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Predictors of HA1c Goal Attainment in Patients Treated With Insulin at a VA Pharmacist-Managed Insulin Clinic (FULL)
Showing up to appointments and adherence to treatment recommendations correlated with glycemic goal attainment for patients.
About 30.3 million Americans (9.4%) have diabetes mellitus (DM).1 Veterans are disproportionately affected—about 1 in 4 of those who receive US Department of Veterans Affairs (VA) care have DM.2 The consequences of uncontrolled DM include microvascular complications (eg, retinopathy, neuropathy, and nephropathy) and macrovascular complications (eg, cardiovascular disease).
The American Diabetes Association (ADA) recommends achieving a goal hemoglobin A1c (HbA1c) level of < 7% to prevent these complications. However, a goal of < 8% HbA1c may be more appropriate for certain patients when a more strict goal may be impractical or have the potential to cause harm.3 Furthermore, guidelines developed by the VA and the US Department of Defense suggest a target HbA1c range of 7.0% to 8.5% for patients with established microvascular or macrovascular disease, comorbid conditions, or a life expectancy of 5 to 10 years.4
Despite the existence of evidence showing the importance of glycemic control in preventing morbidity and mortality associated with DM, many patients have inadequate glycemic control. Diabetes mellitus is the seventh leading cause of death in the US. Moreover, DM is a known risk factor for heart disease, stroke, and kidney disease, which are the first, fifth, and ninth leading causes of death in the US, respectively.5
Because DM management requires ongoing and comprehensive maintenance and monitoring, the ADA supports a collaborative, multidisciplinary, and patient-centered approach to delivery of care.3 Collaborative teams involving pharmacists have been shown to improve outcomes and cost savings for chronic diseases, including DM.6-12 In 1995, the VA launched a national policy providing clinical pharmacists with prescribing privileges that would aid in the provision of coordinated medication management for patients with chronic illnesses.13 The policy created a framework for collaborative drug therapy management (CDTM) models, which grants pharmacists the ability to perform patient assessments, order laboratory tests, and modify medications within a scope of practice.
Since the initiation of these services, several examples of successful DM management services using clinical pharmacists within the VA exist in the literature.14-16 However, even with intensive chronic disease and drug therapy management, not all patients who enroll in these services successfully reach clinical goals. Although these pharmacist-driven services seem to demonstrate overall benefit and cost savings to veteran patients and the VA system, little published data exist to help determine patient behaviors that are associated with glycemic goal attainment when using these services.
At the Corporal Michael J. Crescenz VA Medical Center in (CMCVAMC) Philadelphia, Pennsylvania, where this study was performed, primary care providers may refer patients with uncontrolled DM to the pharmacist disease state management (DSM) clinic. The clinic is a form of a CDTM and receives numerous referrals per year, with many patients discharged for successfully meeting glycemic targets.
However, a percentage of patients fail to attain glycemic goals despite involvement in this clinic. We observed specific patient behaviors that delayed glycemic goal attainment. This study examined whether these behaviors correlated with prolonged glycemic goal attainment. The purpose of this study was to identify patient behaviors that led to glycemic goal attainment in insulin-treated patients referred to this pharmacist DSM clinic.
Methods
This study was performed as a single-center retrospective chart review. The protocol and data collection documents were approved by the CMCVAMC Institutional Review Board. It included patients referred to a pharmacist-led DSM clinic for insulin titration/optimization from January 1, 2011 through December 31, 2012. Data were collected through June 30, 2013, to allow for 6 months after the last referral date of December 31, 2012.
This study included patients who were on insulin therapy at the time of pharmacy consult, who attended at least 3 consecutive pharmacy DSM clinic visits, and had an HbA1c ≥ 8% at the time of initial clinic consult. Patients who failed to have 3 consecutive pharmacy DSM clinic visits, were insulin-naïve at the time of referral, aged ≥ 90, lacked at least 1 follow-up HbA1c result while enrolled in the clinic, or had HbA1c < 8% were excluded.
Among the patients who met eligibility criteria, charts within the Computerized Patient Record System (CPRS) were reviewed in a chronologic order within the respective study time frame. A convenience sample of 100 patients were enrolled in each treatment arm: the goal-attained arm or the goal-not-attained arm.
The primary study variable was HbA1c goal attainment, which was defined in this investigation as at least 1 HbA1c reading of < 8% while enrolled in the DSM clinic during the review period. Secondary variables included specific patient factors such as optimal frequency of self-monitoring of blood glucose (SMBG) testing, adherence to pharmacist’s instructions for changes to glucose-lowering medications, adherence to bringing glucose meter/glucose log book to clinic appointments, and percentage of visits attended. Definitions for each variable are provided in Table 1. 
We hypothesized that patients who were more adherent to treatment plans, regularly attend clinic visits, and appropriately monitor their glucose levels were more likely to meet their glycemic goals.
Statistical Analysis
Univariate descriptive statistics described the individual variables/predictors of HbA1c goal attainment. As the study’s purpose was to identify patient factors and characteristics associated with HbA1c goal attainment, a logistic regression model framework was used for all covariates to evaluate each measured variable’s independent association with HbA1c. The univariate tests were used to compare patient characteristics between the 2 study groups: Pearson chi-square test was used for nominal data, and a paired t test (for normally distributed data) or Wilcoxon rank sum test (for non-normally distributed data) was used for continuous variables. Variables having a P value < .2 underwent a multivariate analysis stepwise logistic regression model to identify patient factors and characteristics associated with HbA1c goal attainment. A Fisher exact test was used to determine gender effect on HbA1c goal attainment, categoric variables were analyzed using Pearson chi-square test, and an unpaired t test was used for continuous data. The backward elimination approach to inclusion of variables in the model was used to build the most parsimonious and best-fitting model, and the Hosmer-Lemeshow goodness-of-fit tests was used to assess model fit. Data analyses were performed using IBM SPSS, version 18.0 (Armonk, NY).
Results
Five hundred eighty-four patient records were reviewed, and 207 patients met inclusion criteria: 102 patient records were reviewed for the goal-attained arm, and 105 patient records for the goal-not-attained arm. Most patients were excluded from the analysis due to not having 3 consecutive visits during the specified period or having an HbA1c of < 8% at the time of referral to the pharmacist DSM clinic.
The patients in this study had type 2 diabetes for about 11 years, were overwhelmingly male (99%), were aged about 61 years, and were taking on average 13 medications at the time of referral to the pharmacist DSM clinic. Mean HbA1c at time of enrollment was slightly higher in the goal-not-attained arm vs goal-attained arm (10.7% vs 10.2%, respectively), but the difference was not statistically significant (P = .066). A little more than half the patients in both study arms were on basal + prandial insulin regimens (Table 2).
Patients who attained their goal HbA1cwere more likely to bring their glucose meter/glucose log book to at least 80% of their appointments (P < .001). Additionally, this same cohort followed insulin dosing instructions at least 80% of the time (P < .001). 
Five variables were included in the multivariate analysis because they had a P value ≤ .2 in univariate analyses: (1) patient adherence to instructions (P < .001); (2) duration in clinic (P < .001); (3) patient bringingglucose meter or glucose log to appointments (P < .001); (4) percentage of scheduled appointments patient attended (P = .015); and (5) baseline HbA1c (P = .066). 
Discussion
The development and constant modification of clinical practicing guidelines has made DM treatment a focus and priority.3,4 Additionally, the collaborative approach to health care and creation of VA pharmacist-driven services have demonstrated successful patient outcomes.6-16 Despite these efforts, further insight is needed to improve the management of DM. Our study identified specific behavioral factors that correlated to veteran patients to attaining their HbA1c goal of < 8% within a VA pharmacist DSM clinic. Additionally, it highlighted factors that contributed to patients not achieving their glycemic goals.
Our univariate analysis showed behaviors such as showing up for appointments and following directions regimens to correlate with glycemic goal attainment. However, following directions was the only behavioral factor that correlated to glycemic goal attainment in our multivariate analysis. Additionally, our findings indicated that factors for HbA1c goal attainment included patients who brought their glucose meter/glucose log book and attended clinic appointments at least 80% of the time, respectively.
These findings can help further refine the process for identifying patients who are most likely to achieve glycemic goals when referred to pharmacist DSM clinics or to any DM treatment program. Assessment of a patient’s motivation and ability to attend clinic appointments, bring their glucose meter/glucose log book, and to follow instructions provided at these appointments are reasonable screening questions to ask before referring that patient to a diabetes care program or service. Currently, this is not performed during the consult process to the pharmacist DSM clinic at the respective VA.
Additionally, our findings show that patients who met goal did so, on average, within 6 months of referral to the pharmacist DSM clinic. This finding may have occurred because patients who successfully reach HbA1c goal in 2 consecutive checks are discharged from the clinic. Patients who do not meet this goal continue with the clinic, thus increasing their duration of enrollment in this service. This finding could help clinical pharmacists estimate how long patients will be followed by the service, thus allowing for a more accurate estimation of workload and clinic capacity. Additionally, this finding provides insight if the patient should remain in clinic or be transferred to another program. Our findings aligned with previous studies showing the link between patient behaviors and glycemic goal attainment.17-19
Limitations
This study has a few notable limitations. First, it is limited to 1 VA medical center, so our findings may not be extrapolated easily to other institutions of the Veterans Health Administration. Ideally, future studies centered on identifying factors that lead to successful glycemic goal attainment would be helpful from multiple VA institutions. This would encourage more factors to be identified and trends to be strengthened. Ultimately, this would allow for more global changes to the consult process from primary care to pharmacist DSM clinics nationally vs at a local VA institution. Additionally, this study was limited to a specific retrospective time frame, therefore limiting its ability to identify trends. This study also relied on some subjective factors, such as the patient’s self-report of properly following the clinic instructions. Another limitation was that our investigation was not designed to characterize the specific pharmacist’s interventions that improved glycemic control. Future studies would benefit from the inclusion of specific interventions and their effect on glycemic goal attainment.
Conclusion
This retrospective study offers insight to specific patient behavioral factors that correlate with glycemic goal attainment in a VA pharmacist DSM clinic. Behavioral factors linked to HbA1c goal attainment of < 8% included appointment keeping, bringing glucose meter/glucose log book at least 80% of the time to these appointments, and following clinic instructions. This investigation also found that patients who attain glycemic goals generally do so within 6 months of enrollment. Furthermore, this study provided insight that following the clinic instructions a majority of the time strongly contributes to glycemic goal attainment. We believe that an assessment of patients’ behaviors prior to referrals to diabetes management programs will yield useful information about possible barriers to glycemic goal attainment.
1. Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Published 2017. Accessed September 25, 2018.
2. Gaspar JL, Dahlke ME, Kasper B. Efficacy of patient aligned care team pharmacist service in reaching diabetes and hyperlipidemia treatment goals. Fed Pract. 2015;32(11):42-47.
3. American Diabetes Association. Standards of medical care in diabetes—2017. Diabetes Care. 2017;40(suppl 1):S6-S135.
4. US Department of Veterans Affairs, Department of Defense. VA/DoD clinical practice guideline for the management of type 2 diabetes mellitus in primary care. https://www.healthquality.va.gov/guidelines/CD/diabetes/VADoDDMCPGFinal508.pdf. Published April 2017. Accessed September 7, 2018.
5. Centers for Disease Control and Prevention. Deaths: leading causes for 2014. Natl Vital Stat Rep. 2016;65(5):1-96.
6. Nigro SC, Garwood CL, Berlie H, et al. Clinical pharmacists as key members of the patient-centered medical home: an opinion statement of the Ambulatory Care Practice and Research Network of the American College of Clinical Pharmacy. Pharmacotherapy. 2014;34(1):96-108.
7. Smith M, Bates DW, Bodenheimer T, et al. Why pharmacists belong in the medical home. Health Aff (Millwood). 2010;29(5):906-913.
8. Chisholm-Burns MA, Kim Lee J, Spivey CA, et al. US Pharmacists’ effect as team members on patient care. Med Care. 2010;48(10):923-933.
9. Wubben DP, Vivian EM. Effects of pharmacist outpatient interventions on adults with diabetes mellitus: a systematic review. Pharmacotherapy. 2008;28(4):421-436.
10. Touchette DR, Doloresco F, Suda KJ, et al. Economic evaluations of clinical pharmacy services: 2006-2010. Pharmacotherapy. 2014;34(8):771-793.
11. Giberson S, Yoder S, Lee MP. Improving patient and health system outcomes through advanced pharmacy practice. A report of the U.S. Surgeon General. American College of Clinical Pharmacy. https://www.accp.com/docs/positions/misc/Improving_Patient_and_Health_System_Outcomes.pdf. Published December 2011. Accessed September 10, 2018.
12. Isetts BJ, Schondelmeyer SW, Artz MB, et al. Clinical and economic outcomes of medication therapy management services: the Minnesota experience. J Am Pharm Assoc (2003). 2008;48(2):203-211.
13. Ourth H, Groppi J, Morreale AP, Quicci-Roberts K. Clinical pharmacist prescribing activities in the Veterans Health Administration. Am J Health Syst Pharm. 2016;73(18):1406-1415.
14. Taveira TH, Friedmann PD, Cohen LB, et al. Pharmacist-led group medical appointment model in type 2 diabetes. Diabetes Educ. 2010;36(1):109-117.
15. Edwards KL, Hadley RL, Baby N, Yeary JC, Chastain LM, Brown CD. Utilizing clinical pharmacy specialists to address access to care barriers in the veteran population for the management of diabetes. J Pharm Pract. 2017;30(4):412-418.
16. Cripps RJ, Gourley ES, Johnson W, et al. An evaluation of diabetes-related measures of control after 6 months of clinical pharmacy specialist intervention. J Pharm Prac. 2011;24(3):332-338.
17. Jones H, Edwards L, Vallis TM, et al; Diabetes Stages of Change (DiSC) Study. Changes in diabetes self-care behaviors make a difference in glycemic control. Diabetes Care. 2003;26(3):732-737.
18. Schetman JM, Schorling JB, Voss JD. Appointment adherence and disparities in outcomes among patients with diabetes. J Gen Intern Med. 2008;23(10):1685-1687.
19. Rhee, MK, Slocum W, Zeimer DC, et al. Patient adherence improves glycemic control. Diabetes Educ. 2005;31(2):240-250.
Showing up to appointments and adherence to treatment recommendations correlated with glycemic goal attainment for patients.
Showing up to appointments and adherence to treatment recommendations correlated with glycemic goal attainment for patients.
About 30.3 million Americans (9.4%) have diabetes mellitus (DM).1 Veterans are disproportionately affected—about 1 in 4 of those who receive US Department of Veterans Affairs (VA) care have DM.2 The consequences of uncontrolled DM include microvascular complications (eg, retinopathy, neuropathy, and nephropathy) and macrovascular complications (eg, cardiovascular disease).
The American Diabetes Association (ADA) recommends achieving a goal hemoglobin A1c (HbA1c) level of < 7% to prevent these complications. However, a goal of < 8% HbA1c may be more appropriate for certain patients when a more strict goal may be impractical or have the potential to cause harm.3 Furthermore, guidelines developed by the VA and the US Department of Defense suggest a target HbA1c range of 7.0% to 8.5% for patients with established microvascular or macrovascular disease, comorbid conditions, or a life expectancy of 5 to 10 years.4
Despite the existence of evidence showing the importance of glycemic control in preventing morbidity and mortality associated with DM, many patients have inadequate glycemic control. Diabetes mellitus is the seventh leading cause of death in the US. Moreover, DM is a known risk factor for heart disease, stroke, and kidney disease, which are the first, fifth, and ninth leading causes of death in the US, respectively.5
Because DM management requires ongoing and comprehensive maintenance and monitoring, the ADA supports a collaborative, multidisciplinary, and patient-centered approach to delivery of care.3 Collaborative teams involving pharmacists have been shown to improve outcomes and cost savings for chronic diseases, including DM.6-12 In 1995, the VA launched a national policy providing clinical pharmacists with prescribing privileges that would aid in the provision of coordinated medication management for patients with chronic illnesses.13 The policy created a framework for collaborative drug therapy management (CDTM) models, which grants pharmacists the ability to perform patient assessments, order laboratory tests, and modify medications within a scope of practice.
Since the initiation of these services, several examples of successful DM management services using clinical pharmacists within the VA exist in the literature.14-16 However, even with intensive chronic disease and drug therapy management, not all patients who enroll in these services successfully reach clinical goals. Although these pharmacist-driven services seem to demonstrate overall benefit and cost savings to veteran patients and the VA system, little published data exist to help determine patient behaviors that are associated with glycemic goal attainment when using these services.
At the Corporal Michael J. Crescenz VA Medical Center in (CMCVAMC) Philadelphia, Pennsylvania, where this study was performed, primary care providers may refer patients with uncontrolled DM to the pharmacist disease state management (DSM) clinic. The clinic is a form of a CDTM and receives numerous referrals per year, with many patients discharged for successfully meeting glycemic targets.
However, a percentage of patients fail to attain glycemic goals despite involvement in this clinic. We observed specific patient behaviors that delayed glycemic goal attainment. This study examined whether these behaviors correlated with prolonged glycemic goal attainment. The purpose of this study was to identify patient behaviors that led to glycemic goal attainment in insulin-treated patients referred to this pharmacist DSM clinic.
Methods
This study was performed as a single-center retrospective chart review. The protocol and data collection documents were approved by the CMCVAMC Institutional Review Board. It included patients referred to a pharmacist-led DSM clinic for insulin titration/optimization from January 1, 2011 through December 31, 2012. Data were collected through June 30, 2013, to allow for 6 months after the last referral date of December 31, 2012.
This study included patients who were on insulin therapy at the time of pharmacy consult, who attended at least 3 consecutive pharmacy DSM clinic visits, and had an HbA1c ≥ 8% at the time of initial clinic consult. Patients who failed to have 3 consecutive pharmacy DSM clinic visits, were insulin-naïve at the time of referral, aged ≥ 90, lacked at least 1 follow-up HbA1c result while enrolled in the clinic, or had HbA1c < 8% were excluded.
Among the patients who met eligibility criteria, charts within the Computerized Patient Record System (CPRS) were reviewed in a chronologic order within the respective study time frame. A convenience sample of 100 patients were enrolled in each treatment arm: the goal-attained arm or the goal-not-attained arm.
The primary study variable was HbA1c goal attainment, which was defined in this investigation as at least 1 HbA1c reading of < 8% while enrolled in the DSM clinic during the review period. Secondary variables included specific patient factors such as optimal frequency of self-monitoring of blood glucose (SMBG) testing, adherence to pharmacist’s instructions for changes to glucose-lowering medications, adherence to bringing glucose meter/glucose log book to clinic appointments, and percentage of visits attended. Definitions for each variable are provided in Table 1.
We hypothesized that patients who were more adherent to treatment plans, regularly attend clinic visits, and appropriately monitor their glucose levels were more likely to meet their glycemic goals.
Statistical Analysis
Univariate descriptive statistics described the individual variables/predictors of HbA1c goal attainment. As the study’s purpose was to identify patient factors and characteristics associated with HbA1c goal attainment, a logistic regression model framework was used for all covariates to evaluate each measured variable’s independent association with HbA1c. The univariate tests were used to compare patient characteristics between the 2 study groups: Pearson chi-square test was used for nominal data, and a paired t test (for normally distributed data) or Wilcoxon rank sum test (for non-normally distributed data) was used for continuous variables. Variables having a P value < .2 underwent a multivariate analysis stepwise logistic regression model to identify patient factors and characteristics associated with HbA1c goal attainment. A Fisher exact test was used to determine gender effect on HbA1c goal attainment, categoric variables were analyzed using Pearson chi-square test, and an unpaired t test was used for continuous data. The backward elimination approach to inclusion of variables in the model was used to build the most parsimonious and best-fitting model, and the Hosmer-Lemeshow goodness-of-fit tests was used to assess model fit. Data analyses were performed using IBM SPSS, version 18.0 (Armonk, NY).
Results
Five hundred eighty-four patient records were reviewed, and 207 patients met inclusion criteria: 102 patient records were reviewed for the goal-attained arm, and 105 patient records for the goal-not-attained arm. Most patients were excluded from the analysis due to not having 3 consecutive visits during the specified period or having an HbA1c of < 8% at the time of referral to the pharmacist DSM clinic.
The patients in this study had type 2 diabetes for about 11 years, were overwhelmingly male (99%), were aged about 61 years, and were taking on average 13 medications at the time of referral to the pharmacist DSM clinic. Mean HbA1c at time of enrollment was slightly higher in the goal-not-attained arm vs goal-attained arm (10.7% vs 10.2%, respectively), but the difference was not statistically significant (P = .066). A little more than half the patients in both study arms were on basal + prandial insulin regimens (Table 2).
Patients who attained their goal HbA1cwere more likely to bring their glucose meter/glucose log book to at least 80% of their appointments (P < .001). Additionally, this same cohort followed insulin dosing instructions at least 80% of the time (P < .001).
Five variables were included in the multivariate analysis because they had a P value ≤ .2 in univariate analyses: (1) patient adherence to instructions (P < .001); (2) duration in clinic (P < .001); (3) patient bringingglucose meter or glucose log to appointments (P < .001); (4) percentage of scheduled appointments patient attended (P = .015); and (5) baseline HbA1c (P = .066).
Discussion
The development and constant modification of clinical practicing guidelines has made DM treatment a focus and priority.3,4 Additionally, the collaborative approach to health care and creation of VA pharmacist-driven services have demonstrated successful patient outcomes.6-16 Despite these efforts, further insight is needed to improve the management of DM. Our study identified specific behavioral factors that correlated to veteran patients to attaining their HbA1c goal of < 8% within a VA pharmacist DSM clinic. Additionally, it highlighted factors that contributed to patients not achieving their glycemic goals.
Our univariate analysis showed behaviors such as showing up for appointments and following directions regimens to correlate with glycemic goal attainment. However, following directions was the only behavioral factor that correlated to glycemic goal attainment in our multivariate analysis. Additionally, our findings indicated that factors for HbA1c goal attainment included patients who brought their glucose meter/glucose log book and attended clinic appointments at least 80% of the time, respectively.
These findings can help further refine the process for identifying patients who are most likely to achieve glycemic goals when referred to pharmacist DSM clinics or to any DM treatment program. Assessment of a patient’s motivation and ability to attend clinic appointments, bring their glucose meter/glucose log book, and to follow instructions provided at these appointments are reasonable screening questions to ask before referring that patient to a diabetes care program or service. Currently, this is not performed during the consult process to the pharmacist DSM clinic at the respective VA.
Additionally, our findings show that patients who met goal did so, on average, within 6 months of referral to the pharmacist DSM clinic. This finding may have occurred because patients who successfully reach HbA1c goal in 2 consecutive checks are discharged from the clinic. Patients who do not meet this goal continue with the clinic, thus increasing their duration of enrollment in this service. This finding could help clinical pharmacists estimate how long patients will be followed by the service, thus allowing for a more accurate estimation of workload and clinic capacity. Additionally, this finding provides insight if the patient should remain in clinic or be transferred to another program. Our findings aligned with previous studies showing the link between patient behaviors and glycemic goal attainment.17-19
Limitations
This study has a few notable limitations. First, it is limited to 1 VA medical center, so our findings may not be extrapolated easily to other institutions of the Veterans Health Administration. Ideally, future studies centered on identifying factors that lead to successful glycemic goal attainment would be helpful from multiple VA institutions. This would encourage more factors to be identified and trends to be strengthened. Ultimately, this would allow for more global changes to the consult process from primary care to pharmacist DSM clinics nationally vs at a local VA institution. Additionally, this study was limited to a specific retrospective time frame, therefore limiting its ability to identify trends. This study also relied on some subjective factors, such as the patient’s self-report of properly following the clinic instructions. Another limitation was that our investigation was not designed to characterize the specific pharmacist’s interventions that improved glycemic control. Future studies would benefit from the inclusion of specific interventions and their effect on glycemic goal attainment.
Conclusion
This retrospective study offers insight to specific patient behavioral factors that correlate with glycemic goal attainment in a VA pharmacist DSM clinic. Behavioral factors linked to HbA1c goal attainment of < 8% included appointment keeping, bringing glucose meter/glucose log book at least 80% of the time to these appointments, and following clinic instructions. This investigation also found that patients who attain glycemic goals generally do so within 6 months of enrollment. Furthermore, this study provided insight that following the clinic instructions a majority of the time strongly contributes to glycemic goal attainment. We believe that an assessment of patients’ behaviors prior to referrals to diabetes management programs will yield useful information about possible barriers to glycemic goal attainment.
About 30.3 million Americans (9.4%) have diabetes mellitus (DM).1 Veterans are disproportionately affected—about 1 in 4 of those who receive US Department of Veterans Affairs (VA) care have DM.2 The consequences of uncontrolled DM include microvascular complications (eg, retinopathy, neuropathy, and nephropathy) and macrovascular complications (eg, cardiovascular disease).
The American Diabetes Association (ADA) recommends achieving a goal hemoglobin A1c (HbA1c) level of < 7% to prevent these complications. However, a goal of < 8% HbA1c may be more appropriate for certain patients when a more strict goal may be impractical or have the potential to cause harm.3 Furthermore, guidelines developed by the VA and the US Department of Defense suggest a target HbA1c range of 7.0% to 8.5% for patients with established microvascular or macrovascular disease, comorbid conditions, or a life expectancy of 5 to 10 years.4
Despite the existence of evidence showing the importance of glycemic control in preventing morbidity and mortality associated with DM, many patients have inadequate glycemic control. Diabetes mellitus is the seventh leading cause of death in the US. Moreover, DM is a known risk factor for heart disease, stroke, and kidney disease, which are the first, fifth, and ninth leading causes of death in the US, respectively.5
Because DM management requires ongoing and comprehensive maintenance and monitoring, the ADA supports a collaborative, multidisciplinary, and patient-centered approach to delivery of care.3 Collaborative teams involving pharmacists have been shown to improve outcomes and cost savings for chronic diseases, including DM.6-12 In 1995, the VA launched a national policy providing clinical pharmacists with prescribing privileges that would aid in the provision of coordinated medication management for patients with chronic illnesses.13 The policy created a framework for collaborative drug therapy management (CDTM) models, which grants pharmacists the ability to perform patient assessments, order laboratory tests, and modify medications within a scope of practice.
Since the initiation of these services, several examples of successful DM management services using clinical pharmacists within the VA exist in the literature.14-16 However, even with intensive chronic disease and drug therapy management, not all patients who enroll in these services successfully reach clinical goals. Although these pharmacist-driven services seem to demonstrate overall benefit and cost savings to veteran patients and the VA system, little published data exist to help determine patient behaviors that are associated with glycemic goal attainment when using these services.
At the Corporal Michael J. Crescenz VA Medical Center in (CMCVAMC) Philadelphia, Pennsylvania, where this study was performed, primary care providers may refer patients with uncontrolled DM to the pharmacist disease state management (DSM) clinic. The clinic is a form of a CDTM and receives numerous referrals per year, with many patients discharged for successfully meeting glycemic targets.
However, a percentage of patients fail to attain glycemic goals despite involvement in this clinic. We observed specific patient behaviors that delayed glycemic goal attainment. This study examined whether these behaviors correlated with prolonged glycemic goal attainment. The purpose of this study was to identify patient behaviors that led to glycemic goal attainment in insulin-treated patients referred to this pharmacist DSM clinic.
Methods
This study was performed as a single-center retrospective chart review. The protocol and data collection documents were approved by the CMCVAMC Institutional Review Board. It included patients referred to a pharmacist-led DSM clinic for insulin titration/optimization from January 1, 2011 through December 31, 2012. Data were collected through June 30, 2013, to allow for 6 months after the last referral date of December 31, 2012.
This study included patients who were on insulin therapy at the time of pharmacy consult, who attended at least 3 consecutive pharmacy DSM clinic visits, and had an HbA1c ≥ 8% at the time of initial clinic consult. Patients who failed to have 3 consecutive pharmacy DSM clinic visits, were insulin-naïve at the time of referral, aged ≥ 90, lacked at least 1 follow-up HbA1c result while enrolled in the clinic, or had HbA1c < 8% were excluded.
Among the patients who met eligibility criteria, charts within the Computerized Patient Record System (CPRS) were reviewed in a chronologic order within the respective study time frame. A convenience sample of 100 patients were enrolled in each treatment arm: the goal-attained arm or the goal-not-attained arm.
The primary study variable was HbA1c goal attainment, which was defined in this investigation as at least 1 HbA1c reading of < 8% while enrolled in the DSM clinic during the review period. Secondary variables included specific patient factors such as optimal frequency of self-monitoring of blood glucose (SMBG) testing, adherence to pharmacist’s instructions for changes to glucose-lowering medications, adherence to bringing glucose meter/glucose log book to clinic appointments, and percentage of visits attended. Definitions for each variable are provided in Table 1.
We hypothesized that patients who were more adherent to treatment plans, regularly attend clinic visits, and appropriately monitor their glucose levels were more likely to meet their glycemic goals.
Statistical Analysis
Univariate descriptive statistics described the individual variables/predictors of HbA1c goal attainment. As the study’s purpose was to identify patient factors and characteristics associated with HbA1c goal attainment, a logistic regression model framework was used for all covariates to evaluate each measured variable’s independent association with HbA1c. The univariate tests were used to compare patient characteristics between the 2 study groups: Pearson chi-square test was used for nominal data, and a paired t test (for normally distributed data) or Wilcoxon rank sum test (for non-normally distributed data) was used for continuous variables. Variables having a P value < .2 underwent a multivariate analysis stepwise logistic regression model to identify patient factors and characteristics associated with HbA1c goal attainment. A Fisher exact test was used to determine gender effect on HbA1c goal attainment, categoric variables were analyzed using Pearson chi-square test, and an unpaired t test was used for continuous data. The backward elimination approach to inclusion of variables in the model was used to build the most parsimonious and best-fitting model, and the Hosmer-Lemeshow goodness-of-fit tests was used to assess model fit. Data analyses were performed using IBM SPSS, version 18.0 (Armonk, NY).
Results
Five hundred eighty-four patient records were reviewed, and 207 patients met inclusion criteria: 102 patient records were reviewed for the goal-attained arm, and 105 patient records for the goal-not-attained arm. Most patients were excluded from the analysis due to not having 3 consecutive visits during the specified period or having an HbA1c of < 8% at the time of referral to the pharmacist DSM clinic.
The patients in this study had type 2 diabetes for about 11 years, were overwhelmingly male (99%), were aged about 61 years, and were taking on average 13 medications at the time of referral to the pharmacist DSM clinic. Mean HbA1c at time of enrollment was slightly higher in the goal-not-attained arm vs goal-attained arm (10.7% vs 10.2%, respectively), but the difference was not statistically significant (P = .066). A little more than half the patients in both study arms were on basal + prandial insulin regimens (Table 2).
Patients who attained their goal HbA1cwere more likely to bring their glucose meter/glucose log book to at least 80% of their appointments (P < .001). Additionally, this same cohort followed insulin dosing instructions at least 80% of the time (P < .001).
Five variables were included in the multivariate analysis because they had a P value ≤ .2 in univariate analyses: (1) patient adherence to instructions (P < .001); (2) duration in clinic (P < .001); (3) patient bringingglucose meter or glucose log to appointments (P < .001); (4) percentage of scheduled appointments patient attended (P = .015); and (5) baseline HbA1c (P = .066).
Discussion
The development and constant modification of clinical practicing guidelines has made DM treatment a focus and priority.3,4 Additionally, the collaborative approach to health care and creation of VA pharmacist-driven services have demonstrated successful patient outcomes.6-16 Despite these efforts, further insight is needed to improve the management of DM. Our study identified specific behavioral factors that correlated to veteran patients to attaining their HbA1c goal of < 8% within a VA pharmacist DSM clinic. Additionally, it highlighted factors that contributed to patients not achieving their glycemic goals.
Our univariate analysis showed behaviors such as showing up for appointments and following directions regimens to correlate with glycemic goal attainment. However, following directions was the only behavioral factor that correlated to glycemic goal attainment in our multivariate analysis. Additionally, our findings indicated that factors for HbA1c goal attainment included patients who brought their glucose meter/glucose log book and attended clinic appointments at least 80% of the time, respectively.
These findings can help further refine the process for identifying patients who are most likely to achieve glycemic goals when referred to pharmacist DSM clinics or to any DM treatment program. Assessment of a patient’s motivation and ability to attend clinic appointments, bring their glucose meter/glucose log book, and to follow instructions provided at these appointments are reasonable screening questions to ask before referring that patient to a diabetes care program or service. Currently, this is not performed during the consult process to the pharmacist DSM clinic at the respective VA.
Additionally, our findings show that patients who met goal did so, on average, within 6 months of referral to the pharmacist DSM clinic. This finding may have occurred because patients who successfully reach HbA1c goal in 2 consecutive checks are discharged from the clinic. Patients who do not meet this goal continue with the clinic, thus increasing their duration of enrollment in this service. This finding could help clinical pharmacists estimate how long patients will be followed by the service, thus allowing for a more accurate estimation of workload and clinic capacity. Additionally, this finding provides insight if the patient should remain in clinic or be transferred to another program. Our findings aligned with previous studies showing the link between patient behaviors and glycemic goal attainment.17-19
Limitations
This study has a few notable limitations. First, it is limited to 1 VA medical center, so our findings may not be extrapolated easily to other institutions of the Veterans Health Administration. Ideally, future studies centered on identifying factors that lead to successful glycemic goal attainment would be helpful from multiple VA institutions. This would encourage more factors to be identified and trends to be strengthened. Ultimately, this would allow for more global changes to the consult process from primary care to pharmacist DSM clinics nationally vs at a local VA institution. Additionally, this study was limited to a specific retrospective time frame, therefore limiting its ability to identify trends. This study also relied on some subjective factors, such as the patient’s self-report of properly following the clinic instructions. Another limitation was that our investigation was not designed to characterize the specific pharmacist’s interventions that improved glycemic control. Future studies would benefit from the inclusion of specific interventions and their effect on glycemic goal attainment.
Conclusion
This retrospective study offers insight to specific patient behavioral factors that correlate with glycemic goal attainment in a VA pharmacist DSM clinic. Behavioral factors linked to HbA1c goal attainment of < 8% included appointment keeping, bringing glucose meter/glucose log book at least 80% of the time to these appointments, and following clinic instructions. This investigation also found that patients who attain glycemic goals generally do so within 6 months of enrollment. Furthermore, this study provided insight that following the clinic instructions a majority of the time strongly contributes to glycemic goal attainment. We believe that an assessment of patients’ behaviors prior to referrals to diabetes management programs will yield useful information about possible barriers to glycemic goal attainment.
1. Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Published 2017. Accessed September 25, 2018.
2. Gaspar JL, Dahlke ME, Kasper B. Efficacy of patient aligned care team pharmacist service in reaching diabetes and hyperlipidemia treatment goals. Fed Pract. 2015;32(11):42-47.
3. American Diabetes Association. Standards of medical care in diabetes—2017. Diabetes Care. 2017;40(suppl 1):S6-S135.
4. US Department of Veterans Affairs, Department of Defense. VA/DoD clinical practice guideline for the management of type 2 diabetes mellitus in primary care. https://www.healthquality.va.gov/guidelines/CD/diabetes/VADoDDMCPGFinal508.pdf. Published April 2017. Accessed September 7, 2018.
5. Centers for Disease Control and Prevention. Deaths: leading causes for 2014. Natl Vital Stat Rep. 2016;65(5):1-96.
6. Nigro SC, Garwood CL, Berlie H, et al. Clinical pharmacists as key members of the patient-centered medical home: an opinion statement of the Ambulatory Care Practice and Research Network of the American College of Clinical Pharmacy. Pharmacotherapy. 2014;34(1):96-108.
7. Smith M, Bates DW, Bodenheimer T, et al. Why pharmacists belong in the medical home. Health Aff (Millwood). 2010;29(5):906-913.
8. Chisholm-Burns MA, Kim Lee J, Spivey CA, et al. US Pharmacists’ effect as team members on patient care. Med Care. 2010;48(10):923-933.
9. Wubben DP, Vivian EM. Effects of pharmacist outpatient interventions on adults with diabetes mellitus: a systematic review. Pharmacotherapy. 2008;28(4):421-436.
10. Touchette DR, Doloresco F, Suda KJ, et al. Economic evaluations of clinical pharmacy services: 2006-2010. Pharmacotherapy. 2014;34(8):771-793.
11. Giberson S, Yoder S, Lee MP. Improving patient and health system outcomes through advanced pharmacy practice. A report of the U.S. Surgeon General. American College of Clinical Pharmacy. https://www.accp.com/docs/positions/misc/Improving_Patient_and_Health_System_Outcomes.pdf. Published December 2011. Accessed September 10, 2018.
12. Isetts BJ, Schondelmeyer SW, Artz MB, et al. Clinical and economic outcomes of medication therapy management services: the Minnesota experience. J Am Pharm Assoc (2003). 2008;48(2):203-211.
13. Ourth H, Groppi J, Morreale AP, Quicci-Roberts K. Clinical pharmacist prescribing activities in the Veterans Health Administration. Am J Health Syst Pharm. 2016;73(18):1406-1415.
14. Taveira TH, Friedmann PD, Cohen LB, et al. Pharmacist-led group medical appointment model in type 2 diabetes. Diabetes Educ. 2010;36(1):109-117.
15. Edwards KL, Hadley RL, Baby N, Yeary JC, Chastain LM, Brown CD. Utilizing clinical pharmacy specialists to address access to care barriers in the veteran population for the management of diabetes. J Pharm Pract. 2017;30(4):412-418.
16. Cripps RJ, Gourley ES, Johnson W, et al. An evaluation of diabetes-related measures of control after 6 months of clinical pharmacy specialist intervention. J Pharm Prac. 2011;24(3):332-338.
17. Jones H, Edwards L, Vallis TM, et al; Diabetes Stages of Change (DiSC) Study. Changes in diabetes self-care behaviors make a difference in glycemic control. Diabetes Care. 2003;26(3):732-737.
18. Schetman JM, Schorling JB, Voss JD. Appointment adherence and disparities in outcomes among patients with diabetes. J Gen Intern Med. 2008;23(10):1685-1687.
19. Rhee, MK, Slocum W, Zeimer DC, et al. Patient adherence improves glycemic control. Diabetes Educ. 2005;31(2):240-250.
1. Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Published 2017. Accessed September 25, 2018.
2. Gaspar JL, Dahlke ME, Kasper B. Efficacy of patient aligned care team pharmacist service in reaching diabetes and hyperlipidemia treatment goals. Fed Pract. 2015;32(11):42-47.
3. American Diabetes Association. Standards of medical care in diabetes—2017. Diabetes Care. 2017;40(suppl 1):S6-S135.
4. US Department of Veterans Affairs, Department of Defense. VA/DoD clinical practice guideline for the management of type 2 diabetes mellitus in primary care. https://www.healthquality.va.gov/guidelines/CD/diabetes/VADoDDMCPGFinal508.pdf. Published April 2017. Accessed September 7, 2018.
5. Centers for Disease Control and Prevention. Deaths: leading causes for 2014. Natl Vital Stat Rep. 2016;65(5):1-96.
6. Nigro SC, Garwood CL, Berlie H, et al. Clinical pharmacists as key members of the patient-centered medical home: an opinion statement of the Ambulatory Care Practice and Research Network of the American College of Clinical Pharmacy. Pharmacotherapy. 2014;34(1):96-108.
7. Smith M, Bates DW, Bodenheimer T, et al. Why pharmacists belong in the medical home. Health Aff (Millwood). 2010;29(5):906-913.
8. Chisholm-Burns MA, Kim Lee J, Spivey CA, et al. US Pharmacists’ effect as team members on patient care. Med Care. 2010;48(10):923-933.
9. Wubben DP, Vivian EM. Effects of pharmacist outpatient interventions on adults with diabetes mellitus: a systematic review. Pharmacotherapy. 2008;28(4):421-436.
10. Touchette DR, Doloresco F, Suda KJ, et al. Economic evaluations of clinical pharmacy services: 2006-2010. Pharmacotherapy. 2014;34(8):771-793.
11. Giberson S, Yoder S, Lee MP. Improving patient and health system outcomes through advanced pharmacy practice. A report of the U.S. Surgeon General. American College of Clinical Pharmacy. https://www.accp.com/docs/positions/misc/Improving_Patient_and_Health_System_Outcomes.pdf. Published December 2011. Accessed September 10, 2018.
12. Isetts BJ, Schondelmeyer SW, Artz MB, et al. Clinical and economic outcomes of medication therapy management services: the Minnesota experience. J Am Pharm Assoc (2003). 2008;48(2):203-211.
13. Ourth H, Groppi J, Morreale AP, Quicci-Roberts K. Clinical pharmacist prescribing activities in the Veterans Health Administration. Am J Health Syst Pharm. 2016;73(18):1406-1415.
14. Taveira TH, Friedmann PD, Cohen LB, et al. Pharmacist-led group medical appointment model in type 2 diabetes. Diabetes Educ. 2010;36(1):109-117.
15. Edwards KL, Hadley RL, Baby N, Yeary JC, Chastain LM, Brown CD. Utilizing clinical pharmacy specialists to address access to care barriers in the veteran population for the management of diabetes. J Pharm Pract. 2017;30(4):412-418.
16. Cripps RJ, Gourley ES, Johnson W, et al. An evaluation of diabetes-related measures of control after 6 months of clinical pharmacy specialist intervention. J Pharm Prac. 2011;24(3):332-338.
17. Jones H, Edwards L, Vallis TM, et al; Diabetes Stages of Change (DiSC) Study. Changes in diabetes self-care behaviors make a difference in glycemic control. Diabetes Care. 2003;26(3):732-737.
18. Schetman JM, Schorling JB, Voss JD. Appointment adherence and disparities in outcomes among patients with diabetes. J Gen Intern Med. 2008;23(10):1685-1687.
19. Rhee, MK, Slocum W, Zeimer DC, et al. Patient adherence improves glycemic control. Diabetes Educ. 2005;31(2):240-250.
Heart Failure in Older Adults: A Geriatrician Call for Action (FULL)
As the population ages, heart failure is becoming a major public health challenge; clinicians need further evidence-based treatments to bridge the existing gap between guidelines and real-world clinical practice.
In 2050, persons aged ≥ 85 years, also known as the oldest old, are projected to reach 18 million, accounting for 4.5% of the US population, up from 2.5% in 2030.1 These patients are the fastest growing segment of the US population.
Advances in treating cardiovascular (CV) disease over the past 2 decades have led to an increased incidence of heart failure (HF) and hospitalizations among older patients.2 Total costs of care for persons with HF have exceeded $30 billion annually and are expected to rise to more than $70 billion by 2030 due to growth of the aging population.3,4 Moreover, the Framingham Study reported mortality increases with advancing age (HR 1.27 and 1.61 per decade in men and women, respectively).5
The prevalence of HF is also high and increasing over time. The National Health and Nutrition Examination Survey reported that about 5.7 million Americans have HF.6 The prevalence of HF is expected to reach 8 million by 2030.6 The higher numbers of HF among patients with advanced age is associated with age-related changes in CV structure and function, including reduced responsiveness to β-adrenergic stimulation, impaired left ventricular diastolic filling, and increased vascular stiffness. In addition, age-related changes in other systems might contribute to a HF diagnosis or worsening of the condition.7
Older adults experience physiologic changes in pharmacokinetics and pharmacodynamics, including decreased volume of distribution and creatinine clearance, which lead to significant changes in drug concentration and effectiveness.8
Geriatric patients aged > 65 years who have comorbidities and those who reside in long-term care settings are underrepresented in clinical trials, leading clinicians to make treatment decisions based on data from younger, community-dwelling individuals. Researchers have questioned whether to include elderly patients and those with comorbidities in clinical trials, given that their diminished response may produce less conclusive results with smaller treatment effects. Exclusion criteria based on comorbid conditions or functional status disqualify many older adults from clinical trials.
This article reviews evidence from major randomized controlled trials over the past 2 decades and explores their applicability to support HF treatment guidelines in patients with advanced age (Table). 
Pharmacotherapy for Heart Failure
Angiotensin-Converting Enzyme Inhibitors
Several randomized clinical trials have found that angiotensin-converting enzyme (ACE) inhibitors improve symptoms in patients with HF. The CooperativeNorth Scandinavian Enalapril Survival Study (CONSENSUS), demonstrated that enalapril improves survival in patients with New York Heart Association (NYHA) class IV HF with reduced ejection fraction (HFrEF) when added to standard therapy.9 However, the duration of beneficial effect of reduced mortality could not be assessed because the benefit of enalapril in NYHA class I to III HF was not evaluated, and follow-up data are limited. The average age of patients in the study was 71 years, and individuals with significant comorbidities were excluded.
ACE inhibitors also were found to reduce mortality even in asymptomatic patients with HFrEF in the Studies of Left Ventricular Dysfunction trial (SOLVD).10 Enalapril was found to reduce 4-year mortality by 16% and decrease HF hospitalizations when added to conventional therapy consisting primarily of digitalis, diuretics, and nitrates in patients with HFrEF. In this trial, patients aged ≥ 80 years were excluded as well as those with serum creatinine > 2 mg/dL or other conditions that could shorten survival or otherwise impede participation in a long-term trial.
PARADIGM-HF trial patients with HFrEF were randomized to enalapril or the angiotensin receptor-neprilysin inhibitor LCZ696. After a median of 27 months of follow-up, treatment with the angiotensin receptor-neprilysin inhibitor demonstrated greater reduction in CV mortality and HF hospitalizations than enalapril did and was associated with reduced all-cause mortality.11 The trial was stopped early because of evidence of overwhelming benefit with LCZ696. This study of mainly white men included no patients aged ≥ 75 years.
Angiotensin Receptor Blockers
Although less studied than ACE inhibitors, angiotensin receptor blockers (ARBs) share similar benefits. Among patients with symptomatic HFrEF taking an ACE inhibitor, the addition of candesartan reduced the risk of CV death and HF hospitalization as demonstrated in the Candesartan in Heart Failure Assessment of Reduction Mortality and Morbidity (CHARM-added and CHARM-alternative trials).12,13 The CHARM-added trial targeted patients with left ventricular ejection fraction (LVEF) ≤ 40% and NYHA class II to IV HF symptoms who were taking an ACE inhibitor. Adding candesartan reduced CV mortality by 37.9% and HF hospitalization by 42.3% compared with that of placebo.
The CHARM-alternative study found that use of candesartan in symptomatic HFrEF patients who do not tolerate ACE inhibitors,resulted in a 20% reduction in CV mortality as well as a 40% reduction in hospitalization for HF. Among patients with HF with preserved ejection fraction (HFpEF) and NYHA class II to IV symptoms, adding candesartan modestly reduced the rate of HF-related hospitalizations and had no effect on CV mortality in the CHARM-preserved study.14 The CHARM trials examined mostly white men, but 26% of patients were aged > 75 years. However, there was no subgroup analysis for patients aged > 75 years. The study excluded patients with serum creatinine > 2 mg/dL.
Other ARB trials included the following:
- The I-PRESERVE trial, which found that irbesartan did not improve outcomes of patients with HF with preserved ejection fraction (HFpEF).15 The study of mostly white patients did not include patients aged ≥ 80 years.
- A randomized trial of valsartan in HF improved symptoms and mortality in NYHA II to IV HF but showed no benefit when added to ACE inhibitors.16 The trial had no patients aged ≥ 75 years and excluded those with several common comorbidities.
- A randomized, double-blind trial studied the effects of high-dose vs low-dose losartan on clinical outcomes in 3,846 patients with HF and demonstrated that high-dose losartan (150 mg/d) reduces all-cause mortality and hospitalization for HF more effectively than does low-dose losartan (50 mg/d).17 The study, however, had several exclusion criteria, and no patients were aged ≥ 75 years.
Mineralocorticoid Receptor Antagonists
Major studies of aldosterone antagonists demonstrated extra benefit when added to ACE inhibitors/ARBs in patients with HFrEF and NYHA class II HF.18,19
In the RALES study, spironolactone was found to reduce all-cause mortality by 30% and symptoms in NYHA III HF without a significant increase in the risk of serious hyperkalemia or renal failure.18 Most patients were white men aged < 80 years. This study demonstrated the importance of closely following serum potassium levels after initiating aldosterone antagonists in patients with subclinical renal disease because extensive structural damage within the kidney occurs before serum creatinine increases. Patients with advanced renal failure or those who cannot have close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists. Patients with cancer and liver failure were excluded from this trial.
In the Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure study, (EMPHASIS-HF Study) eplerenone was found to reduce all-cause mortality and hospitalization for HFrEF.19 Similar to RALES, patients were mostly white males aged < 80 years, and patients with clinically significant, coexisting conditions were excluded.
The 2014 Treatment of Preserved Cardiac FunctionHeart Failure with an Aldosterone Antagonist Trial (TOPCAT) randomized 3,445 patients with well-controlled blood pressure to spironolactone or placebo.20 Inclusion criteria were LVEF ≥ 45%, findings of HF, and either a HF hospitalization or elevated B-type natriuretic peptide level. There was no difference in the primary composite outcome of CV mortality, aborted cardiac arrest, or HF hospitalization over the 3.3-year follow-up period. The study found that among patients with HFpEF, spironolactone does not reduce the composite endpoint of CV mortality, aborted cardiac arrest, or HF hospitalizations compared with that of placebo.20 In the trial, 29% of patients were aged > 75 years, and most were white men. There was no subgroup analysis for older patients.20 In all 3 trials, patients with kidney injury (serum creatinine of ≥ 2.5 or estimated glomerular filtration rate of ≤ 30 mL/min) were excluded because of the risk of hyperkalemia.
An observational study after the RALES trial demonstrated a nearly 4-fold increase in admissions for hyperkalemia with a 6-fold increase in associated mortality in patients taking spirolactone.21 Therefore, it is important to closely follow serum potassium levels after initiating aldosterone antagonists in older patients with subclinical renal disease. Patients with advanced renal failure or those without close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists.
Antithrombotic Therapy
The large multicenter, double-blind randomized trial WARCEF found no added benefit with warfarin vs aspirin for patients with HFrEF in sinus rhythm.22 There was no reduced time to first stroke or death, and the reduced ischemic stroke risk was offset by an increase in major hemorrhage. It is not clear whether subgroup analysis for the etiology of patients’ HF was performed in WARCEF.
The Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial (N = 1,587) found that treatment with warfarin resulted in significantly fewer strokes in patients with ischemic cardiomyopathy.23 Randomization was not stratified by age group in both trials, and baseline characteristics included mostly white men, and no patients were older than aged > 75 years.
The risk of bleeding with prophylactic aspirin use for CV disease is dose dependent and increases with higher aspirin doses.24 The use of aspirin, 325 mg/d, in the WARCEF study might have contributed to the increased risk of hemorrhage.
Recently published results of COMMANDER HF found that the addition of rivaroxaban at a dose of 2.5 mg twice daily to standard care, including clinically selected antiplatelet therapies was not associated with a significantly lower rate of the composite primary outcome composite outcome of death, myocardial infarction (MI), or stroke among 5,022 patients with a recent episode of worsening heart failure compared with that of placebo.25
Several medical conditions are known to increase bleeding risk, including hypertension, cerebrovascular disease, ischemic stroke, serious heart disease, diabetes mellitus, renal insufficiency, alcoholism, liver disease, and falls.26 Many of these conditions are common among very old patients and should be considered when estimating risk–benefit ratio of oral anticoagulation therapy.
β-blockers
In several large studies, β-blockers have been shown to be effective in reducing mortality in patients with HFrEF. In the Cardiac Insufficiency Bisoprolol Study II, bisoprolol improved all-cause mortality and all-cause hospitalizations, and reduced sudden death in patients with NYHA III or IV HF.27 In the Carvedilol or Metoprolol European Trial (COMET), carvedilol was superior to metoprolol in reducing all-cause mortality for patients with NYHA II or IV HF.28 Both trials included mostly white men; patients with several comorbidities were excluded, and no patients were aged > 80 years.
COMET compared carvedilol with metoprolol tartrate, the short-acting form of metoprolol that has not shown a survival benefit for patients with HF. However, the Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure trial demonstrated survival benefits with metoprolol CR/XL and included patients aged > 80 years.29
In the SENIORS study, patients treated with nebivolol had a 4.2% absolute risk reduction in a composite of mortality or hospital admission at a mean follow-up of 21 months.30 It is reasonable to use nebivolol for managing HF in older patients. Careful monitoring of heart rate is necessary when prescribing β-blockers for older patients.
Cardiac Glycosides
Digoxin with diuretics was the first-line treatment for HF for many decades and the mainstay of HF therapy until the first large HF trials were performed in the 1980s. One trial initiated by the Digoxin Investigation Group (DIG) studied patients with HFrEF who were already receiving treatment for HF (including 94% taking ACE inhibitors and 82% on diuretics) and randomized them to either digoxin or placebo.31 The study found no significant difference in mortality between the groups at the 3-year follow-up; however, the digoxin group had significantly fewer hospitalizations compared with that of the placebo group.
A post-hoc analysis of patients by age found no difference in mortality between patients aged 70 to 79 years and those ≥ 80 years, with a persistent benefit in fewer hospitalizations. Digoxin continues to be recommended as a reasonable medication for treating symptomatic HFrEF. However, caution is advised in older patients, especially women, who are at higher risk of digoxin toxicity.
No current evidence exists that digoxin adds any benefits for patients with HFpEF of any age and therefore, it should not be used.
Diuretics
Diuretic therapy is important for managing shortness of breath and congestion related to fluid volume overload in patients with HF. Although diuretics have not been shown to reduce mortality in patients with HF, they are the mainstay treatment for patients with HFpEF.32 In a post-hoc analysis of the DIG study, diuretic use was associated with increased risk of mortality and hospitalizations in patients aged > 65 years.33 Hyponatremia is one of the most serious adverse effects (AEs) with these agents and occurs in about one-fifth of elderly patients taking diuretics.
In severe cases hyponatremia can cause a range of problems, including weakness, confusion, postural giddiness, postural hypotension, falls, transient hemiparesis, and seizures. In older patients with diminished renal reserve, diuretics are more likely to precipitate prerenal uremia than it does in younger patients. Prerequisites for diuretic use are an accurate diagnosis, careful monitoring of blood pressure and serum electrolytes, and regular review of their efficacy, AEs, and the need for continued treatment.
Statins
The Controlled Rosuvastatin Multinational Trial in Heart Failure demonstrated that low-dose rosuvastatin (10 mg/d) does not improve survival among patients with moderate-to-severe ischemic cardiomyopathy but could reduce the rate of CV hospitalizations.34 Patients in this study had a mean age of 73 years, and 41% of them were aged ≥ 75 years. However, the study used a low-dose rosuvastatin, and patients with several common comorbidities were excluded. Evidence exists that treatment with other statins may improve outcomes in patients with HF. There is also evidence that among elderly patients with HF, low serum total cholesterol is independently associated with a worse prognosis.35
Comorbidities
Anemia
In patients with iron-deficiency anemia (ferritin 15-100 ng/mL or 100-299 ng/mL with transferrin saturation < 20%) and symptomatic HFrEF (LVEF ≤ 40% with NYHA II to IV HF), oral iron replacement had no effect on exercise capacity as measured using change in peak oxygen uptake.36 However, IV iron replacement might be a reasonable option to improve functional status and quality of life (QOL) for patients with HF.37 In these studies, participants were aged < 75 years, and there is no evidence that treating other types of anemia improves outcomes in patients with HF.
Hypertension
The Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated that controlling blood pressure to a goal systolic pressure of < 120 mm Hg is associated with significant reduction in the mortality among patients with increased CV risk (aged > 75 years, vascular disease, kidney injury, or a Framingham Risk Score >15%).38 The SPRINT study included patients aged > 75 (25%); however, the study excluded older adults living in nursing homes and those with diabetes mellitus, symptomatic HF, dementia, or stroke. The subgroup analysis did not stratify patients based on age nor provided sufficient evidence regarding treatment targets for this vulnerable population. Therefore, clinicians cannot draw any conclusions about managing hypertension among patients with HF from this study.
Sleep Apnea
Sleep apnea is common among patients with HF. A study of adults with chronic HF treated with evidence-based therapies found that 61% of participants had central or obstructive sleep apnea.39 In elderly patients, sleep apnea is further complicated by insomnia and disturbance of sleep cycle that often occur with the aging process.
It is crucial to differentiate central sleep apnea from obstructive sleep apnea, because the treatment approaches differ. Central sleep apnea is associated with poor prognosis in patients with HF.40 Adaptive servo ventilation for central sleep apnea uses a noninvasive ventilator to delivering servo controlled inspiratory pressure support on top of expiratory positive airway pressure. Adaptive servo ventilation for central sleep apnea is associated with higher all-cause mortality and CV mortality.41 Continuous positive airway pressure for obstructive sleep apnea improves sleep quality, reduces the apnea-hypopnea index, and improves nocturnal oxygenation.42
Depression
Clinically significant depression occurs in 21% of patients with HF, and the relationship between depression and poor HF outcomes is consistent and strong across several endpoints. However, in a randomized, 12-week study, the selective serotonin reuptake inhibitor sertraline did not improve depression symptoms or clinical status among patients with HF.43 Depression symptoms might overlap with fatigue and low energy expenditure experienced by oldest old patients with HF who do not have depression.
Furthermore, studies describing depression treatments among patients with HF are too small and heterogeneous to permit definitive conclusions about intervention effectiveness. These results identify areas requiring further development, raise questions regarding the association between depression and clinical outcomes in patients with HF, and provide information on depression prevalence that may help researchers design studies with appropriate depression measures and adequately powered sample sizes.
Frailty
Although frailty is prevalent in the elderly and is independently associated with poor outcomes, there is no standardized definition for frailty. The Fried Frailty Index is a widely used scale that incorporates criteria including weakness, slowness, exhaustion, and low physical activity in the diagnosis of frailty.44 However these symptoms are common among patients with advanced HF with and without depression or frailty.
Frailty should be defined collaboratively by the clinician and the patient and should include multidimensional aspects of health, function, and well-being. The treatment goal for patients with HF with frailty is to establish patient-centered goals based on preferences of care.45
Discussion
Although several novel approaches to improve outcomes of patients with HF have been developed, it continues to be the leading cause of cardiovascular death among older patients and the leading cause of hospital admissions.46 About 50% of newly diagnosed patients with HF die within 5 years.47 Current guidelines for managing HF are based on clinical trials that either include few or completely exclude patients aged > 80 years, minorities, and patients with comorbidities clinicians encounter daily in clinical practice.
Furthermore, most clinical trials are designed with mortality as the primary endpoint, which might be as important to our patients with advanced age as their ability to function with a reasonable QOL and less dependence on caregivers.
Decision making in managing HF in our oldest patients should start with an open discussion of the disease and its prognosis, goals of care, and available treatment options. The discussion should also cover all dimensions of suffering, including physical, spiritual, and psychosocial domains. Interviews of patients dying of HF and their caregivers conducted in the United Kingdom identified several communication and transition of care challenges specific to treating this population.48 The study revealed in most cases, patients did not recall receiving any written information about the severity of their disease and often did not understand the association among symptoms, such as shortness of breath, edema, and HF. Patients and caregivers did not feel involved in the decision-making process regarding their illness.
The concurrent presence of comorbidity, frailty, and cognitive impairment in our aging population with HF might add to the burden of the primary condition. Care often is perceived as fragmented. Polypharmacy negatively impacts HF management by increasing risk of drug nonadherence, drug interactions, and AEs in an already vulnerable population. There is a need for more effective interpersonal and easy to understand communication and resources.
In many situations, support services might be best facilitated by a dedicated palliative medicine team with significant experience in managing patients with HF.Although palliative medicine should always be considered for patients with HF with advanced age,consultations often are not obtained unless the patient decides to forgo medical treatment or until the last month of life.49
Although not all end-of-life symptoms can realistically be palliated, earlier involvement of multidisciplinary palliative medicine specialists may improve symptom control, functional status, and QOL. The team may help patients and caregivers cope with uncertainty, and make informed decisions that are person centered based on value system and beliefs.51
Conclusion
Randomized control trials as well as thoughtful observational studies of HF in patients with advanced age and comorbidities, although challenging, are needed to create the evidence base for treatment interventions and assessing their impact on mortality, morbidity, and QOL in this rapidly growing segment of our population.
Given the lack of evidence for HF treatment in patients with advanced age, the clinician should weigh the knowledge of the effect of aging on the CV system, and the lived experience of patients with HF, with the evidence that exists for making the best decision to relieve bothersome symptoms and improve outcomes of care as determined by patients and their caregivers.
Often the most important intervention we can offer our patients, especially those nearing the end of life, is dedicating our time to truly and actively listen with empathy, understating, and respect for their autonomy and for their decision making. And in doing so we accept our own limitations with humility.
Acknowledgments
Dr. Kheirbek received funds from the Veterans Affairs Capitol Health Care Network to establish the Center for Health and Aging at the Washington DC VA Medical Center.
1. Ortman JM, Velkoff AV, Hogan H. An aging nation: the older population in the United States. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed September 30, 2018.
2. Fang J, Mensah GA, Croft JB, Keenan NL. Heart failure-related hospitalization in the U.S., 1979 to 2004. J Am Coll Cardiol. 2008;52(6):428-434.
3. Heidenreich PA, Albert NM, Allen LA, et al; American Heart Association Advocacy Coordinating Committee; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Stroke Council. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013;6(3):606-619.
4. National Heart, Lung, and Blood Institute, National Institutes of Health. Incidence and Prevalence: 2006 Chart Book on Cardiovascular and Lung Diseases. Bethesda, MD: National Institutes of Health; 2006.
5. Curtis LH, Whellan DJ, Hammill BG, et al. Incidence and prevalence of heart failure in elderly persons, 1994-2003. Arch Intern Med. 2008;168(4):418-424.
6. Writing Group, Mozaffarian D, Benjamin EJ, et al; American Heart Association Statistics Committee; Stroke Statistics Subcommittee. Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation. 2016;133(4):e38-e360.
7. Lakatta EG, Levy D. Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part I: aging arteries: a “set up” for vascular disease. Circulation. 2003;107(1):139-146.
8. Mangoni AA, Jackson SH. Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications. Br J Clin Pharmacol. 2004;57(1):6-14.
9. CONSENSUS Trial Study Group. Effects of enalapril on mortality in severe congestive heart failure. Results of the Cooperative North Scandinavian Enalapril Survival Study (CONSENSUS). N Engl J Med. 1987;316(23):1429-1435.
10. SOLVD Investigators; Yusuf S, Pitt B, Davis CE, Hood WB Jr, Cohn JN. Effect of enalapril on mortality and the development of heart failure in asymptomatic patients with reduced left ventricular ejection fractions. N Engl J Med. 1992;327(10):685-691.
11. McMurray JJ, Packer M, Desai AS, et al; PARADIGM-HF Investigators and Committees. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med. 2014;371(11):993-1004.
12. McMurray JJ, Ostergren J, Swedberg K, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and reduced left-ventricular systolic function taking angiotensin-converting-enzyme inhibitors: the CHARM-Added trial. Lancet. 2003;362(9386):767-771.
13. Granger CB, McMurray JJ, Yusuf S, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and reduced left-ventricular systolic function intolerant to angiotensin-converting-enzyme inhibitors: the CHARM-Alternative trial. Lancet. 2003;362(9386):772-776.
14. Yusuf S, Pfeffer MA, Swedberg K, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and preserved left-ventricular ejection fraction: the CHARM-Preserved Trial. Lancet. 2003;362(9386):777-781.
15. Massie BM, Carson PE, McMurray JJ, et al; I-PRESERVE Investigators. Irbesartan in patients with heart failure and preserved ejection fraction. N Engl J Med. 2008;359(23):2456-2467.
16. Cohn JN, Tognoni G; Valsartan Heart Failure Trial Investigators. A randomized trial of the angiotensin-receptor blocker valsartan in chronic heart failure. N Engl J Med. 2001;345(23):1667-1675.
17. Konstam MA, Neaton JD, Dickstein K, et al; HEAAL Investigators. Effects of high-dose versus low-dose losartan on clinical outcomes in patients with heart failure (HEAAL study): a randomised, double-blind trial. Lancet. 2009;374(9704):1840-1848.
18. Pitt B, Zannad F, Remme WJ, et al. The effect of spironolactone on morbidity and mortality in patients with severe heart failure. Randomized Aldactone Evaluation Study Investigators. N Engl J Med. 1999;341(10):709-717.
19. Zannad F, McMurray JJ, Krum H, et al; EMPHASIS-HF Study Group. Eplerenone in patients with systolic heart failure and mild symptoms. N Engl J Med. 2011;364(1):11-21.
20. Pitt B, Pfeffer MA, Assmann SF, et al; TOPCAT Investigators. Spironolactone for heart failure with preserved ejection fraction. N Engl J Med. 2014;370(15):1383-1392.
21. Juurlink DN, Mamdani MM, Lee DS, et al. Rates of hyperkalemia after publication of the Randomized Aldactone Evaluation Study. N Engl J Med. 2004;351(6):543-551.
22. Homma S, Thompson JL, Pullicino PM, et al; WARCEF Investigators. Warfarin and aspirin in patients with heart failure and sinus rhythm. N Engl J Med. 2012;366(20):1859-1869.
23. Massie BM, Collins JF, Ammon SE, et al; WATCH Trial Investigators. Randomized trial of warfarin, aspirin, and clopidogrel in patients with chronic heart failure: the Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial. Circulation. 2009;119(12):1616-1624.
24. Campbell CL, Smyth S, Montalescot G, Steinhubl SR. Aspirin dose for the prevention of cardiovascular disease: a systematic review. JAMA. 2007;297(18):2018-2024.
25. Zannad F, Anker, SD, Byra WM, et al; COMMANDER HF Investigators. Rivaroxaban in patients with heart failure, sinus rhythm, and coronary disease. N Engl J Med. 2018;379(14):1332-1342.
26. Schulman S, Beyth RJ, Kearon C, Levine MN. Hemorrhagic complications of anticoagulant and thrombolytic treatment: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest. 2008;133(suppl 6):257S-298S.
27. CIBIS-II Investigators and Committees. The Cardiac Insufficiency Bisoprolol Study II (CIBIS-II): a randomised trial. Lancet. 1999;353(9146):9-13.
28. Poole-Wilson PA, Swedberg K, Cleland JG, et al; Carvedilol Or Metoprolol European Trial Investigators. Comparison of carvedilol and metoprolol on clinical outcomes in patients with chronic heart failure in the Carvedilol Or Metoprolol European Trial (COMET): randomized controlled trial. Lancet. 2003;362(9377):7-13.
29. MERIT-HF Study Group. Effect of metoprolol CR/XL in chronic heart failure: Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure (MERIT-HF). Lancet. 1999;353(9169):2001-2007.
30. Flather MD, Shibata MC, Coats AJ, et al; SENIORS Investigators. Randomized trial to determine the effect of nebivolol on mortality and cardiovascular hospital admission in elderly patients with heart failure (SENIORS). Eur Heart J. 2005;26(3):215-225.
31. Digitalis Investigation Group. The effect of digoxin on mortality and morbidity in patients with heart failure. N Engl J Med. 1997;336(8):525-533.
32. Yancy CW, Jessup M, Bozkurt B, et al; American College of Cardiology Foundation; American Heart Association Task Force on Practice Guidelines. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;62(16):e147-e239.
33 Juurlink DN, Mamdani MM, Lee DS, et al. Rates of hyperkalemia after publication of the Randomized Aldactone Evaluation Study. N Engl J Med. 2004;351(6):543-551.
34. Kjekshus J, Apetrei E, Barrios V, et al; CORONA Group. Rosuvastatin in older patients with systolic heart failure. N Engl J Med. 2007;357(22):2248-2261.
35. Rauchhaus M, Clark AL, Doehner W, et al. The relationship between cholesterol and survival in patients with chronic heart failure. J Am Coll Cardiol. 2003;42(11):1933-1940.
36. Yancy CW, Jessup M, Bozkurt B, et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation. 2017;136(6):e137-e161.
37. Ponikowski P, van Veldhuisen DJ, Comin-Colet J, et al; CONFIRM-HF Investigators. Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency. Eur Heart J. 2015;36(11):657-668.
38. SPRINT Research Group, Wright JT Jr, Williamson JD, et al. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373(22):2103-2116.
39. MacDonald M, Fang J, Pittman SD, White DP, Malhotra A. The current prevalence of sleep disordered breathing in congestive heart failure patients treated with beta-blockers. J Clin Sleep Med. 2008;4(1):38-42.
40. Bradley TD, Floras JS. Sleep Apnea and heart failure: part II: Central sleep apnea. Circulation. 2003;107(13):1822-1826.
41. Cowie MR, Woehrle H, Wegscheider K, et al. Adaptive servo-ventilation for central sleep apnea in systolic heart failure. N Engl J Med. 2015;373(12):1095-1105.
42. McEvoy RD, Antic NA, Heeley E, et al; SAVE Investigators and Coordinators. CPAP for prevention of cardiovascular events in obstructive sleep apnea. N Engl J Med. 2016;375(10):919-931.
43. O’Connor CM, Jiang W, Kuchibhatla M, et al; SADHART-CHF Investigators. Safety and efficacy of sertraline for depression in patients with heart failure: results of the SADHART-CHF (Sertraline Against Depression and Heart Disease in Chronic Heart Failure) trial. J Am Coll Cardiol. 2010;56(9):692-699.
44. Fried LP, Tangen CM, Walston J, et al; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-156.
45. Pilotto A, Addante F, Franceschi M, et al. Multidimensional Prognostic Index based on a comprehensive geriatric assessment predicts short-term mortality in older patients with heart failure. Circ Heart Fail. 2010;3(1):14-20.
46. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428.
47. Goldberg, RJ, Ciampa, J, Lessard D,, et al. Long-term survival after heart failure: a contemporary population-based perspective. Arch Intern Med. 2007;167(5):490-496.
48. Murray SA, Boyd K, Kendall M, Worth A, Benton TF, Clausen H. Dying of lung cancer or cardiac failure: prospective qualitative interview study of patients and their carers in the community. BMJ. 2002;325(7370):929.
49. Gibbs JS, McCoy AS, Gibbs LM, Rogers AE, Addington-Hall JM. Living with and dying from heart failure: the role of palliative care. Heart. 2002;88(suppl 2):ii36-39.
50. Quill TE, Dresser R, Brock DW. The rule of double effect—a critique of its role in end-of-life decision making. N Engl J Med. 1997;337(24):1768-1771.
51. Nieminen MS, Dickstein K, Fonseca C, et al. The patient perspective: quality of life in advanced heart failure with frequent hospitalizations. Int J Cardiol. 2015;191:256-264.
As the population ages, heart failure is becoming a major public health challenge; clinicians need further evidence-based treatments to bridge the existing gap between guidelines and real-world clinical practice.
As the population ages, heart failure is becoming a major public health challenge; clinicians need further evidence-based treatments to bridge the existing gap between guidelines and real-world clinical practice.
In 2050, persons aged ≥ 85 years, also known as the oldest old, are projected to reach 18 million, accounting for 4.5% of the US population, up from 2.5% in 2030.1 These patients are the fastest growing segment of the US population.
Advances in treating cardiovascular (CV) disease over the past 2 decades have led to an increased incidence of heart failure (HF) and hospitalizations among older patients.2 Total costs of care for persons with HF have exceeded $30 billion annually and are expected to rise to more than $70 billion by 2030 due to growth of the aging population.3,4 Moreover, the Framingham Study reported mortality increases with advancing age (HR 1.27 and 1.61 per decade in men and women, respectively).5
The prevalence of HF is also high and increasing over time. The National Health and Nutrition Examination Survey reported that about 5.7 million Americans have HF.6 The prevalence of HF is expected to reach 8 million by 2030.6 The higher numbers of HF among patients with advanced age is associated with age-related changes in CV structure and function, including reduced responsiveness to β-adrenergic stimulation, impaired left ventricular diastolic filling, and increased vascular stiffness. In addition, age-related changes in other systems might contribute to a HF diagnosis or worsening of the condition.7
Older adults experience physiologic changes in pharmacokinetics and pharmacodynamics, including decreased volume of distribution and creatinine clearance, which lead to significant changes in drug concentration and effectiveness.8
Geriatric patients aged > 65 years who have comorbidities and those who reside in long-term care settings are underrepresented in clinical trials, leading clinicians to make treatment decisions based on data from younger, community-dwelling individuals. Researchers have questioned whether to include elderly patients and those with comorbidities in clinical trials, given that their diminished response may produce less conclusive results with smaller treatment effects. Exclusion criteria based on comorbid conditions or functional status disqualify many older adults from clinical trials.
This article reviews evidence from major randomized controlled trials over the past 2 decades and explores their applicability to support HF treatment guidelines in patients with advanced age (Table).
Pharmacotherapy for Heart Failure
Angiotensin-Converting Enzyme Inhibitors
Several randomized clinical trials have found that angiotensin-converting enzyme (ACE) inhibitors improve symptoms in patients with HF. The CooperativeNorth Scandinavian Enalapril Survival Study (CONSENSUS), demonstrated that enalapril improves survival in patients with New York Heart Association (NYHA) class IV HF with reduced ejection fraction (HFrEF) when added to standard therapy.9 However, the duration of beneficial effect of reduced mortality could not be assessed because the benefit of enalapril in NYHA class I to III HF was not evaluated, and follow-up data are limited. The average age of patients in the study was 71 years, and individuals with significant comorbidities were excluded.
ACE inhibitors also were found to reduce mortality even in asymptomatic patients with HFrEF in the Studies of Left Ventricular Dysfunction trial (SOLVD).10 Enalapril was found to reduce 4-year mortality by 16% and decrease HF hospitalizations when added to conventional therapy consisting primarily of digitalis, diuretics, and nitrates in patients with HFrEF. In this trial, patients aged ≥ 80 years were excluded as well as those with serum creatinine > 2 mg/dL or other conditions that could shorten survival or otherwise impede participation in a long-term trial.
PARADIGM-HF trial patients with HFrEF were randomized to enalapril or the angiotensin receptor-neprilysin inhibitor LCZ696. After a median of 27 months of follow-up, treatment with the angiotensin receptor-neprilysin inhibitor demonstrated greater reduction in CV mortality and HF hospitalizations than enalapril did and was associated with reduced all-cause mortality.11 The trial was stopped early because of evidence of overwhelming benefit with LCZ696. This study of mainly white men included no patients aged ≥ 75 years.
Angiotensin Receptor Blockers
Although less studied than ACE inhibitors, angiotensin receptor blockers (ARBs) share similar benefits. Among patients with symptomatic HFrEF taking an ACE inhibitor, the addition of candesartan reduced the risk of CV death and HF hospitalization as demonstrated in the Candesartan in Heart Failure Assessment of Reduction Mortality and Morbidity (CHARM-added and CHARM-alternative trials).12,13 The CHARM-added trial targeted patients with left ventricular ejection fraction (LVEF) ≤ 40% and NYHA class II to IV HF symptoms who were taking an ACE inhibitor. Adding candesartan reduced CV mortality by 37.9% and HF hospitalization by 42.3% compared with that of placebo.
The CHARM-alternative study found that use of candesartan in symptomatic HFrEF patients who do not tolerate ACE inhibitors,resulted in a 20% reduction in CV mortality as well as a 40% reduction in hospitalization for HF. Among patients with HF with preserved ejection fraction (HFpEF) and NYHA class II to IV symptoms, adding candesartan modestly reduced the rate of HF-related hospitalizations and had no effect on CV mortality in the CHARM-preserved study.14 The CHARM trials examined mostly white men, but 26% of patients were aged > 75 years. However, there was no subgroup analysis for patients aged > 75 years. The study excluded patients with serum creatinine > 2 mg/dL.
Other ARB trials included the following:
- The I-PRESERVE trial, which found that irbesartan did not improve outcomes of patients with HF with preserved ejection fraction (HFpEF).15 The study of mostly white patients did not include patients aged ≥ 80 years.
- A randomized trial of valsartan in HF improved symptoms and mortality in NYHA II to IV HF but showed no benefit when added to ACE inhibitors.16 The trial had no patients aged ≥ 75 years and excluded those with several common comorbidities.
- A randomized, double-blind trial studied the effects of high-dose vs low-dose losartan on clinical outcomes in 3,846 patients with HF and demonstrated that high-dose losartan (150 mg/d) reduces all-cause mortality and hospitalization for HF more effectively than does low-dose losartan (50 mg/d).17 The study, however, had several exclusion criteria, and no patients were aged ≥ 75 years.
Mineralocorticoid Receptor Antagonists
Major studies of aldosterone antagonists demonstrated extra benefit when added to ACE inhibitors/ARBs in patients with HFrEF and NYHA class II HF.18,19
In the RALES study, spironolactone was found to reduce all-cause mortality by 30% and symptoms in NYHA III HF without a significant increase in the risk of serious hyperkalemia or renal failure.18 Most patients were white men aged < 80 years. This study demonstrated the importance of closely following serum potassium levels after initiating aldosterone antagonists in patients with subclinical renal disease because extensive structural damage within the kidney occurs before serum creatinine increases. Patients with advanced renal failure or those who cannot have close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists. Patients with cancer and liver failure were excluded from this trial.
In the Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure study, (EMPHASIS-HF Study) eplerenone was found to reduce all-cause mortality and hospitalization for HFrEF.19 Similar to RALES, patients were mostly white males aged < 80 years, and patients with clinically significant, coexisting conditions were excluded.
The 2014 Treatment of Preserved Cardiac FunctionHeart Failure with an Aldosterone Antagonist Trial (TOPCAT) randomized 3,445 patients with well-controlled blood pressure to spironolactone or placebo.20 Inclusion criteria were LVEF ≥ 45%, findings of HF, and either a HF hospitalization or elevated B-type natriuretic peptide level. There was no difference in the primary composite outcome of CV mortality, aborted cardiac arrest, or HF hospitalization over the 3.3-year follow-up period. The study found that among patients with HFpEF, spironolactone does not reduce the composite endpoint of CV mortality, aborted cardiac arrest, or HF hospitalizations compared with that of placebo.20 In the trial, 29% of patients were aged > 75 years, and most were white men. There was no subgroup analysis for older patients.20 In all 3 trials, patients with kidney injury (serum creatinine of ≥ 2.5 or estimated glomerular filtration rate of ≤ 30 mL/min) were excluded because of the risk of hyperkalemia.
An observational study after the RALES trial demonstrated a nearly 4-fold increase in admissions for hyperkalemia with a 6-fold increase in associated mortality in patients taking spirolactone.21 Therefore, it is important to closely follow serum potassium levels after initiating aldosterone antagonists in older patients with subclinical renal disease. Patients with advanced renal failure or those without close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists.
Antithrombotic Therapy
The large multicenter, double-blind randomized trial WARCEF found no added benefit with warfarin vs aspirin for patients with HFrEF in sinus rhythm.22 There was no reduced time to first stroke or death, and the reduced ischemic stroke risk was offset by an increase in major hemorrhage. It is not clear whether subgroup analysis for the etiology of patients’ HF was performed in WARCEF.
The Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial (N = 1,587) found that treatment with warfarin resulted in significantly fewer strokes in patients with ischemic cardiomyopathy.23 Randomization was not stratified by age group in both trials, and baseline characteristics included mostly white men, and no patients were older than aged > 75 years.
The risk of bleeding with prophylactic aspirin use for CV disease is dose dependent and increases with higher aspirin doses.24 The use of aspirin, 325 mg/d, in the WARCEF study might have contributed to the increased risk of hemorrhage.
Recently published results of COMMANDER HF found that the addition of rivaroxaban at a dose of 2.5 mg twice daily to standard care, including clinically selected antiplatelet therapies was not associated with a significantly lower rate of the composite primary outcome composite outcome of death, myocardial infarction (MI), or stroke among 5,022 patients with a recent episode of worsening heart failure compared with that of placebo.25
Several medical conditions are known to increase bleeding risk, including hypertension, cerebrovascular disease, ischemic stroke, serious heart disease, diabetes mellitus, renal insufficiency, alcoholism, liver disease, and falls.26 Many of these conditions are common among very old patients and should be considered when estimating risk–benefit ratio of oral anticoagulation therapy.
β-blockers
In several large studies, β-blockers have been shown to be effective in reducing mortality in patients with HFrEF. In the Cardiac Insufficiency Bisoprolol Study II, bisoprolol improved all-cause mortality and all-cause hospitalizations, and reduced sudden death in patients with NYHA III or IV HF.27 In the Carvedilol or Metoprolol European Trial (COMET), carvedilol was superior to metoprolol in reducing all-cause mortality for patients with NYHA II or IV HF.28 Both trials included mostly white men; patients with several comorbidities were excluded, and no patients were aged > 80 years.
COMET compared carvedilol with metoprolol tartrate, the short-acting form of metoprolol that has not shown a survival benefit for patients with HF. However, the Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure trial demonstrated survival benefits with metoprolol CR/XL and included patients aged > 80 years.29
In the SENIORS study, patients treated with nebivolol had a 4.2% absolute risk reduction in a composite of mortality or hospital admission at a mean follow-up of 21 months.30 It is reasonable to use nebivolol for managing HF in older patients. Careful monitoring of heart rate is necessary when prescribing β-blockers for older patients.
Cardiac Glycosides
Digoxin with diuretics was the first-line treatment for HF for many decades and the mainstay of HF therapy until the first large HF trials were performed in the 1980s. One trial initiated by the Digoxin Investigation Group (DIG) studied patients with HFrEF who were already receiving treatment for HF (including 94% taking ACE inhibitors and 82% on diuretics) and randomized them to either digoxin or placebo.31 The study found no significant difference in mortality between the groups at the 3-year follow-up; however, the digoxin group had significantly fewer hospitalizations compared with that of the placebo group.
A post-hoc analysis of patients by age found no difference in mortality between patients aged 70 to 79 years and those ≥ 80 years, with a persistent benefit in fewer hospitalizations. Digoxin continues to be recommended as a reasonable medication for treating symptomatic HFrEF. However, caution is advised in older patients, especially women, who are at higher risk of digoxin toxicity.
No current evidence exists that digoxin adds any benefits for patients with HFpEF of any age and therefore, it should not be used.
Diuretics
Diuretic therapy is important for managing shortness of breath and congestion related to fluid volume overload in patients with HF. Although diuretics have not been shown to reduce mortality in patients with HF, they are the mainstay treatment for patients with HFpEF.32 In a post-hoc analysis of the DIG study, diuretic use was associated with increased risk of mortality and hospitalizations in patients aged > 65 years.33 Hyponatremia is one of the most serious adverse effects (AEs) with these agents and occurs in about one-fifth of elderly patients taking diuretics.
In severe cases hyponatremia can cause a range of problems, including weakness, confusion, postural giddiness, postural hypotension, falls, transient hemiparesis, and seizures. In older patients with diminished renal reserve, diuretics are more likely to precipitate prerenal uremia than it does in younger patients. Prerequisites for diuretic use are an accurate diagnosis, careful monitoring of blood pressure and serum electrolytes, and regular review of their efficacy, AEs, and the need for continued treatment.
Statins
The Controlled Rosuvastatin Multinational Trial in Heart Failure demonstrated that low-dose rosuvastatin (10 mg/d) does not improve survival among patients with moderate-to-severe ischemic cardiomyopathy but could reduce the rate of CV hospitalizations.34 Patients in this study had a mean age of 73 years, and 41% of them were aged ≥ 75 years. However, the study used a low-dose rosuvastatin, and patients with several common comorbidities were excluded. Evidence exists that treatment with other statins may improve outcomes in patients with HF. There is also evidence that among elderly patients with HF, low serum total cholesterol is independently associated with a worse prognosis.35
Comorbidities
Anemia
In patients with iron-deficiency anemia (ferritin 15-100 ng/mL or 100-299 ng/mL with transferrin saturation < 20%) and symptomatic HFrEF (LVEF ≤ 40% with NYHA II to IV HF), oral iron replacement had no effect on exercise capacity as measured using change in peak oxygen uptake.36 However, IV iron replacement might be a reasonable option to improve functional status and quality of life (QOL) for patients with HF.37 In these studies, participants were aged < 75 years, and there is no evidence that treating other types of anemia improves outcomes in patients with HF.
Hypertension
The Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated that controlling blood pressure to a goal systolic pressure of < 120 mm Hg is associated with significant reduction in the mortality among patients with increased CV risk (aged > 75 years, vascular disease, kidney injury, or a Framingham Risk Score >15%).38 The SPRINT study included patients aged > 75 (25%); however, the study excluded older adults living in nursing homes and those with diabetes mellitus, symptomatic HF, dementia, or stroke. The subgroup analysis did not stratify patients based on age nor provided sufficient evidence regarding treatment targets for this vulnerable population. Therefore, clinicians cannot draw any conclusions about managing hypertension among patients with HF from this study.
Sleep Apnea
Sleep apnea is common among patients with HF. A study of adults with chronic HF treated with evidence-based therapies found that 61% of participants had central or obstructive sleep apnea.39 In elderly patients, sleep apnea is further complicated by insomnia and disturbance of sleep cycle that often occur with the aging process.
It is crucial to differentiate central sleep apnea from obstructive sleep apnea, because the treatment approaches differ. Central sleep apnea is associated with poor prognosis in patients with HF.40 Adaptive servo ventilation for central sleep apnea uses a noninvasive ventilator to delivering servo controlled inspiratory pressure support on top of expiratory positive airway pressure. Adaptive servo ventilation for central sleep apnea is associated with higher all-cause mortality and CV mortality.41 Continuous positive airway pressure for obstructive sleep apnea improves sleep quality, reduces the apnea-hypopnea index, and improves nocturnal oxygenation.42
Depression
Clinically significant depression occurs in 21% of patients with HF, and the relationship between depression and poor HF outcomes is consistent and strong across several endpoints. However, in a randomized, 12-week study, the selective serotonin reuptake inhibitor sertraline did not improve depression symptoms or clinical status among patients with HF.43 Depression symptoms might overlap with fatigue and low energy expenditure experienced by oldest old patients with HF who do not have depression.
Furthermore, studies describing depression treatments among patients with HF are too small and heterogeneous to permit definitive conclusions about intervention effectiveness. These results identify areas requiring further development, raise questions regarding the association between depression and clinical outcomes in patients with HF, and provide information on depression prevalence that may help researchers design studies with appropriate depression measures and adequately powered sample sizes.
Frailty
Although frailty is prevalent in the elderly and is independently associated with poor outcomes, there is no standardized definition for frailty. The Fried Frailty Index is a widely used scale that incorporates criteria including weakness, slowness, exhaustion, and low physical activity in the diagnosis of frailty.44 However these symptoms are common among patients with advanced HF with and without depression or frailty.
Frailty should be defined collaboratively by the clinician and the patient and should include multidimensional aspects of health, function, and well-being. The treatment goal for patients with HF with frailty is to establish patient-centered goals based on preferences of care.45
Discussion
Although several novel approaches to improve outcomes of patients with HF have been developed, it continues to be the leading cause of cardiovascular death among older patients and the leading cause of hospital admissions.46 About 50% of newly diagnosed patients with HF die within 5 years.47 Current guidelines for managing HF are based on clinical trials that either include few or completely exclude patients aged > 80 years, minorities, and patients with comorbidities clinicians encounter daily in clinical practice.
Furthermore, most clinical trials are designed with mortality as the primary endpoint, which might be as important to our patients with advanced age as their ability to function with a reasonable QOL and less dependence on caregivers.
Decision making in managing HF in our oldest patients should start with an open discussion of the disease and its prognosis, goals of care, and available treatment options. The discussion should also cover all dimensions of suffering, including physical, spiritual, and psychosocial domains. Interviews of patients dying of HF and their caregivers conducted in the United Kingdom identified several communication and transition of care challenges specific to treating this population.48 The study revealed in most cases, patients did not recall receiving any written information about the severity of their disease and often did not understand the association among symptoms, such as shortness of breath, edema, and HF. Patients and caregivers did not feel involved in the decision-making process regarding their illness.
The concurrent presence of comorbidity, frailty, and cognitive impairment in our aging population with HF might add to the burden of the primary condition. Care often is perceived as fragmented. Polypharmacy negatively impacts HF management by increasing risk of drug nonadherence, drug interactions, and AEs in an already vulnerable population. There is a need for more effective interpersonal and easy to understand communication and resources.
In many situations, support services might be best facilitated by a dedicated palliative medicine team with significant experience in managing patients with HF.Although palliative medicine should always be considered for patients with HF with advanced age,consultations often are not obtained unless the patient decides to forgo medical treatment or until the last month of life.49
Although not all end-of-life symptoms can realistically be palliated, earlier involvement of multidisciplinary palliative medicine specialists may improve symptom control, functional status, and QOL. The team may help patients and caregivers cope with uncertainty, and make informed decisions that are person centered based on value system and beliefs.51
Conclusion
Randomized control trials as well as thoughtful observational studies of HF in patients with advanced age and comorbidities, although challenging, are needed to create the evidence base for treatment interventions and assessing their impact on mortality, morbidity, and QOL in this rapidly growing segment of our population.
Given the lack of evidence for HF treatment in patients with advanced age, the clinician should weigh the knowledge of the effect of aging on the CV system, and the lived experience of patients with HF, with the evidence that exists for making the best decision to relieve bothersome symptoms and improve outcomes of care as determined by patients and their caregivers.
Often the most important intervention we can offer our patients, especially those nearing the end of life, is dedicating our time to truly and actively listen with empathy, understating, and respect for their autonomy and for their decision making. And in doing so we accept our own limitations with humility.
Acknowledgments
Dr. Kheirbek received funds from the Veterans Affairs Capitol Health Care Network to establish the Center for Health and Aging at the Washington DC VA Medical Center.
In 2050, persons aged ≥ 85 years, also known as the oldest old, are projected to reach 18 million, accounting for 4.5% of the US population, up from 2.5% in 2030.1 These patients are the fastest growing segment of the US population.
Advances in treating cardiovascular (CV) disease over the past 2 decades have led to an increased incidence of heart failure (HF) and hospitalizations among older patients.2 Total costs of care for persons with HF have exceeded $30 billion annually and are expected to rise to more than $70 billion by 2030 due to growth of the aging population.3,4 Moreover, the Framingham Study reported mortality increases with advancing age (HR 1.27 and 1.61 per decade in men and women, respectively).5
The prevalence of HF is also high and increasing over time. The National Health and Nutrition Examination Survey reported that about 5.7 million Americans have HF.6 The prevalence of HF is expected to reach 8 million by 2030.6 The higher numbers of HF among patients with advanced age is associated with age-related changes in CV structure and function, including reduced responsiveness to β-adrenergic stimulation, impaired left ventricular diastolic filling, and increased vascular stiffness. In addition, age-related changes in other systems might contribute to a HF diagnosis or worsening of the condition.7
Older adults experience physiologic changes in pharmacokinetics and pharmacodynamics, including decreased volume of distribution and creatinine clearance, which lead to significant changes in drug concentration and effectiveness.8
Geriatric patients aged > 65 years who have comorbidities and those who reside in long-term care settings are underrepresented in clinical trials, leading clinicians to make treatment decisions based on data from younger, community-dwelling individuals. Researchers have questioned whether to include elderly patients and those with comorbidities in clinical trials, given that their diminished response may produce less conclusive results with smaller treatment effects. Exclusion criteria based on comorbid conditions or functional status disqualify many older adults from clinical trials.
This article reviews evidence from major randomized controlled trials over the past 2 decades and explores their applicability to support HF treatment guidelines in patients with advanced age (Table).
Pharmacotherapy for Heart Failure
Angiotensin-Converting Enzyme Inhibitors
Several randomized clinical trials have found that angiotensin-converting enzyme (ACE) inhibitors improve symptoms in patients with HF. The CooperativeNorth Scandinavian Enalapril Survival Study (CONSENSUS), demonstrated that enalapril improves survival in patients with New York Heart Association (NYHA) class IV HF with reduced ejection fraction (HFrEF) when added to standard therapy.9 However, the duration of beneficial effect of reduced mortality could not be assessed because the benefit of enalapril in NYHA class I to III HF was not evaluated, and follow-up data are limited. The average age of patients in the study was 71 years, and individuals with significant comorbidities were excluded.
ACE inhibitors also were found to reduce mortality even in asymptomatic patients with HFrEF in the Studies of Left Ventricular Dysfunction trial (SOLVD).10 Enalapril was found to reduce 4-year mortality by 16% and decrease HF hospitalizations when added to conventional therapy consisting primarily of digitalis, diuretics, and nitrates in patients with HFrEF. In this trial, patients aged ≥ 80 years were excluded as well as those with serum creatinine > 2 mg/dL or other conditions that could shorten survival or otherwise impede participation in a long-term trial.
PARADIGM-HF trial patients with HFrEF were randomized to enalapril or the angiotensin receptor-neprilysin inhibitor LCZ696. After a median of 27 months of follow-up, treatment with the angiotensin receptor-neprilysin inhibitor demonstrated greater reduction in CV mortality and HF hospitalizations than enalapril did and was associated with reduced all-cause mortality.11 The trial was stopped early because of evidence of overwhelming benefit with LCZ696. This study of mainly white men included no patients aged ≥ 75 years.
Angiotensin Receptor Blockers
Although less studied than ACE inhibitors, angiotensin receptor blockers (ARBs) share similar benefits. Among patients with symptomatic HFrEF taking an ACE inhibitor, the addition of candesartan reduced the risk of CV death and HF hospitalization as demonstrated in the Candesartan in Heart Failure Assessment of Reduction Mortality and Morbidity (CHARM-added and CHARM-alternative trials).12,13 The CHARM-added trial targeted patients with left ventricular ejection fraction (LVEF) ≤ 40% and NYHA class II to IV HF symptoms who were taking an ACE inhibitor. Adding candesartan reduced CV mortality by 37.9% and HF hospitalization by 42.3% compared with that of placebo.
The CHARM-alternative study found that use of candesartan in symptomatic HFrEF patients who do not tolerate ACE inhibitors,resulted in a 20% reduction in CV mortality as well as a 40% reduction in hospitalization for HF. Among patients with HF with preserved ejection fraction (HFpEF) and NYHA class II to IV symptoms, adding candesartan modestly reduced the rate of HF-related hospitalizations and had no effect on CV mortality in the CHARM-preserved study.14 The CHARM trials examined mostly white men, but 26% of patients were aged > 75 years. However, there was no subgroup analysis for patients aged > 75 years. The study excluded patients with serum creatinine > 2 mg/dL.
Other ARB trials included the following:
- The I-PRESERVE trial, which found that irbesartan did not improve outcomes of patients with HF with preserved ejection fraction (HFpEF).15 The study of mostly white patients did not include patients aged ≥ 80 years.
- A randomized trial of valsartan in HF improved symptoms and mortality in NYHA II to IV HF but showed no benefit when added to ACE inhibitors.16 The trial had no patients aged ≥ 75 years and excluded those with several common comorbidities.
- A randomized, double-blind trial studied the effects of high-dose vs low-dose losartan on clinical outcomes in 3,846 patients with HF and demonstrated that high-dose losartan (150 mg/d) reduces all-cause mortality and hospitalization for HF more effectively than does low-dose losartan (50 mg/d).17 The study, however, had several exclusion criteria, and no patients were aged ≥ 75 years.
Mineralocorticoid Receptor Antagonists
Major studies of aldosterone antagonists demonstrated extra benefit when added to ACE inhibitors/ARBs in patients with HFrEF and NYHA class II HF.18,19
In the RALES study, spironolactone was found to reduce all-cause mortality by 30% and symptoms in NYHA III HF without a significant increase in the risk of serious hyperkalemia or renal failure.18 Most patients were white men aged < 80 years. This study demonstrated the importance of closely following serum potassium levels after initiating aldosterone antagonists in patients with subclinical renal disease because extensive structural damage within the kidney occurs before serum creatinine increases. Patients with advanced renal failure or those who cannot have close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists. Patients with cancer and liver failure were excluded from this trial.
In the Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure study, (EMPHASIS-HF Study) eplerenone was found to reduce all-cause mortality and hospitalization for HFrEF.19 Similar to RALES, patients were mostly white males aged < 80 years, and patients with clinically significant, coexisting conditions were excluded.
The 2014 Treatment of Preserved Cardiac FunctionHeart Failure with an Aldosterone Antagonist Trial (TOPCAT) randomized 3,445 patients with well-controlled blood pressure to spironolactone or placebo.20 Inclusion criteria were LVEF ≥ 45%, findings of HF, and either a HF hospitalization or elevated B-type natriuretic peptide level. There was no difference in the primary composite outcome of CV mortality, aborted cardiac arrest, or HF hospitalization over the 3.3-year follow-up period. The study found that among patients with HFpEF, spironolactone does not reduce the composite endpoint of CV mortality, aborted cardiac arrest, or HF hospitalizations compared with that of placebo.20 In the trial, 29% of patients were aged > 75 years, and most were white men. There was no subgroup analysis for older patients.20 In all 3 trials, patients with kidney injury (serum creatinine of ≥ 2.5 or estimated glomerular filtration rate of ≤ 30 mL/min) were excluded because of the risk of hyperkalemia.
An observational study after the RALES trial demonstrated a nearly 4-fold increase in admissions for hyperkalemia with a 6-fold increase in associated mortality in patients taking spirolactone.21 Therefore, it is important to closely follow serum potassium levels after initiating aldosterone antagonists in older patients with subclinical renal disease. Patients with advanced renal failure or those without close monitoring of serum potassium levels have an unfavorable risk–benefit ratio with aldosterone antagonists.
Antithrombotic Therapy
The large multicenter, double-blind randomized trial WARCEF found no added benefit with warfarin vs aspirin for patients with HFrEF in sinus rhythm.22 There was no reduced time to first stroke or death, and the reduced ischemic stroke risk was offset by an increase in major hemorrhage. It is not clear whether subgroup analysis for the etiology of patients’ HF was performed in WARCEF.
The Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial (N = 1,587) found that treatment with warfarin resulted in significantly fewer strokes in patients with ischemic cardiomyopathy.23 Randomization was not stratified by age group in both trials, and baseline characteristics included mostly white men, and no patients were older than aged > 75 years.
The risk of bleeding with prophylactic aspirin use for CV disease is dose dependent and increases with higher aspirin doses.24 The use of aspirin, 325 mg/d, in the WARCEF study might have contributed to the increased risk of hemorrhage.
Recently published results of COMMANDER HF found that the addition of rivaroxaban at a dose of 2.5 mg twice daily to standard care, including clinically selected antiplatelet therapies was not associated with a significantly lower rate of the composite primary outcome composite outcome of death, myocardial infarction (MI), or stroke among 5,022 patients with a recent episode of worsening heart failure compared with that of placebo.25
Several medical conditions are known to increase bleeding risk, including hypertension, cerebrovascular disease, ischemic stroke, serious heart disease, diabetes mellitus, renal insufficiency, alcoholism, liver disease, and falls.26 Many of these conditions are common among very old patients and should be considered when estimating risk–benefit ratio of oral anticoagulation therapy.
β-blockers
In several large studies, β-blockers have been shown to be effective in reducing mortality in patients with HFrEF. In the Cardiac Insufficiency Bisoprolol Study II, bisoprolol improved all-cause mortality and all-cause hospitalizations, and reduced sudden death in patients with NYHA III or IV HF.27 In the Carvedilol or Metoprolol European Trial (COMET), carvedilol was superior to metoprolol in reducing all-cause mortality for patients with NYHA II or IV HF.28 Both trials included mostly white men; patients with several comorbidities were excluded, and no patients were aged > 80 years.
COMET compared carvedilol with metoprolol tartrate, the short-acting form of metoprolol that has not shown a survival benefit for patients with HF. However, the Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure trial demonstrated survival benefits with metoprolol CR/XL and included patients aged > 80 years.29
In the SENIORS study, patients treated with nebivolol had a 4.2% absolute risk reduction in a composite of mortality or hospital admission at a mean follow-up of 21 months.30 It is reasonable to use nebivolol for managing HF in older patients. Careful monitoring of heart rate is necessary when prescribing β-blockers for older patients.
Cardiac Glycosides
Digoxin with diuretics was the first-line treatment for HF for many decades and the mainstay of HF therapy until the first large HF trials were performed in the 1980s. One trial initiated by the Digoxin Investigation Group (DIG) studied patients with HFrEF who were already receiving treatment for HF (including 94% taking ACE inhibitors and 82% on diuretics) and randomized them to either digoxin or placebo.31 The study found no significant difference in mortality between the groups at the 3-year follow-up; however, the digoxin group had significantly fewer hospitalizations compared with that of the placebo group.
A post-hoc analysis of patients by age found no difference in mortality between patients aged 70 to 79 years and those ≥ 80 years, with a persistent benefit in fewer hospitalizations. Digoxin continues to be recommended as a reasonable medication for treating symptomatic HFrEF. However, caution is advised in older patients, especially women, who are at higher risk of digoxin toxicity.
No current evidence exists that digoxin adds any benefits for patients with HFpEF of any age and therefore, it should not be used.
Diuretics
Diuretic therapy is important for managing shortness of breath and congestion related to fluid volume overload in patients with HF. Although diuretics have not been shown to reduce mortality in patients with HF, they are the mainstay treatment for patients with HFpEF.32 In a post-hoc analysis of the DIG study, diuretic use was associated with increased risk of mortality and hospitalizations in patients aged > 65 years.33 Hyponatremia is one of the most serious adverse effects (AEs) with these agents and occurs in about one-fifth of elderly patients taking diuretics.
In severe cases hyponatremia can cause a range of problems, including weakness, confusion, postural giddiness, postural hypotension, falls, transient hemiparesis, and seizures. In older patients with diminished renal reserve, diuretics are more likely to precipitate prerenal uremia than it does in younger patients. Prerequisites for diuretic use are an accurate diagnosis, careful monitoring of blood pressure and serum electrolytes, and regular review of their efficacy, AEs, and the need for continued treatment.
Statins
The Controlled Rosuvastatin Multinational Trial in Heart Failure demonstrated that low-dose rosuvastatin (10 mg/d) does not improve survival among patients with moderate-to-severe ischemic cardiomyopathy but could reduce the rate of CV hospitalizations.34 Patients in this study had a mean age of 73 years, and 41% of them were aged ≥ 75 years. However, the study used a low-dose rosuvastatin, and patients with several common comorbidities were excluded. Evidence exists that treatment with other statins may improve outcomes in patients with HF. There is also evidence that among elderly patients with HF, low serum total cholesterol is independently associated with a worse prognosis.35
Comorbidities
Anemia
In patients with iron-deficiency anemia (ferritin 15-100 ng/mL or 100-299 ng/mL with transferrin saturation < 20%) and symptomatic HFrEF (LVEF ≤ 40% with NYHA II to IV HF), oral iron replacement had no effect on exercise capacity as measured using change in peak oxygen uptake.36 However, IV iron replacement might be a reasonable option to improve functional status and quality of life (QOL) for patients with HF.37 In these studies, participants were aged < 75 years, and there is no evidence that treating other types of anemia improves outcomes in patients with HF.
Hypertension
The Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated that controlling blood pressure to a goal systolic pressure of < 120 mm Hg is associated with significant reduction in the mortality among patients with increased CV risk (aged > 75 years, vascular disease, kidney injury, or a Framingham Risk Score >15%).38 The SPRINT study included patients aged > 75 (25%); however, the study excluded older adults living in nursing homes and those with diabetes mellitus, symptomatic HF, dementia, or stroke. The subgroup analysis did not stratify patients based on age nor provided sufficient evidence regarding treatment targets for this vulnerable population. Therefore, clinicians cannot draw any conclusions about managing hypertension among patients with HF from this study.
Sleep Apnea
Sleep apnea is common among patients with HF. A study of adults with chronic HF treated with evidence-based therapies found that 61% of participants had central or obstructive sleep apnea.39 In elderly patients, sleep apnea is further complicated by insomnia and disturbance of sleep cycle that often occur with the aging process.
It is crucial to differentiate central sleep apnea from obstructive sleep apnea, because the treatment approaches differ. Central sleep apnea is associated with poor prognosis in patients with HF.40 Adaptive servo ventilation for central sleep apnea uses a noninvasive ventilator to delivering servo controlled inspiratory pressure support on top of expiratory positive airway pressure. Adaptive servo ventilation for central sleep apnea is associated with higher all-cause mortality and CV mortality.41 Continuous positive airway pressure for obstructive sleep apnea improves sleep quality, reduces the apnea-hypopnea index, and improves nocturnal oxygenation.42
Depression
Clinically significant depression occurs in 21% of patients with HF, and the relationship between depression and poor HF outcomes is consistent and strong across several endpoints. However, in a randomized, 12-week study, the selective serotonin reuptake inhibitor sertraline did not improve depression symptoms or clinical status among patients with HF.43 Depression symptoms might overlap with fatigue and low energy expenditure experienced by oldest old patients with HF who do not have depression.
Furthermore, studies describing depression treatments among patients with HF are too small and heterogeneous to permit definitive conclusions about intervention effectiveness. These results identify areas requiring further development, raise questions regarding the association between depression and clinical outcomes in patients with HF, and provide information on depression prevalence that may help researchers design studies with appropriate depression measures and adequately powered sample sizes.
Frailty
Although frailty is prevalent in the elderly and is independently associated with poor outcomes, there is no standardized definition for frailty. The Fried Frailty Index is a widely used scale that incorporates criteria including weakness, slowness, exhaustion, and low physical activity in the diagnosis of frailty.44 However these symptoms are common among patients with advanced HF with and without depression or frailty.
Frailty should be defined collaboratively by the clinician and the patient and should include multidimensional aspects of health, function, and well-being. The treatment goal for patients with HF with frailty is to establish patient-centered goals based on preferences of care.45
Discussion
Although several novel approaches to improve outcomes of patients with HF have been developed, it continues to be the leading cause of cardiovascular death among older patients and the leading cause of hospital admissions.46 About 50% of newly diagnosed patients with HF die within 5 years.47 Current guidelines for managing HF are based on clinical trials that either include few or completely exclude patients aged > 80 years, minorities, and patients with comorbidities clinicians encounter daily in clinical practice.
Furthermore, most clinical trials are designed with mortality as the primary endpoint, which might be as important to our patients with advanced age as their ability to function with a reasonable QOL and less dependence on caregivers.
Decision making in managing HF in our oldest patients should start with an open discussion of the disease and its prognosis, goals of care, and available treatment options. The discussion should also cover all dimensions of suffering, including physical, spiritual, and psychosocial domains. Interviews of patients dying of HF and their caregivers conducted in the United Kingdom identified several communication and transition of care challenges specific to treating this population.48 The study revealed in most cases, patients did not recall receiving any written information about the severity of their disease and often did not understand the association among symptoms, such as shortness of breath, edema, and HF. Patients and caregivers did not feel involved in the decision-making process regarding their illness.
The concurrent presence of comorbidity, frailty, and cognitive impairment in our aging population with HF might add to the burden of the primary condition. Care often is perceived as fragmented. Polypharmacy negatively impacts HF management by increasing risk of drug nonadherence, drug interactions, and AEs in an already vulnerable population. There is a need for more effective interpersonal and easy to understand communication and resources.
In many situations, support services might be best facilitated by a dedicated palliative medicine team with significant experience in managing patients with HF.Although palliative medicine should always be considered for patients with HF with advanced age,consultations often are not obtained unless the patient decides to forgo medical treatment or until the last month of life.49
Although not all end-of-life symptoms can realistically be palliated, earlier involvement of multidisciplinary palliative medicine specialists may improve symptom control, functional status, and QOL. The team may help patients and caregivers cope with uncertainty, and make informed decisions that are person centered based on value system and beliefs.51
Conclusion
Randomized control trials as well as thoughtful observational studies of HF in patients with advanced age and comorbidities, although challenging, are needed to create the evidence base for treatment interventions and assessing their impact on mortality, morbidity, and QOL in this rapidly growing segment of our population.
Given the lack of evidence for HF treatment in patients with advanced age, the clinician should weigh the knowledge of the effect of aging on the CV system, and the lived experience of patients with HF, with the evidence that exists for making the best decision to relieve bothersome symptoms and improve outcomes of care as determined by patients and their caregivers.
Often the most important intervention we can offer our patients, especially those nearing the end of life, is dedicating our time to truly and actively listen with empathy, understating, and respect for their autonomy and for their decision making. And in doing so we accept our own limitations with humility.
Acknowledgments
Dr. Kheirbek received funds from the Veterans Affairs Capitol Health Care Network to establish the Center for Health and Aging at the Washington DC VA Medical Center.
1. Ortman JM, Velkoff AV, Hogan H. An aging nation: the older population in the United States. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed September 30, 2018.
2. Fang J, Mensah GA, Croft JB, Keenan NL. Heart failure-related hospitalization in the U.S., 1979 to 2004. J Am Coll Cardiol. 2008;52(6):428-434.
3. Heidenreich PA, Albert NM, Allen LA, et al; American Heart Association Advocacy Coordinating Committee; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Stroke Council. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013;6(3):606-619.
4. National Heart, Lung, and Blood Institute, National Institutes of Health. Incidence and Prevalence: 2006 Chart Book on Cardiovascular and Lung Diseases. Bethesda, MD: National Institutes of Health; 2006.
5. Curtis LH, Whellan DJ, Hammill BG, et al. Incidence and prevalence of heart failure in elderly persons, 1994-2003. Arch Intern Med. 2008;168(4):418-424.
6. Writing Group, Mozaffarian D, Benjamin EJ, et al; American Heart Association Statistics Committee; Stroke Statistics Subcommittee. Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation. 2016;133(4):e38-e360.
7. Lakatta EG, Levy D. Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part I: aging arteries: a “set up” for vascular disease. Circulation. 2003;107(1):139-146.
8. Mangoni AA, Jackson SH. Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications. Br J Clin Pharmacol. 2004;57(1):6-14.
9. CONSENSUS Trial Study Group. Effects of enalapril on mortality in severe congestive heart failure. Results of the Cooperative North Scandinavian Enalapril Survival Study (CONSENSUS). N Engl J Med. 1987;316(23):1429-1435.
10. SOLVD Investigators; Yusuf S, Pitt B, Davis CE, Hood WB Jr, Cohn JN. Effect of enalapril on mortality and the development of heart failure in asymptomatic patients with reduced left ventricular ejection fractions. N Engl J Med. 1992;327(10):685-691.
11. McMurray JJ, Packer M, Desai AS, et al; PARADIGM-HF Investigators and Committees. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med. 2014;371(11):993-1004.
12. McMurray JJ, Ostergren J, Swedberg K, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and reduced left-ventricular systolic function taking angiotensin-converting-enzyme inhibitors: the CHARM-Added trial. Lancet. 2003;362(9386):767-771.
13. Granger CB, McMurray JJ, Yusuf S, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and reduced left-ventricular systolic function intolerant to angiotensin-converting-enzyme inhibitors: the CHARM-Alternative trial. Lancet. 2003;362(9386):772-776.
14. Yusuf S, Pfeffer MA, Swedberg K, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and preserved left-ventricular ejection fraction: the CHARM-Preserved Trial. Lancet. 2003;362(9386):777-781.
15. Massie BM, Carson PE, McMurray JJ, et al; I-PRESERVE Investigators. Irbesartan in patients with heart failure and preserved ejection fraction. N Engl J Med. 2008;359(23):2456-2467.
16. Cohn JN, Tognoni G; Valsartan Heart Failure Trial Investigators. A randomized trial of the angiotensin-receptor blocker valsartan in chronic heart failure. N Engl J Med. 2001;345(23):1667-1675.
17. Konstam MA, Neaton JD, Dickstein K, et al; HEAAL Investigators. Effects of high-dose versus low-dose losartan on clinical outcomes in patients with heart failure (HEAAL study): a randomised, double-blind trial. Lancet. 2009;374(9704):1840-1848.
18. Pitt B, Zannad F, Remme WJ, et al. The effect of spironolactone on morbidity and mortality in patients with severe heart failure. Randomized Aldactone Evaluation Study Investigators. N Engl J Med. 1999;341(10):709-717.
19. Zannad F, McMurray JJ, Krum H, et al; EMPHASIS-HF Study Group. Eplerenone in patients with systolic heart failure and mild symptoms. N Engl J Med. 2011;364(1):11-21.
20. Pitt B, Pfeffer MA, Assmann SF, et al; TOPCAT Investigators. Spironolactone for heart failure with preserved ejection fraction. N Engl J Med. 2014;370(15):1383-1392.
21. Juurlink DN, Mamdani MM, Lee DS, et al. Rates of hyperkalemia after publication of the Randomized Aldactone Evaluation Study. N Engl J Med. 2004;351(6):543-551.
22. Homma S, Thompson JL, Pullicino PM, et al; WARCEF Investigators. Warfarin and aspirin in patients with heart failure and sinus rhythm. N Engl J Med. 2012;366(20):1859-1869.
23. Massie BM, Collins JF, Ammon SE, et al; WATCH Trial Investigators. Randomized trial of warfarin, aspirin, and clopidogrel in patients with chronic heart failure: the Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial. Circulation. 2009;119(12):1616-1624.
24. Campbell CL, Smyth S, Montalescot G, Steinhubl SR. Aspirin dose for the prevention of cardiovascular disease: a systematic review. JAMA. 2007;297(18):2018-2024.
25. Zannad F, Anker, SD, Byra WM, et al; COMMANDER HF Investigators. Rivaroxaban in patients with heart failure, sinus rhythm, and coronary disease. N Engl J Med. 2018;379(14):1332-1342.
26. Schulman S, Beyth RJ, Kearon C, Levine MN. Hemorrhagic complications of anticoagulant and thrombolytic treatment: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest. 2008;133(suppl 6):257S-298S.
27. CIBIS-II Investigators and Committees. The Cardiac Insufficiency Bisoprolol Study II (CIBIS-II): a randomised trial. Lancet. 1999;353(9146):9-13.
28. Poole-Wilson PA, Swedberg K, Cleland JG, et al; Carvedilol Or Metoprolol European Trial Investigators. Comparison of carvedilol and metoprolol on clinical outcomes in patients with chronic heart failure in the Carvedilol Or Metoprolol European Trial (COMET): randomized controlled trial. Lancet. 2003;362(9377):7-13.
29. MERIT-HF Study Group. Effect of metoprolol CR/XL in chronic heart failure: Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure (MERIT-HF). Lancet. 1999;353(9169):2001-2007.
30. Flather MD, Shibata MC, Coats AJ, et al; SENIORS Investigators. Randomized trial to determine the effect of nebivolol on mortality and cardiovascular hospital admission in elderly patients with heart failure (SENIORS). Eur Heart J. 2005;26(3):215-225.
31. Digitalis Investigation Group. The effect of digoxin on mortality and morbidity in patients with heart failure. N Engl J Med. 1997;336(8):525-533.
32. Yancy CW, Jessup M, Bozkurt B, et al; American College of Cardiology Foundation; American Heart Association Task Force on Practice Guidelines. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;62(16):e147-e239.
33 Juurlink DN, Mamdani MM, Lee DS, et al. Rates of hyperkalemia after publication of the Randomized Aldactone Evaluation Study. N Engl J Med. 2004;351(6):543-551.
34. Kjekshus J, Apetrei E, Barrios V, et al; CORONA Group. Rosuvastatin in older patients with systolic heart failure. N Engl J Med. 2007;357(22):2248-2261.
35. Rauchhaus M, Clark AL, Doehner W, et al. The relationship between cholesterol and survival in patients with chronic heart failure. J Am Coll Cardiol. 2003;42(11):1933-1940.
36. Yancy CW, Jessup M, Bozkurt B, et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation. 2017;136(6):e137-e161.
37. Ponikowski P, van Veldhuisen DJ, Comin-Colet J, et al; CONFIRM-HF Investigators. Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency. Eur Heart J. 2015;36(11):657-668.
38. SPRINT Research Group, Wright JT Jr, Williamson JD, et al. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373(22):2103-2116.
39. MacDonald M, Fang J, Pittman SD, White DP, Malhotra A. The current prevalence of sleep disordered breathing in congestive heart failure patients treated with beta-blockers. J Clin Sleep Med. 2008;4(1):38-42.
40. Bradley TD, Floras JS. Sleep Apnea and heart failure: part II: Central sleep apnea. Circulation. 2003;107(13):1822-1826.
41. Cowie MR, Woehrle H, Wegscheider K, et al. Adaptive servo-ventilation for central sleep apnea in systolic heart failure. N Engl J Med. 2015;373(12):1095-1105.
42. McEvoy RD, Antic NA, Heeley E, et al; SAVE Investigators and Coordinators. CPAP for prevention of cardiovascular events in obstructive sleep apnea. N Engl J Med. 2016;375(10):919-931.
43. O’Connor CM, Jiang W, Kuchibhatla M, et al; SADHART-CHF Investigators. Safety and efficacy of sertraline for depression in patients with heart failure: results of the SADHART-CHF (Sertraline Against Depression and Heart Disease in Chronic Heart Failure) trial. J Am Coll Cardiol. 2010;56(9):692-699.
44. Fried LP, Tangen CM, Walston J, et al; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-156.
45. Pilotto A, Addante F, Franceschi M, et al. Multidimensional Prognostic Index based on a comprehensive geriatric assessment predicts short-term mortality in older patients with heart failure. Circ Heart Fail. 2010;3(1):14-20.
46. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428.
47. Goldberg, RJ, Ciampa, J, Lessard D,, et al. Long-term survival after heart failure: a contemporary population-based perspective. Arch Intern Med. 2007;167(5):490-496.
48. Murray SA, Boyd K, Kendall M, Worth A, Benton TF, Clausen H. Dying of lung cancer or cardiac failure: prospective qualitative interview study of patients and their carers in the community. BMJ. 2002;325(7370):929.
49. Gibbs JS, McCoy AS, Gibbs LM, Rogers AE, Addington-Hall JM. Living with and dying from heart failure: the role of palliative care. Heart. 2002;88(suppl 2):ii36-39.
50. Quill TE, Dresser R, Brock DW. The rule of double effect—a critique of its role in end-of-life decision making. N Engl J Med. 1997;337(24):1768-1771.
51. Nieminen MS, Dickstein K, Fonseca C, et al. The patient perspective: quality of life in advanced heart failure with frequent hospitalizations. Int J Cardiol. 2015;191:256-264.
1. Ortman JM, Velkoff AV, Hogan H. An aging nation: the older population in the United States. https://www.census.gov/prod/2014pubs/p25-1140.pdf. Published May 2014. Accessed September 30, 2018.
2. Fang J, Mensah GA, Croft JB, Keenan NL. Heart failure-related hospitalization in the U.S., 1979 to 2004. J Am Coll Cardiol. 2008;52(6):428-434.
3. Heidenreich PA, Albert NM, Allen LA, et al; American Heart Association Advocacy Coordinating Committee; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Stroke Council. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail. 2013;6(3):606-619.
4. National Heart, Lung, and Blood Institute, National Institutes of Health. Incidence and Prevalence: 2006 Chart Book on Cardiovascular and Lung Diseases. Bethesda, MD: National Institutes of Health; 2006.
5. Curtis LH, Whellan DJ, Hammill BG, et al. Incidence and prevalence of heart failure in elderly persons, 1994-2003. Arch Intern Med. 2008;168(4):418-424.
6. Writing Group, Mozaffarian D, Benjamin EJ, et al; American Heart Association Statistics Committee; Stroke Statistics Subcommittee. Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation. 2016;133(4):e38-e360.
7. Lakatta EG, Levy D. Arterial and cardiac aging: major shareholders in cardiovascular disease enterprises: Part I: aging arteries: a “set up” for vascular disease. Circulation. 2003;107(1):139-146.
8. Mangoni AA, Jackson SH. Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications. Br J Clin Pharmacol. 2004;57(1):6-14.
9. CONSENSUS Trial Study Group. Effects of enalapril on mortality in severe congestive heart failure. Results of the Cooperative North Scandinavian Enalapril Survival Study (CONSENSUS). N Engl J Med. 1987;316(23):1429-1435.
10. SOLVD Investigators; Yusuf S, Pitt B, Davis CE, Hood WB Jr, Cohn JN. Effect of enalapril on mortality and the development of heart failure in asymptomatic patients with reduced left ventricular ejection fractions. N Engl J Med. 1992;327(10):685-691.
11. McMurray JJ, Packer M, Desai AS, et al; PARADIGM-HF Investigators and Committees. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med. 2014;371(11):993-1004.
12. McMurray JJ, Ostergren J, Swedberg K, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and reduced left-ventricular systolic function taking angiotensin-converting-enzyme inhibitors: the CHARM-Added trial. Lancet. 2003;362(9386):767-771.
13. Granger CB, McMurray JJ, Yusuf S, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and reduced left-ventricular systolic function intolerant to angiotensin-converting-enzyme inhibitors: the CHARM-Alternative trial. Lancet. 2003;362(9386):772-776.
14. Yusuf S, Pfeffer MA, Swedberg K, et al; CHARM Investigators and Committees. Effects of candesartan in patients with chronic heart failure and preserved left-ventricular ejection fraction: the CHARM-Preserved Trial. Lancet. 2003;362(9386):777-781.
15. Massie BM, Carson PE, McMurray JJ, et al; I-PRESERVE Investigators. Irbesartan in patients with heart failure and preserved ejection fraction. N Engl J Med. 2008;359(23):2456-2467.
16. Cohn JN, Tognoni G; Valsartan Heart Failure Trial Investigators. A randomized trial of the angiotensin-receptor blocker valsartan in chronic heart failure. N Engl J Med. 2001;345(23):1667-1675.
17. Konstam MA, Neaton JD, Dickstein K, et al; HEAAL Investigators. Effects of high-dose versus low-dose losartan on clinical outcomes in patients with heart failure (HEAAL study): a randomised, double-blind trial. Lancet. 2009;374(9704):1840-1848.
18. Pitt B, Zannad F, Remme WJ, et al. The effect of spironolactone on morbidity and mortality in patients with severe heart failure. Randomized Aldactone Evaluation Study Investigators. N Engl J Med. 1999;341(10):709-717.
19. Zannad F, McMurray JJ, Krum H, et al; EMPHASIS-HF Study Group. Eplerenone in patients with systolic heart failure and mild symptoms. N Engl J Med. 2011;364(1):11-21.
20. Pitt B, Pfeffer MA, Assmann SF, et al; TOPCAT Investigators. Spironolactone for heart failure with preserved ejection fraction. N Engl J Med. 2014;370(15):1383-1392.
21. Juurlink DN, Mamdani MM, Lee DS, et al. Rates of hyperkalemia after publication of the Randomized Aldactone Evaluation Study. N Engl J Med. 2004;351(6):543-551.
22. Homma S, Thompson JL, Pullicino PM, et al; WARCEF Investigators. Warfarin and aspirin in patients with heart failure and sinus rhythm. N Engl J Med. 2012;366(20):1859-1869.
23. Massie BM, Collins JF, Ammon SE, et al; WATCH Trial Investigators. Randomized trial of warfarin, aspirin, and clopidogrel in patients with chronic heart failure: the Warfarin and Antiplatelet Therapy in Chronic Heart Failure (WATCH) trial. Circulation. 2009;119(12):1616-1624.
24. Campbell CL, Smyth S, Montalescot G, Steinhubl SR. Aspirin dose for the prevention of cardiovascular disease: a systematic review. JAMA. 2007;297(18):2018-2024.
25. Zannad F, Anker, SD, Byra WM, et al; COMMANDER HF Investigators. Rivaroxaban in patients with heart failure, sinus rhythm, and coronary disease. N Engl J Med. 2018;379(14):1332-1342.
26. Schulman S, Beyth RJ, Kearon C, Levine MN. Hemorrhagic complications of anticoagulant and thrombolytic treatment: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest. 2008;133(suppl 6):257S-298S.
27. CIBIS-II Investigators and Committees. The Cardiac Insufficiency Bisoprolol Study II (CIBIS-II): a randomised trial. Lancet. 1999;353(9146):9-13.
28. Poole-Wilson PA, Swedberg K, Cleland JG, et al; Carvedilol Or Metoprolol European Trial Investigators. Comparison of carvedilol and metoprolol on clinical outcomes in patients with chronic heart failure in the Carvedilol Or Metoprolol European Trial (COMET): randomized controlled trial. Lancet. 2003;362(9377):7-13.
29. MERIT-HF Study Group. Effect of metoprolol CR/XL in chronic heart failure: Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure (MERIT-HF). Lancet. 1999;353(9169):2001-2007.
30. Flather MD, Shibata MC, Coats AJ, et al; SENIORS Investigators. Randomized trial to determine the effect of nebivolol on mortality and cardiovascular hospital admission in elderly patients with heart failure (SENIORS). Eur Heart J. 2005;26(3):215-225.
31. Digitalis Investigation Group. The effect of digoxin on mortality and morbidity in patients with heart failure. N Engl J Med. 1997;336(8):525-533.
32. Yancy CW, Jessup M, Bozkurt B, et al; American College of Cardiology Foundation; American Heart Association Task Force on Practice Guidelines. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;62(16):e147-e239.
33 Juurlink DN, Mamdani MM, Lee DS, et al. Rates of hyperkalemia after publication of the Randomized Aldactone Evaluation Study. N Engl J Med. 2004;351(6):543-551.
34. Kjekshus J, Apetrei E, Barrios V, et al; CORONA Group. Rosuvastatin in older patients with systolic heart failure. N Engl J Med. 2007;357(22):2248-2261.
35. Rauchhaus M, Clark AL, Doehner W, et al. The relationship between cholesterol and survival in patients with chronic heart failure. J Am Coll Cardiol. 2003;42(11):1933-1940.
36. Yancy CW, Jessup M, Bozkurt B, et al. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation. 2017;136(6):e137-e161.
37. Ponikowski P, van Veldhuisen DJ, Comin-Colet J, et al; CONFIRM-HF Investigators. Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency. Eur Heart J. 2015;36(11):657-668.
38. SPRINT Research Group, Wright JT Jr, Williamson JD, et al. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373(22):2103-2116.
39. MacDonald M, Fang J, Pittman SD, White DP, Malhotra A. The current prevalence of sleep disordered breathing in congestive heart failure patients treated with beta-blockers. J Clin Sleep Med. 2008;4(1):38-42.
40. Bradley TD, Floras JS. Sleep Apnea and heart failure: part II: Central sleep apnea. Circulation. 2003;107(13):1822-1826.
41. Cowie MR, Woehrle H, Wegscheider K, et al. Adaptive servo-ventilation for central sleep apnea in systolic heart failure. N Engl J Med. 2015;373(12):1095-1105.
42. McEvoy RD, Antic NA, Heeley E, et al; SAVE Investigators and Coordinators. CPAP for prevention of cardiovascular events in obstructive sleep apnea. N Engl J Med. 2016;375(10):919-931.
43. O’Connor CM, Jiang W, Kuchibhatla M, et al; SADHART-CHF Investigators. Safety and efficacy of sertraline for depression in patients with heart failure: results of the SADHART-CHF (Sertraline Against Depression and Heart Disease in Chronic Heart Failure) trial. J Am Coll Cardiol. 2010;56(9):692-699.
44. Fried LP, Tangen CM, Walston J, et al; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-156.
45. Pilotto A, Addante F, Franceschi M, et al. Multidimensional Prognostic Index based on a comprehensive geriatric assessment predicts short-term mortality in older patients with heart failure. Circ Heart Fail. 2010;3(1):14-20.
46. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-1428.
47. Goldberg, RJ, Ciampa, J, Lessard D,, et al. Long-term survival after heart failure: a contemporary population-based perspective. Arch Intern Med. 2007;167(5):490-496.
48. Murray SA, Boyd K, Kendall M, Worth A, Benton TF, Clausen H. Dying of lung cancer or cardiac failure: prospective qualitative interview study of patients and their carers in the community. BMJ. 2002;325(7370):929.
49. Gibbs JS, McCoy AS, Gibbs LM, Rogers AE, Addington-Hall JM. Living with and dying from heart failure: the role of palliative care. Heart. 2002;88(suppl 2):ii36-39.
50. Quill TE, Dresser R, Brock DW. The rule of double effect—a critique of its role in end-of-life decision making. N Engl J Med. 1997;337(24):1768-1771.
51. Nieminen MS, Dickstein K, Fonseca C, et al. The patient perspective: quality of life in advanced heart failure with frequent hospitalizations. Int J Cardiol. 2015;191:256-264.
Lipoprotein(a) Elevation: A New Diagnostic Code with Relevance to Service Members and Veterans (FULL)
Cardiovascular disease (CVD) remains the leading cause of global mortality. In 2015, 41.5% of the US population had at least 1 form of CVD and CVD accounted for nearly 18 million deaths worldwide.1,2 The major disease categories represented include myocardial infarction (MI), sudden death, strokes, calcific aortic valve stenosis (CAVS), and peripheral vascular disease.1,2 In terms of health care costs, quality of life, and caregiver burden, the overall impact of disease prevalence continues to rise.1,3-6 There is an urgent need for more precise and earlier CVD risk assessment to guide lifestyle and therapeutic interventions for prevention of disease progression as well as potential reversal of preclinical disease. Even at a young age, visible coronary atherosclerosis has been found in up to 11% of “healthy” active individuals during autopsies for trauma fatalities.7,8
The impact of CVD on the US and global populations is profound. In 2011, CVD prevalence was predicted to reach 40% by 2030.9 That estimate was exceeded in 2015, and it is now predicted that by 2035, 45% of the US population will suffer from some form of clinical or preclinical CVD. In 2015, the decadeslong decline in CVD mortality was reversed for the first time since 1969, showing a 1% increase in deaths from CVD.1 Nearly 300,000 of those using US Department of Veterans Affairs (VA) services were hospitalized for CVD between 2010 and 2014.10 The annual direct and indirect costs related to CVD in the US are estimated at $329.7 billion, and these costs are predicted to top $1 trillion by 2035.1 Heart attack, coronary atherosclerosis, and stroke accounted for 3 of the 10 most expensive conditions treated in US hospitals in 2013.11 Globally, the estimate for CVD-related direct and indirect costs was $863 billion in 2010 and may exceed $1 trillion by 2030.12
The nature of military service adds additional risk factors, such as posttraumatic stress disorder, depression, sleep disorders and physical trauma which increase CVD morbidity/ mortality in service members, veterans, and their families.13-16 In addition, living in lowerincome areas (countries or neighborhoods) can increase the risk of both CVD incidence and fatalities, particularly in younger individuals.17-20 The Military Health System (MHS) and VA are responsible for the care of those individuals who have voluntarily taken on these additional risks through their time in service. This responsibility calls for rapid translation to practice tools and resources that can support interventions to minimize as many modifiable risk factors as possible and improve longterm health. This strategy aligns with the World Health Organization’s (WHO) focus on prevention of disease progression through interventions targeting modifiable risk.3-6,21-23 The driving force behind the launch of the US Department of Health and Human Services (HHS) Million Hearts program was the goal of preventing 1 million heart attacks and strokes by 2017 with risk reduction through aspirin, blood pressure control, cholesterol management, smoking cessation, sodium reduction, and physical activity.24,25 While some reductions in CVD events have been documented, the outcomes fell short of the goals set, highlighting both the need and value of continued and expanded efforts for CVD risk reduction.26
More precise assessment of risk factors during preventative care, as well as after a diagnosis of CVD, may improve the timeliness and precision of earlier interventions (both lifestyle and therapeutic) that reduce CVD morbidity and mortality.27 Personalized or precision medicine approaches take into account differences in socioeconomic, environmental, and lifestyle factors that are potentially reversible, as well as gender, race, and ethnicity.28-31 Current methods of predicting CVD risk have considerable room for improvement.27 About 40% of patients with newly diagnosed CVD have normal traditional cholesterol profiles, including those whose first cardiac event proves fatal.29-33 Currently available risk scores (hundreds have been described in the literature) mischaracterize risk in minority populations and women, and have shown deficiencies in identifying preclinical atherosclerosis.34,35 The failure to recognize preclinical CVD in military personnel during their active duty life cycle results in missed opportunities for improved health and readiness sustainment.
Most CVD risk prediction models incorporate some form of blood lipids. Total cholesterol (TC) is most commonly used in clinical practice, along with high-density lipoprotein (HDLC), low-density lipoprotein (LDLC), and triglycerides (TG).23,27,36 High LDLC and/or TC are well established as lipid-related CVD risk factors and are incorporated into many CVD risk scoring systems/models described in the literature.27 LDLC reduction is commonly recommended as CVD prevention, but even with optimal statin treatment, there is still considerable residual risk for new and recurrent CVD events.28,32,34,35,37-42
Incorporating novel biomarkers and alternative lipid measurements may improve risk prediction and aid targeted treatment, ultimately reducing CVD events.27 Apolipoprotein B (ApoB) is a major atherogenic component embedded in LDL and VLDL correlating to non-HDLC and may be useful in the setting of triglycerides ≥ 200 mg/d as levels > 130 mg/ dL appear to be risk-enhancing, but measurements may be unreliable.43 According to the 2018 Cholesterol Guidelines, lipoprotein(a) [Lp(a)] elevation also is recognized as a risk-enhancing factor that is particularly implicated when there is a strong family history of premature atherosclerotic CVD or personal history of CVD not explained by major risk factors.43
Lp(a) elevation is a largely underrecognized category of lipid disorder that impacts up to 20% to 30% of the population globally and within the US, although there is considerable variability by geographic location and ethnicity.44 Globally, Lp(a) elevation places > 1 billion people at moderate to high risk for CVD.44 Lp(a) has a strong genetic component and is recognized as a distinct and independent risk factor for MI, sudden death, strokes and CAVS. Lp(a) has an extensive body of evidence to support its distinct role both as a causal factor in CVD and as an augmentation to traditional risk factors.44-48
Lipoproteni(a) Elevation Use For Diagnosis
The importance of Lp(a) elevation as a clinical diagnosis rather than a laboratory abnormality alone was brought forward by the Lipoprotein(a) Foundation. Its founder, Sandra Tremulis, is a survivor of an acute coronary event that occurred when she was 39-years old, despite running marathons and having none of the traditional CVD lifestyle risk factors.49 This experience inspired her to create the Lipoprotein(a) Foundation to give a voice to families living with or at risk for CVD due to Lp(a) elevation.
As often happens in the progress of medicine, patients and their families drive change based on their personal experiences with the gaps in standard clinical practice. It was this foundation—not a member of the medical establishment—that submitted the formal request for the addition of new ICD-10-CM diagnostic and family history codes for Lp(a) elevation during the Centers for Disease Control and Prevention (CDC) September 2017 ICD-10-CM Coordination and Maintenance Committee meeting.50 In June 2018, the final ICD-10-CM code addenda for 2019 was released and included the new codes E78.41 (Elevated Lp[a]) and Z83.430 (Family history of elevated Lp[a]).52 After the new codes were approved, both the American Heart Association and the National Lipid Association added recommendations regarding Lp(a) testing to their clinical practice guidelines.43,52
Practically, these codes standardize billing and payment for legitimate clinical work and laboratory testing. Prior to the addition of Lp(a) elevation as a clinical diagnosis, testing and treatment of Lp(a) elevation was considered experimental and not medically necessary until after a cardiovascular event had already occurred. Services for Lp(a) elevation were therefore not reimbursed by many healthcare organizations and insurance companies. The new ICD-10-CM codes encourage the assessment of Lp(a) both in individuals with early onset major CVD events and in presumably fit, healthy individuals, particularly when there is a family history of Lp(a) elevation. Given that Lp(a) levels do not change significantly over time, the current understanding is that only a single measurement is needed to define the individual risk over a lifetime.41,42,44,45 As therapies targeting Lp(a) levels evolve, repeated measurements may be indicated to monitor response and direct changes in management. “Elevated Lipoprotein(a)” is the first laboratory testing abnormality that has achieved the status of a clinical diagnosis.
Lp(a) Measurements
There is considerable complexity to the measurement of lipoproteins in blood samples due to heterogeneity in both density and size of particles as illustrated in the Figure.53
For traditional lipids measured in clinical practice, the size and density ranges from small high-density lipoprotein (HDL) through LDLC and intermediate- density lipoprotein (IDL) to the largest least dense particles in the very low-density lipoprotein (VLDL) and chylomicron remnant fractions. Standard lipid profiles consist of mass concentration measurements (mg/dL) of TC, TG, HDLC, and LDLC.53 Non-HDLC (calculated as: TC−HDLC) consists of all cholesterol found in atherogenic lipoproteins, including remnant-C and Lp(a). Until recently, the cholesterol content of Lp(a), corresponding to about 30% of Lp(a) total mass, was included in the TC, non-HDLC and LDLC measurements with no separate reporting by the majority of clinical laboratories.
After > 50 years of research on the structure and biochemistry of Lp(a), the physiology and biological functions of these complex and polymorphic lipoprotein particles are not fully understood. Lp(a) is composed of a lipoprotein particle similar in composition to LDL (protein and lipid), containing 1 molecule of ApoB wrapped around a core of cholesteryl ester and triglyceride with phospholipids and unesterified cholesterol at its surface.48 The presence of a unique hydrophilic, highly glycosylated protein referred to as apolopoprotienA (apo[a]), covalently attached to ApoB-100 by a single disulfide bridge, differentiates Lp(a) from LDL.48 Cholesterol rich ApoB is an important component within many lipoproteins pathogenic for atherosclerosis and CVD.45,47,53
The apo(a) contributes to the increased density of Lp(a) compared to LDLC with associated reduced binding affinity to the LDL receptor. This reduced receptor binding affinity is a presumed mechanism for the lack of Lp(a) plasma level response to statin therapies, which increase hepatic LDL receptor activity.47 Apo(a) evolved from the plasminogen gene through duplication and remodeling and demonstrates extensive heterogeneity in protein size, with > 40 different apo(a) isoforms resulting in > 40 different Lp(a) particle sizes. Size of the apo(a) particle is determined by the number of pleated structures known as kringles. Most people (> 80%) carry 2 different-sized apo(a) isoforms. Plasma Lp(a) level is determined by the net production of apo(a) in each isoform, and the smaller apo(a) isoforms are associated with higher plasma levels of Lp(a).45
Given the heterogeneity in Lp(a) molecular weight, which can vary even within individuals, recommendations have been made for reporting results as particle numbers or concentrations (nmol/L or mmol/L) rather than as mass concentration (mg/dL).55 However, the majority of the large CVD morbidity and mortality outcomes studies used Lp(a) mass concentration levels in mg/ dL to characterize risk levels.56,57 There is no standardized method to convert Lp(a) measurements from mg/dL to nmol/L.55 Current assays using WHO standardized reagents and controls are reliable for categorizing risk levels.58
The European Atherosclerosis Society consensus panel recommended that desirable Lp(a) levels should be below the 80th percentile (< 50 mg/dL or < 125 nmol/L) in patients with intermediate or high CVD risk.59 Subsequent epidemiological and Mendelian randomization studies have been performed in general populations with no history of CVD and demonstrated that increased CVD risk can be detected with Lp(a) levels as low as 25 to 30 mg/dL.56,60-63 In secondary prevention populations with prior CVD and optimal treatment (statins, antiplatelet drugs), recurrent event risk was also increased with elevated Lp(a).63-66
Using immunoturbidometric assays, Varvel and colleagues reported the prevalence of elevated Lp(a) mass concentration levels (mg/dL) in > 500,000 US patients undergoing clinical evaluations based on data from a referral laboratory of patients.58 The mean Lp(a) levels were 34.0 mg/dL with median (interquartile range [IQR]) levels at 17 (7-47) mg/dL and overall range of 0 to 907 mg/dL.58 Females had higher Lp(a) levels compared to males but no ethnic or racial breakdown was provided. Lp(a) levels > 30 mg/dL and > 50 mg/dL were present in 35% and 24% of subjects, respectively. Table 1 displays the relationship between various Lp(a) level cut-offs to mean levels of LDLC, estimated LDLC corrected for Lp(a), TC, HDLC, and TG.58 The data demonstrate that Lp(a) elevation cannot be inferred from LDLC levels nor from any of the other traditional lipoprotein measures. Patients with high risk Lp(a) levels may have normal LDLC. While Lp(a) thresholds have been identified for stratification of CVD risk, the target levels for risk reduction have not been specifically defined, particularly since therapies are not widely available for reduction of Lp(a). Table 2 provides an overview of clinical lipoprotein measurements that may be reasonable targets for therapeutic interventions and reduction of CVD risk.44,53,55 In general, existing studies suggest that radical reduction (> 80%) is required to impact long-term outcomes, particularly in individuals with severe disease.68,69
LDLC reduction alone leaves a residual CVD risk that is greater than the risk reduced.40 In addition, the autoimmune inflammation and lipid specific autoantibodies play an important role in increased CVD morbidity and mortality risk.70,71 The presence of autoantibodies such as antiphospholipid antibodies (without a specific autoimmune disease diagnosis) increases the risk of subclinical atherosclerosis.72,73 Certain autoimmune diseases such as systemic lupus erythematosus are recognized as independent risk factors for CVD.74,75 Autoantibodies appear to mediate CVD events and mortality risk, independent of traditional therapies for risk reduction.73 Further research is needed to clarify the role of autoantibodies as markers of increased or decreased CVD risk and their mechanism of action.
Autoantibodies directed at new antigens in lipoproteins within atherosclerotic lesions can modulate the impact of atherosclerosis via activation of the innate and adaptive immune system.76 The lipid-associated neopeptides are recognized as damage-associated or danger- associated molecular patterns (DAMPs), also known as alarmins, which signal molecules that can trigger and perpetuate noninfectious inflammatory responses.77-79 Plasma autoantibodies (immunoglobulin M and G [IgM, IgG]) modify proinflammatory oxidation-specific epitopes on oxidized phospholipids (oxPL) within lipoproteins and are linked with markers of inflammation and CVD events.80-82 Modified LDLC and ApoB-100 immune complexes with specific autoantibodies in the IgG class are associated with increased CVD.76 These and other risk-modulating autoantibodies may explain some of the variability in CVD outcomes by ethnicity and between individuals.
Some antibodies to oxidized LDL (ox-LDL) may have a protective role in the development of atherosclerosis.83,84 In a cohort of > 500 women, the number of carotid atherosclerotic plaques and total carotid plaque area were inversely correlated with a specific IgM autoantibody (MDA-p210).84 High concentrations of Lp(a)- containing circulating immune complexes and Lp(a)-specific IgM and IgG have been described in patients with coronary heart disease (CHD).85 Like ox-LDL, oxidized Lp(a) [ox-Lp(a)] is more potent than native Lp(a) in increasing atherosclerosis risk and is increased in patients with CHD compared to healthy controls.86-88 Ox-Lp(a) levels may represent an even stronger risk marker for CVD than ox-LDL.85
Possible Mechanisms of Pathogenesis
While the precise quantification of Lp(a) in human plasma (or serum) has been challenging, current clinical laboratories use standardized international reference reagents and controls in their assays. Most current Lp(a) assays are based on immunological methods (eg, immunonephelometry, immunoturbidimetry, or enzyme linked immunosorbent assay [ELISA]) using antibodies against apo(a).89 Apo(a) contains 10 subtypes of kringle IV and 1 copy of kringle V. Some assays use antibodies against kringle-IV type 2; however, it has been recommended that newer methods should use antibodies against the specific bridging kringle-IV Type 9 domain, which has a more stable bond and is present as a single copy.48,89 Other approaches to Lp(a) measurement include ultraperformance liquid chromatography/mass spectrometry that can determine both the concentration and particle size of apo(a).48,90 For routine clinical care, currently available assays reporting in mg/dL can be considered fairly accurate for separating low-risk from moderate-to-high-risk patients.45
The physiologic role of Lp(a) in humans remains to be fully defined and individuals with extremely low plasma Lp(a) levels present no disease or deficiency syndromes.91 Lp(a) accumulates in endothelial injuries and binds to components of the vessel wall and subendothelial matrix, presumably due to the strong lysine binding site in apo(a).46 Mediated by apo(a), the binding stimulates chemotactic activation of monocytes/macrophages and thereby modulating angiogenesis and inflammation.89 Lp(a) may contribute to CVD and CAVS via its LDL-like component, with proinflammatory effects of oxidized phospholipids (OxPL) on both ApoB and apo(a) and antifibrinolytic/prothrombotic effects of apo(a).92 In Vitro studies have demonstrated that apo(a) modifies cellular function of cultured vascular endothelial cells (promoting stress fiber formation, endothelial contraction and vascular permeability), smooth muscles, and monocytes/ macrophages (promoting differentiation of proinflammatory M1-1 type macrophages) via complex mechanisms of cell signaling and cytokine production.89 Lp(a) is the only monogenetic risk factor for aortic valve calcification and stenosis93 and is strongly linked specifically with the single nucleotide polymorphism (SNP) rs10455872 in the gene LPA encoding for apo(a).94
CVD Risk Predictive Value
There are a large number of studies demonstrating that Lp(a) elevations are an independent predictor of adverse cardiovascular outcomes including MI, sudden death, strokes, calcific aortic valve stenosis and peripheral vascular disease (Table 3). The Copenhagen City Heart Study and Copenhagen General Population Study are well known prospective population- based cohort studies that track outcomes through national patient registries.95 These studies demonstrate increased risk for MI, CHD, CAVS, and heart failure when subjects with very high Lp(a) levels (50-115 mg/dL) are compared with subjects with very low Lp(a) levels (< 5 mg/dL).96-100 Subjects with less extreme Lp(a) elevations (> 30 mg/dL) also show increased risk of CVD when they have comorbid LDLC elevations.101 However, the Copenhagen studies are composed exclusively of white subjects and the effects of Lp(a) are known to vary with race or ethnicity.
The Multi-Ethnic Study of Atherosclerosis (MESA) recruited an ethnically diverse sample of > 6,000 Americans, aged 45 to 84 years, without CVD, into an ongoing prospective cohort study. Research using subjects from this study has found consistently increased risk of CHD, heart failure, subclinical aortic valve calcification, and more severe CAVS in white subjects with elevated Lp(a).60,102,103 Black subjects with elevated Lp(a) had increased risk of CHD and more severe CAVS and Hispanic subjects with Lp(a) elevation were at higher risk for CHD.60,102 So far, no studies of MESA subjects have identified a relationship between Lp(a) elevation and CVD events for Asian-Americans subjects (predominantly of Chinese descent). There is a need for ongoing research to more precisely define relevant cut-off levels by race, ethnicity and sex.
The Atherosclerosis Risk in Communities (ARIC) Study was a prospective multiethnic cohort study including > 15,000 US adults, aged 45 to 64 years.103 Lp(a) elevations in this cohort were associated with greater risks for first CVD events, heart failure, and recurrent CVD events.61,64,105 The risk of stroke for subjects with elevated Lp(a) was greater for black and white women, and for black men.61,106 However, a meta-analysis of case-control studies showed increased ischemic stroke risk in both men and women with elevated Lp(a).57
A recent European meta-analysis collected blood samples and outcome data from > 50,000 subjects in 7 prospective cohort studies. Using a central laboratory to standardize Lp(a) measurements, researchers found increased risk of major coronary events and new CVD in subjects with Lp(a) > 50 mg/dL compared to those below that threshold.107
Although many of these studies show modest increases in risk of CVD events with Lp(a) elevation, it should be noted that other studies do not demonstrate such consistent associations. This is particularly true in studies of women and nonwhite ethnic groups.103,108-112 The variability of study results may be due to other confounding factors such as autoantibodies that either upregulate or downregulate atherogenicity of LDLC and potentially other lipoproteins. This is particularly relevant to women who have an increased risk for autoimmune disease.
Lp(a) has significant genetic heritability—75% in Europeans and 85% in African Americans.113 In whites, the LPA gene on chromosome 6p26- 27 with the polymorphism genetic variants rs10455872 and rs3798220 is consistently associated with elevated Lp(a) levels.63,100,113 However, the degree of Lp(a) elevation associated with these specific genetic variants varies by ethnicity.78,113,115
Lifestyle and Cardiovascular Health
It is noteworthy that the Lp(a) genetic risks can also be modified by lifestyle risk reduction even in the absence of significant blood level reductions. For example, Khera and colleagues constructed a genetic risk profile for CVD that included genes related to Lp(a).116 Subjects with high genetic risk were more likely to experience CVD events compared with subjects with low genetic risk. However, risks for CVD were attenuated by 4 healthy lifestyle factors: current nonsmoker, body mass index < 30, at least weekly physical activity, and a healthy diet. Subjects with high genetic risk and an unhealthy lifestyle (0 or 1 of the 4 healthy lifestyle factors) were the most likely to develop CVD (Hazard ratio [HR], 3.5), but that risk was lower for subjects with healthy (3 or 4 of the 4 healthy lifestyle factors) and intermediate lifestyles (2 of the 4 healthy lifestyle factors) (HR, 1.9 and 2.2, respectively), despite despite high genetic risk for CVD.
While the independent CVD risk associated with elevated Lp(a) does not appear to be responsive to lifestyle risk reduction alone, certainly elevated LDLC and traditional risk factors can increase the overall CVD risk and are worthy of preventive interventions. In particular, inflammation from any source exacerbates CVD risk. Proatherogenic diet, insufficient sleep, lack of exercise, and maladaptive stress responses are other targets for personalized CVD risk reduction. 28,117 Studies of dietary modifications and other lifestyle factors have shown reduced risk of CVD events, despite lack of reduction in Lp(a) levels.119,120 It is noteworthy that statin therapy (with or without ezetimibe) fails to impact CAVS progression, likely because statins either raise or have no effect on Lp(a) levels.92,119
Until recently, there has been no evidence supporting any therapeutic intervention causing clinically meaningful reductions in Lp(a). Table 4 lists major drug classes and their effects on Lp(a) and CVD outcomes; however, a detailed discussion of each of these therapies is beyond the scope of this review. Drugs that reduce Lp(a) by 20-30% have varying effects on CVD outcomes, from no effect122,123 to a 10% to 20% decrease in CVD events when compared with a placebo.124,125 Because these drugs also produce substantial reductions in LDLC, it is not possible to determine how much of the beneficial effects are due to reductions in Lp(a).
Lipoprotein apheresis produces profound reductions in Lp(a) of 60 to 80% in very highrisk populations.69,126 Within-subjects comparisons show up to 80% reductions in CVD events, relative to event rates prior to treatment initiation.69,127 Early trials of antisense oligonucleotide against apo(a) therapies show potential to produce similar outcomes.128,129 These treatments may be particularly effective in patients with isolated Lp(a) elevations.
Summary
Lp(a) elevation is a major contributor to cardiovascular disease risk and has been recognized as an ICD-10-CM coded clinical diagnosis, the first laboratory abnormality to be defined a clinical disease in the asymptomatic healthy young individuals. This change addresses currently under- diagnosed CVD risk independent of LDLC reduction strategies. A brief overview of recent guidelines for the clinical use of Lp(a) testing from the American Heart Association43,151 and the National Lipid Association52 can be found in Table 5. Although drug therapies for lowering Lp(a) levels remain limited, new treatment options are actively being developed.
Many Americans with high Lp(a) have not yet been identified. Expanded one-time screening can inform these patients of their cardiovascular risk and increase their access to early, aggressive lifestyle modification and optimal lipid-lowering therapy. Given the further increased CVD risk factors for military service members and veterans, a case can be made for broader screening and enhanced surveillance of elevated Lp(a) in these presumably healthy and fit individuals as well as management focused on modifiable risk factors.
Acknowledgements
This program initiative was conducted by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. as part of the Integrative Cardiac Health Project at Walter Reed National Military Medical Center (WRNMMC), and is made possible by a cooperative agreement that was awarded and administered by the US Army Medical Research & Materiel Command (USAMRMC), at Fort Detrick under Contract Number: W81XWH-16-2-0007. It reflects literature review preparatory work for a research protocol but does not involve an actual research project. The work in this manuscript was supported by the staff of the Integrative Cardiac Health Project (ICHP) with special thanks to Claire Fuller, Elaine Walizer, Dr. Mariam Kashani and the entire health coaching team.
1. American Heart Association. Cardiovascular disease: a costly burden for America, projections through 2035. http://www.heart.org/idc/groups /heart-public/@wcm/@adv/documents/downloadable /ucm_491543.pdf. Accessed October 10, 2019.
2. Benjamin EJ, Virani SS, Callaway CW, et al. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation. 2018;137(12):e67-e492.
3. Roth GA, Johnson C, Abajobir A, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1-25.
4. Thrift AG, Cadilhac DA, Thayabaranathan T, et al. Global stroke statistics. Int J Stroke. 2014;9(1):6-18.
5. Murray CJ, Barber RM, Foreman KJ, et al; GBD 2013 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition. Lancet. 2015;386(10009):2145-2191.
6. Mukherjee D, Patil CG. Epidemiology and the global burden of stroke. World Neurosurg. 2011;76(6 suppl):S85-S90.
7. Joseph A, Ackerman D, Talley JD, Johnstone J, Kupersmith J. Manifestations of coronary atherosclerosis in young trauma victims—an autopsy study. J Am Coll Cardiol. 1993;22(2):459-467.
8. Webber BJ, Seguin PG, Burnett DG, Clark LL, Otto JL. Prevalence of and risk factors for autopsy-determined atherosclerosis among US service members, 2001-2011. JAMA. 2012;308(24):2577-2583.
9. Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123(8):933-944.
10. Krishnamurthi N, Francis J, Fihn SD, Meyer CS, Whooley MA. Leading causes of cardiovascular hospitalization in 8.45 million US veterans. PLoS One. 2018;13(3):e0193996.
11. Torio CM, Moore BJ. National inpatient hospital costs: the most expensive conditions by payer. Agency for Healthcare Research and Quality Statistical Brief No. 204. http:// www.hcup-us.ahrq.gov/reports/statbriefs/sb204-Most -Expensive-Hospital-Conditions.pdf. Published May 2016. Accessed October 10, 2019.
12. Bloom DE, Cafiero ET, Jané-Llopis E, et al. The global economic burden of noncommunicable diseases. https:// www.weforum.org/reports/global-economic-burden-non -communicable-diseases. Published September 18, 2011. Accessed October 10, 2019.
13. Crum-Cianflone NF, Bagnell ME, Schaller E, et al. Impact of combat deployment and posttraumatic stress disorder on newly reported coronary heart disease among US active duty and reserve forces. Circulation. 2014;129(18):1813-1820.
14. Fryar CD, Herrick K, Afful J, Ogden CL. Cardiovascular disease risk factors among male veterans, U.S., 2009- 2012. Am J Prev Med. 2016;50(1):101-105.
15. Ulmer CS, Bosworth HB, Germain A, et al; VA Mid-Atlantic Mental Illness Research Education and Clinical Center Registry Workgroup. Associations between sleep difficulties and risk factors for cardiovascular disease in veterans and active duty military personnel of the Iraq and Afghanistan conflicts. J Behav Med. 2015;38(3):544-555.
16. Lutwak N, Dill C. Military sexual trauma increases risk of post-traumatic stress disorder and depression thereby amplifying the possibility of suicidal ideation and cardiovascular disease. Mil Med. 2013;178(4):359-361.
17. Bowry ADK, Lewey J, Dugani SB, Choudhry NK. The burden of cardiovascular disease in low- and middle-income countries: epidemiology and management. Can J Cardiol. 2015;31(9):1151-1159.
18. Reinier K, Stecker EC, Vickers C, Gunson K, Jui J, Chugh SS. Incidence of sudden cardiac arrest is higher in areas of low socioeconomic status: a prospective two year study in a large United States community. Resuscitation. 2006;70(2):186-192.
19. Reinier K, Thomas E, Andrusiek DL, et al; Resuscitation Outcomes Consortium Investigators. Socioeconomic status and incidence of sudden cardiac arrest. CMAJ. 2011;183(15):1705-1712.
20. Yusuf S, Rangarajan S, Teo K, et al; PURE Investigators. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med. 2014;371(9):818-827.
21. World Health Organization. Health topics: cardiovascular disease. http://www.who.int/cardiovascular_diseases/en/. Updated 2019. Accessed October 10, 2019.
22. Berkowitz AL. Stroke and the noncommunicable diseases: A global burden in need of global advocacy. Neurology. 2015;84(21):2183-2184.
23. Holt T. Predicting cardiovascular disease. BMJ. 2016;353:i2621.
24. Centers for Disease Control and Prevention. Million hearts: strategies to reduce the prevalence of leading cardiovascular disease risk factors—United States, 2011. MMWR Morb Mortal Wkly Rep. 2011;60(36):1248-1251.
25. Fryar CD, Chen TC, Li X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999- 2010. NCHS Data Brief. 2012;103:1-8.
26. Ritchey MD, Loustalot F, Wall HK, et al. Million Hearts: description of the national surveillance and modeling methodology used to monitor the number of cardiovascular events prevented during 2012-2016. J Am Heart Assoc. 2017;6(5):pii:e00602.
27. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/ AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 pt B):2935-2959.
28. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): casecontrol study. Lancet. 2004;364(9438):937-952.
29. Eckel RH, Jakicic JM, Ard JD, et al. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 pt B):2960-2984.
30. Bansilal S, Castellano JM, Fuster V. Global burden of CVD: focus on secondary prevention of cardiovascular disease. Int J Cardiol. 2015;201(suppl 1):S1-S7.
31. Havranek EP, Mujahid MS, Barr DA, et al. Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2015;132(9):873-898.
32. Miedema MD, Garberich RF, Schnaidt LJ, et al. Statin eligibility and outpatient care prior to ST-segment elevation myocardial infarction. J Am Heart Assoc. 2017;6(4): pii: e005333.
33. Noheria A, Teodorescu C, Uy-Evanado A, et al. Distinctive profile of sudden cardiac arrest in middle-aged vs. older adults: a community-based study. Int J Cardiol. 2013;168(4):3495-3499.
34. Lieb W, Enserro DM, Larson MG, Vasan RS. Residual cardiovascular risk in individuals on lipid-lowering treatment: quantifying absolute and relative risk in the community. Open Heart. 2018;5(1):e000722.
35. Sachdeva A, Cannon CP, Deedwania PC, et al. Lipid levels in patients hospitalized with coronary artery disease: an analysis of 136,905 hospitalizations in Get With The Guidelines. Am Heart J. 2009;157(1):111-117.e2.
36. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416.
37. Fulcher J, O’Connell R, Voysey M, et al; Cholesterol Treatment Trialists (CTT) Collaboration. Efficacy and safety of LDL-lowering therapy among men and women: metaanalysis of individual data from 174,000 participants in 27 randomised trials. Lancet. 2015;385(9976):1397-1405.
38. Perrone V, Sangiorgi D, Buda S, Degli Esposti L. Residual cardiovascular risk in patients who received lipid-lowering treatment in a real-life setting: retrospective study. Clinicoecon Outcomes Res. 2016;8:649-655.
39. Sirimarco G, Labreuche J, Bruckert E, et al; PERFORM and SPARCL Investigators. Atherogenic dyslipidemia and residual cardiovascular risk in statin-treated patients. Stroke. 2014;45(5):1429-1436.
40. Kones R. Molecular sources of residual cardiovascular risk, clinical signals, and innovative solutions: relationship with subclinical disease, undertreatment, and poor adherence: implications of new evidence upon optimizing cardiovascular patient outcomes. Vasc Health Risk Manag. 2013;9:617-670.
41. Hayashi M, Shimizu W, Albert CM. The spectrum of epidemiology underlying sudden cardiac death. Circ Res. 2015;116(12):1887-1906.
42. Downs JR, O’Malley PG. Management of dyslipidemia for cardiovascular disease risk reduction: synopsis of the 2014 U.S. Department of Veterans Affairs and U.S. Department of Defense clinical practice guideline. Ann Intern Med. 2015;163(4):291-297.
43. Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/ AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/ NLA/PCNA Guideline on the management of blood cholesterol: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;73(24):3168-3209.
44. Tsimikas S, Fazio S, Ferdinand KC, et al. NHLBI Working Group recommendations to reduce lipoprotein(a)-mediated risk of cardiovascular disease and aortic stenosis. J Am Coll Cardiol. 2018;71(2):177-192.
45. Tsimikas S. A test in context: Lipoprotein(a): diagnosis, prognosis, controversies, and emerging therapies. J Am Coll Cardiol. 2017;69(6):692-711.
46. Ellis KL, Boffa MB, Sahebkar A, Koschinsky ML, Watts GF. The renaissance of lipoprotein(a): brave new world for preventive cardiology? Prog Lipid Res. 2017;68:57-82.
47. Thompson GR, Seed M. Lipoprotein(a): the underestimated cardiovascular risk factor. Heart. 2014;100(7):534-535.
48. Marcovina SM, Albers JJ. Lipoprotein (a) measurements for clinical application. J Lipid Res. 2016;57(4):526-537.
49. Tremulis SR. Founder’s Story: Lipoprotein(a) Foundation. https://www.lipoproteinafoundation.org/page /Sandrastory. Accessed October 10, 2019.
50. Centers for Disease Control and Prevention. ICD-10 Coordination and Maintenance Committee meeting, September 12-13, 2017 diagnosis agenda. https://www.cdc. gov/nchs/data/icd/Topic_Packet_Sept_2017.pdf. Accessed October 10, 2019.
51. Centers for Medicare & Medicaid Services. 2019 ICD- 10-CM codes descriptions in tabular order. https://www. cms.gov/Medicare/Coding/ICD10/2019-ICD-10-CM.html. Accessed October 10, 2019.
52. Wilson DP, Jacobson TA, Jones PH, et al. Use of Lipoprotein(a) in clinical practice: a biomarker whose time has come. A scientific statement from the National Lipid Association. J Clin Lipidol. 2019;13(3):374-392.
53. Langlois MR, Chapman MJ, Cobbaert C, et al. Quantifying atherogenic lipoproteins: current and future challenges in the era of personalized medicine and very low concentrations of ldl cholesterol. A consensus statement from EAS and EFLM. Clin Chem. 2018;64(7):1006-1033.
54. Shapiro MD, Fazio S. Apolipoprotein B-containing lipoproteins and atherosclerotic cardiovascular disease. F1000Res. 2017;6:134.
55. Tsimikas S, Fazio S, Viney NJ, Xia S, Witztum JL, Marcovina SM. Relationship of lipoprotein(a) molar concentrations and mass according to lipoprotein(a) thresholds and apolipoprotein(a) isoform size. J Clin Lipidol. 2018;12(5):1313-1323.
56. Erqou S, Kaptoge S, Perry PL, et al; Emerging Risk Factors Collaboration. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA. 2009;302(4):412-423.
57. Nave AH, Lange KS, Leonards CO, et al. Lipoprotein (a) as a risk factor for ischemic stroke: a meta-analysis. Atherosclerosis. 2015;242(2):496-503.
58. Varvel S, McConnell JP, Tsimikas S. Prevalence of elevated Lp(a) mass levels and patient thresholds in 532,359 patients in the United States. Arterioscler Thromb Vasc Biol. 2016;36(11):2239-2245.
59. Nordestgaard BG, Chapman MJ, Ray K, et al; European Atherosclerosis Society Consensus Panel. Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J. 2010;31(23):2844-2853.
60. Guan W, Cao J, Steffen BT, et al. Race is a key variable in assigning lipoprotein(a) cutoff values for coronary heart disease risk assessment: the Multi-Ethnic Study of Atherosclerosis. Arterioscler Thromb Vasc Biol. 2015;35(4):996-1001.
61. Virani SS, Brautbar A, Davis BC, et al. Associations between lipoprotein(a) levels and cardiovascular outcomes in black and white subjects: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 2012;125(2):241-249.
62. Tsimikas S, Mallat Z, Talmud PJ, et al. Oxidation-specific biomarkers, lipoprotein(a), and risk of fatal and nonfatal coronary events. J Am Coll Cardiol. 2010;56(12):946-955.
63. Clarke R, Peden JF, Hopewell JC, et al; PROCARDIS Consortium. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. New Eng J Med. 2009;361(26):2518-2528.
64. Wattanakit K, Folsom AR, Chambless LE, Nieto FJ. Risk factors for cardiovascular event recurrence in the Atherosclerosis Risk in Communities (ARIC) study. Am Heart J. 2005;149(4):606-612.
65. Ruotolo G, Lincoff MA, Menon V, et al. Lipoprotein(a) is a determinant of residual cardiovascular risk in the setting of optimal LDL-C in statin-treated patients with atherosclerotic cardiovascular disease [Abstract 17400]. Circulation. 2018;136(suppl 1):A17400.
66. Suwa S, Ogita M, Miyauchi K, et al. Impact of lipoprotein (a) on long-term outcomes in patients with coronary artery disease treated with statin after a first percutaneous coronary intervention. J Atheroscler Thromb. 2017;24(11):1125-1131.
67. Nestel PJ, Barnes EH, Tonkin AM, et al. Plasma lipoprotein(a) concentration predicts future coronary and cardiovascular events in patients with stable coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33(12):2902-2908.
68. Burgess S, Ference BA, et al. Association of LPA variants with risk of coronary disease and the implications for lipoprotein(a)-lowering therapies: a Mendelian randomization analysis. JAMA Cardiol. 2018;3(7):619-627.
69. Roeseler E, Julius U, Heigl F, et al; Pro(a)LiFe-Study Group. Lipoprotein apheresis for lipoprotein(a)-associated cardiovascular disease: prospective 5 years of followup and apolipoprotein(a) characterization. Arterioscler Thromb Vasc Biol. 2016;36(9):2019-2027.
70. Matsuura E, Atzeni F, Sarzi-Puttini P, Turiel M, Lopez LR, Nurmohamed MT. Is atherosclerosis an autoimmune disease? BMC Med. 2014;12:47.
71. Ahearn J, Shields KJ, Liu CC, Manzi S. Cardiovascular disease biomarkers across autoimmune diseases. Clin Immunol. 2015;161(1):59-63.
72. Di Minno MND, Emmi G, Ambrosino P, et al. Subclinical atherosclerosis in asymptomatic carriers of persistent antiphospholipid antibodies positivity: a cross-sectional study. Int J Cardiol. 2019;274:1-6.
72. Di Minno MND, Emmi G, Ambrosino P, et al. Subclinical atherosclerosis in asymptomatic carriers of persistent antiphospholipid antibodies positivity: a cross-sectional study. Int J Cardiol. 2019;274:1-6.
73. Iseme RA, McEvoy M, Kelly B, et al. A role for autoantibodies in atherogenesis. Cardiovasc Res. 2017;113(10):1102-1112.
74. Sinicato NA, da Silva Cardoso PA, Appenzeller S. Risk factors in cardiovascular disease in systemic lupus erythematosus. Curr Cardiol Rev. 2013;9(1):15-19.
75. Sciatti E, Cavazzana I, Vizzardi E, et al. Systemic lupus erythematosus and endothelial dysfunction: a close relationship. Curr Rheumatol Rev. 2018;15(3):177-188.
76. Prasad A, Clopton P, Ayers C, et al. Relationship of autoantibodies to MDA-LDL and ApoB-Immune complexes to sex, ethnicity, subclinical atherosclerosis, and cardiovascular events. Arterioscler Thromb Vasc Biol. 2017;37(6):1213-1221.
77. Miller YI, Choi SH, Wiesner P, et al. Oxidation-specific epitopes are danger-associated molecular patterns recognized by pattern recognition receptors of innate immunity. Circ Res. 2011;108(2):235-248.
78. Libby P, Lichtman AH, Hansson GK. Immune effector mechanisms implicated in atherosclerosis: from mice to humans. Immunity. 2013;38(6):1092-1104.
79. Binder CJ, Papac-Milicevic N, Witztum JL. Innate sensing of oxidation-specific epitopes in health and disease. Nat Rev Immunol. 2016;16(8):485-497.
80. Freigang S. The regulation of inflammation by oxidized phospholipids. Eur J Immunol. 2016;46(8):1818-1825.
81. Ravandi A, Boekholdt SM, Mallat Z, et al. Relationship of oxidized LDL with markers of oxidation and inflammation and cardiovascular events: results from the EPIC-Norfolk Study. J Lipid Res. 2011;52(10):1829-1836.
82. Tsimikas S, Willeit P, Willeit J, et al. Oxidation-specific biomarkers, prospective 15-year cardiovascular and stroke outcomes, and net reclassification of cardiovascular events. J Am Coll Cardiol. 2012;60(21):2218-2229.
83. Cinoku I, Mavragani CP, Tellis CC, Nezos A, Tselepis AD, Moutsopoulos HM. Autoantibodies to ox-LDL in Sjogren’s syndrome: are they atheroprotective? Clin Exp Rheumatol. 2018;36 Suppl 112(3):61-67.
84. Fagerberg B, Prahl Gullberg U, Alm R, Nilsson J, Fredrikson GN. Circulating autoantibodies against the apolipoprotein B-100 peptides p45 and p210 in relation to the occurrence of carotid plaques in 64-year-old women. PLoS One. 2015;10(3):e0120744.
85. Klesareva EA, Afanas’eva OI, Donskikh VV, Adamova IY, Pokrovskii SN. Characteristics of lipoprotein(a)-containing circulating immune complexes as markers of coronary heart disease. Bull Exp Biol Med. 2016;162(2):231-236.
86. Morishita R, Ishii J, Kusumi Y, et al. Association of serum oxidized lipoprotein(a) concentration with coronary artery disease: potential role of oxidized lipoprotein(a) in the vasucular wall. J Atheroscler Thromb. 2009;16(4):410-418.
87. Wang J, Zhang C, Gong J, et al. Development of new enzyme-linked immunosorbent assay for oxidized lipoprotein(a) by using purified human oxidized lipoprotein(a) autoantibodies as capture antibody. Clin Chim Acta. 2007;385(1-2):73-78.
88. Wang JJ, Han AZ, Meng Y, et al. Measurement of oxidized lipoprotein (a) in patients with acute coronary syndromes and stable coronary artery disease by 2 ELISAs: using different capture antibody against oxidized lipoprotein (a) or oxidized LDL. Clin Biochem. 2010;43(6):571-575.
89. Orso E, Schmitz G. Lipoprotein(a) and its role in inflammation, atherosclerosis and malignancies. Clin Res Cardiol Suppl. 2017;12(Suppl 1):31-37.
90. Lassman ME, McLaughlin TM, Zhou H, et al. Simultaneous quantitation and size characterization of apolipoprotein(a) by ultra-performance liquid chromatography/ mass spectrometry. Rapid Commun Mass Spectrom. 2014;28(10):1101-1106.
91. Lippi G, Guidi G. Lipoprotein(a): from ancestral benefit to modern pathogen? QJM. 2000;93(2):75-84.
92. van der Valk FM, Bekkering S, Kroon J, et al. Oxidized phospholipids on lipoprotein(a) elicit arterial wall inflammation and an inflammatory monocyte response in humans. Circulation. 2016;134(8):611-624.
93. Yeang C, Wilkinson MJ, Tsimikas S. Lipoprotein(a) and oxidized phospholipids in calcific aortic valve stenosis. Curr Opin Cardiol. 2016;31(4):440-450.
94. Thanassoulis G, Campbell CY, Owens DS, et al; CHARGE Extracoronary Calcium Working Group. Genetic associations with valvular calcification and aortic stenosis. N Engl J Med. 2013;368(6):503-512.
95. Aguib Y, Al Suwaidi J. The Copenhagen City Heart Study (Osterbroundersogelsen). Glob Cardiol Sci Pract. 2015;2015(3):33.
96. Kamstrup PR, Benn M, Tybjaerg-Hansen A, Nordestgaard BG. Extreme lipoprotein(a) levels and risk of myocardial infarction in the general population: the Copenhagen City Heart Study. Circulation. 2008;117(2):176-184.
97. Kamstrup PR, Tybjærg-Hansen A, Steffensen R, Nordestgaard BG. Genetically elevated lipoprotein(a) and increased risk of myocardial infarction. JAMA. 2009;301(22):2331-2339.
98. Kamstrup PR, Tybjaerg-Hansen A, Nordestgaard BG. Extreme lipoprotein(a) levels and improved cardiovascular risk prediction. J Am Coll Cardiol.2013;61(11):1146-1156.
99. Kamstrup PR, Tybjaerg-Hansen A, Nordestgaard BG. Elevated lipoprotein(a) and risk of aortic valve stenosis in the general population. J Am Coll Cardiol. 2014;63(5):470-477.
100. Kamstrup PR, Nordestgaard BG. Elevated lipoprotein(a) levels, LPA risk genotypes, and increased risk of heart failure in the general population. JACC Heart Fail.2016;4(1):78-87.
101. Verbeek R, Hoogeveen RM, Langsted A, et al. Cardiovascular disease risk associated with elevated lipoprotein(a) attenuates at low low-density lipoprotein cholesterol levels in a primary prevention setting. Eur Heart J. 2018;39(27):2589-2596.
102. Cao J, Steffen BT, Budoff M, et al. Lipoprotein(a) levels are associated with subclinical calcific aortic valve disease in white and black individuals: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. 2016;36(5):1003-1009.
103. Steffen BT, Duprez D, Bertoni AG, Guan W, Tsai M. Lp(a) [lipoprotein(a)]-related risk of heart failure is evident in whites but not in other racial/ethnic groups.Arterioscler Thromb Vasc Biol. 2018;38(10):2498-2504.
104. ARIC Investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epidemiol. 1989;129(4):687-702.
105. Agarwala A, Pokharel Y, Saeed A, et al. The association of lipoprotein(a) with incident heart failure hospitalization: Atherosclerosis Risk in Communities study. Atherosclerosis. 2017;262:131-137.
106. Ohira T, Schreiner PJ, Morrisett JD, Chambless LE, Rosamond WD, Folsom AR. Lipoprotein(a) and incident ischemic stroke: the Atherosclerosis Risk in Communities (ARIC) study. Stroke. 2006;37(6):1407-1412.
107. Waldeyer C, Makarova N, Zeller T, et al. Lipoprotein(a) and the risk of cardiovascular disease in the European population: results from the BiomarCaRE consortium. Eur Heart J. 2017;38(32):2490-2498.
108. Cook NR, Mora S, Ridker PM. Lipoprotein(a) and cardiovascular risk prediction among women. J Am Coll Cardiol. 2018;72(3):287-296.
109. Suk Danik J, Rifai N, Buring JE, Ridker PM. Lipoprotein(a), measured with an assay independent of apolipoprotein(a) isoform size, and risk of future cardiovascular events among initially healthy women. JAMA. 2006;296(11):1363-1370.
110. Suk Danik J, Rifai N, Buring JE, Ridker PM. Lipoprotein(a), hormone replacement therapy, and risk of future cardiovascular events. J Am Coll Cardiol. 2008;52(2):124-131.
111. Chien KL, Hsu HC, Su TC, Sung FC, Chen MF, Lee YT. Lipoprotein(a) and cardiovascular disease in ethnic Chinese: the Chin-Shan Community Cardiovascular Cohort Study. Clin Chem. 2008;54(2):285-291.
112. Lee SR, Prasad A, Choi YS, et al. LPA gene, ethnicity, and cardiovascular events. Circulation.2017;135(3):251-263.
113. Zekavat SM, Ruotsalainen S, Handsaker RE, et al. Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries. Nat Commun.2018;9(1):2606.
114. Zewinger S, Kleber ME, Tragante V, et al. Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study. Lancet Diabetes Endocrinol. 2017;5(7):534-543.
115. Li J, Lange LA, Sabourin J, et al. Genome- and exomewide association study of serum lipoprotein (a) in the Jackson Heart Study. J Hum Genet. 2015;60(12):755-761.
116. Khera AV, Emdin CA, Drake I, et al, Kathiresan S. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N Engl J Med.2016;375(24):2349-2358.
117. Nordestgaard BG, Langsted A. Lipoprotein(a) as a cause of cardiovascular disease: insights from epidemiology, genetics, and biology. J Lipid Res.2016;57(11):1953-1975.
118. Sofi F, Cesari F, Casini A, Macchi C, Abbate R, Gensini GF. Insomnia and risk of cardiovascular disease: a metaanalysis. Eur J Prev Cardiol.2014;21(1):57-64.
119. Estruch R, Ros E, Salas-Salvado J, et al. Primary prevention of cardiovascular disease with a mediterranean diet supplemented with extra-virgin olive oil or nuts. N Engl J Med.2018;378(25):e34.
120. Perrot N, Verbeek R, Sandhu M, et al. Ideal cardiovascular health influences cardiovascular disease risk associated with high lipoprotein(a) levels and genotype: The EPICNorfolk prospective population study. Atherosclerosis. 2017;256:47-52.
121. Teo KK, Corsi DJ, Tam JW, Dumesnil JG, Chan KL. Lipid lowering on progression of mild to moderate aortic stenosis: meta-analysis of the randomized placebocontrolled clinical trials on 2344 patients. Can J Cardiol. 2011;27(6):800-808.
122. Albers JJ, Slee A, O’Brien KD, et al. Relationship of apolipoproteins A-1 and B, and lipoprotein(a) to cardiovascular outcomes: the AIM-HIGH trial (Atherothrombosis Intervention in Metabolic Syndrome with Low HDL/High Triglyceride and Impact on Global Health Outcomes). J Am Coll Cardiol. 2013;62(17):1575-1579.
123. Lincoff AM, Nicholls SJ, Riesmeyer JS, et al; ACCELERATE Investigators. Evacetrapib and cardiovascular outcomes in high-risk vascular disease. N Engl J Med. 2017;376(20):1933-1942.
124. Schmidt AF, Pearce LS, Wilkins JT, Overington JP, Hingorani AD, Casas JP. PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database Syst Rev.2017;4:CD011748.
125. Bowman L, Hopewell JC, Chen F, et al; PHS3/TIM155-REVEAL Collaborative Group. Effects of anacetrapib in patients with atherosclerotic vascular disease.
126. Leebmann J, Roeseler E, Julius U, et al; Pro(a)LiFe Study Group. Lipoprotein apheresis in patients with maximally tolerated lipid-lowering therapy, lipoprotein(a)-hyperlipoproteinemia, and progressive cardiovascular disease: prospective observational multicenter study. Circulation. 2013;128(24):2567-2576.
127. Heigl F, Hettich R, Lotz N, et al. Efficacy, safety, and tolerability of long-term lipoprotein apheresis in patients with LDL- or Lp(a) hyperlipoproteinemia: Findings gathered from more than 36,000 treatments at one center in Germany. Atheroscler Suppl. 2015;18:154-162.
128. Viney NJ, van Capelleveen JC, Geary RS, et al. Antisense oligonucleotides targeting apolipoprotein(a) in people with raised lipoprotein(a): two randomised, double-blind, placebo-controlled, dose-ranging trials. Lancet. 2016;388(10057):2239-2253.
129. Graham MJ, Viney N, Crooke RM, Tsimikas S. Antisense inhibition of apolipoprotein (a) to lower plasma lipoprotein (a) levels in humans. J Lipid Res. 2016;57(3):340-351.
130. Keene D, Price C, Shun-Shin MJ, Francis DP. Effect on cardiovascular risk of high density lipoprotein targeted drug treatments niacin, fibrates, and CETP inhibitors: meta-analysis of randomised controlled trials including 117,411 patients. BMJ. 2014;349:g4379.
131. Nicholls SJ, Ruotolo G, Brewer HB, et al. Evacetrapib alone or in combination with statins lowers lipoprotein(a) and total and small LDL particle concentrations in mildly hypercholesterolemic patients. J Clin Lipidol. 2016;10(3):519-527.e4.
132. Schwartz GG, Ballantyne CM, Barter PJ, et al. Association of lipoprotein(a) with risk of recurrent ischemic events following acute coronary syndrome: analysis of the dal-outcomes randomized clinical trial. JAMA Cardiol.2018;3(2):164-168.
133. Schwartz GG, Olsson AG, Abt M, et al; dal-OUTCOMES Investigators. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med.2012;367(22):2089-2099.
134. Thomas T, Zhou H, Karmally W, et al. CETP (Cholesteryl Ester Transfer Protein) inhibition with anacetrapib decreases production of lipoprotein(a) in mildly hypercholesterolemic subjects. Arterioscler Thromb Vasc Biol.2017;37(9):1770-1775.
135. Khera AV, Everett BM, Caulfield MP, et al. Lipoprotein(a) concentrations, rosuvastatin therapy, and residual vascular risk: an analysis from the JUPITER Trial (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin). Circulation. 2014;129(6):635-642.
136. Yeang C, Hung MY, Byun YS, et al. Effect of therapeutic interventions on oxidized phospholipids on apolipoprotein B100 and lipoprotein(a). J Clin Lipidol. 2016;10(3):594-603.
137. Zhou Z, Rahme E, Pilote L. Are statins created equal? Evidence from randomized trials of pravastatin, simvastatin, and atorvastatin for cardiovascular disease prevention.Am Heart J. 2006;151(2):273-281.
138. Ridker PM, MacFadyen JG, Fonseca FA, et al; JUPITER Study Group. Number needed to treat with rosuvastatin to prevent first cardiovascular events and death among men and women with low low-density lipoprotein cholesterol and elevated high-sensitivity C-reactive protein: justification for the use of statins in prevention: an intervention trial evaluating rosuvastatin (JUPITER). Circ Cardiovasc Qual Outcomes. 2009;2(6):616-623.
139. Raal FJ, Giugliano RP, Sabatine MS, et al. Reduction in lipoprotein(a) with PCSK9 monoclonal antibody evolocumab (AMG 145): a pooled analysis of more than 1,300 patients in 4 phase II trials. J Am Coll Cardiol.2014;63(13):1278-1288.
140. Sabatine MS, Giugliano RP, Wiviott SD, et al. Efficacy and safety of evolocumab in reducing lipids and cardiovascular events. N Engl J Med. 2015;372(16):1500-1509.
141. Koren MJ, Sabatine MS, Giugliano RP, et al. Long-term low-density lipoprotein cholesterol-lowering efficacy, persistence, and safety of evolocumab in treatment of hypercholesterolemia: results up to 4 years from the open-label OSLER-1 extension study. JAMA Cardiol.2017;2(6):598-607.
142. Desai NR, Kohli P, Giugliano RP, et al. AMG145, a monoclonal antibody against proprotein convertase subtilisin kexin type 9, significantly reduces lipoprotein(a) in hypercholesterolemic patients receiving statin therapy: an analysis from the LDL-C Assessment with Proprotein Convertase Subtilisin Kexin Type 9 Monoclonal Antibody Inhibition Combined with Statin Therapy (LAPLACE)-Thrombolysis in Myocardial Infarction (TIMI) 57 trial. Circulation.2013;128(9):962-969.
143. Schwartz GG, Steg PG, Szarek M, et al; ODYSSEY OUTCOMES Committees and Investigators. Alirocumab and cardiovascular outcomes after acute coronary syndrome.N Engl J Med. 2018;379(22):2097-2107.
144. Sabatine MS, Giugliano RP, Keech AC, et al; FOURIER Steering Committee and Investigators. Evolocumab and clinical outcomes in patients with cardiovascular Disease.N Engl J Med. 2017;376(18):1713-1722.
145. Karatasakis A, Danek BA, Karacsonyi J, et al. Effect of PCSK9 inhibitors on clinical outcomes in patients with hypercholesterolemia: A meta-analysis of 35 randomized controlled trials. J Am Heart Assoc. 2017;6(12):e006910.
146. Santos RD, Duell PB, East C, et al. Long-term efficacy and safety of mipomersen in patients with familial hypercholesterolaemia: 2-year interim results of an open-label extension.Eur Heart J. 2015;36(9):566-575.
147. Duell PB, Santos RD, Kirwan BA, Witztum JL, Tsimikas S, Kastelein JJP. Long-term mipomersen treatment is associated with a reduction in cardiovascular events in patients with familial hypercholesterolemia. J Clin Lipidol. 2016;10(4):1011-1021.
148. McGowan MP, Tardif JC, Ceska R, et al. Randomized, placebo-controlled trial of mipomersen in patients with severe hypercholesterolemia receiving maximally tolerated lipid-lowering therapy. PLoS One.2012;7(11):e49006.
149. Jaeger BR, Richter Y, Nagel D, et al. Longitudinal cohort study on the effectiveness of lipid apheresis treatment to reduce high lipoprotein(a) levels and prevent major adverse coronary events. Nat Clin Pract Cardiovasc Med.2009;6(3):229-239.
150. Rosada A, Kassner U, Vogt A, Willhauck M, Parhofer K, Steinhagen-Thiessen E. Does regular lipid apheresis in Does regular lipid apheresis in patients with isolated elevated lipoprotein(a) levels reduce the incidence of cardiovascular events? Artif Organs. 2014;38(2):135-141.
151. Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e596-e646.
Cardiovascular disease (CVD) remains the leading cause of global mortality. In 2015, 41.5% of the US population had at least 1 form of CVD and CVD accounted for nearly 18 million deaths worldwide.1,2 The major disease categories represented include myocardial infarction (MI), sudden death, strokes, calcific aortic valve stenosis (CAVS), and peripheral vascular disease.1,2 In terms of health care costs, quality of life, and caregiver burden, the overall impact of disease prevalence continues to rise.1,3-6 There is an urgent need for more precise and earlier CVD risk assessment to guide lifestyle and therapeutic interventions for prevention of disease progression as well as potential reversal of preclinical disease. Even at a young age, visible coronary atherosclerosis has been found in up to 11% of “healthy” active individuals during autopsies for trauma fatalities.7,8
The impact of CVD on the US and global populations is profound. In 2011, CVD prevalence was predicted to reach 40% by 2030.9 That estimate was exceeded in 2015, and it is now predicted that by 2035, 45% of the US population will suffer from some form of clinical or preclinical CVD. In 2015, the decadeslong decline in CVD mortality was reversed for the first time since 1969, showing a 1% increase in deaths from CVD.1 Nearly 300,000 of those using US Department of Veterans Affairs (VA) services were hospitalized for CVD between 2010 and 2014.10 The annual direct and indirect costs related to CVD in the US are estimated at $329.7 billion, and these costs are predicted to top $1 trillion by 2035.1 Heart attack, coronary atherosclerosis, and stroke accounted for 3 of the 10 most expensive conditions treated in US hospitals in 2013.11 Globally, the estimate for CVD-related direct and indirect costs was $863 billion in 2010 and may exceed $1 trillion by 2030.12
The nature of military service adds additional risk factors, such as posttraumatic stress disorder, depression, sleep disorders and physical trauma which increase CVD morbidity/ mortality in service members, veterans, and their families.13-16 In addition, living in lowerincome areas (countries or neighborhoods) can increase the risk of both CVD incidence and fatalities, particularly in younger individuals.17-20 The Military Health System (MHS) and VA are responsible for the care of those individuals who have voluntarily taken on these additional risks through their time in service. This responsibility calls for rapid translation to practice tools and resources that can support interventions to minimize as many modifiable risk factors as possible and improve longterm health. This strategy aligns with the World Health Organization’s (WHO) focus on prevention of disease progression through interventions targeting modifiable risk.3-6,21-23 The driving force behind the launch of the US Department of Health and Human Services (HHS) Million Hearts program was the goal of preventing 1 million heart attacks and strokes by 2017 with risk reduction through aspirin, blood pressure control, cholesterol management, smoking cessation, sodium reduction, and physical activity.24,25 While some reductions in CVD events have been documented, the outcomes fell short of the goals set, highlighting both the need and value of continued and expanded efforts for CVD risk reduction.26
More precise assessment of risk factors during preventative care, as well as after a diagnosis of CVD, may improve the timeliness and precision of earlier interventions (both lifestyle and therapeutic) that reduce CVD morbidity and mortality.27 Personalized or precision medicine approaches take into account differences in socioeconomic, environmental, and lifestyle factors that are potentially reversible, as well as gender, race, and ethnicity.28-31 Current methods of predicting CVD risk have considerable room for improvement.27 About 40% of patients with newly diagnosed CVD have normal traditional cholesterol profiles, including those whose first cardiac event proves fatal.29-33 Currently available risk scores (hundreds have been described in the literature) mischaracterize risk in minority populations and women, and have shown deficiencies in identifying preclinical atherosclerosis.34,35 The failure to recognize preclinical CVD in military personnel during their active duty life cycle results in missed opportunities for improved health and readiness sustainment.
Most CVD risk prediction models incorporate some form of blood lipids. Total cholesterol (TC) is most commonly used in clinical practice, along with high-density lipoprotein (HDLC), low-density lipoprotein (LDLC), and triglycerides (TG).23,27,36 High LDLC and/or TC are well established as lipid-related CVD risk factors and are incorporated into many CVD risk scoring systems/models described in the literature.27 LDLC reduction is commonly recommended as CVD prevention, but even with optimal statin treatment, there is still considerable residual risk for new and recurrent CVD events.28,32,34,35,37-42
Incorporating novel biomarkers and alternative lipid measurements may improve risk prediction and aid targeted treatment, ultimately reducing CVD events.27 Apolipoprotein B (ApoB) is a major atherogenic component embedded in LDL and VLDL correlating to non-HDLC and may be useful in the setting of triglycerides ≥ 200 mg/d as levels > 130 mg/ dL appear to be risk-enhancing, but measurements may be unreliable.43 According to the 2018 Cholesterol Guidelines, lipoprotein(a) [Lp(a)] elevation also is recognized as a risk-enhancing factor that is particularly implicated when there is a strong family history of premature atherosclerotic CVD or personal history of CVD not explained by major risk factors.43
Lp(a) elevation is a largely underrecognized category of lipid disorder that impacts up to 20% to 30% of the population globally and within the US, although there is considerable variability by geographic location and ethnicity.44 Globally, Lp(a) elevation places > 1 billion people at moderate to high risk for CVD.44 Lp(a) has a strong genetic component and is recognized as a distinct and independent risk factor for MI, sudden death, strokes and CAVS. Lp(a) has an extensive body of evidence to support its distinct role both as a causal factor in CVD and as an augmentation to traditional risk factors.44-48
Lipoproteni(a) Elevation Use For Diagnosis
The importance of Lp(a) elevation as a clinical diagnosis rather than a laboratory abnormality alone was brought forward by the Lipoprotein(a) Foundation. Its founder, Sandra Tremulis, is a survivor of an acute coronary event that occurred when she was 39-years old, despite running marathons and having none of the traditional CVD lifestyle risk factors.49 This experience inspired her to create the Lipoprotein(a) Foundation to give a voice to families living with or at risk for CVD due to Lp(a) elevation.
As often happens in the progress of medicine, patients and their families drive change based on their personal experiences with the gaps in standard clinical practice. It was this foundation—not a member of the medical establishment—that submitted the formal request for the addition of new ICD-10-CM diagnostic and family history codes for Lp(a) elevation during the Centers for Disease Control and Prevention (CDC) September 2017 ICD-10-CM Coordination and Maintenance Committee meeting.50 In June 2018, the final ICD-10-CM code addenda for 2019 was released and included the new codes E78.41 (Elevated Lp[a]) and Z83.430 (Family history of elevated Lp[a]).52 After the new codes were approved, both the American Heart Association and the National Lipid Association added recommendations regarding Lp(a) testing to their clinical practice guidelines.43,52
Practically, these codes standardize billing and payment for legitimate clinical work and laboratory testing. Prior to the addition of Lp(a) elevation as a clinical diagnosis, testing and treatment of Lp(a) elevation was considered experimental and not medically necessary until after a cardiovascular event had already occurred. Services for Lp(a) elevation were therefore not reimbursed by many healthcare organizations and insurance companies. The new ICD-10-CM codes encourage the assessment of Lp(a) both in individuals with early onset major CVD events and in presumably fit, healthy individuals, particularly when there is a family history of Lp(a) elevation. Given that Lp(a) levels do not change significantly over time, the current understanding is that only a single measurement is needed to define the individual risk over a lifetime.41,42,44,45 As therapies targeting Lp(a) levels evolve, repeated measurements may be indicated to monitor response and direct changes in management. “Elevated Lipoprotein(a)” is the first laboratory testing abnormality that has achieved the status of a clinical diagnosis.
Lp(a) Measurements
There is considerable complexity to the measurement of lipoproteins in blood samples due to heterogeneity in both density and size of particles as illustrated in the Figure.53
For traditional lipids measured in clinical practice, the size and density ranges from small high-density lipoprotein (HDL) through LDLC and intermediate- density lipoprotein (IDL) to the largest least dense particles in the very low-density lipoprotein (VLDL) and chylomicron remnant fractions. Standard lipid profiles consist of mass concentration measurements (mg/dL) of TC, TG, HDLC, and LDLC.53 Non-HDLC (calculated as: TC−HDLC) consists of all cholesterol found in atherogenic lipoproteins, including remnant-C and Lp(a). Until recently, the cholesterol content of Lp(a), corresponding to about 30% of Lp(a) total mass, was included in the TC, non-HDLC and LDLC measurements with no separate reporting by the majority of clinical laboratories.
After > 50 years of research on the structure and biochemistry of Lp(a), the physiology and biological functions of these complex and polymorphic lipoprotein particles are not fully understood. Lp(a) is composed of a lipoprotein particle similar in composition to LDL (protein and lipid), containing 1 molecule of ApoB wrapped around a core of cholesteryl ester and triglyceride with phospholipids and unesterified cholesterol at its surface.48 The presence of a unique hydrophilic, highly glycosylated protein referred to as apolopoprotienA (apo[a]), covalently attached to ApoB-100 by a single disulfide bridge, differentiates Lp(a) from LDL.48 Cholesterol rich ApoB is an important component within many lipoproteins pathogenic for atherosclerosis and CVD.45,47,53
The apo(a) contributes to the increased density of Lp(a) compared to LDLC with associated reduced binding affinity to the LDL receptor. This reduced receptor binding affinity is a presumed mechanism for the lack of Lp(a) plasma level response to statin therapies, which increase hepatic LDL receptor activity.47 Apo(a) evolved from the plasminogen gene through duplication and remodeling and demonstrates extensive heterogeneity in protein size, with > 40 different apo(a) isoforms resulting in > 40 different Lp(a) particle sizes. Size of the apo(a) particle is determined by the number of pleated structures known as kringles. Most people (> 80%) carry 2 different-sized apo(a) isoforms. Plasma Lp(a) level is determined by the net production of apo(a) in each isoform, and the smaller apo(a) isoforms are associated with higher plasma levels of Lp(a).45
Given the heterogeneity in Lp(a) molecular weight, which can vary even within individuals, recommendations have been made for reporting results as particle numbers or concentrations (nmol/L or mmol/L) rather than as mass concentration (mg/dL).55 However, the majority of the large CVD morbidity and mortality outcomes studies used Lp(a) mass concentration levels in mg/ dL to characterize risk levels.56,57 There is no standardized method to convert Lp(a) measurements from mg/dL to nmol/L.55 Current assays using WHO standardized reagents and controls are reliable for categorizing risk levels.58
The European Atherosclerosis Society consensus panel recommended that desirable Lp(a) levels should be below the 80th percentile (< 50 mg/dL or < 125 nmol/L) in patients with intermediate or high CVD risk.59 Subsequent epidemiological and Mendelian randomization studies have been performed in general populations with no history of CVD and demonstrated that increased CVD risk can be detected with Lp(a) levels as low as 25 to 30 mg/dL.56,60-63 In secondary prevention populations with prior CVD and optimal treatment (statins, antiplatelet drugs), recurrent event risk was also increased with elevated Lp(a).63-66
Using immunoturbidometric assays, Varvel and colleagues reported the prevalence of elevated Lp(a) mass concentration levels (mg/dL) in > 500,000 US patients undergoing clinical evaluations based on data from a referral laboratory of patients.58 The mean Lp(a) levels were 34.0 mg/dL with median (interquartile range [IQR]) levels at 17 (7-47) mg/dL and overall range of 0 to 907 mg/dL.58 Females had higher Lp(a) levels compared to males but no ethnic or racial breakdown was provided. Lp(a) levels > 30 mg/dL and > 50 mg/dL were present in 35% and 24% of subjects, respectively. Table 1 displays the relationship between various Lp(a) level cut-offs to mean levels of LDLC, estimated LDLC corrected for Lp(a), TC, HDLC, and TG.58 The data demonstrate that Lp(a) elevation cannot be inferred from LDLC levels nor from any of the other traditional lipoprotein measures. Patients with high risk Lp(a) levels may have normal LDLC. While Lp(a) thresholds have been identified for stratification of CVD risk, the target levels for risk reduction have not been specifically defined, particularly since therapies are not widely available for reduction of Lp(a). Table 2 provides an overview of clinical lipoprotein measurements that may be reasonable targets for therapeutic interventions and reduction of CVD risk.44,53,55 In general, existing studies suggest that radical reduction (> 80%) is required to impact long-term outcomes, particularly in individuals with severe disease.68,69
LDLC reduction alone leaves a residual CVD risk that is greater than the risk reduced.40 In addition, the autoimmune inflammation and lipid specific autoantibodies play an important role in increased CVD morbidity and mortality risk.70,71 The presence of autoantibodies such as antiphospholipid antibodies (without a specific autoimmune disease diagnosis) increases the risk of subclinical atherosclerosis.72,73 Certain autoimmune diseases such as systemic lupus erythematosus are recognized as independent risk factors for CVD.74,75 Autoantibodies appear to mediate CVD events and mortality risk, independent of traditional therapies for risk reduction.73 Further research is needed to clarify the role of autoantibodies as markers of increased or decreased CVD risk and their mechanism of action.
Autoantibodies directed at new antigens in lipoproteins within atherosclerotic lesions can modulate the impact of atherosclerosis via activation of the innate and adaptive immune system.76 The lipid-associated neopeptides are recognized as damage-associated or danger- associated molecular patterns (DAMPs), also known as alarmins, which signal molecules that can trigger and perpetuate noninfectious inflammatory responses.77-79 Plasma autoantibodies (immunoglobulin M and G [IgM, IgG]) modify proinflammatory oxidation-specific epitopes on oxidized phospholipids (oxPL) within lipoproteins and are linked with markers of inflammation and CVD events.80-82 Modified LDLC and ApoB-100 immune complexes with specific autoantibodies in the IgG class are associated with increased CVD.76 These and other risk-modulating autoantibodies may explain some of the variability in CVD outcomes by ethnicity and between individuals.
Some antibodies to oxidized LDL (ox-LDL) may have a protective role in the development of atherosclerosis.83,84 In a cohort of > 500 women, the number of carotid atherosclerotic plaques and total carotid plaque area were inversely correlated with a specific IgM autoantibody (MDA-p210).84 High concentrations of Lp(a)- containing circulating immune complexes and Lp(a)-specific IgM and IgG have been described in patients with coronary heart disease (CHD).85 Like ox-LDL, oxidized Lp(a) [ox-Lp(a)] is more potent than native Lp(a) in increasing atherosclerosis risk and is increased in patients with CHD compared to healthy controls.86-88 Ox-Lp(a) levels may represent an even stronger risk marker for CVD than ox-LDL.85
Possible Mechanisms of Pathogenesis
While the precise quantification of Lp(a) in human plasma (or serum) has been challenging, current clinical laboratories use standardized international reference reagents and controls in their assays. Most current Lp(a) assays are based on immunological methods (eg, immunonephelometry, immunoturbidimetry, or enzyme linked immunosorbent assay [ELISA]) using antibodies against apo(a).89 Apo(a) contains 10 subtypes of kringle IV and 1 copy of kringle V. Some assays use antibodies against kringle-IV type 2; however, it has been recommended that newer methods should use antibodies against the specific bridging kringle-IV Type 9 domain, which has a more stable bond and is present as a single copy.48,89 Other approaches to Lp(a) measurement include ultraperformance liquid chromatography/mass spectrometry that can determine both the concentration and particle size of apo(a).48,90 For routine clinical care, currently available assays reporting in mg/dL can be considered fairly accurate for separating low-risk from moderate-to-high-risk patients.45
The physiologic role of Lp(a) in humans remains to be fully defined and individuals with extremely low plasma Lp(a) levels present no disease or deficiency syndromes.91 Lp(a) accumulates in endothelial injuries and binds to components of the vessel wall and subendothelial matrix, presumably due to the strong lysine binding site in apo(a).46 Mediated by apo(a), the binding stimulates chemotactic activation of monocytes/macrophages and thereby modulating angiogenesis and inflammation.89 Lp(a) may contribute to CVD and CAVS via its LDL-like component, with proinflammatory effects of oxidized phospholipids (OxPL) on both ApoB and apo(a) and antifibrinolytic/prothrombotic effects of apo(a).92 In Vitro studies have demonstrated that apo(a) modifies cellular function of cultured vascular endothelial cells (promoting stress fiber formation, endothelial contraction and vascular permeability), smooth muscles, and monocytes/ macrophages (promoting differentiation of proinflammatory M1-1 type macrophages) via complex mechanisms of cell signaling and cytokine production.89 Lp(a) is the only monogenetic risk factor for aortic valve calcification and stenosis93 and is strongly linked specifically with the single nucleotide polymorphism (SNP) rs10455872 in the gene LPA encoding for apo(a).94
CVD Risk Predictive Value
There are a large number of studies demonstrating that Lp(a) elevations are an independent predictor of adverse cardiovascular outcomes including MI, sudden death, strokes, calcific aortic valve stenosis and peripheral vascular disease (Table 3). The Copenhagen City Heart Study and Copenhagen General Population Study are well known prospective population- based cohort studies that track outcomes through national patient registries.95 These studies demonstrate increased risk for MI, CHD, CAVS, and heart failure when subjects with very high Lp(a) levels (50-115 mg/dL) are compared with subjects with very low Lp(a) levels (< 5 mg/dL).96-100 Subjects with less extreme Lp(a) elevations (> 30 mg/dL) also show increased risk of CVD when they have comorbid LDLC elevations.101 However, the Copenhagen studies are composed exclusively of white subjects and the effects of Lp(a) are known to vary with race or ethnicity.
The Multi-Ethnic Study of Atherosclerosis (MESA) recruited an ethnically diverse sample of > 6,000 Americans, aged 45 to 84 years, without CVD, into an ongoing prospective cohort study. Research using subjects from this study has found consistently increased risk of CHD, heart failure, subclinical aortic valve calcification, and more severe CAVS in white subjects with elevated Lp(a).60,102,103 Black subjects with elevated Lp(a) had increased risk of CHD and more severe CAVS and Hispanic subjects with Lp(a) elevation were at higher risk for CHD.60,102 So far, no studies of MESA subjects have identified a relationship between Lp(a) elevation and CVD events for Asian-Americans subjects (predominantly of Chinese descent). There is a need for ongoing research to more precisely define relevant cut-off levels by race, ethnicity and sex.
The Atherosclerosis Risk in Communities (ARIC) Study was a prospective multiethnic cohort study including > 15,000 US adults, aged 45 to 64 years.103 Lp(a) elevations in this cohort were associated with greater risks for first CVD events, heart failure, and recurrent CVD events.61,64,105 The risk of stroke for subjects with elevated Lp(a) was greater for black and white women, and for black men.61,106 However, a meta-analysis of case-control studies showed increased ischemic stroke risk in both men and women with elevated Lp(a).57
A recent European meta-analysis collected blood samples and outcome data from > 50,000 subjects in 7 prospective cohort studies. Using a central laboratory to standardize Lp(a) measurements, researchers found increased risk of major coronary events and new CVD in subjects with Lp(a) > 50 mg/dL compared to those below that threshold.107
Although many of these studies show modest increases in risk of CVD events with Lp(a) elevation, it should be noted that other studies do not demonstrate such consistent associations. This is particularly true in studies of women and nonwhite ethnic groups.103,108-112 The variability of study results may be due to other confounding factors such as autoantibodies that either upregulate or downregulate atherogenicity of LDLC and potentially other lipoproteins. This is particularly relevant to women who have an increased risk for autoimmune disease.
Lp(a) has significant genetic heritability—75% in Europeans and 85% in African Americans.113 In whites, the LPA gene on chromosome 6p26- 27 with the polymorphism genetic variants rs10455872 and rs3798220 is consistently associated with elevated Lp(a) levels.63,100,113 However, the degree of Lp(a) elevation associated with these specific genetic variants varies by ethnicity.78,113,115
Lifestyle and Cardiovascular Health
It is noteworthy that the Lp(a) genetic risks can also be modified by lifestyle risk reduction even in the absence of significant blood level reductions. For example, Khera and colleagues constructed a genetic risk profile for CVD that included genes related to Lp(a).116 Subjects with high genetic risk were more likely to experience CVD events compared with subjects with low genetic risk. However, risks for CVD were attenuated by 4 healthy lifestyle factors: current nonsmoker, body mass index < 30, at least weekly physical activity, and a healthy diet. Subjects with high genetic risk and an unhealthy lifestyle (0 or 1 of the 4 healthy lifestyle factors) were the most likely to develop CVD (Hazard ratio [HR], 3.5), but that risk was lower for subjects with healthy (3 or 4 of the 4 healthy lifestyle factors) and intermediate lifestyles (2 of the 4 healthy lifestyle factors) (HR, 1.9 and 2.2, respectively), despite despite high genetic risk for CVD.
While the independent CVD risk associated with elevated Lp(a) does not appear to be responsive to lifestyle risk reduction alone, certainly elevated LDLC and traditional risk factors can increase the overall CVD risk and are worthy of preventive interventions. In particular, inflammation from any source exacerbates CVD risk. Proatherogenic diet, insufficient sleep, lack of exercise, and maladaptive stress responses are other targets for personalized CVD risk reduction. 28,117 Studies of dietary modifications and other lifestyle factors have shown reduced risk of CVD events, despite lack of reduction in Lp(a) levels.119,120 It is noteworthy that statin therapy (with or without ezetimibe) fails to impact CAVS progression, likely because statins either raise or have no effect on Lp(a) levels.92,119
Until recently, there has been no evidence supporting any therapeutic intervention causing clinically meaningful reductions in Lp(a). Table 4 lists major drug classes and their effects on Lp(a) and CVD outcomes; however, a detailed discussion of each of these therapies is beyond the scope of this review. Drugs that reduce Lp(a) by 20-30% have varying effects on CVD outcomes, from no effect122,123 to a 10% to 20% decrease in CVD events when compared with a placebo.124,125 Because these drugs also produce substantial reductions in LDLC, it is not possible to determine how much of the beneficial effects are due to reductions in Lp(a).
Lipoprotein apheresis produces profound reductions in Lp(a) of 60 to 80% in very highrisk populations.69,126 Within-subjects comparisons show up to 80% reductions in CVD events, relative to event rates prior to treatment initiation.69,127 Early trials of antisense oligonucleotide against apo(a) therapies show potential to produce similar outcomes.128,129 These treatments may be particularly effective in patients with isolated Lp(a) elevations.
Summary
Lp(a) elevation is a major contributor to cardiovascular disease risk and has been recognized as an ICD-10-CM coded clinical diagnosis, the first laboratory abnormality to be defined a clinical disease in the asymptomatic healthy young individuals. This change addresses currently under- diagnosed CVD risk independent of LDLC reduction strategies. A brief overview of recent guidelines for the clinical use of Lp(a) testing from the American Heart Association43,151 and the National Lipid Association52 can be found in Table 5. Although drug therapies for lowering Lp(a) levels remain limited, new treatment options are actively being developed.
Many Americans with high Lp(a) have not yet been identified. Expanded one-time screening can inform these patients of their cardiovascular risk and increase their access to early, aggressive lifestyle modification and optimal lipid-lowering therapy. Given the further increased CVD risk factors for military service members and veterans, a case can be made for broader screening and enhanced surveillance of elevated Lp(a) in these presumably healthy and fit individuals as well as management focused on modifiable risk factors.
Acknowledgements
This program initiative was conducted by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. as part of the Integrative Cardiac Health Project at Walter Reed National Military Medical Center (WRNMMC), and is made possible by a cooperative agreement that was awarded and administered by the US Army Medical Research & Materiel Command (USAMRMC), at Fort Detrick under Contract Number: W81XWH-16-2-0007. It reflects literature review preparatory work for a research protocol but does not involve an actual research project. The work in this manuscript was supported by the staff of the Integrative Cardiac Health Project (ICHP) with special thanks to Claire Fuller, Elaine Walizer, Dr. Mariam Kashani and the entire health coaching team.
Cardiovascular disease (CVD) remains the leading cause of global mortality. In 2015, 41.5% of the US population had at least 1 form of CVD and CVD accounted for nearly 18 million deaths worldwide.1,2 The major disease categories represented include myocardial infarction (MI), sudden death, strokes, calcific aortic valve stenosis (CAVS), and peripheral vascular disease.1,2 In terms of health care costs, quality of life, and caregiver burden, the overall impact of disease prevalence continues to rise.1,3-6 There is an urgent need for more precise and earlier CVD risk assessment to guide lifestyle and therapeutic interventions for prevention of disease progression as well as potential reversal of preclinical disease. Even at a young age, visible coronary atherosclerosis has been found in up to 11% of “healthy” active individuals during autopsies for trauma fatalities.7,8
The impact of CVD on the US and global populations is profound. In 2011, CVD prevalence was predicted to reach 40% by 2030.9 That estimate was exceeded in 2015, and it is now predicted that by 2035, 45% of the US population will suffer from some form of clinical or preclinical CVD. In 2015, the decadeslong decline in CVD mortality was reversed for the first time since 1969, showing a 1% increase in deaths from CVD.1 Nearly 300,000 of those using US Department of Veterans Affairs (VA) services were hospitalized for CVD between 2010 and 2014.10 The annual direct and indirect costs related to CVD in the US are estimated at $329.7 billion, and these costs are predicted to top $1 trillion by 2035.1 Heart attack, coronary atherosclerosis, and stroke accounted for 3 of the 10 most expensive conditions treated in US hospitals in 2013.11 Globally, the estimate for CVD-related direct and indirect costs was $863 billion in 2010 and may exceed $1 trillion by 2030.12
The nature of military service adds additional risk factors, such as posttraumatic stress disorder, depression, sleep disorders and physical trauma which increase CVD morbidity/ mortality in service members, veterans, and their families.13-16 In addition, living in lowerincome areas (countries or neighborhoods) can increase the risk of both CVD incidence and fatalities, particularly in younger individuals.17-20 The Military Health System (MHS) and VA are responsible for the care of those individuals who have voluntarily taken on these additional risks through their time in service. This responsibility calls for rapid translation to practice tools and resources that can support interventions to minimize as many modifiable risk factors as possible and improve longterm health. This strategy aligns with the World Health Organization’s (WHO) focus on prevention of disease progression through interventions targeting modifiable risk.3-6,21-23 The driving force behind the launch of the US Department of Health and Human Services (HHS) Million Hearts program was the goal of preventing 1 million heart attacks and strokes by 2017 with risk reduction through aspirin, blood pressure control, cholesterol management, smoking cessation, sodium reduction, and physical activity.24,25 While some reductions in CVD events have been documented, the outcomes fell short of the goals set, highlighting both the need and value of continued and expanded efforts for CVD risk reduction.26
More precise assessment of risk factors during preventative care, as well as after a diagnosis of CVD, may improve the timeliness and precision of earlier interventions (both lifestyle and therapeutic) that reduce CVD morbidity and mortality.27 Personalized or precision medicine approaches take into account differences in socioeconomic, environmental, and lifestyle factors that are potentially reversible, as well as gender, race, and ethnicity.28-31 Current methods of predicting CVD risk have considerable room for improvement.27 About 40% of patients with newly diagnosed CVD have normal traditional cholesterol profiles, including those whose first cardiac event proves fatal.29-33 Currently available risk scores (hundreds have been described in the literature) mischaracterize risk in minority populations and women, and have shown deficiencies in identifying preclinical atherosclerosis.34,35 The failure to recognize preclinical CVD in military personnel during their active duty life cycle results in missed opportunities for improved health and readiness sustainment.
Most CVD risk prediction models incorporate some form of blood lipids. Total cholesterol (TC) is most commonly used in clinical practice, along with high-density lipoprotein (HDLC), low-density lipoprotein (LDLC), and triglycerides (TG).23,27,36 High LDLC and/or TC are well established as lipid-related CVD risk factors and are incorporated into many CVD risk scoring systems/models described in the literature.27 LDLC reduction is commonly recommended as CVD prevention, but even with optimal statin treatment, there is still considerable residual risk for new and recurrent CVD events.28,32,34,35,37-42
Incorporating novel biomarkers and alternative lipid measurements may improve risk prediction and aid targeted treatment, ultimately reducing CVD events.27 Apolipoprotein B (ApoB) is a major atherogenic component embedded in LDL and VLDL correlating to non-HDLC and may be useful in the setting of triglycerides ≥ 200 mg/d as levels > 130 mg/ dL appear to be risk-enhancing, but measurements may be unreliable.43 According to the 2018 Cholesterol Guidelines, lipoprotein(a) [Lp(a)] elevation also is recognized as a risk-enhancing factor that is particularly implicated when there is a strong family history of premature atherosclerotic CVD or personal history of CVD not explained by major risk factors.43
Lp(a) elevation is a largely underrecognized category of lipid disorder that impacts up to 20% to 30% of the population globally and within the US, although there is considerable variability by geographic location and ethnicity.44 Globally, Lp(a) elevation places > 1 billion people at moderate to high risk for CVD.44 Lp(a) has a strong genetic component and is recognized as a distinct and independent risk factor for MI, sudden death, strokes and CAVS. Lp(a) has an extensive body of evidence to support its distinct role both as a causal factor in CVD and as an augmentation to traditional risk factors.44-48
Lipoproteni(a) Elevation Use For Diagnosis
The importance of Lp(a) elevation as a clinical diagnosis rather than a laboratory abnormality alone was brought forward by the Lipoprotein(a) Foundation. Its founder, Sandra Tremulis, is a survivor of an acute coronary event that occurred when she was 39-years old, despite running marathons and having none of the traditional CVD lifestyle risk factors.49 This experience inspired her to create the Lipoprotein(a) Foundation to give a voice to families living with or at risk for CVD due to Lp(a) elevation.
As often happens in the progress of medicine, patients and their families drive change based on their personal experiences with the gaps in standard clinical practice. It was this foundation—not a member of the medical establishment—that submitted the formal request for the addition of new ICD-10-CM diagnostic and family history codes for Lp(a) elevation during the Centers for Disease Control and Prevention (CDC) September 2017 ICD-10-CM Coordination and Maintenance Committee meeting.50 In June 2018, the final ICD-10-CM code addenda for 2019 was released and included the new codes E78.41 (Elevated Lp[a]) and Z83.430 (Family history of elevated Lp[a]).52 After the new codes were approved, both the American Heart Association and the National Lipid Association added recommendations regarding Lp(a) testing to their clinical practice guidelines.43,52
Practically, these codes standardize billing and payment for legitimate clinical work and laboratory testing. Prior to the addition of Lp(a) elevation as a clinical diagnosis, testing and treatment of Lp(a) elevation was considered experimental and not medically necessary until after a cardiovascular event had already occurred. Services for Lp(a) elevation were therefore not reimbursed by many healthcare organizations and insurance companies. The new ICD-10-CM codes encourage the assessment of Lp(a) both in individuals with early onset major CVD events and in presumably fit, healthy individuals, particularly when there is a family history of Lp(a) elevation. Given that Lp(a) levels do not change significantly over time, the current understanding is that only a single measurement is needed to define the individual risk over a lifetime.41,42,44,45 As therapies targeting Lp(a) levels evolve, repeated measurements may be indicated to monitor response and direct changes in management. “Elevated Lipoprotein(a)” is the first laboratory testing abnormality that has achieved the status of a clinical diagnosis.
Lp(a) Measurements
There is considerable complexity to the measurement of lipoproteins in blood samples due to heterogeneity in both density and size of particles as illustrated in the Figure.53
For traditional lipids measured in clinical practice, the size and density ranges from small high-density lipoprotein (HDL) through LDLC and intermediate- density lipoprotein (IDL) to the largest least dense particles in the very low-density lipoprotein (VLDL) and chylomicron remnant fractions. Standard lipid profiles consist of mass concentration measurements (mg/dL) of TC, TG, HDLC, and LDLC.53 Non-HDLC (calculated as: TC−HDLC) consists of all cholesterol found in atherogenic lipoproteins, including remnant-C and Lp(a). Until recently, the cholesterol content of Lp(a), corresponding to about 30% of Lp(a) total mass, was included in the TC, non-HDLC and LDLC measurements with no separate reporting by the majority of clinical laboratories.
After > 50 years of research on the structure and biochemistry of Lp(a), the physiology and biological functions of these complex and polymorphic lipoprotein particles are not fully understood. Lp(a) is composed of a lipoprotein particle similar in composition to LDL (protein and lipid), containing 1 molecule of ApoB wrapped around a core of cholesteryl ester and triglyceride with phospholipids and unesterified cholesterol at its surface.48 The presence of a unique hydrophilic, highly glycosylated protein referred to as apolopoprotienA (apo[a]), covalently attached to ApoB-100 by a single disulfide bridge, differentiates Lp(a) from LDL.48 Cholesterol rich ApoB is an important component within many lipoproteins pathogenic for atherosclerosis and CVD.45,47,53
The apo(a) contributes to the increased density of Lp(a) compared to LDLC with associated reduced binding affinity to the LDL receptor. This reduced receptor binding affinity is a presumed mechanism for the lack of Lp(a) plasma level response to statin therapies, which increase hepatic LDL receptor activity.47 Apo(a) evolved from the plasminogen gene through duplication and remodeling and demonstrates extensive heterogeneity in protein size, with > 40 different apo(a) isoforms resulting in > 40 different Lp(a) particle sizes. Size of the apo(a) particle is determined by the number of pleated structures known as kringles. Most people (> 80%) carry 2 different-sized apo(a) isoforms. Plasma Lp(a) level is determined by the net production of apo(a) in each isoform, and the smaller apo(a) isoforms are associated with higher plasma levels of Lp(a).45
Given the heterogeneity in Lp(a) molecular weight, which can vary even within individuals, recommendations have been made for reporting results as particle numbers or concentrations (nmol/L or mmol/L) rather than as mass concentration (mg/dL).55 However, the majority of the large CVD morbidity and mortality outcomes studies used Lp(a) mass concentration levels in mg/ dL to characterize risk levels.56,57 There is no standardized method to convert Lp(a) measurements from mg/dL to nmol/L.55 Current assays using WHO standardized reagents and controls are reliable for categorizing risk levels.58
The European Atherosclerosis Society consensus panel recommended that desirable Lp(a) levels should be below the 80th percentile (< 50 mg/dL or < 125 nmol/L) in patients with intermediate or high CVD risk.59 Subsequent epidemiological and Mendelian randomization studies have been performed in general populations with no history of CVD and demonstrated that increased CVD risk can be detected with Lp(a) levels as low as 25 to 30 mg/dL.56,60-63 In secondary prevention populations with prior CVD and optimal treatment (statins, antiplatelet drugs), recurrent event risk was also increased with elevated Lp(a).63-66
Using immunoturbidometric assays, Varvel and colleagues reported the prevalence of elevated Lp(a) mass concentration levels (mg/dL) in > 500,000 US patients undergoing clinical evaluations based on data from a referral laboratory of patients.58 The mean Lp(a) levels were 34.0 mg/dL with median (interquartile range [IQR]) levels at 17 (7-47) mg/dL and overall range of 0 to 907 mg/dL.58 Females had higher Lp(a) levels compared to males but no ethnic or racial breakdown was provided. Lp(a) levels > 30 mg/dL and > 50 mg/dL were present in 35% and 24% of subjects, respectively. Table 1 displays the relationship between various Lp(a) level cut-offs to mean levels of LDLC, estimated LDLC corrected for Lp(a), TC, HDLC, and TG.58 The data demonstrate that Lp(a) elevation cannot be inferred from LDLC levels nor from any of the other traditional lipoprotein measures. Patients with high risk Lp(a) levels may have normal LDLC. While Lp(a) thresholds have been identified for stratification of CVD risk, the target levels for risk reduction have not been specifically defined, particularly since therapies are not widely available for reduction of Lp(a). Table 2 provides an overview of clinical lipoprotein measurements that may be reasonable targets for therapeutic interventions and reduction of CVD risk.44,53,55 In general, existing studies suggest that radical reduction (> 80%) is required to impact long-term outcomes, particularly in individuals with severe disease.68,69
LDLC reduction alone leaves a residual CVD risk that is greater than the risk reduced.40 In addition, the autoimmune inflammation and lipid specific autoantibodies play an important role in increased CVD morbidity and mortality risk.70,71 The presence of autoantibodies such as antiphospholipid antibodies (without a specific autoimmune disease diagnosis) increases the risk of subclinical atherosclerosis.72,73 Certain autoimmune diseases such as systemic lupus erythematosus are recognized as independent risk factors for CVD.74,75 Autoantibodies appear to mediate CVD events and mortality risk, independent of traditional therapies for risk reduction.73 Further research is needed to clarify the role of autoantibodies as markers of increased or decreased CVD risk and their mechanism of action.
Autoantibodies directed at new antigens in lipoproteins within atherosclerotic lesions can modulate the impact of atherosclerosis via activation of the innate and adaptive immune system.76 The lipid-associated neopeptides are recognized as damage-associated or danger- associated molecular patterns (DAMPs), also known as alarmins, which signal molecules that can trigger and perpetuate noninfectious inflammatory responses.77-79 Plasma autoantibodies (immunoglobulin M and G [IgM, IgG]) modify proinflammatory oxidation-specific epitopes on oxidized phospholipids (oxPL) within lipoproteins and are linked with markers of inflammation and CVD events.80-82 Modified LDLC and ApoB-100 immune complexes with specific autoantibodies in the IgG class are associated with increased CVD.76 These and other risk-modulating autoantibodies may explain some of the variability in CVD outcomes by ethnicity and between individuals.
Some antibodies to oxidized LDL (ox-LDL) may have a protective role in the development of atherosclerosis.83,84 In a cohort of > 500 women, the number of carotid atherosclerotic plaques and total carotid plaque area were inversely correlated with a specific IgM autoantibody (MDA-p210).84 High concentrations of Lp(a)- containing circulating immune complexes and Lp(a)-specific IgM and IgG have been described in patients with coronary heart disease (CHD).85 Like ox-LDL, oxidized Lp(a) [ox-Lp(a)] is more potent than native Lp(a) in increasing atherosclerosis risk and is increased in patients with CHD compared to healthy controls.86-88 Ox-Lp(a) levels may represent an even stronger risk marker for CVD than ox-LDL.85
Possible Mechanisms of Pathogenesis
While the precise quantification of Lp(a) in human plasma (or serum) has been challenging, current clinical laboratories use standardized international reference reagents and controls in their assays. Most current Lp(a) assays are based on immunological methods (eg, immunonephelometry, immunoturbidimetry, or enzyme linked immunosorbent assay [ELISA]) using antibodies against apo(a).89 Apo(a) contains 10 subtypes of kringle IV and 1 copy of kringle V. Some assays use antibodies against kringle-IV type 2; however, it has been recommended that newer methods should use antibodies against the specific bridging kringle-IV Type 9 domain, which has a more stable bond and is present as a single copy.48,89 Other approaches to Lp(a) measurement include ultraperformance liquid chromatography/mass spectrometry that can determine both the concentration and particle size of apo(a).48,90 For routine clinical care, currently available assays reporting in mg/dL can be considered fairly accurate for separating low-risk from moderate-to-high-risk patients.45
The physiologic role of Lp(a) in humans remains to be fully defined and individuals with extremely low plasma Lp(a) levels present no disease or deficiency syndromes.91 Lp(a) accumulates in endothelial injuries and binds to components of the vessel wall and subendothelial matrix, presumably due to the strong lysine binding site in apo(a).46 Mediated by apo(a), the binding stimulates chemotactic activation of monocytes/macrophages and thereby modulating angiogenesis and inflammation.89 Lp(a) may contribute to CVD and CAVS via its LDL-like component, with proinflammatory effects of oxidized phospholipids (OxPL) on both ApoB and apo(a) and antifibrinolytic/prothrombotic effects of apo(a).92 In Vitro studies have demonstrated that apo(a) modifies cellular function of cultured vascular endothelial cells (promoting stress fiber formation, endothelial contraction and vascular permeability), smooth muscles, and monocytes/ macrophages (promoting differentiation of proinflammatory M1-1 type macrophages) via complex mechanisms of cell signaling and cytokine production.89 Lp(a) is the only monogenetic risk factor for aortic valve calcification and stenosis93 and is strongly linked specifically with the single nucleotide polymorphism (SNP) rs10455872 in the gene LPA encoding for apo(a).94
CVD Risk Predictive Value
There are a large number of studies demonstrating that Lp(a) elevations are an independent predictor of adverse cardiovascular outcomes including MI, sudden death, strokes, calcific aortic valve stenosis and peripheral vascular disease (Table 3). The Copenhagen City Heart Study and Copenhagen General Population Study are well known prospective population- based cohort studies that track outcomes through national patient registries.95 These studies demonstrate increased risk for MI, CHD, CAVS, and heart failure when subjects with very high Lp(a) levels (50-115 mg/dL) are compared with subjects with very low Lp(a) levels (< 5 mg/dL).96-100 Subjects with less extreme Lp(a) elevations (> 30 mg/dL) also show increased risk of CVD when they have comorbid LDLC elevations.101 However, the Copenhagen studies are composed exclusively of white subjects and the effects of Lp(a) are known to vary with race or ethnicity.
The Multi-Ethnic Study of Atherosclerosis (MESA) recruited an ethnically diverse sample of > 6,000 Americans, aged 45 to 84 years, without CVD, into an ongoing prospective cohort study. Research using subjects from this study has found consistently increased risk of CHD, heart failure, subclinical aortic valve calcification, and more severe CAVS in white subjects with elevated Lp(a).60,102,103 Black subjects with elevated Lp(a) had increased risk of CHD and more severe CAVS and Hispanic subjects with Lp(a) elevation were at higher risk for CHD.60,102 So far, no studies of MESA subjects have identified a relationship between Lp(a) elevation and CVD events for Asian-Americans subjects (predominantly of Chinese descent). There is a need for ongoing research to more precisely define relevant cut-off levels by race, ethnicity and sex.
The Atherosclerosis Risk in Communities (ARIC) Study was a prospective multiethnic cohort study including > 15,000 US adults, aged 45 to 64 years.103 Lp(a) elevations in this cohort were associated with greater risks for first CVD events, heart failure, and recurrent CVD events.61,64,105 The risk of stroke for subjects with elevated Lp(a) was greater for black and white women, and for black men.61,106 However, a meta-analysis of case-control studies showed increased ischemic stroke risk in both men and women with elevated Lp(a).57
A recent European meta-analysis collected blood samples and outcome data from > 50,000 subjects in 7 prospective cohort studies. Using a central laboratory to standardize Lp(a) measurements, researchers found increased risk of major coronary events and new CVD in subjects with Lp(a) > 50 mg/dL compared to those below that threshold.107
Although many of these studies show modest increases in risk of CVD events with Lp(a) elevation, it should be noted that other studies do not demonstrate such consistent associations. This is particularly true in studies of women and nonwhite ethnic groups.103,108-112 The variability of study results may be due to other confounding factors such as autoantibodies that either upregulate or downregulate atherogenicity of LDLC and potentially other lipoproteins. This is particularly relevant to women who have an increased risk for autoimmune disease.
Lp(a) has significant genetic heritability—75% in Europeans and 85% in African Americans.113 In whites, the LPA gene on chromosome 6p26- 27 with the polymorphism genetic variants rs10455872 and rs3798220 is consistently associated with elevated Lp(a) levels.63,100,113 However, the degree of Lp(a) elevation associated with these specific genetic variants varies by ethnicity.78,113,115
Lifestyle and Cardiovascular Health
It is noteworthy that the Lp(a) genetic risks can also be modified by lifestyle risk reduction even in the absence of significant blood level reductions. For example, Khera and colleagues constructed a genetic risk profile for CVD that included genes related to Lp(a).116 Subjects with high genetic risk were more likely to experience CVD events compared with subjects with low genetic risk. However, risks for CVD were attenuated by 4 healthy lifestyle factors: current nonsmoker, body mass index < 30, at least weekly physical activity, and a healthy diet. Subjects with high genetic risk and an unhealthy lifestyle (0 or 1 of the 4 healthy lifestyle factors) were the most likely to develop CVD (Hazard ratio [HR], 3.5), but that risk was lower for subjects with healthy (3 or 4 of the 4 healthy lifestyle factors) and intermediate lifestyles (2 of the 4 healthy lifestyle factors) (HR, 1.9 and 2.2, respectively), despite despite high genetic risk for CVD.
While the independent CVD risk associated with elevated Lp(a) does not appear to be responsive to lifestyle risk reduction alone, certainly elevated LDLC and traditional risk factors can increase the overall CVD risk and are worthy of preventive interventions. In particular, inflammation from any source exacerbates CVD risk. Proatherogenic diet, insufficient sleep, lack of exercise, and maladaptive stress responses are other targets for personalized CVD risk reduction. 28,117 Studies of dietary modifications and other lifestyle factors have shown reduced risk of CVD events, despite lack of reduction in Lp(a) levels.119,120 It is noteworthy that statin therapy (with or without ezetimibe) fails to impact CAVS progression, likely because statins either raise or have no effect on Lp(a) levels.92,119
Until recently, there has been no evidence supporting any therapeutic intervention causing clinically meaningful reductions in Lp(a). Table 4 lists major drug classes and their effects on Lp(a) and CVD outcomes; however, a detailed discussion of each of these therapies is beyond the scope of this review. Drugs that reduce Lp(a) by 20-30% have varying effects on CVD outcomes, from no effect122,123 to a 10% to 20% decrease in CVD events when compared with a placebo.124,125 Because these drugs also produce substantial reductions in LDLC, it is not possible to determine how much of the beneficial effects are due to reductions in Lp(a).
Lipoprotein apheresis produces profound reductions in Lp(a) of 60 to 80% in very highrisk populations.69,126 Within-subjects comparisons show up to 80% reductions in CVD events, relative to event rates prior to treatment initiation.69,127 Early trials of antisense oligonucleotide against apo(a) therapies show potential to produce similar outcomes.128,129 These treatments may be particularly effective in patients with isolated Lp(a) elevations.
Summary
Lp(a) elevation is a major contributor to cardiovascular disease risk and has been recognized as an ICD-10-CM coded clinical diagnosis, the first laboratory abnormality to be defined a clinical disease in the asymptomatic healthy young individuals. This change addresses currently under- diagnosed CVD risk independent of LDLC reduction strategies. A brief overview of recent guidelines for the clinical use of Lp(a) testing from the American Heart Association43,151 and the National Lipid Association52 can be found in Table 5. Although drug therapies for lowering Lp(a) levels remain limited, new treatment options are actively being developed.
Many Americans with high Lp(a) have not yet been identified. Expanded one-time screening can inform these patients of their cardiovascular risk and increase their access to early, aggressive lifestyle modification and optimal lipid-lowering therapy. Given the further increased CVD risk factors for military service members and veterans, a case can be made for broader screening and enhanced surveillance of elevated Lp(a) in these presumably healthy and fit individuals as well as management focused on modifiable risk factors.
Acknowledgements
This program initiative was conducted by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. as part of the Integrative Cardiac Health Project at Walter Reed National Military Medical Center (WRNMMC), and is made possible by a cooperative agreement that was awarded and administered by the US Army Medical Research & Materiel Command (USAMRMC), at Fort Detrick under Contract Number: W81XWH-16-2-0007. It reflects literature review preparatory work for a research protocol but does not involve an actual research project. The work in this manuscript was supported by the staff of the Integrative Cardiac Health Project (ICHP) with special thanks to Claire Fuller, Elaine Walizer, Dr. Mariam Kashani and the entire health coaching team.
1. American Heart Association. Cardiovascular disease: a costly burden for America, projections through 2035. http://www.heart.org/idc/groups /heart-public/@wcm/@adv/documents/downloadable /ucm_491543.pdf. Accessed October 10, 2019.
2. Benjamin EJ, Virani SS, Callaway CW, et al. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation. 2018;137(12):e67-e492.
3. Roth GA, Johnson C, Abajobir A, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1-25.
4. Thrift AG, Cadilhac DA, Thayabaranathan T, et al. Global stroke statistics. Int J Stroke. 2014;9(1):6-18.
5. Murray CJ, Barber RM, Foreman KJ, et al; GBD 2013 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition. Lancet. 2015;386(10009):2145-2191.
6. Mukherjee D, Patil CG. Epidemiology and the global burden of stroke. World Neurosurg. 2011;76(6 suppl):S85-S90.
7. Joseph A, Ackerman D, Talley JD, Johnstone J, Kupersmith J. Manifestations of coronary atherosclerosis in young trauma victims—an autopsy study. J Am Coll Cardiol. 1993;22(2):459-467.
8. Webber BJ, Seguin PG, Burnett DG, Clark LL, Otto JL. Prevalence of and risk factors for autopsy-determined atherosclerosis among US service members, 2001-2011. JAMA. 2012;308(24):2577-2583.
9. Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123(8):933-944.
10. Krishnamurthi N, Francis J, Fihn SD, Meyer CS, Whooley MA. Leading causes of cardiovascular hospitalization in 8.45 million US veterans. PLoS One. 2018;13(3):e0193996.
11. Torio CM, Moore BJ. National inpatient hospital costs: the most expensive conditions by payer. Agency for Healthcare Research and Quality Statistical Brief No. 204. http:// www.hcup-us.ahrq.gov/reports/statbriefs/sb204-Most -Expensive-Hospital-Conditions.pdf. Published May 2016. Accessed October 10, 2019.
12. Bloom DE, Cafiero ET, Jané-Llopis E, et al. The global economic burden of noncommunicable diseases. https:// www.weforum.org/reports/global-economic-burden-non -communicable-diseases. Published September 18, 2011. Accessed October 10, 2019.
13. Crum-Cianflone NF, Bagnell ME, Schaller E, et al. Impact of combat deployment and posttraumatic stress disorder on newly reported coronary heart disease among US active duty and reserve forces. Circulation. 2014;129(18):1813-1820.
14. Fryar CD, Herrick K, Afful J, Ogden CL. Cardiovascular disease risk factors among male veterans, U.S., 2009- 2012. Am J Prev Med. 2016;50(1):101-105.
15. Ulmer CS, Bosworth HB, Germain A, et al; VA Mid-Atlantic Mental Illness Research Education and Clinical Center Registry Workgroup. Associations between sleep difficulties and risk factors for cardiovascular disease in veterans and active duty military personnel of the Iraq and Afghanistan conflicts. J Behav Med. 2015;38(3):544-555.
16. Lutwak N, Dill C. Military sexual trauma increases risk of post-traumatic stress disorder and depression thereby amplifying the possibility of suicidal ideation and cardiovascular disease. Mil Med. 2013;178(4):359-361.
17. Bowry ADK, Lewey J, Dugani SB, Choudhry NK. The burden of cardiovascular disease in low- and middle-income countries: epidemiology and management. Can J Cardiol. 2015;31(9):1151-1159.
18. Reinier K, Stecker EC, Vickers C, Gunson K, Jui J, Chugh SS. Incidence of sudden cardiac arrest is higher in areas of low socioeconomic status: a prospective two year study in a large United States community. Resuscitation. 2006;70(2):186-192.
19. Reinier K, Thomas E, Andrusiek DL, et al; Resuscitation Outcomes Consortium Investigators. Socioeconomic status and incidence of sudden cardiac arrest. CMAJ. 2011;183(15):1705-1712.
20. Yusuf S, Rangarajan S, Teo K, et al; PURE Investigators. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med. 2014;371(9):818-827.
21. World Health Organization. Health topics: cardiovascular disease. http://www.who.int/cardiovascular_diseases/en/. Updated 2019. Accessed October 10, 2019.
22. Berkowitz AL. Stroke and the noncommunicable diseases: A global burden in need of global advocacy. Neurology. 2015;84(21):2183-2184.
23. Holt T. Predicting cardiovascular disease. BMJ. 2016;353:i2621.
24. Centers for Disease Control and Prevention. Million hearts: strategies to reduce the prevalence of leading cardiovascular disease risk factors—United States, 2011. MMWR Morb Mortal Wkly Rep. 2011;60(36):1248-1251.
25. Fryar CD, Chen TC, Li X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999- 2010. NCHS Data Brief. 2012;103:1-8.
26. Ritchey MD, Loustalot F, Wall HK, et al. Million Hearts: description of the national surveillance and modeling methodology used to monitor the number of cardiovascular events prevented during 2012-2016. J Am Heart Assoc. 2017;6(5):pii:e00602.
27. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/ AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 pt B):2935-2959.
28. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): casecontrol study. Lancet. 2004;364(9438):937-952.
29. Eckel RH, Jakicic JM, Ard JD, et al. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 pt B):2960-2984.
30. Bansilal S, Castellano JM, Fuster V. Global burden of CVD: focus on secondary prevention of cardiovascular disease. Int J Cardiol. 2015;201(suppl 1):S1-S7.
31. Havranek EP, Mujahid MS, Barr DA, et al. Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2015;132(9):873-898.
32. Miedema MD, Garberich RF, Schnaidt LJ, et al. Statin eligibility and outpatient care prior to ST-segment elevation myocardial infarction. J Am Heart Assoc. 2017;6(4): pii: e005333.
33. Noheria A, Teodorescu C, Uy-Evanado A, et al. Distinctive profile of sudden cardiac arrest in middle-aged vs. older adults: a community-based study. Int J Cardiol. 2013;168(4):3495-3499.
34. Lieb W, Enserro DM, Larson MG, Vasan RS. Residual cardiovascular risk in individuals on lipid-lowering treatment: quantifying absolute and relative risk in the community. Open Heart. 2018;5(1):e000722.
35. Sachdeva A, Cannon CP, Deedwania PC, et al. Lipid levels in patients hospitalized with coronary artery disease: an analysis of 136,905 hospitalizations in Get With The Guidelines. Am Heart J. 2009;157(1):111-117.e2.
36. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416.
37. Fulcher J, O’Connell R, Voysey M, et al; Cholesterol Treatment Trialists (CTT) Collaboration. Efficacy and safety of LDL-lowering therapy among men and women: metaanalysis of individual data from 174,000 participants in 27 randomised trials. Lancet. 2015;385(9976):1397-1405.
38. Perrone V, Sangiorgi D, Buda S, Degli Esposti L. Residual cardiovascular risk in patients who received lipid-lowering treatment in a real-life setting: retrospective study. Clinicoecon Outcomes Res. 2016;8:649-655.
39. Sirimarco G, Labreuche J, Bruckert E, et al; PERFORM and SPARCL Investigators. Atherogenic dyslipidemia and residual cardiovascular risk in statin-treated patients. Stroke. 2014;45(5):1429-1436.
40. Kones R. Molecular sources of residual cardiovascular risk, clinical signals, and innovative solutions: relationship with subclinical disease, undertreatment, and poor adherence: implications of new evidence upon optimizing cardiovascular patient outcomes. Vasc Health Risk Manag. 2013;9:617-670.
41. Hayashi M, Shimizu W, Albert CM. The spectrum of epidemiology underlying sudden cardiac death. Circ Res. 2015;116(12):1887-1906.
42. Downs JR, O’Malley PG. Management of dyslipidemia for cardiovascular disease risk reduction: synopsis of the 2014 U.S. Department of Veterans Affairs and U.S. Department of Defense clinical practice guideline. Ann Intern Med. 2015;163(4):291-297.
43. Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/ AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/ NLA/PCNA Guideline on the management of blood cholesterol: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;73(24):3168-3209.
44. Tsimikas S, Fazio S, Ferdinand KC, et al. NHLBI Working Group recommendations to reduce lipoprotein(a)-mediated risk of cardiovascular disease and aortic stenosis. J Am Coll Cardiol. 2018;71(2):177-192.
45. Tsimikas S. A test in context: Lipoprotein(a): diagnosis, prognosis, controversies, and emerging therapies. J Am Coll Cardiol. 2017;69(6):692-711.
46. Ellis KL, Boffa MB, Sahebkar A, Koschinsky ML, Watts GF. The renaissance of lipoprotein(a): brave new world for preventive cardiology? Prog Lipid Res. 2017;68:57-82.
47. Thompson GR, Seed M. Lipoprotein(a): the underestimated cardiovascular risk factor. Heart. 2014;100(7):534-535.
48. Marcovina SM, Albers JJ. Lipoprotein (a) measurements for clinical application. J Lipid Res. 2016;57(4):526-537.
49. Tremulis SR. Founder’s Story: Lipoprotein(a) Foundation. https://www.lipoproteinafoundation.org/page /Sandrastory. Accessed October 10, 2019.
50. Centers for Disease Control and Prevention. ICD-10 Coordination and Maintenance Committee meeting, September 12-13, 2017 diagnosis agenda. https://www.cdc. gov/nchs/data/icd/Topic_Packet_Sept_2017.pdf. Accessed October 10, 2019.
51. Centers for Medicare & Medicaid Services. 2019 ICD- 10-CM codes descriptions in tabular order. https://www. cms.gov/Medicare/Coding/ICD10/2019-ICD-10-CM.html. Accessed October 10, 2019.
52. Wilson DP, Jacobson TA, Jones PH, et al. Use of Lipoprotein(a) in clinical practice: a biomarker whose time has come. A scientific statement from the National Lipid Association. J Clin Lipidol. 2019;13(3):374-392.
53. Langlois MR, Chapman MJ, Cobbaert C, et al. Quantifying atherogenic lipoproteins: current and future challenges in the era of personalized medicine and very low concentrations of ldl cholesterol. A consensus statement from EAS and EFLM. Clin Chem. 2018;64(7):1006-1033.
54. Shapiro MD, Fazio S. Apolipoprotein B-containing lipoproteins and atherosclerotic cardiovascular disease. F1000Res. 2017;6:134.
55. Tsimikas S, Fazio S, Viney NJ, Xia S, Witztum JL, Marcovina SM. Relationship of lipoprotein(a) molar concentrations and mass according to lipoprotein(a) thresholds and apolipoprotein(a) isoform size. J Clin Lipidol. 2018;12(5):1313-1323.
56. Erqou S, Kaptoge S, Perry PL, et al; Emerging Risk Factors Collaboration. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA. 2009;302(4):412-423.
57. Nave AH, Lange KS, Leonards CO, et al. Lipoprotein (a) as a risk factor for ischemic stroke: a meta-analysis. Atherosclerosis. 2015;242(2):496-503.
58. Varvel S, McConnell JP, Tsimikas S. Prevalence of elevated Lp(a) mass levels and patient thresholds in 532,359 patients in the United States. Arterioscler Thromb Vasc Biol. 2016;36(11):2239-2245.
59. Nordestgaard BG, Chapman MJ, Ray K, et al; European Atherosclerosis Society Consensus Panel. Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J. 2010;31(23):2844-2853.
60. Guan W, Cao J, Steffen BT, et al. Race is a key variable in assigning lipoprotein(a) cutoff values for coronary heart disease risk assessment: the Multi-Ethnic Study of Atherosclerosis. Arterioscler Thromb Vasc Biol. 2015;35(4):996-1001.
61. Virani SS, Brautbar A, Davis BC, et al. Associations between lipoprotein(a) levels and cardiovascular outcomes in black and white subjects: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 2012;125(2):241-249.
62. Tsimikas S, Mallat Z, Talmud PJ, et al. Oxidation-specific biomarkers, lipoprotein(a), and risk of fatal and nonfatal coronary events. J Am Coll Cardiol. 2010;56(12):946-955.
63. Clarke R, Peden JF, Hopewell JC, et al; PROCARDIS Consortium. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. New Eng J Med. 2009;361(26):2518-2528.
64. Wattanakit K, Folsom AR, Chambless LE, Nieto FJ. Risk factors for cardiovascular event recurrence in the Atherosclerosis Risk in Communities (ARIC) study. Am Heart J. 2005;149(4):606-612.
65. Ruotolo G, Lincoff MA, Menon V, et al. Lipoprotein(a) is a determinant of residual cardiovascular risk in the setting of optimal LDL-C in statin-treated patients with atherosclerotic cardiovascular disease [Abstract 17400]. Circulation. 2018;136(suppl 1):A17400.
66. Suwa S, Ogita M, Miyauchi K, et al. Impact of lipoprotein (a) on long-term outcomes in patients with coronary artery disease treated with statin after a first percutaneous coronary intervention. J Atheroscler Thromb. 2017;24(11):1125-1131.
67. Nestel PJ, Barnes EH, Tonkin AM, et al. Plasma lipoprotein(a) concentration predicts future coronary and cardiovascular events in patients with stable coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33(12):2902-2908.
68. Burgess S, Ference BA, et al. Association of LPA variants with risk of coronary disease and the implications for lipoprotein(a)-lowering therapies: a Mendelian randomization analysis. JAMA Cardiol. 2018;3(7):619-627.
69. Roeseler E, Julius U, Heigl F, et al; Pro(a)LiFe-Study Group. Lipoprotein apheresis for lipoprotein(a)-associated cardiovascular disease: prospective 5 years of followup and apolipoprotein(a) characterization. Arterioscler Thromb Vasc Biol. 2016;36(9):2019-2027.
70. Matsuura E, Atzeni F, Sarzi-Puttini P, Turiel M, Lopez LR, Nurmohamed MT. Is atherosclerosis an autoimmune disease? BMC Med. 2014;12:47.
71. Ahearn J, Shields KJ, Liu CC, Manzi S. Cardiovascular disease biomarkers across autoimmune diseases. Clin Immunol. 2015;161(1):59-63.
72. Di Minno MND, Emmi G, Ambrosino P, et al. Subclinical atherosclerosis in asymptomatic carriers of persistent antiphospholipid antibodies positivity: a cross-sectional study. Int J Cardiol. 2019;274:1-6.
72. Di Minno MND, Emmi G, Ambrosino P, et al. Subclinical atherosclerosis in asymptomatic carriers of persistent antiphospholipid antibodies positivity: a cross-sectional study. Int J Cardiol. 2019;274:1-6.
73. Iseme RA, McEvoy M, Kelly B, et al. A role for autoantibodies in atherogenesis. Cardiovasc Res. 2017;113(10):1102-1112.
74. Sinicato NA, da Silva Cardoso PA, Appenzeller S. Risk factors in cardiovascular disease in systemic lupus erythematosus. Curr Cardiol Rev. 2013;9(1):15-19.
75. Sciatti E, Cavazzana I, Vizzardi E, et al. Systemic lupus erythematosus and endothelial dysfunction: a close relationship. Curr Rheumatol Rev. 2018;15(3):177-188.
76. Prasad A, Clopton P, Ayers C, et al. Relationship of autoantibodies to MDA-LDL and ApoB-Immune complexes to sex, ethnicity, subclinical atherosclerosis, and cardiovascular events. Arterioscler Thromb Vasc Biol. 2017;37(6):1213-1221.
77. Miller YI, Choi SH, Wiesner P, et al. Oxidation-specific epitopes are danger-associated molecular patterns recognized by pattern recognition receptors of innate immunity. Circ Res. 2011;108(2):235-248.
78. Libby P, Lichtman AH, Hansson GK. Immune effector mechanisms implicated in atherosclerosis: from mice to humans. Immunity. 2013;38(6):1092-1104.
79. Binder CJ, Papac-Milicevic N, Witztum JL. Innate sensing of oxidation-specific epitopes in health and disease. Nat Rev Immunol. 2016;16(8):485-497.
80. Freigang S. The regulation of inflammation by oxidized phospholipids. Eur J Immunol. 2016;46(8):1818-1825.
81. Ravandi A, Boekholdt SM, Mallat Z, et al. Relationship of oxidized LDL with markers of oxidation and inflammation and cardiovascular events: results from the EPIC-Norfolk Study. J Lipid Res. 2011;52(10):1829-1836.
82. Tsimikas S, Willeit P, Willeit J, et al. Oxidation-specific biomarkers, prospective 15-year cardiovascular and stroke outcomes, and net reclassification of cardiovascular events. J Am Coll Cardiol. 2012;60(21):2218-2229.
83. Cinoku I, Mavragani CP, Tellis CC, Nezos A, Tselepis AD, Moutsopoulos HM. Autoantibodies to ox-LDL in Sjogren’s syndrome: are they atheroprotective? Clin Exp Rheumatol. 2018;36 Suppl 112(3):61-67.
84. Fagerberg B, Prahl Gullberg U, Alm R, Nilsson J, Fredrikson GN. Circulating autoantibodies against the apolipoprotein B-100 peptides p45 and p210 in relation to the occurrence of carotid plaques in 64-year-old women. PLoS One. 2015;10(3):e0120744.
85. Klesareva EA, Afanas’eva OI, Donskikh VV, Adamova IY, Pokrovskii SN. Characteristics of lipoprotein(a)-containing circulating immune complexes as markers of coronary heart disease. Bull Exp Biol Med. 2016;162(2):231-236.
86. Morishita R, Ishii J, Kusumi Y, et al. Association of serum oxidized lipoprotein(a) concentration with coronary artery disease: potential role of oxidized lipoprotein(a) in the vasucular wall. J Atheroscler Thromb. 2009;16(4):410-418.
87. Wang J, Zhang C, Gong J, et al. Development of new enzyme-linked immunosorbent assay for oxidized lipoprotein(a) by using purified human oxidized lipoprotein(a) autoantibodies as capture antibody. Clin Chim Acta. 2007;385(1-2):73-78.
88. Wang JJ, Han AZ, Meng Y, et al. Measurement of oxidized lipoprotein (a) in patients with acute coronary syndromes and stable coronary artery disease by 2 ELISAs: using different capture antibody against oxidized lipoprotein (a) or oxidized LDL. Clin Biochem. 2010;43(6):571-575.
89. Orso E, Schmitz G. Lipoprotein(a) and its role in inflammation, atherosclerosis and malignancies. Clin Res Cardiol Suppl. 2017;12(Suppl 1):31-37.
90. Lassman ME, McLaughlin TM, Zhou H, et al. Simultaneous quantitation and size characterization of apolipoprotein(a) by ultra-performance liquid chromatography/ mass spectrometry. Rapid Commun Mass Spectrom. 2014;28(10):1101-1106.
91. Lippi G, Guidi G. Lipoprotein(a): from ancestral benefit to modern pathogen? QJM. 2000;93(2):75-84.
92. van der Valk FM, Bekkering S, Kroon J, et al. Oxidized phospholipids on lipoprotein(a) elicit arterial wall inflammation and an inflammatory monocyte response in humans. Circulation. 2016;134(8):611-624.
93. Yeang C, Wilkinson MJ, Tsimikas S. Lipoprotein(a) and oxidized phospholipids in calcific aortic valve stenosis. Curr Opin Cardiol. 2016;31(4):440-450.
94. Thanassoulis G, Campbell CY, Owens DS, et al; CHARGE Extracoronary Calcium Working Group. Genetic associations with valvular calcification and aortic stenosis. N Engl J Med. 2013;368(6):503-512.
95. Aguib Y, Al Suwaidi J. The Copenhagen City Heart Study (Osterbroundersogelsen). Glob Cardiol Sci Pract. 2015;2015(3):33.
96. Kamstrup PR, Benn M, Tybjaerg-Hansen A, Nordestgaard BG. Extreme lipoprotein(a) levels and risk of myocardial infarction in the general population: the Copenhagen City Heart Study. Circulation. 2008;117(2):176-184.
97. Kamstrup PR, Tybjærg-Hansen A, Steffensen R, Nordestgaard BG. Genetically elevated lipoprotein(a) and increased risk of myocardial infarction. JAMA. 2009;301(22):2331-2339.
98. Kamstrup PR, Tybjaerg-Hansen A, Nordestgaard BG. Extreme lipoprotein(a) levels and improved cardiovascular risk prediction. J Am Coll Cardiol.2013;61(11):1146-1156.
99. Kamstrup PR, Tybjaerg-Hansen A, Nordestgaard BG. Elevated lipoprotein(a) and risk of aortic valve stenosis in the general population. J Am Coll Cardiol. 2014;63(5):470-477.
100. Kamstrup PR, Nordestgaard BG. Elevated lipoprotein(a) levels, LPA risk genotypes, and increased risk of heart failure in the general population. JACC Heart Fail.2016;4(1):78-87.
101. Verbeek R, Hoogeveen RM, Langsted A, et al. Cardiovascular disease risk associated with elevated lipoprotein(a) attenuates at low low-density lipoprotein cholesterol levels in a primary prevention setting. Eur Heart J. 2018;39(27):2589-2596.
102. Cao J, Steffen BT, Budoff M, et al. Lipoprotein(a) levels are associated with subclinical calcific aortic valve disease in white and black individuals: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. 2016;36(5):1003-1009.
103. Steffen BT, Duprez D, Bertoni AG, Guan W, Tsai M. Lp(a) [lipoprotein(a)]-related risk of heart failure is evident in whites but not in other racial/ethnic groups.Arterioscler Thromb Vasc Biol. 2018;38(10):2498-2504.
104. ARIC Investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epidemiol. 1989;129(4):687-702.
105. Agarwala A, Pokharel Y, Saeed A, et al. The association of lipoprotein(a) with incident heart failure hospitalization: Atherosclerosis Risk in Communities study. Atherosclerosis. 2017;262:131-137.
106. Ohira T, Schreiner PJ, Morrisett JD, Chambless LE, Rosamond WD, Folsom AR. Lipoprotein(a) and incident ischemic stroke: the Atherosclerosis Risk in Communities (ARIC) study. Stroke. 2006;37(6):1407-1412.
107. Waldeyer C, Makarova N, Zeller T, et al. Lipoprotein(a) and the risk of cardiovascular disease in the European population: results from the BiomarCaRE consortium. Eur Heart J. 2017;38(32):2490-2498.
108. Cook NR, Mora S, Ridker PM. Lipoprotein(a) and cardiovascular risk prediction among women. J Am Coll Cardiol. 2018;72(3):287-296.
109. Suk Danik J, Rifai N, Buring JE, Ridker PM. Lipoprotein(a), measured with an assay independent of apolipoprotein(a) isoform size, and risk of future cardiovascular events among initially healthy women. JAMA. 2006;296(11):1363-1370.
110. Suk Danik J, Rifai N, Buring JE, Ridker PM. Lipoprotein(a), hormone replacement therapy, and risk of future cardiovascular events. J Am Coll Cardiol. 2008;52(2):124-131.
111. Chien KL, Hsu HC, Su TC, Sung FC, Chen MF, Lee YT. Lipoprotein(a) and cardiovascular disease in ethnic Chinese: the Chin-Shan Community Cardiovascular Cohort Study. Clin Chem. 2008;54(2):285-291.
112. Lee SR, Prasad A, Choi YS, et al. LPA gene, ethnicity, and cardiovascular events. Circulation.2017;135(3):251-263.
113. Zekavat SM, Ruotsalainen S, Handsaker RE, et al. Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries. Nat Commun.2018;9(1):2606.
114. Zewinger S, Kleber ME, Tragante V, et al. Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study. Lancet Diabetes Endocrinol. 2017;5(7):534-543.
115. Li J, Lange LA, Sabourin J, et al. Genome- and exomewide association study of serum lipoprotein (a) in the Jackson Heart Study. J Hum Genet. 2015;60(12):755-761.
116. Khera AV, Emdin CA, Drake I, et al, Kathiresan S. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N Engl J Med.2016;375(24):2349-2358.
117. Nordestgaard BG, Langsted A. Lipoprotein(a) as a cause of cardiovascular disease: insights from epidemiology, genetics, and biology. J Lipid Res.2016;57(11):1953-1975.
118. Sofi F, Cesari F, Casini A, Macchi C, Abbate R, Gensini GF. Insomnia and risk of cardiovascular disease: a metaanalysis. Eur J Prev Cardiol.2014;21(1):57-64.
119. Estruch R, Ros E, Salas-Salvado J, et al. Primary prevention of cardiovascular disease with a mediterranean diet supplemented with extra-virgin olive oil or nuts. N Engl J Med.2018;378(25):e34.
120. Perrot N, Verbeek R, Sandhu M, et al. Ideal cardiovascular health influences cardiovascular disease risk associated with high lipoprotein(a) levels and genotype: The EPICNorfolk prospective population study. Atherosclerosis. 2017;256:47-52.
121. Teo KK, Corsi DJ, Tam JW, Dumesnil JG, Chan KL. Lipid lowering on progression of mild to moderate aortic stenosis: meta-analysis of the randomized placebocontrolled clinical trials on 2344 patients. Can J Cardiol. 2011;27(6):800-808.
122. Albers JJ, Slee A, O’Brien KD, et al. Relationship of apolipoproteins A-1 and B, and lipoprotein(a) to cardiovascular outcomes: the AIM-HIGH trial (Atherothrombosis Intervention in Metabolic Syndrome with Low HDL/High Triglyceride and Impact on Global Health Outcomes). J Am Coll Cardiol. 2013;62(17):1575-1579.
123. Lincoff AM, Nicholls SJ, Riesmeyer JS, et al; ACCELERATE Investigators. Evacetrapib and cardiovascular outcomes in high-risk vascular disease. N Engl J Med. 2017;376(20):1933-1942.
124. Schmidt AF, Pearce LS, Wilkins JT, Overington JP, Hingorani AD, Casas JP. PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database Syst Rev.2017;4:CD011748.
125. Bowman L, Hopewell JC, Chen F, et al; PHS3/TIM155-REVEAL Collaborative Group. Effects of anacetrapib in patients with atherosclerotic vascular disease.
126. Leebmann J, Roeseler E, Julius U, et al; Pro(a)LiFe Study Group. Lipoprotein apheresis in patients with maximally tolerated lipid-lowering therapy, lipoprotein(a)-hyperlipoproteinemia, and progressive cardiovascular disease: prospective observational multicenter study. Circulation. 2013;128(24):2567-2576.
127. Heigl F, Hettich R, Lotz N, et al. Efficacy, safety, and tolerability of long-term lipoprotein apheresis in patients with LDL- or Lp(a) hyperlipoproteinemia: Findings gathered from more than 36,000 treatments at one center in Germany. Atheroscler Suppl. 2015;18:154-162.
128. Viney NJ, van Capelleveen JC, Geary RS, et al. Antisense oligonucleotides targeting apolipoprotein(a) in people with raised lipoprotein(a): two randomised, double-blind, placebo-controlled, dose-ranging trials. Lancet. 2016;388(10057):2239-2253.
129. Graham MJ, Viney N, Crooke RM, Tsimikas S. Antisense inhibition of apolipoprotein (a) to lower plasma lipoprotein (a) levels in humans. J Lipid Res. 2016;57(3):340-351.
130. Keene D, Price C, Shun-Shin MJ, Francis DP. Effect on cardiovascular risk of high density lipoprotein targeted drug treatments niacin, fibrates, and CETP inhibitors: meta-analysis of randomised controlled trials including 117,411 patients. BMJ. 2014;349:g4379.
131. Nicholls SJ, Ruotolo G, Brewer HB, et al. Evacetrapib alone or in combination with statins lowers lipoprotein(a) and total and small LDL particle concentrations in mildly hypercholesterolemic patients. J Clin Lipidol. 2016;10(3):519-527.e4.
132. Schwartz GG, Ballantyne CM, Barter PJ, et al. Association of lipoprotein(a) with risk of recurrent ischemic events following acute coronary syndrome: analysis of the dal-outcomes randomized clinical trial. JAMA Cardiol.2018;3(2):164-168.
133. Schwartz GG, Olsson AG, Abt M, et al; dal-OUTCOMES Investigators. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med.2012;367(22):2089-2099.
134. Thomas T, Zhou H, Karmally W, et al. CETP (Cholesteryl Ester Transfer Protein) inhibition with anacetrapib decreases production of lipoprotein(a) in mildly hypercholesterolemic subjects. Arterioscler Thromb Vasc Biol.2017;37(9):1770-1775.
135. Khera AV, Everett BM, Caulfield MP, et al. Lipoprotein(a) concentrations, rosuvastatin therapy, and residual vascular risk: an analysis from the JUPITER Trial (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin). Circulation. 2014;129(6):635-642.
136. Yeang C, Hung MY, Byun YS, et al. Effect of therapeutic interventions on oxidized phospholipids on apolipoprotein B100 and lipoprotein(a). J Clin Lipidol. 2016;10(3):594-603.
137. Zhou Z, Rahme E, Pilote L. Are statins created equal? Evidence from randomized trials of pravastatin, simvastatin, and atorvastatin for cardiovascular disease prevention.Am Heart J. 2006;151(2):273-281.
138. Ridker PM, MacFadyen JG, Fonseca FA, et al; JUPITER Study Group. Number needed to treat with rosuvastatin to prevent first cardiovascular events and death among men and women with low low-density lipoprotein cholesterol and elevated high-sensitivity C-reactive protein: justification for the use of statins in prevention: an intervention trial evaluating rosuvastatin (JUPITER). Circ Cardiovasc Qual Outcomes. 2009;2(6):616-623.
139. Raal FJ, Giugliano RP, Sabatine MS, et al. Reduction in lipoprotein(a) with PCSK9 monoclonal antibody evolocumab (AMG 145): a pooled analysis of more than 1,300 patients in 4 phase II trials. J Am Coll Cardiol.2014;63(13):1278-1288.
140. Sabatine MS, Giugliano RP, Wiviott SD, et al. Efficacy and safety of evolocumab in reducing lipids and cardiovascular events. N Engl J Med. 2015;372(16):1500-1509.
141. Koren MJ, Sabatine MS, Giugliano RP, et al. Long-term low-density lipoprotein cholesterol-lowering efficacy, persistence, and safety of evolocumab in treatment of hypercholesterolemia: results up to 4 years from the open-label OSLER-1 extension study. JAMA Cardiol.2017;2(6):598-607.
142. Desai NR, Kohli P, Giugliano RP, et al. AMG145, a monoclonal antibody against proprotein convertase subtilisin kexin type 9, significantly reduces lipoprotein(a) in hypercholesterolemic patients receiving statin therapy: an analysis from the LDL-C Assessment with Proprotein Convertase Subtilisin Kexin Type 9 Monoclonal Antibody Inhibition Combined with Statin Therapy (LAPLACE)-Thrombolysis in Myocardial Infarction (TIMI) 57 trial. Circulation.2013;128(9):962-969.
143. Schwartz GG, Steg PG, Szarek M, et al; ODYSSEY OUTCOMES Committees and Investigators. Alirocumab and cardiovascular outcomes after acute coronary syndrome.N Engl J Med. 2018;379(22):2097-2107.
144. Sabatine MS, Giugliano RP, Keech AC, et al; FOURIER Steering Committee and Investigators. Evolocumab and clinical outcomes in patients with cardiovascular Disease.N Engl J Med. 2017;376(18):1713-1722.
145. Karatasakis A, Danek BA, Karacsonyi J, et al. Effect of PCSK9 inhibitors on clinical outcomes in patients with hypercholesterolemia: A meta-analysis of 35 randomized controlled trials. J Am Heart Assoc. 2017;6(12):e006910.
146. Santos RD, Duell PB, East C, et al. Long-term efficacy and safety of mipomersen in patients with familial hypercholesterolaemia: 2-year interim results of an open-label extension.Eur Heart J. 2015;36(9):566-575.
147. Duell PB, Santos RD, Kirwan BA, Witztum JL, Tsimikas S, Kastelein JJP. Long-term mipomersen treatment is associated with a reduction in cardiovascular events in patients with familial hypercholesterolemia. J Clin Lipidol. 2016;10(4):1011-1021.
148. McGowan MP, Tardif JC, Ceska R, et al. Randomized, placebo-controlled trial of mipomersen in patients with severe hypercholesterolemia receiving maximally tolerated lipid-lowering therapy. PLoS One.2012;7(11):e49006.
149. Jaeger BR, Richter Y, Nagel D, et al. Longitudinal cohort study on the effectiveness of lipid apheresis treatment to reduce high lipoprotein(a) levels and prevent major adverse coronary events. Nat Clin Pract Cardiovasc Med.2009;6(3):229-239.
150. Rosada A, Kassner U, Vogt A, Willhauck M, Parhofer K, Steinhagen-Thiessen E. Does regular lipid apheresis in Does regular lipid apheresis in patients with isolated elevated lipoprotein(a) levels reduce the incidence of cardiovascular events? Artif Organs. 2014;38(2):135-141.
151. Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e596-e646.
1. American Heart Association. Cardiovascular disease: a costly burden for America, projections through 2035. http://www.heart.org/idc/groups /heart-public/@wcm/@adv/documents/downloadable /ucm_491543.pdf. Accessed October 10, 2019.
2. Benjamin EJ, Virani SS, Callaway CW, et al. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation. 2018;137(12):e67-e492.
3. Roth GA, Johnson C, Abajobir A, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1-25.
4. Thrift AG, Cadilhac DA, Thayabaranathan T, et al. Global stroke statistics. Int J Stroke. 2014;9(1):6-18.
5. Murray CJ, Barber RM, Foreman KJ, et al; GBD 2013 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition. Lancet. 2015;386(10009):2145-2191.
6. Mukherjee D, Patil CG. Epidemiology and the global burden of stroke. World Neurosurg. 2011;76(6 suppl):S85-S90.
7. Joseph A, Ackerman D, Talley JD, Johnstone J, Kupersmith J. Manifestations of coronary atherosclerosis in young trauma victims—an autopsy study. J Am Coll Cardiol. 1993;22(2):459-467.
8. Webber BJ, Seguin PG, Burnett DG, Clark LL, Otto JL. Prevalence of and risk factors for autopsy-determined atherosclerosis among US service members, 2001-2011. JAMA. 2012;308(24):2577-2583.
9. Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123(8):933-944.
10. Krishnamurthi N, Francis J, Fihn SD, Meyer CS, Whooley MA. Leading causes of cardiovascular hospitalization in 8.45 million US veterans. PLoS One. 2018;13(3):e0193996.
11. Torio CM, Moore BJ. National inpatient hospital costs: the most expensive conditions by payer. Agency for Healthcare Research and Quality Statistical Brief No. 204. http:// www.hcup-us.ahrq.gov/reports/statbriefs/sb204-Most -Expensive-Hospital-Conditions.pdf. Published May 2016. Accessed October 10, 2019.
12. Bloom DE, Cafiero ET, Jané-Llopis E, et al. The global economic burden of noncommunicable diseases. https:// www.weforum.org/reports/global-economic-burden-non -communicable-diseases. Published September 18, 2011. Accessed October 10, 2019.
13. Crum-Cianflone NF, Bagnell ME, Schaller E, et al. Impact of combat deployment and posttraumatic stress disorder on newly reported coronary heart disease among US active duty and reserve forces. Circulation. 2014;129(18):1813-1820.
14. Fryar CD, Herrick K, Afful J, Ogden CL. Cardiovascular disease risk factors among male veterans, U.S., 2009- 2012. Am J Prev Med. 2016;50(1):101-105.
15. Ulmer CS, Bosworth HB, Germain A, et al; VA Mid-Atlantic Mental Illness Research Education and Clinical Center Registry Workgroup. Associations between sleep difficulties and risk factors for cardiovascular disease in veterans and active duty military personnel of the Iraq and Afghanistan conflicts. J Behav Med. 2015;38(3):544-555.
16. Lutwak N, Dill C. Military sexual trauma increases risk of post-traumatic stress disorder and depression thereby amplifying the possibility of suicidal ideation and cardiovascular disease. Mil Med. 2013;178(4):359-361.
17. Bowry ADK, Lewey J, Dugani SB, Choudhry NK. The burden of cardiovascular disease in low- and middle-income countries: epidemiology and management. Can J Cardiol. 2015;31(9):1151-1159.
18. Reinier K, Stecker EC, Vickers C, Gunson K, Jui J, Chugh SS. Incidence of sudden cardiac arrest is higher in areas of low socioeconomic status: a prospective two year study in a large United States community. Resuscitation. 2006;70(2):186-192.
19. Reinier K, Thomas E, Andrusiek DL, et al; Resuscitation Outcomes Consortium Investigators. Socioeconomic status and incidence of sudden cardiac arrest. CMAJ. 2011;183(15):1705-1712.
20. Yusuf S, Rangarajan S, Teo K, et al; PURE Investigators. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med. 2014;371(9):818-827.
21. World Health Organization. Health topics: cardiovascular disease. http://www.who.int/cardiovascular_diseases/en/. Updated 2019. Accessed October 10, 2019.
22. Berkowitz AL. Stroke and the noncommunicable diseases: A global burden in need of global advocacy. Neurology. 2015;84(21):2183-2184.
23. Holt T. Predicting cardiovascular disease. BMJ. 2016;353:i2621.
24. Centers for Disease Control and Prevention. Million hearts: strategies to reduce the prevalence of leading cardiovascular disease risk factors—United States, 2011. MMWR Morb Mortal Wkly Rep. 2011;60(36):1248-1251.
25. Fryar CD, Chen TC, Li X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999- 2010. NCHS Data Brief. 2012;103:1-8.
26. Ritchey MD, Loustalot F, Wall HK, et al. Million Hearts: description of the national surveillance and modeling methodology used to monitor the number of cardiovascular events prevented during 2012-2016. J Am Heart Assoc. 2017;6(5):pii:e00602.
27. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/ AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 pt B):2935-2959.
28. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): casecontrol study. Lancet. 2004;364(9438):937-952.
29. Eckel RH, Jakicic JM, Ard JD, et al. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 pt B):2960-2984.
30. Bansilal S, Castellano JM, Fuster V. Global burden of CVD: focus on secondary prevention of cardiovascular disease. Int J Cardiol. 2015;201(suppl 1):S1-S7.
31. Havranek EP, Mujahid MS, Barr DA, et al. Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2015;132(9):873-898.
32. Miedema MD, Garberich RF, Schnaidt LJ, et al. Statin eligibility and outpatient care prior to ST-segment elevation myocardial infarction. J Am Heart Assoc. 2017;6(4): pii: e005333.
33. Noheria A, Teodorescu C, Uy-Evanado A, et al. Distinctive profile of sudden cardiac arrest in middle-aged vs. older adults: a community-based study. Int J Cardiol. 2013;168(4):3495-3499.
34. Lieb W, Enserro DM, Larson MG, Vasan RS. Residual cardiovascular risk in individuals on lipid-lowering treatment: quantifying absolute and relative risk in the community. Open Heart. 2018;5(1):e000722.
35. Sachdeva A, Cannon CP, Deedwania PC, et al. Lipid levels in patients hospitalized with coronary artery disease: an analysis of 136,905 hospitalizations in Get With The Guidelines. Am Heart J. 2009;157(1):111-117.e2.
36. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416.
37. Fulcher J, O’Connell R, Voysey M, et al; Cholesterol Treatment Trialists (CTT) Collaboration. Efficacy and safety of LDL-lowering therapy among men and women: metaanalysis of individual data from 174,000 participants in 27 randomised trials. Lancet. 2015;385(9976):1397-1405.
38. Perrone V, Sangiorgi D, Buda S, Degli Esposti L. Residual cardiovascular risk in patients who received lipid-lowering treatment in a real-life setting: retrospective study. Clinicoecon Outcomes Res. 2016;8:649-655.
39. Sirimarco G, Labreuche J, Bruckert E, et al; PERFORM and SPARCL Investigators. Atherogenic dyslipidemia and residual cardiovascular risk in statin-treated patients. Stroke. 2014;45(5):1429-1436.
40. Kones R. Molecular sources of residual cardiovascular risk, clinical signals, and innovative solutions: relationship with subclinical disease, undertreatment, and poor adherence: implications of new evidence upon optimizing cardiovascular patient outcomes. Vasc Health Risk Manag. 2013;9:617-670.
41. Hayashi M, Shimizu W, Albert CM. The spectrum of epidemiology underlying sudden cardiac death. Circ Res. 2015;116(12):1887-1906.
42. Downs JR, O’Malley PG. Management of dyslipidemia for cardiovascular disease risk reduction: synopsis of the 2014 U.S. Department of Veterans Affairs and U.S. Department of Defense clinical practice guideline. Ann Intern Med. 2015;163(4):291-297.
43. Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/ AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/ NLA/PCNA Guideline on the management of blood cholesterol: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;73(24):3168-3209.
44. Tsimikas S, Fazio S, Ferdinand KC, et al. NHLBI Working Group recommendations to reduce lipoprotein(a)-mediated risk of cardiovascular disease and aortic stenosis. J Am Coll Cardiol. 2018;71(2):177-192.
45. Tsimikas S. A test in context: Lipoprotein(a): diagnosis, prognosis, controversies, and emerging therapies. J Am Coll Cardiol. 2017;69(6):692-711.
46. Ellis KL, Boffa MB, Sahebkar A, Koschinsky ML, Watts GF. The renaissance of lipoprotein(a): brave new world for preventive cardiology? Prog Lipid Res. 2017;68:57-82.
47. Thompson GR, Seed M. Lipoprotein(a): the underestimated cardiovascular risk factor. Heart. 2014;100(7):534-535.
48. Marcovina SM, Albers JJ. Lipoprotein (a) measurements for clinical application. J Lipid Res. 2016;57(4):526-537.
49. Tremulis SR. Founder’s Story: Lipoprotein(a) Foundation. https://www.lipoproteinafoundation.org/page /Sandrastory. Accessed October 10, 2019.
50. Centers for Disease Control and Prevention. ICD-10 Coordination and Maintenance Committee meeting, September 12-13, 2017 diagnosis agenda. https://www.cdc. gov/nchs/data/icd/Topic_Packet_Sept_2017.pdf. Accessed October 10, 2019.
51. Centers for Medicare & Medicaid Services. 2019 ICD- 10-CM codes descriptions in tabular order. https://www. cms.gov/Medicare/Coding/ICD10/2019-ICD-10-CM.html. Accessed October 10, 2019.
52. Wilson DP, Jacobson TA, Jones PH, et al. Use of Lipoprotein(a) in clinical practice: a biomarker whose time has come. A scientific statement from the National Lipid Association. J Clin Lipidol. 2019;13(3):374-392.
53. Langlois MR, Chapman MJ, Cobbaert C, et al. Quantifying atherogenic lipoproteins: current and future challenges in the era of personalized medicine and very low concentrations of ldl cholesterol. A consensus statement from EAS and EFLM. Clin Chem. 2018;64(7):1006-1033.
54. Shapiro MD, Fazio S. Apolipoprotein B-containing lipoproteins and atherosclerotic cardiovascular disease. F1000Res. 2017;6:134.
55. Tsimikas S, Fazio S, Viney NJ, Xia S, Witztum JL, Marcovina SM. Relationship of lipoprotein(a) molar concentrations and mass according to lipoprotein(a) thresholds and apolipoprotein(a) isoform size. J Clin Lipidol. 2018;12(5):1313-1323.
56. Erqou S, Kaptoge S, Perry PL, et al; Emerging Risk Factors Collaboration. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA. 2009;302(4):412-423.
57. Nave AH, Lange KS, Leonards CO, et al. Lipoprotein (a) as a risk factor for ischemic stroke: a meta-analysis. Atherosclerosis. 2015;242(2):496-503.
58. Varvel S, McConnell JP, Tsimikas S. Prevalence of elevated Lp(a) mass levels and patient thresholds in 532,359 patients in the United States. Arterioscler Thromb Vasc Biol. 2016;36(11):2239-2245.
59. Nordestgaard BG, Chapman MJ, Ray K, et al; European Atherosclerosis Society Consensus Panel. Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J. 2010;31(23):2844-2853.
60. Guan W, Cao J, Steffen BT, et al. Race is a key variable in assigning lipoprotein(a) cutoff values for coronary heart disease risk assessment: the Multi-Ethnic Study of Atherosclerosis. Arterioscler Thromb Vasc Biol. 2015;35(4):996-1001.
61. Virani SS, Brautbar A, Davis BC, et al. Associations between lipoprotein(a) levels and cardiovascular outcomes in black and white subjects: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation. 2012;125(2):241-249.
62. Tsimikas S, Mallat Z, Talmud PJ, et al. Oxidation-specific biomarkers, lipoprotein(a), and risk of fatal and nonfatal coronary events. J Am Coll Cardiol. 2010;56(12):946-955.
63. Clarke R, Peden JF, Hopewell JC, et al; PROCARDIS Consortium. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. New Eng J Med. 2009;361(26):2518-2528.
64. Wattanakit K, Folsom AR, Chambless LE, Nieto FJ. Risk factors for cardiovascular event recurrence in the Atherosclerosis Risk in Communities (ARIC) study. Am Heart J. 2005;149(4):606-612.
65. Ruotolo G, Lincoff MA, Menon V, et al. Lipoprotein(a) is a determinant of residual cardiovascular risk in the setting of optimal LDL-C in statin-treated patients with atherosclerotic cardiovascular disease [Abstract 17400]. Circulation. 2018;136(suppl 1):A17400.
66. Suwa S, Ogita M, Miyauchi K, et al. Impact of lipoprotein (a) on long-term outcomes in patients with coronary artery disease treated with statin after a first percutaneous coronary intervention. J Atheroscler Thromb. 2017;24(11):1125-1131.
67. Nestel PJ, Barnes EH, Tonkin AM, et al. Plasma lipoprotein(a) concentration predicts future coronary and cardiovascular events in patients with stable coronary heart disease. Arterioscler Thromb Vasc Biol. 2013;33(12):2902-2908.
68. Burgess S, Ference BA, et al. Association of LPA variants with risk of coronary disease and the implications for lipoprotein(a)-lowering therapies: a Mendelian randomization analysis. JAMA Cardiol. 2018;3(7):619-627.
69. Roeseler E, Julius U, Heigl F, et al; Pro(a)LiFe-Study Group. Lipoprotein apheresis for lipoprotein(a)-associated cardiovascular disease: prospective 5 years of followup and apolipoprotein(a) characterization. Arterioscler Thromb Vasc Biol. 2016;36(9):2019-2027.
70. Matsuura E, Atzeni F, Sarzi-Puttini P, Turiel M, Lopez LR, Nurmohamed MT. Is atherosclerosis an autoimmune disease? BMC Med. 2014;12:47.
71. Ahearn J, Shields KJ, Liu CC, Manzi S. Cardiovascular disease biomarkers across autoimmune diseases. Clin Immunol. 2015;161(1):59-63.
72. Di Minno MND, Emmi G, Ambrosino P, et al. Subclinical atherosclerosis in asymptomatic carriers of persistent antiphospholipid antibodies positivity: a cross-sectional study. Int J Cardiol. 2019;274:1-6.
72. Di Minno MND, Emmi G, Ambrosino P, et al. Subclinical atherosclerosis in asymptomatic carriers of persistent antiphospholipid antibodies positivity: a cross-sectional study. Int J Cardiol. 2019;274:1-6.
73. Iseme RA, McEvoy M, Kelly B, et al. A role for autoantibodies in atherogenesis. Cardiovasc Res. 2017;113(10):1102-1112.
74. Sinicato NA, da Silva Cardoso PA, Appenzeller S. Risk factors in cardiovascular disease in systemic lupus erythematosus. Curr Cardiol Rev. 2013;9(1):15-19.
75. Sciatti E, Cavazzana I, Vizzardi E, et al. Systemic lupus erythematosus and endothelial dysfunction: a close relationship. Curr Rheumatol Rev. 2018;15(3):177-188.
76. Prasad A, Clopton P, Ayers C, et al. Relationship of autoantibodies to MDA-LDL and ApoB-Immune complexes to sex, ethnicity, subclinical atherosclerosis, and cardiovascular events. Arterioscler Thromb Vasc Biol. 2017;37(6):1213-1221.
77. Miller YI, Choi SH, Wiesner P, et al. Oxidation-specific epitopes are danger-associated molecular patterns recognized by pattern recognition receptors of innate immunity. Circ Res. 2011;108(2):235-248.
78. Libby P, Lichtman AH, Hansson GK. Immune effector mechanisms implicated in atherosclerosis: from mice to humans. Immunity. 2013;38(6):1092-1104.
79. Binder CJ, Papac-Milicevic N, Witztum JL. Innate sensing of oxidation-specific epitopes in health and disease. Nat Rev Immunol. 2016;16(8):485-497.
80. Freigang S. The regulation of inflammation by oxidized phospholipids. Eur J Immunol. 2016;46(8):1818-1825.
81. Ravandi A, Boekholdt SM, Mallat Z, et al. Relationship of oxidized LDL with markers of oxidation and inflammation and cardiovascular events: results from the EPIC-Norfolk Study. J Lipid Res. 2011;52(10):1829-1836.
82. Tsimikas S, Willeit P, Willeit J, et al. Oxidation-specific biomarkers, prospective 15-year cardiovascular and stroke outcomes, and net reclassification of cardiovascular events. J Am Coll Cardiol. 2012;60(21):2218-2229.
83. Cinoku I, Mavragani CP, Tellis CC, Nezos A, Tselepis AD, Moutsopoulos HM. Autoantibodies to ox-LDL in Sjogren’s syndrome: are they atheroprotective? Clin Exp Rheumatol. 2018;36 Suppl 112(3):61-67.
84. Fagerberg B, Prahl Gullberg U, Alm R, Nilsson J, Fredrikson GN. Circulating autoantibodies against the apolipoprotein B-100 peptides p45 and p210 in relation to the occurrence of carotid plaques in 64-year-old women. PLoS One. 2015;10(3):e0120744.
85. Klesareva EA, Afanas’eva OI, Donskikh VV, Adamova IY, Pokrovskii SN. Characteristics of lipoprotein(a)-containing circulating immune complexes as markers of coronary heart disease. Bull Exp Biol Med. 2016;162(2):231-236.
86. Morishita R, Ishii J, Kusumi Y, et al. Association of serum oxidized lipoprotein(a) concentration with coronary artery disease: potential role of oxidized lipoprotein(a) in the vasucular wall. J Atheroscler Thromb. 2009;16(4):410-418.
87. Wang J, Zhang C, Gong J, et al. Development of new enzyme-linked immunosorbent assay for oxidized lipoprotein(a) by using purified human oxidized lipoprotein(a) autoantibodies as capture antibody. Clin Chim Acta. 2007;385(1-2):73-78.
88. Wang JJ, Han AZ, Meng Y, et al. Measurement of oxidized lipoprotein (a) in patients with acute coronary syndromes and stable coronary artery disease by 2 ELISAs: using different capture antibody against oxidized lipoprotein (a) or oxidized LDL. Clin Biochem. 2010;43(6):571-575.
89. Orso E, Schmitz G. Lipoprotein(a) and its role in inflammation, atherosclerosis and malignancies. Clin Res Cardiol Suppl. 2017;12(Suppl 1):31-37.
90. Lassman ME, McLaughlin TM, Zhou H, et al. Simultaneous quantitation and size characterization of apolipoprotein(a) by ultra-performance liquid chromatography/ mass spectrometry. Rapid Commun Mass Spectrom. 2014;28(10):1101-1106.
91. Lippi G, Guidi G. Lipoprotein(a): from ancestral benefit to modern pathogen? QJM. 2000;93(2):75-84.
92. van der Valk FM, Bekkering S, Kroon J, et al. Oxidized phospholipids on lipoprotein(a) elicit arterial wall inflammation and an inflammatory monocyte response in humans. Circulation. 2016;134(8):611-624.
93. Yeang C, Wilkinson MJ, Tsimikas S. Lipoprotein(a) and oxidized phospholipids in calcific aortic valve stenosis. Curr Opin Cardiol. 2016;31(4):440-450.
94. Thanassoulis G, Campbell CY, Owens DS, et al; CHARGE Extracoronary Calcium Working Group. Genetic associations with valvular calcification and aortic stenosis. N Engl J Med. 2013;368(6):503-512.
95. Aguib Y, Al Suwaidi J. The Copenhagen City Heart Study (Osterbroundersogelsen). Glob Cardiol Sci Pract. 2015;2015(3):33.
96. Kamstrup PR, Benn M, Tybjaerg-Hansen A, Nordestgaard BG. Extreme lipoprotein(a) levels and risk of myocardial infarction in the general population: the Copenhagen City Heart Study. Circulation. 2008;117(2):176-184.
97. Kamstrup PR, Tybjærg-Hansen A, Steffensen R, Nordestgaard BG. Genetically elevated lipoprotein(a) and increased risk of myocardial infarction. JAMA. 2009;301(22):2331-2339.
98. Kamstrup PR, Tybjaerg-Hansen A, Nordestgaard BG. Extreme lipoprotein(a) levels and improved cardiovascular risk prediction. J Am Coll Cardiol.2013;61(11):1146-1156.
99. Kamstrup PR, Tybjaerg-Hansen A, Nordestgaard BG. Elevated lipoprotein(a) and risk of aortic valve stenosis in the general population. J Am Coll Cardiol. 2014;63(5):470-477.
100. Kamstrup PR, Nordestgaard BG. Elevated lipoprotein(a) levels, LPA risk genotypes, and increased risk of heart failure in the general population. JACC Heart Fail.2016;4(1):78-87.
101. Verbeek R, Hoogeveen RM, Langsted A, et al. Cardiovascular disease risk associated with elevated lipoprotein(a) attenuates at low low-density lipoprotein cholesterol levels in a primary prevention setting. Eur Heart J. 2018;39(27):2589-2596.
102. Cao J, Steffen BT, Budoff M, et al. Lipoprotein(a) levels are associated with subclinical calcific aortic valve disease in white and black individuals: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. 2016;36(5):1003-1009.
103. Steffen BT, Duprez D, Bertoni AG, Guan W, Tsai M. Lp(a) [lipoprotein(a)]-related risk of heart failure is evident in whites but not in other racial/ethnic groups.Arterioscler Thromb Vasc Biol. 2018;38(10):2498-2504.
104. ARIC Investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epidemiol. 1989;129(4):687-702.
105. Agarwala A, Pokharel Y, Saeed A, et al. The association of lipoprotein(a) with incident heart failure hospitalization: Atherosclerosis Risk in Communities study. Atherosclerosis. 2017;262:131-137.
106. Ohira T, Schreiner PJ, Morrisett JD, Chambless LE, Rosamond WD, Folsom AR. Lipoprotein(a) and incident ischemic stroke: the Atherosclerosis Risk in Communities (ARIC) study. Stroke. 2006;37(6):1407-1412.
107. Waldeyer C, Makarova N, Zeller T, et al. Lipoprotein(a) and the risk of cardiovascular disease in the European population: results from the BiomarCaRE consortium. Eur Heart J. 2017;38(32):2490-2498.
108. Cook NR, Mora S, Ridker PM. Lipoprotein(a) and cardiovascular risk prediction among women. J Am Coll Cardiol. 2018;72(3):287-296.
109. Suk Danik J, Rifai N, Buring JE, Ridker PM. Lipoprotein(a), measured with an assay independent of apolipoprotein(a) isoform size, and risk of future cardiovascular events among initially healthy women. JAMA. 2006;296(11):1363-1370.
110. Suk Danik J, Rifai N, Buring JE, Ridker PM. Lipoprotein(a), hormone replacement therapy, and risk of future cardiovascular events. J Am Coll Cardiol. 2008;52(2):124-131.
111. Chien KL, Hsu HC, Su TC, Sung FC, Chen MF, Lee YT. Lipoprotein(a) and cardiovascular disease in ethnic Chinese: the Chin-Shan Community Cardiovascular Cohort Study. Clin Chem. 2008;54(2):285-291.
112. Lee SR, Prasad A, Choi YS, et al. LPA gene, ethnicity, and cardiovascular events. Circulation.2017;135(3):251-263.
113. Zekavat SM, Ruotsalainen S, Handsaker RE, et al. Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries. Nat Commun.2018;9(1):2606.
114. Zewinger S, Kleber ME, Tragante V, et al. Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study. Lancet Diabetes Endocrinol. 2017;5(7):534-543.
115. Li J, Lange LA, Sabourin J, et al. Genome- and exomewide association study of serum lipoprotein (a) in the Jackson Heart Study. J Hum Genet. 2015;60(12):755-761.
116. Khera AV, Emdin CA, Drake I, et al, Kathiresan S. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N Engl J Med.2016;375(24):2349-2358.
117. Nordestgaard BG, Langsted A. Lipoprotein(a) as a cause of cardiovascular disease: insights from epidemiology, genetics, and biology. J Lipid Res.2016;57(11):1953-1975.
118. Sofi F, Cesari F, Casini A, Macchi C, Abbate R, Gensini GF. Insomnia and risk of cardiovascular disease: a metaanalysis. Eur J Prev Cardiol.2014;21(1):57-64.
119. Estruch R, Ros E, Salas-Salvado J, et al. Primary prevention of cardiovascular disease with a mediterranean diet supplemented with extra-virgin olive oil or nuts. N Engl J Med.2018;378(25):e34.
120. Perrot N, Verbeek R, Sandhu M, et al. Ideal cardiovascular health influences cardiovascular disease risk associated with high lipoprotein(a) levels and genotype: The EPICNorfolk prospective population study. Atherosclerosis. 2017;256:47-52.
121. Teo KK, Corsi DJ, Tam JW, Dumesnil JG, Chan KL. Lipid lowering on progression of mild to moderate aortic stenosis: meta-analysis of the randomized placebocontrolled clinical trials on 2344 patients. Can J Cardiol. 2011;27(6):800-808.
122. Albers JJ, Slee A, O’Brien KD, et al. Relationship of apolipoproteins A-1 and B, and lipoprotein(a) to cardiovascular outcomes: the AIM-HIGH trial (Atherothrombosis Intervention in Metabolic Syndrome with Low HDL/High Triglyceride and Impact on Global Health Outcomes). J Am Coll Cardiol. 2013;62(17):1575-1579.
123. Lincoff AM, Nicholls SJ, Riesmeyer JS, et al; ACCELERATE Investigators. Evacetrapib and cardiovascular outcomes in high-risk vascular disease. N Engl J Med. 2017;376(20):1933-1942.
124. Schmidt AF, Pearce LS, Wilkins JT, Overington JP, Hingorani AD, Casas JP. PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database Syst Rev.2017;4:CD011748.
125. Bowman L, Hopewell JC, Chen F, et al; PHS3/TIM155-REVEAL Collaborative Group. Effects of anacetrapib in patients with atherosclerotic vascular disease.
126. Leebmann J, Roeseler E, Julius U, et al; Pro(a)LiFe Study Group. Lipoprotein apheresis in patients with maximally tolerated lipid-lowering therapy, lipoprotein(a)-hyperlipoproteinemia, and progressive cardiovascular disease: prospective observational multicenter study. Circulation. 2013;128(24):2567-2576.
127. Heigl F, Hettich R, Lotz N, et al. Efficacy, safety, and tolerability of long-term lipoprotein apheresis in patients with LDL- or Lp(a) hyperlipoproteinemia: Findings gathered from more than 36,000 treatments at one center in Germany. Atheroscler Suppl. 2015;18:154-162.
128. Viney NJ, van Capelleveen JC, Geary RS, et al. Antisense oligonucleotides targeting apolipoprotein(a) in people with raised lipoprotein(a): two randomised, double-blind, placebo-controlled, dose-ranging trials. Lancet. 2016;388(10057):2239-2253.
129. Graham MJ, Viney N, Crooke RM, Tsimikas S. Antisense inhibition of apolipoprotein (a) to lower plasma lipoprotein (a) levels in humans. J Lipid Res. 2016;57(3):340-351.
130. Keene D, Price C, Shun-Shin MJ, Francis DP. Effect on cardiovascular risk of high density lipoprotein targeted drug treatments niacin, fibrates, and CETP inhibitors: meta-analysis of randomised controlled trials including 117,411 patients. BMJ. 2014;349:g4379.
131. Nicholls SJ, Ruotolo G, Brewer HB, et al. Evacetrapib alone or in combination with statins lowers lipoprotein(a) and total and small LDL particle concentrations in mildly hypercholesterolemic patients. J Clin Lipidol. 2016;10(3):519-527.e4.
132. Schwartz GG, Ballantyne CM, Barter PJ, et al. Association of lipoprotein(a) with risk of recurrent ischemic events following acute coronary syndrome: analysis of the dal-outcomes randomized clinical trial. JAMA Cardiol.2018;3(2):164-168.
133. Schwartz GG, Olsson AG, Abt M, et al; dal-OUTCOMES Investigators. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med.2012;367(22):2089-2099.
134. Thomas T, Zhou H, Karmally W, et al. CETP (Cholesteryl Ester Transfer Protein) inhibition with anacetrapib decreases production of lipoprotein(a) in mildly hypercholesterolemic subjects. Arterioscler Thromb Vasc Biol.2017;37(9):1770-1775.
135. Khera AV, Everett BM, Caulfield MP, et al. Lipoprotein(a) concentrations, rosuvastatin therapy, and residual vascular risk: an analysis from the JUPITER Trial (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin). Circulation. 2014;129(6):635-642.
136. Yeang C, Hung MY, Byun YS, et al. Effect of therapeutic interventions on oxidized phospholipids on apolipoprotein B100 and lipoprotein(a). J Clin Lipidol. 2016;10(3):594-603.
137. Zhou Z, Rahme E, Pilote L. Are statins created equal? Evidence from randomized trials of pravastatin, simvastatin, and atorvastatin for cardiovascular disease prevention.Am Heart J. 2006;151(2):273-281.
138. Ridker PM, MacFadyen JG, Fonseca FA, et al; JUPITER Study Group. Number needed to treat with rosuvastatin to prevent first cardiovascular events and death among men and women with low low-density lipoprotein cholesterol and elevated high-sensitivity C-reactive protein: justification for the use of statins in prevention: an intervention trial evaluating rosuvastatin (JUPITER). Circ Cardiovasc Qual Outcomes. 2009;2(6):616-623.
139. Raal FJ, Giugliano RP, Sabatine MS, et al. Reduction in lipoprotein(a) with PCSK9 monoclonal antibody evolocumab (AMG 145): a pooled analysis of more than 1,300 patients in 4 phase II trials. J Am Coll Cardiol.2014;63(13):1278-1288.
140. Sabatine MS, Giugliano RP, Wiviott SD, et al. Efficacy and safety of evolocumab in reducing lipids and cardiovascular events. N Engl J Med. 2015;372(16):1500-1509.
141. Koren MJ, Sabatine MS, Giugliano RP, et al. Long-term low-density lipoprotein cholesterol-lowering efficacy, persistence, and safety of evolocumab in treatment of hypercholesterolemia: results up to 4 years from the open-label OSLER-1 extension study. JAMA Cardiol.2017;2(6):598-607.
142. Desai NR, Kohli P, Giugliano RP, et al. AMG145, a monoclonal antibody against proprotein convertase subtilisin kexin type 9, significantly reduces lipoprotein(a) in hypercholesterolemic patients receiving statin therapy: an analysis from the LDL-C Assessment with Proprotein Convertase Subtilisin Kexin Type 9 Monoclonal Antibody Inhibition Combined with Statin Therapy (LAPLACE)-Thrombolysis in Myocardial Infarction (TIMI) 57 trial. Circulation.2013;128(9):962-969.
143. Schwartz GG, Steg PG, Szarek M, et al; ODYSSEY OUTCOMES Committees and Investigators. Alirocumab and cardiovascular outcomes after acute coronary syndrome.N Engl J Med. 2018;379(22):2097-2107.
144. Sabatine MS, Giugliano RP, Keech AC, et al; FOURIER Steering Committee and Investigators. Evolocumab and clinical outcomes in patients with cardiovascular Disease.N Engl J Med. 2017;376(18):1713-1722.
145. Karatasakis A, Danek BA, Karacsonyi J, et al. Effect of PCSK9 inhibitors on clinical outcomes in patients with hypercholesterolemia: A meta-analysis of 35 randomized controlled trials. J Am Heart Assoc. 2017;6(12):e006910.
146. Santos RD, Duell PB, East C, et al. Long-term efficacy and safety of mipomersen in patients with familial hypercholesterolaemia: 2-year interim results of an open-label extension.Eur Heart J. 2015;36(9):566-575.
147. Duell PB, Santos RD, Kirwan BA, Witztum JL, Tsimikas S, Kastelein JJP. Long-term mipomersen treatment is associated with a reduction in cardiovascular events in patients with familial hypercholesterolemia. J Clin Lipidol. 2016;10(4):1011-1021.
148. McGowan MP, Tardif JC, Ceska R, et al. Randomized, placebo-controlled trial of mipomersen in patients with severe hypercholesterolemia receiving maximally tolerated lipid-lowering therapy. PLoS One.2012;7(11):e49006.
149. Jaeger BR, Richter Y, Nagel D, et al. Longitudinal cohort study on the effectiveness of lipid apheresis treatment to reduce high lipoprotein(a) levels and prevent major adverse coronary events. Nat Clin Pract Cardiovasc Med.2009;6(3):229-239.
150. Rosada A, Kassner U, Vogt A, Willhauck M, Parhofer K, Steinhagen-Thiessen E. Does regular lipid apheresis in Does regular lipid apheresis in patients with isolated elevated lipoprotein(a) levels reduce the incidence of cardiovascular events? Artif Organs. 2014;38(2):135-141.
151. Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e596-e646.
Open Clinical Trials for Native Americans With Diabetes Mellitus(FULL)
Providing access to clinical trials for patients with diabetes mellitus can be a challenge, but a significant number of trials are now recruiting patients. The clinical trials listed below are all open as of October 31, 2019; and are focused on diabetes mellitus-related treatments for American Indians. For additional information and full inclusion/exclusion criteria, please consult clinicaltrials.gov.
Cross-Sectional and Longitudinal Studies of “Pre-Diabetes” in the Pima Indians
The Pima Indians of Arizona have the highest prevalence and incidence of type 2 diabetes of any population in the world. Prospective analyses in this population have identified insulin resistance and a defect in early insulin secretion as risk factors for the development of the disease. To identify the genetic and environmental determinants of diabetes we plan to study Pima Indian families to determine: (1) if there are genes that segregate with metabolic risk factors for diabetes which might therefore be genetic markers for type 2 diabetes; and (2) the mechanisms mediating genetic and environmental determinants of insulin resistance and impaired insulin secretion.
ID: NCT00340132
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Clifton Bogardus, MD, [email protected]
Location: NIDDK, Phoenix, AZ
Empaglifozin in Early Diabetic Kidney Disease
Diabetes is common among American Indian people and diabetic kidney disease is a common complication. Kidney disease caused by diabetes can lead to the need for kidney replacement, by dialysis or kidney transplant, and is also associated with higher risk of early death. A new diabetes medicine called empagliflozin may slow kidney disease from type 2 diabetes. Researchers want to learn if it protects the kidneys when used in very early stages of diabetic kidney disease.
ID: NCT03173963
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Helen C Looker, [email protected]
Location: NIDDK, Phoenix, AZ
Family Investigation of Nephropathy and Diabetes
The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. FIND will be conducted in 11 centers and in many ethnic groups throughout the United States. Two different strategies will be used to localize genes predisposing to kidney disease: a family-based genetic linkage study and a case-control study that utilizes admixture linkage disequilibrium. The center will conduct family-based linkage studies among American Indian populations in the southwestern United States.
ID: NCT00342927
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: William C Knowler, MD, [email protected]
Location: NIDDK, Phoenix, AZ
Look AHEAD: Action for Health in Diabetes
The Look AHEAD study is a multi-center, randomized clinical trial to examine the long-term effects of a lifestyle intervention designed to achieve and maintain weight loss. The study will investigate the effects of the intervention on heart attacks, stroke and cardiovascular-related death in individuals with type 2 diabetes who are also overweight or obese.
ID: NCT00017953
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Location: Southwestern American Indian Center, Phoenix, AZ
Vitamin D and Type 2 Diabetes Study
The goal of the Vitamin D and type 2 diabetes (D2d) study is to determine if vitamin D supplementation works to delay the onset of type 2 diabetes in people at risk for the disease and to gain a better understand how vitamin D affects glucose (sugar) metabolism.
ID: NCT01942694
Sponsor: Tufts Medical Center
Locations: Southwest American Indian Center; Phoenix, AZ; Orlando VA Medical Center, FL; Atlanta VA Medical Center, Decatur, GA; Omaha VA Medical Center, NE
Reducing Diabetes Risk Factors in American Indian Children: Tribal Turning Point (TTP)
This study will evaluate a behavioral intervention designed to reduce risk factors for type 2 diabetes in American Indian youth aged 7-10 years.
ID: NCT03573856
Sponsor: University of Colorado, Denver
Contact: Katherine Sauder, PhD, [email protected]; Dana Dabelea, MD, PhD, [email protected]
Location: Childrens Hospital Colorado, Aurora
Strong Men, Strong Communities Diabetes Risk Reduction in American Indian Men (SMSC)
SMSC will inform the design and implementation of culturally informed, community-based lifestyle interventions for diabetes prevention in AI men in our partner communities and elsewhere, as well as in men of other minority groups who experience a heavy burden of diabetes.
ID: NCT02953977
Sponsor: Washington State University
Contact: Kaimi Sinclair, PhD, MPH, [email protected] Location: IREACH, Seattle, WA
Growing Resilience in Wind River Indian Reservation (GR)
The Growing Resilience research leverages reservation-based assets of land, family, culture, and front-line tribal health organizations to develop and evaluate home food gardens as a family-based health promotion intervention to reduce disparities suffered by Native Americans in nearly every measure of health. Home gardening interventions show great promise for enabling families to improve their health, and this study aims to fulfill that promise with university and Wind River Indian Reservation partners. The investigators will develop an empowering, scalable, and sustainable family-based health promotion intervention with, by, and for Native American families and conduct the first randomized controlled trial to assess the health impacts of home gardens.
ID: NCT02672748
Sponsor: University of Wyoming
Location: University of Wyoming, Laramie
A Comparative Effectiveness Study of Major Glycemia-lowering Medications for Treatment of Type 2 Diabetes (GRADE)
The GRADE Study is a pragmatic, unmasked clinical trial that will compare commonly used diabetes medications, when combined with metformin, on glycemia-lowering effectiveness and patient-centered outcomes.
ID: NCT01794143
Sponsor: GRADE Study Group
Location: Southwestern American Indian Center, Phoenix, AZ
Home-Based Kidney Care in Native Americans of New Mexico (HBKC)
New Mexico American Indians are experiencing an epidemic of chronic kidney disease due primarily to the high rates of obesity and diabetes. The present study entitled Home-Based Kidney Care is designed to delay / reduce rates of end stage renal disease by early interventions in chronic kidney disease (CKD). Investigators propose to assess the safety and efficacy of conducting a full-scale study to determine if home based care delivered by a collaborative team composed of community health workers, the Albuquerque Area Indian Health Board and University of New Mexico faculty will decrease the risk for the development and the progression of CKD.
ID: NCT03179085
Sponsor: University of New Mexico
Contact: Vallabh Shah, PhD, [email protected]; Kevin English, PhD, [email protected]
Location: University of New Mexico, Albuquerque
Home-based Prediabetes Care in Acoma Pueblo - Study 1
Our major goal of implementing educational interventions to slow the current rate of increase in diabetes in Native communities is aligned with the National Institute of Health (NIGMS) and New Mexico INBRE’s vision in reducing health disparity using innovative interventions. The investigators propose following aims: (1) Recruit and Screen 300 community members in Acoma Pueblo, New Mexico to identify incident cases of pre-diabetes for the proposed study of Home Based Diabetes Care (HBDC); (2) Enroll 150 Acoma Natives aged 21-70 years, at risk for type 2 diabetes mellitus and conduct HBDC for a 16-week lifestyle intervention in a longitudinal cohort study.
ID: NCT04029298
Sponsor: University of New Mexico
Contact: Matthew Bouchonville, MD, [email protected]; Vallabh Shah, PhD, [email protected]
Providing access to clinical trials for patients with diabetes mellitus can be a challenge, but a significant number of trials are now recruiting patients. The clinical trials listed below are all open as of October 31, 2019; and are focused on diabetes mellitus-related treatments for American Indians. For additional information and full inclusion/exclusion criteria, please consult clinicaltrials.gov.
Cross-Sectional and Longitudinal Studies of “Pre-Diabetes” in the Pima Indians
The Pima Indians of Arizona have the highest prevalence and incidence of type 2 diabetes of any population in the world. Prospective analyses in this population have identified insulin resistance and a defect in early insulin secretion as risk factors for the development of the disease. To identify the genetic and environmental determinants of diabetes we plan to study Pima Indian families to determine: (1) if there are genes that segregate with metabolic risk factors for diabetes which might therefore be genetic markers for type 2 diabetes; and (2) the mechanisms mediating genetic and environmental determinants of insulin resistance and impaired insulin secretion.
ID: NCT00340132
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Clifton Bogardus, MD, [email protected]
Location: NIDDK, Phoenix, AZ
Empaglifozin in Early Diabetic Kidney Disease
Diabetes is common among American Indian people and diabetic kidney disease is a common complication. Kidney disease caused by diabetes can lead to the need for kidney replacement, by dialysis or kidney transplant, and is also associated with higher risk of early death. A new diabetes medicine called empagliflozin may slow kidney disease from type 2 diabetes. Researchers want to learn if it protects the kidneys when used in very early stages of diabetic kidney disease.
ID: NCT03173963
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Helen C Looker, [email protected]
Location: NIDDK, Phoenix, AZ
Family Investigation of Nephropathy and Diabetes
The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. FIND will be conducted in 11 centers and in many ethnic groups throughout the United States. Two different strategies will be used to localize genes predisposing to kidney disease: a family-based genetic linkage study and a case-control study that utilizes admixture linkage disequilibrium. The center will conduct family-based linkage studies among American Indian populations in the southwestern United States.
ID: NCT00342927
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: William C Knowler, MD, [email protected]
Location: NIDDK, Phoenix, AZ
Look AHEAD: Action for Health in Diabetes
The Look AHEAD study is a multi-center, randomized clinical trial to examine the long-term effects of a lifestyle intervention designed to achieve and maintain weight loss. The study will investigate the effects of the intervention on heart attacks, stroke and cardiovascular-related death in individuals with type 2 diabetes who are also overweight or obese.
ID: NCT00017953
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Location: Southwestern American Indian Center, Phoenix, AZ
Vitamin D and Type 2 Diabetes Study
The goal of the Vitamin D and type 2 diabetes (D2d) study is to determine if vitamin D supplementation works to delay the onset of type 2 diabetes in people at risk for the disease and to gain a better understand how vitamin D affects glucose (sugar) metabolism.
ID: NCT01942694
Sponsor: Tufts Medical Center
Locations: Southwest American Indian Center; Phoenix, AZ; Orlando VA Medical Center, FL; Atlanta VA Medical Center, Decatur, GA; Omaha VA Medical Center, NE
Reducing Diabetes Risk Factors in American Indian Children: Tribal Turning Point (TTP)
This study will evaluate a behavioral intervention designed to reduce risk factors for type 2 diabetes in American Indian youth aged 7-10 years.
ID: NCT03573856
Sponsor: University of Colorado, Denver
Contact: Katherine Sauder, PhD, [email protected]; Dana Dabelea, MD, PhD, [email protected]
Location: Childrens Hospital Colorado, Aurora
Strong Men, Strong Communities Diabetes Risk Reduction in American Indian Men (SMSC)
SMSC will inform the design and implementation of culturally informed, community-based lifestyle interventions for diabetes prevention in AI men in our partner communities and elsewhere, as well as in men of other minority groups who experience a heavy burden of diabetes.
ID: NCT02953977
Sponsor: Washington State University
Contact: Kaimi Sinclair, PhD, MPH, [email protected] Location: IREACH, Seattle, WA
Growing Resilience in Wind River Indian Reservation (GR)
The Growing Resilience research leverages reservation-based assets of land, family, culture, and front-line tribal health organizations to develop and evaluate home food gardens as a family-based health promotion intervention to reduce disparities suffered by Native Americans in nearly every measure of health. Home gardening interventions show great promise for enabling families to improve their health, and this study aims to fulfill that promise with university and Wind River Indian Reservation partners. The investigators will develop an empowering, scalable, and sustainable family-based health promotion intervention with, by, and for Native American families and conduct the first randomized controlled trial to assess the health impacts of home gardens.
ID: NCT02672748
Sponsor: University of Wyoming
Location: University of Wyoming, Laramie
A Comparative Effectiveness Study of Major Glycemia-lowering Medications for Treatment of Type 2 Diabetes (GRADE)
The GRADE Study is a pragmatic, unmasked clinical trial that will compare commonly used diabetes medications, when combined with metformin, on glycemia-lowering effectiveness and patient-centered outcomes.
ID: NCT01794143
Sponsor: GRADE Study Group
Location: Southwestern American Indian Center, Phoenix, AZ
Home-Based Kidney Care in Native Americans of New Mexico (HBKC)
New Mexico American Indians are experiencing an epidemic of chronic kidney disease due primarily to the high rates of obesity and diabetes. The present study entitled Home-Based Kidney Care is designed to delay / reduce rates of end stage renal disease by early interventions in chronic kidney disease (CKD). Investigators propose to assess the safety and efficacy of conducting a full-scale study to determine if home based care delivered by a collaborative team composed of community health workers, the Albuquerque Area Indian Health Board and University of New Mexico faculty will decrease the risk for the development and the progression of CKD.
ID: NCT03179085
Sponsor: University of New Mexico
Contact: Vallabh Shah, PhD, [email protected]; Kevin English, PhD, [email protected]
Location: University of New Mexico, Albuquerque
Home-based Prediabetes Care in Acoma Pueblo - Study 1
Our major goal of implementing educational interventions to slow the current rate of increase in diabetes in Native communities is aligned with the National Institute of Health (NIGMS) and New Mexico INBRE’s vision in reducing health disparity using innovative interventions. The investigators propose following aims: (1) Recruit and Screen 300 community members in Acoma Pueblo, New Mexico to identify incident cases of pre-diabetes for the proposed study of Home Based Diabetes Care (HBDC); (2) Enroll 150 Acoma Natives aged 21-70 years, at risk for type 2 diabetes mellitus and conduct HBDC for a 16-week lifestyle intervention in a longitudinal cohort study.
ID: NCT04029298
Sponsor: University of New Mexico
Contact: Matthew Bouchonville, MD, [email protected]; Vallabh Shah, PhD, [email protected]
Providing access to clinical trials for patients with diabetes mellitus can be a challenge, but a significant number of trials are now recruiting patients. The clinical trials listed below are all open as of October 31, 2019; and are focused on diabetes mellitus-related treatments for American Indians. For additional information and full inclusion/exclusion criteria, please consult clinicaltrials.gov.
Cross-Sectional and Longitudinal Studies of “Pre-Diabetes” in the Pima Indians
The Pima Indians of Arizona have the highest prevalence and incidence of type 2 diabetes of any population in the world. Prospective analyses in this population have identified insulin resistance and a defect in early insulin secretion as risk factors for the development of the disease. To identify the genetic and environmental determinants of diabetes we plan to study Pima Indian families to determine: (1) if there are genes that segregate with metabolic risk factors for diabetes which might therefore be genetic markers for type 2 diabetes; and (2) the mechanisms mediating genetic and environmental determinants of insulin resistance and impaired insulin secretion.
ID: NCT00340132
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Clifton Bogardus, MD, [email protected]
Location: NIDDK, Phoenix, AZ
Empaglifozin in Early Diabetic Kidney Disease
Diabetes is common among American Indian people and diabetic kidney disease is a common complication. Kidney disease caused by diabetes can lead to the need for kidney replacement, by dialysis or kidney transplant, and is also associated with higher risk of early death. A new diabetes medicine called empagliflozin may slow kidney disease from type 2 diabetes. Researchers want to learn if it protects the kidneys when used in very early stages of diabetic kidney disease.
ID: NCT03173963
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: Helen C Looker, [email protected]
Location: NIDDK, Phoenix, AZ
Family Investigation of Nephropathy and Diabetes
The Family Investigation of Nephropathy and Diabetes (FIND) is a multicenter study designed to identify genetic determinants of diabetic kidney disease. FIND will be conducted in 11 centers and in many ethnic groups throughout the United States. Two different strategies will be used to localize genes predisposing to kidney disease: a family-based genetic linkage study and a case-control study that utilizes admixture linkage disequilibrium. The center will conduct family-based linkage studies among American Indian populations in the southwestern United States.
ID: NCT00342927
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Contact: William C Knowler, MD, [email protected]
Location: NIDDK, Phoenix, AZ
Look AHEAD: Action for Health in Diabetes
The Look AHEAD study is a multi-center, randomized clinical trial to examine the long-term effects of a lifestyle intervention designed to achieve and maintain weight loss. The study will investigate the effects of the intervention on heart attacks, stroke and cardiovascular-related death in individuals with type 2 diabetes who are also overweight or obese.
ID: NCT00017953
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Location: Southwestern American Indian Center, Phoenix, AZ
Vitamin D and Type 2 Diabetes Study
The goal of the Vitamin D and type 2 diabetes (D2d) study is to determine if vitamin D supplementation works to delay the onset of type 2 diabetes in people at risk for the disease and to gain a better understand how vitamin D affects glucose (sugar) metabolism.
ID: NCT01942694
Sponsor: Tufts Medical Center
Locations: Southwest American Indian Center; Phoenix, AZ; Orlando VA Medical Center, FL; Atlanta VA Medical Center, Decatur, GA; Omaha VA Medical Center, NE
Reducing Diabetes Risk Factors in American Indian Children: Tribal Turning Point (TTP)
This study will evaluate a behavioral intervention designed to reduce risk factors for type 2 diabetes in American Indian youth aged 7-10 years.
ID: NCT03573856
Sponsor: University of Colorado, Denver
Contact: Katherine Sauder, PhD, [email protected]; Dana Dabelea, MD, PhD, [email protected]
Location: Childrens Hospital Colorado, Aurora
Strong Men, Strong Communities Diabetes Risk Reduction in American Indian Men (SMSC)
SMSC will inform the design and implementation of culturally informed, community-based lifestyle interventions for diabetes prevention in AI men in our partner communities and elsewhere, as well as in men of other minority groups who experience a heavy burden of diabetes.
ID: NCT02953977
Sponsor: Washington State University
Contact: Kaimi Sinclair, PhD, MPH, [email protected] Location: IREACH, Seattle, WA
Growing Resilience in Wind River Indian Reservation (GR)
The Growing Resilience research leverages reservation-based assets of land, family, culture, and front-line tribal health organizations to develop and evaluate home food gardens as a family-based health promotion intervention to reduce disparities suffered by Native Americans in nearly every measure of health. Home gardening interventions show great promise for enabling families to improve their health, and this study aims to fulfill that promise with university and Wind River Indian Reservation partners. The investigators will develop an empowering, scalable, and sustainable family-based health promotion intervention with, by, and for Native American families and conduct the first randomized controlled trial to assess the health impacts of home gardens.
ID: NCT02672748
Sponsor: University of Wyoming
Location: University of Wyoming, Laramie
A Comparative Effectiveness Study of Major Glycemia-lowering Medications for Treatment of Type 2 Diabetes (GRADE)
The GRADE Study is a pragmatic, unmasked clinical trial that will compare commonly used diabetes medications, when combined with metformin, on glycemia-lowering effectiveness and patient-centered outcomes.
ID: NCT01794143
Sponsor: GRADE Study Group
Location: Southwestern American Indian Center, Phoenix, AZ
Home-Based Kidney Care in Native Americans of New Mexico (HBKC)
New Mexico American Indians are experiencing an epidemic of chronic kidney disease due primarily to the high rates of obesity and diabetes. The present study entitled Home-Based Kidney Care is designed to delay / reduce rates of end stage renal disease by early interventions in chronic kidney disease (CKD). Investigators propose to assess the safety and efficacy of conducting a full-scale study to determine if home based care delivered by a collaborative team composed of community health workers, the Albuquerque Area Indian Health Board and University of New Mexico faculty will decrease the risk for the development and the progression of CKD.
ID: NCT03179085
Sponsor: University of New Mexico
Contact: Vallabh Shah, PhD, [email protected]; Kevin English, PhD, [email protected]
Location: University of New Mexico, Albuquerque
Home-based Prediabetes Care in Acoma Pueblo - Study 1
Our major goal of implementing educational interventions to slow the current rate of increase in diabetes in Native communities is aligned with the National Institute of Health (NIGMS) and New Mexico INBRE’s vision in reducing health disparity using innovative interventions. The investigators propose following aims: (1) Recruit and Screen 300 community members in Acoma Pueblo, New Mexico to identify incident cases of pre-diabetes for the proposed study of Home Based Diabetes Care (HBDC); (2) Enroll 150 Acoma Natives aged 21-70 years, at risk for type 2 diabetes mellitus and conduct HBDC for a 16-week lifestyle intervention in a longitudinal cohort study.
ID: NCT04029298
Sponsor: University of New Mexico
Contact: Matthew Bouchonville, MD, [email protected]; Vallabh Shah, PhD, [email protected]
Evaluating a Program Process Change to Improve Completion of Foot Exams and Amputation Risk Assessments for Veterans with Diabetes (FULL)
Individuals with diabetes mellitus (DM), peripheral vascular disease, or end-stage renal disease are at risk for a nontraumatic lower limb amputation.1 Veterans have a high number of risk factors and are especially vulnerable. More than 70% of veterans enrolled in US Department of Veterans Affairs (VA) healthcare are at increased risk for developing DM due to excess weight, poor eating habits, and physical inactivity.2 One in 4 veterans has DM, compared with 1 in 6 in the general population.2
DM can lead to long-term complications including limb amputations. Annually in the US about 73,000 nontraumatic lower limb amputations are performed and > 60% occur among persons with DM.3 Complications from diabetic wounds are the cause of 90% of lower limb amputations, and foot ulcers are the most prevalent complication.4 Diabetic ulcers are slow to heal due to vascular impairments and nerve damage.5 Peripheral vascular disease, a common comorbid condition, contributes to restricted blood flow and can lead to tissue death or gangrene requiring amputation.6
Between 2010 and 2014, VA Portland Healthcare System (VAPORHCS) had one of the highest national amputation rates in VA.7 A clinical chart review found that annual foot examinations and amputation risk assessments (ARAs) were not completed with all at-risk veterans. In 2013, a VA Office of Inspector General (OIG) national report found that more than one-third of veterans enrolled in VA with DM had no documentation of required annual foot exams.8 In 2017, VA released Directive 1410, which outlined the scope of care required to prevent and treat lower limb complications and amputations for veterans at risk for primary or secondary limb loss.1 This national initiative is a comprehensive approach that engages multiprofessional teams to perform routine foot examinations and amputation risk assessments; identify and promptly treat foot ulcers; track, monitor and educate at-risk veterans; and participate in clinical education to enhance staff skills.
To decrease the amputation rate, VAPORHCS redesigned its foot-care program to comply with the national initiative. As is typical in VA, VAPORHCS uses a team-based approach in primary care. The basic 4-member team patient-aligned care team (PACT) consists of a physician or nurse practitioner (NP) primary care provider (PCP), a registered nurse (RN) care manager, a licensed practical nurse (LPN), and a medical staff assistant (MSA) for administrative support. Each PACT cares for about 1,800 veterans. Formerly, LPNs completed the annual diabetic foot exams, and PCPs verified the exams and completed the ARA based on the LPNs’ findings. If patients were moderate risk or high risk, they were referred to podiatry. The VAPORHCS audit found that not all at-risk veterans had both the foot exam and ARA completed, or were referred to podiatry when indicated. There was a need for a process improvement project to develop a seamless program consisting of all recommended foot care components crucial for timely care.
This quality improvement project sought to evaluate the effectiveness of the process changes by examining PCPs’ adoption of, and consistency in completing annual diabetic foot exams and ARAs with veterans. The goals of the project were to evaluate changes in the: (1) Number of accurate diabetic foot exams and amputation risk assessments completed with veterans with DM; (2) Number and timeliness of appropriate referrals to podiatry for an in-depth assessment and treatment of veterans found to be at moderate-to-high risk for lower limb amputations; and (3) Number of administrative text orders entered by PCPs for nurse care managers to offer foot care education and the completion of the education with veterans found to be at normal-to-low risk for lower limb amputations. The institutional review boards of VAPORHCS and Gonzaga University approved the study.
Methods
Established by the American Diabetes Association and endorsed by the American Association of Clinical Endocrinologists, the comprehensive foot exam includes a visual exam, pedal pulse checks, and a sensory exam.9,10 The templated Computerized Patient Record System (CPRS) electronic health record note specifies normal and abnormal parameters of each section. On the same template, the provider assigns an ARA score based on the results of the completed foot exam. Risk scores range from 0 to 3 (0, normal or no risk; 1, low risk, 2; moderate risk; 3, high risk) If the veteran has normal or low risk, the PCP can encourage the veteran to remain at low risk by entering an administrative CPRS text order for the nurse care manager to offer education about daily foot care at the same visit or at a scheduled follow-up visit. This process facilitates nurse care managers to include routine foot care as integral to their usual duties coaching veterans to engage in self-care to manage chronic conditions. If the risk is assessed as moderate or high risk, PCPs are prompted to send a referral to podiatry to repeat the foot exam, verify the ARA score, and provide appropriate foot care treatment and follow-up.
On October 31, 2017, following training on the updated foot exam and ARA template with staff at the 13 VAPORHCS outpatient clinic sites, 2 sites piloted all components of the Comprehensive Foot Care program. An in-person training was completed with PCPs to review the changes of the foot care template in CPRS and to answer their questions about it. PCPs were required to complete both the 3-part foot exam and ARA at least once annually with veterans with DM.
An electronic clinical reminder was built to alert PCPs and PACTs that a veteran was either due or overdue for an exam and risk assessment. VA podiatrists agreed to complete the reminder with veterans under their care. One of the 2 sites was randomly selected for this study. Data were collected from August 1, 2017 to July 31, 2018. Patients were identified from the Diabetes Registry, a database established at VAPORHCS in 2008 to track veterans with DM to ensure quality care.11 Veterans’ personal health identifiers from the registry were used to access their health records to complete chart reviews and assess the completion, accuracy and timeliness of all foot care components.
The Diabetes Registry lists a veterans’ upcoming appointments and tracks their most recent clinic visits; laboratory tests; physical exams; and screening exams for foot, eye, and renal care. Newly diagnosed veterans are uploaded automatically into this registry by tracking all DM-related International Classification of Diseases (ICD-10) codes, hemoglobin A1c (HbA1c) levels ≥ 6.5%, or outpatient prescriptions for insulin or oral hypoglycemic agents.11
Study Design
This quality improvement project evaluated PCPs’ actions in a program process change intended to improve foot care provided with veterans at-risk for nontraumatic lower limb amputations. Audits of CPRS records and the Diabetes Registry determined the results of the practice change. Data on the total number of foot exams, amputation risk scores, appropriate podiatry referrals, administrative orders for nurse coaching, and completed foot care education were collected during the study period. Data collected for the 3-month period preceding the process change established preimplementation comparison vs the postimplementation data. Data were collected at 3, 6, and 9 months after implementation. The foot exams and ARAs were reviewed to determine whether exams and assessments were completed accurately during the pre- and post-implementation timeframes. Incomplete or clearly incorrectly completed documentation were considered inaccurate. For example, it was considered inaccurate if only the foot exam portion was completed in the assessment and the ARA was not.
Data Analysis
Data on the total number of accurately completed foot examinations and ARAs, total number of podiatry referrals, and total number of administrative text orders placed by PCPs, and education completed by nurse care managers were assessed. Statistical significance was evaluated using χ2 and Fisher exact test as appropriate. A Pearson correlation coefficient was used to determine whether there was a statistically significant increase in accurate foot examinations and ARAs as well as total number of podiatry referrals during the study period. Statistical analyses were performed using Stata 14.1 statistical software (College Station, TX).
Results
A total of 1,242 completed diabetic foot examinations were identified from August 1, 2017 to July 31, 2018 using the Diabetes Registry (Table). For the 3 months prior to the change, there were 191 appropriately completed foot examinations and ARAs. This number increased progressively over three 3-month periods (Figure 1). Within the 1-year study period, there was a statistically significant increase in the number of appropriate foot examinations (r = 0.495). PCPs placed 34 podiatry referrals during the prechange period. After the change, the number of appropriate referrals increased statistically significantly in the 3 following 3-month-periods (r = 0.222) (Figure 2).
To determine the accuracy of documentation and ratio of appropriate referrals, the 3-month prech
Notably, at the end of the first year of this evaluation, 119 veterans at the clinic did not show a recorded comprehensive foot examination since receiving a DM diagnosis and 299 veterans were due for an annual examination—a 25.2% gap of veterans without the recommended progression of foot care services. Of those that previously had a recorded foot examination, 51 (17.0%) veterans were found to be ≥ 2 years overdue.
Discussion
DM management requires a comprehensive team-based approach to help monitor for associated complications. At the VA, PACTs are veterans’ initial and primary point of contact for chronic condition management. PACTs have regular opportunities to engage veterans in initial and follow-up care and appropriate self-care. PCPs are critical in placing referrals for specialized care promptly to prevent and minimize complications such as foot ulcers, and ultimately, lower limb amputations.9,10,12
When PCPs assume responsibility for the entire foot examination, they are able to identify problems early.1 Left untreated, foot wounds and ulcers have the potential to grow into serious infections.9 Early risk identification and management can lead to increased patient satisfaction, improved life expectancy, quality of life, and ultimately, lower healthcare costs.12
Multiple studies have shown the clinical importance of foot examinations in preventative care. In one study, researchers found that completing foot examinations, among other early interventions, increased life expectancy and reduced foot complications.13 Diabetic foot management programs involving screening and categorizing patients into low- and high-risk groups had a 47.4% decrease in the incidence of amputations and 37.8% decrease in hospital admissions.14 Amputations were found to be inversely correlated with multidisciplinary foot care programs with reduction of lower limb amputations at 2 years.15 The Centers for Disease Control and Prevention found that comprehensive foot care programs that include a foot examination, ARA, appropriate referrals to specialists, and foot-care education and preventative services can reduce lower limb amputation rates by 45% to 85%.16
With one of the highest amputation rates in VA, VAPORHCS needed an integrated approach to ensure that appropriate foot care occurred regularly with veterans with DM. Prior to the process change, LPNs completed foot examinations and PCPs completed the ARA. Separating these clinical services resulted in few veterans receiving an amputation risk score. Of those with scores, the lack of a standardized program protocol resulted in discrepancies between ARAs from patient to patient as health care providers did not have clear or enough information to select the correct score and make the appropriate referrals. Thus, veterans previously identified as at moderate or high risk also lacked podiatric follow-up care.
The new quality-driven process change corrected the documentation process to nationally accepted standards. The goal was to create a consistent template in the electronic health record for all health care providers. The new template simplifies the documentation process and clarifies the amputation risk score assignment, which allows for proper foot care management. The PCP completes the process from assessment through referral, removing gaps in care and improving efficiency. Although this change was initially met with resistance from PCPs, it led to a significant increase in the number of patients with accurately documented examinations. Similarly, the number of appropriate referrals significantly rose during the study period. The standardized documentation process resulted in improved accurate examinations and ARAs over the past year. The new program also resulted in an increased number of appropriate podiatry referrals for those identified to be at moderate or high risk. This elevation of care is crucial for veterans to receive frequent follow-up visits for preventative care and/or treatment, including surgical modalities to promote limb salvage.
Barriers
This project identified several barriers to the Comprehensive Foot Care program. One major barrier was health care provider resistance to using the new process. For example, VAPORHCS podiatrists are not using the new template with established patients, which requires PCPs to complete the clinical reminder template for quality performance, an additional burden unrelated to clinical care. PCPs that do complete the foot examination/ARA templated note use the podiatrist’s visit note without personally assessing the patient.
PCPs also have been resistant to entering administrative text orders for preventative foot care in normal- or low-risk veterans (4.6% overall), which has resulted in decreased patient education (3.9% overall). Education for normal-risk and low-risk patients is designed to engage veterans in self-care and prevent risk progression, critical to prevention.
It was found that PCPs often did not ask nurses to coach normal- or low-risk veterans on preventative foot care, as suggested by the low rates at which patients were offered education. This is an area we will target with future quality improvement efforts. All patients with DM should have general education about risk factors and appropriate management of them to decrease their risk for complications.9 Preventative foot care education is a critical resource to share with patients during health coaching opportunities to clarify misunderstandings and support change talk when patients are ambivalent or resistant to change. Individual or group-based nurse visits can facilitate better coaching for patients.
At the VA, coaching begins with a conversation about what matters most to the veteran, facilitating the development of a personalized plan based on patients’ values, needs, preferences and goals.9,10,12,17 Coaching allows nurses to assess veterans’ knowledge and willingness to engage in healthy habits; and identify additional resources to help them achieve their goals.
Limitations
There are many limitations to this short quality improvement analysis. For example, only 1 of 2 clinics that piloted the program change was evaluated. In addition, there are 11 other clinics that need additional in-depth education on the program change. Although this analysis was overwhelmingly positive, it may not be as successful at other clinic sites and may be subject to the Hawthorne effect—since the 2 piloted locations knew they were being observed for the quality improvement program and may have made an extra effort to be compliant.18 Additionally, we were unable to track the records of veterans receiving care through the VA Choice Program for this analysis resulting in a lack of documentation of completed diabetic foot examinations and a lack of internal referrals to VA podiatry.
Another major limitation of this project involved calculating the number of referrals placed to podiatry. On January 1, 2018, about halfway through the program evaluation, a national VA decision enabled veterans to self-refer to podiatry, which may have limited the number of podiatry referrals placed by PCPs. Finally, patients could refuse podiatry referrals. In the 9-month postimplementation period, 57 (64.8%) veterans declined podiatry referrals, according to their CPRS records.
Although, there was an improvement in the accuracy of diabetic foot examinations, ARAs, and appropriate podiatry referrals, the ultimate goal of reducing diabetic foot ulcers and lower limb amputations was not tracked due to the limited timeframe of this analysis. Tracking these endpoints with continuous plan-do-study-act cycles are needed for this ongoing quality improvement project.
Conclusion
The goal of the VAPORHCS Comprehensive Foot Care program is to provide veterans with a program that is predictable, easy and consistent to prevent and treat foot ulcers to reduce the rate of lower limb amputations. It requires multidisciplinary team collaboration for success. Implementation of this new comprehensive program has increased the number of accurate annual foot exams, ARAs and podiatry referrals. Despite these improvements, areas of future improvement include emphasizing patient education and ongoing provider compliance with annual assessments.
Author contributions
MHG proposed the program evaluation project idea. TVQ collected and analyzed the data and wrote the manuscript. MHG oversaw the project and edited the manuscript. TVQ is the guarantor of this project and takes responsibility for the contents of this journal article.
Acknowledgments
The authors thank Tyra Haebe, VAPORHCS Prevention of Amputation in Veterans Everywhere (PAVE) Manager, and the entire VAPORHCS PAVE committee for their support in this program evaluation project.
1. US Department of Veterans Affairs, Veterans Health Administration. VHA directive 1410, prevention of amputation in veterans everywhere (PAVE) program. http://vaww.medical surgical.va.gov/podiatry/docs/VHADirective_1410_PAVE.pdf. Published March 31, 2017. Accessed October 11, 2019.
2. US Department of Veterans Affairs. Close to 25 percent of VA patients have diabetes http://www.va.gov/health/NewsFeatures/20111115a.asp. Accessed 14 October 2017
3. Centers for Disease Control and Prevention. National diabetes statistics report, 2017: Estimates of Diabetes and Its Burden in the United States. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Accessed October 11, 2019.
4. Gibson LW, Abbas A: Limb salvage for veterans with diabetes: to care for him who has borne the battle. Crit Care Nurs Clin North Am. 2012;25(1):131-134
5. Boyko EJ, Monteiro-Soares M, Wheeler SGB. “Peripheral arterial disease, foot ulcers, lower extremity amputations, and diabetes.” In: Cowie CC, Casagrande SS, Menke A, et al, eds. Diabetes in America. 3rd ed. Bethesda, MD: National Institutes of Health Publication; 2017:20-21,20-34.
6. National Institute of Health, National Institute of Neurological Disorders and Stroke. Peripheral neuropathy fact sheet. https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Peripheral-Neuropathy-Fact-Sheet. Updated August 13, 2019. Accessed October 11, 2019.
7. US Department of Veterans Affairs, Veterans Health Administration, Support Services Center. Amputation cube, lower amputations 2015. http://vssc.med.va.gov/AlphaIndex. [Nonpublic source, not verified]
8. US Department of Veterans Affairs, Office of Inspector General. Healthcare inspection: Foot care for patients with diabetes and additional risk factors for amputation. https://www.va.gov/oig/pubs/VAOIG-11-00711-74.pdf. Published January 17, 2013. Accessed October 11, 2019.
9. American Diabetes Association. Standards of medical care in diabetes - 2017. Diabetes Care. 2017;40(suppl 1):1-142.
10. Boulton AJM, Armstrong DG, Albert SF, et al. Comprehensive foot examination and risk assessment: a report of the Task Force of the Foot Care Interest Group of the American Diabetes Association, with endorsement by the American Association of Clinical Endocrinologists. Diabetes Care. 2008;31(8):1679-1685.
11. Yang J, McConnachie J, Renfro R, Schreiner S, Tallett S, Winterbottom L. The diabetes registry and future panel management tool https://docplayer.net/19062632-The-diabetes-registry-and.html. Accessed October 11, 2019.
12. National Institute of Health, Centers for Disease Control and Prevention, the National Diabetes Education Program. Working together to manage diabetes: a guide for pharmcy, podiatry, optometry, and dentistry. https://www.cdc.gov/diabetes/ndep/pdfs/ppod-guide.pdf. Accessed October 11, 2019.
13. Ortegon MM, Redekop WK, Niessen LW. Cost-effectiveness of prevention and treatment of the diabetic foot: a Markov analysis. Diabetes Care. 2004;27(4):901-907.
14. Lavery LA, Wunderlich RP, Tredwell JL. Disease management for the diabetic foot: effectiveness of a diabetic foot prevention program to reduce amputations and hospitalizations. Diabetes Res Clin Pract. 2005;70(1):31-37.
15. Paisey RB, Abbott A, Levenson R, et al; South-West Cardiovascular Strategic Clinical Network peer diabetic foot service review team. Diabetes-related major lower limb amputation incidence is strongly related to diabetic foot service provision and improves with enhancement of services: peer review of the south-west of England. Diabet Med. 2017;35(1):53-62.
16. Centers for Disease Control and Prevention. National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States, 2011. https://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf. Published 2011. Accessed October 11, 2019.
17. US Department of Veterans Affairs. Whole health for life. https://www.va.gov/patientcenteredcare/explore/about-whole-health.asp. Updated July 20, 2017. Accessed October 11, 2019.
18. Parsons HM. What happened at Hawthorne? New evidence suggests the Hawthorne effect resulted from operant reinforcement contingencies. Science. 1974;183(4128):922–9322.
Individuals with diabetes mellitus (DM), peripheral vascular disease, or end-stage renal disease are at risk for a nontraumatic lower limb amputation.1 Veterans have a high number of risk factors and are especially vulnerable. More than 70% of veterans enrolled in US Department of Veterans Affairs (VA) healthcare are at increased risk for developing DM due to excess weight, poor eating habits, and physical inactivity.2 One in 4 veterans has DM, compared with 1 in 6 in the general population.2
DM can lead to long-term complications including limb amputations. Annually in the US about 73,000 nontraumatic lower limb amputations are performed and > 60% occur among persons with DM.3 Complications from diabetic wounds are the cause of 90% of lower limb amputations, and foot ulcers are the most prevalent complication.4 Diabetic ulcers are slow to heal due to vascular impairments and nerve damage.5 Peripheral vascular disease, a common comorbid condition, contributes to restricted blood flow and can lead to tissue death or gangrene requiring amputation.6
Between 2010 and 2014, VA Portland Healthcare System (VAPORHCS) had one of the highest national amputation rates in VA.7 A clinical chart review found that annual foot examinations and amputation risk assessments (ARAs) were not completed with all at-risk veterans. In 2013, a VA Office of Inspector General (OIG) national report found that more than one-third of veterans enrolled in VA with DM had no documentation of required annual foot exams.8 In 2017, VA released Directive 1410, which outlined the scope of care required to prevent and treat lower limb complications and amputations for veterans at risk for primary or secondary limb loss.1 This national initiative is a comprehensive approach that engages multiprofessional teams to perform routine foot examinations and amputation risk assessments; identify and promptly treat foot ulcers; track, monitor and educate at-risk veterans; and participate in clinical education to enhance staff skills.
To decrease the amputation rate, VAPORHCS redesigned its foot-care program to comply with the national initiative. As is typical in VA, VAPORHCS uses a team-based approach in primary care. The basic 4-member team patient-aligned care team (PACT) consists of a physician or nurse practitioner (NP) primary care provider (PCP), a registered nurse (RN) care manager, a licensed practical nurse (LPN), and a medical staff assistant (MSA) for administrative support. Each PACT cares for about 1,800 veterans. Formerly, LPNs completed the annual diabetic foot exams, and PCPs verified the exams and completed the ARA based on the LPNs’ findings. If patients were moderate risk or high risk, they were referred to podiatry. The VAPORHCS audit found that not all at-risk veterans had both the foot exam and ARA completed, or were referred to podiatry when indicated. There was a need for a process improvement project to develop a seamless program consisting of all recommended foot care components crucial for timely care.
This quality improvement project sought to evaluate the effectiveness of the process changes by examining PCPs’ adoption of, and consistency in completing annual diabetic foot exams and ARAs with veterans. The goals of the project were to evaluate changes in the: (1) Number of accurate diabetic foot exams and amputation risk assessments completed with veterans with DM; (2) Number and timeliness of appropriate referrals to podiatry for an in-depth assessment and treatment of veterans found to be at moderate-to-high risk for lower limb amputations; and (3) Number of administrative text orders entered by PCPs for nurse care managers to offer foot care education and the completion of the education with veterans found to be at normal-to-low risk for lower limb amputations. The institutional review boards of VAPORHCS and Gonzaga University approved the study.
Methods
Established by the American Diabetes Association and endorsed by the American Association of Clinical Endocrinologists, the comprehensive foot exam includes a visual exam, pedal pulse checks, and a sensory exam.9,10 The templated Computerized Patient Record System (CPRS) electronic health record note specifies normal and abnormal parameters of each section. On the same template, the provider assigns an ARA score based on the results of the completed foot exam. Risk scores range from 0 to 3 (0, normal or no risk; 1, low risk, 2; moderate risk; 3, high risk) If the veteran has normal or low risk, the PCP can encourage the veteran to remain at low risk by entering an administrative CPRS text order for the nurse care manager to offer education about daily foot care at the same visit or at a scheduled follow-up visit. This process facilitates nurse care managers to include routine foot care as integral to their usual duties coaching veterans to engage in self-care to manage chronic conditions. If the risk is assessed as moderate or high risk, PCPs are prompted to send a referral to podiatry to repeat the foot exam, verify the ARA score, and provide appropriate foot care treatment and follow-up.
On October 31, 2017, following training on the updated foot exam and ARA template with staff at the 13 VAPORHCS outpatient clinic sites, 2 sites piloted all components of the Comprehensive Foot Care program. An in-person training was completed with PCPs to review the changes of the foot care template in CPRS and to answer their questions about it. PCPs were required to complete both the 3-part foot exam and ARA at least once annually with veterans with DM.
An electronic clinical reminder was built to alert PCPs and PACTs that a veteran was either due or overdue for an exam and risk assessment. VA podiatrists agreed to complete the reminder with veterans under their care. One of the 2 sites was randomly selected for this study. Data were collected from August 1, 2017 to July 31, 2018. Patients were identified from the Diabetes Registry, a database established at VAPORHCS in 2008 to track veterans with DM to ensure quality care.11 Veterans’ personal health identifiers from the registry were used to access their health records to complete chart reviews and assess the completion, accuracy and timeliness of all foot care components.
The Diabetes Registry lists a veterans’ upcoming appointments and tracks their most recent clinic visits; laboratory tests; physical exams; and screening exams for foot, eye, and renal care. Newly diagnosed veterans are uploaded automatically into this registry by tracking all DM-related International Classification of Diseases (ICD-10) codes, hemoglobin A1c (HbA1c) levels ≥ 6.5%, or outpatient prescriptions for insulin or oral hypoglycemic agents.11
Study Design
This quality improvement project evaluated PCPs’ actions in a program process change intended to improve foot care provided with veterans at-risk for nontraumatic lower limb amputations. Audits of CPRS records and the Diabetes Registry determined the results of the practice change. Data on the total number of foot exams, amputation risk scores, appropriate podiatry referrals, administrative orders for nurse coaching, and completed foot care education were collected during the study period. Data collected for the 3-month period preceding the process change established preimplementation comparison vs the postimplementation data. Data were collected at 3, 6, and 9 months after implementation. The foot exams and ARAs were reviewed to determine whether exams and assessments were completed accurately during the pre- and post-implementation timeframes. Incomplete or clearly incorrectly completed documentation were considered inaccurate. For example, it was considered inaccurate if only the foot exam portion was completed in the assessment and the ARA was not.
Data Analysis
Data on the total number of accurately completed foot examinations and ARAs, total number of podiatry referrals, and total number of administrative text orders placed by PCPs, and education completed by nurse care managers were assessed. Statistical significance was evaluated using χ2 and Fisher exact test as appropriate. A Pearson correlation coefficient was used to determine whether there was a statistically significant increase in accurate foot examinations and ARAs as well as total number of podiatry referrals during the study period. Statistical analyses were performed using Stata 14.1 statistical software (College Station, TX).
Results
A total of 1,242 completed diabetic foot examinations were identified from August 1, 2017 to July 31, 2018 using the Diabetes Registry (Table). For the 3 months prior to the change, there were 191 appropriately completed foot examinations and ARAs. This number increased progressively over three 3-month periods (Figure 1). Within the 1-year study period, there was a statistically significant increase in the number of appropriate foot examinations (r = 0.495). PCPs placed 34 podiatry referrals during the prechange period. After the change, the number of appropriate referrals increased statistically significantly in the 3 following 3-month-periods (r = 0.222) (Figure 2).
To determine the accuracy of documentation and ratio of appropriate referrals, the 3-month prech
Notably, at the end of the first year of this evaluation, 119 veterans at the clinic did not show a recorded comprehensive foot examination since receiving a DM diagnosis and 299 veterans were due for an annual examination—a 25.2% gap of veterans without the recommended progression of foot care services. Of those that previously had a recorded foot examination, 51 (17.0%) veterans were found to be ≥ 2 years overdue.
Discussion
DM management requires a comprehensive team-based approach to help monitor for associated complications. At the VA, PACTs are veterans’ initial and primary point of contact for chronic condition management. PACTs have regular opportunities to engage veterans in initial and follow-up care and appropriate self-care. PCPs are critical in placing referrals for specialized care promptly to prevent and minimize complications such as foot ulcers, and ultimately, lower limb amputations.9,10,12
When PCPs assume responsibility for the entire foot examination, they are able to identify problems early.1 Left untreated, foot wounds and ulcers have the potential to grow into serious infections.9 Early risk identification and management can lead to increased patient satisfaction, improved life expectancy, quality of life, and ultimately, lower healthcare costs.12
Multiple studies have shown the clinical importance of foot examinations in preventative care. In one study, researchers found that completing foot examinations, among other early interventions, increased life expectancy and reduced foot complications.13 Diabetic foot management programs involving screening and categorizing patients into low- and high-risk groups had a 47.4% decrease in the incidence of amputations and 37.8% decrease in hospital admissions.14 Amputations were found to be inversely correlated with multidisciplinary foot care programs with reduction of lower limb amputations at 2 years.15 The Centers for Disease Control and Prevention found that comprehensive foot care programs that include a foot examination, ARA, appropriate referrals to specialists, and foot-care education and preventative services can reduce lower limb amputation rates by 45% to 85%.16
With one of the highest amputation rates in VA, VAPORHCS needed an integrated approach to ensure that appropriate foot care occurred regularly with veterans with DM. Prior to the process change, LPNs completed foot examinations and PCPs completed the ARA. Separating these clinical services resulted in few veterans receiving an amputation risk score. Of those with scores, the lack of a standardized program protocol resulted in discrepancies between ARAs from patient to patient as health care providers did not have clear or enough information to select the correct score and make the appropriate referrals. Thus, veterans previously identified as at moderate or high risk also lacked podiatric follow-up care.
The new quality-driven process change corrected the documentation process to nationally accepted standards. The goal was to create a consistent template in the electronic health record for all health care providers. The new template simplifies the documentation process and clarifies the amputation risk score assignment, which allows for proper foot care management. The PCP completes the process from assessment through referral, removing gaps in care and improving efficiency. Although this change was initially met with resistance from PCPs, it led to a significant increase in the number of patients with accurately documented examinations. Similarly, the number of appropriate referrals significantly rose during the study period. The standardized documentation process resulted in improved accurate examinations and ARAs over the past year. The new program also resulted in an increased number of appropriate podiatry referrals for those identified to be at moderate or high risk. This elevation of care is crucial for veterans to receive frequent follow-up visits for preventative care and/or treatment, including surgical modalities to promote limb salvage.
Barriers
This project identified several barriers to the Comprehensive Foot Care program. One major barrier was health care provider resistance to using the new process. For example, VAPORHCS podiatrists are not using the new template with established patients, which requires PCPs to complete the clinical reminder template for quality performance, an additional burden unrelated to clinical care. PCPs that do complete the foot examination/ARA templated note use the podiatrist’s visit note without personally assessing the patient.
PCPs also have been resistant to entering administrative text orders for preventative foot care in normal- or low-risk veterans (4.6% overall), which has resulted in decreased patient education (3.9% overall). Education for normal-risk and low-risk patients is designed to engage veterans in self-care and prevent risk progression, critical to prevention.
It was found that PCPs often did not ask nurses to coach normal- or low-risk veterans on preventative foot care, as suggested by the low rates at which patients were offered education. This is an area we will target with future quality improvement efforts. All patients with DM should have general education about risk factors and appropriate management of them to decrease their risk for complications.9 Preventative foot care education is a critical resource to share with patients during health coaching opportunities to clarify misunderstandings and support change talk when patients are ambivalent or resistant to change. Individual or group-based nurse visits can facilitate better coaching for patients.
At the VA, coaching begins with a conversation about what matters most to the veteran, facilitating the development of a personalized plan based on patients’ values, needs, preferences and goals.9,10,12,17 Coaching allows nurses to assess veterans’ knowledge and willingness to engage in healthy habits; and identify additional resources to help them achieve their goals.
Limitations
There are many limitations to this short quality improvement analysis. For example, only 1 of 2 clinics that piloted the program change was evaluated. In addition, there are 11 other clinics that need additional in-depth education on the program change. Although this analysis was overwhelmingly positive, it may not be as successful at other clinic sites and may be subject to the Hawthorne effect—since the 2 piloted locations knew they were being observed for the quality improvement program and may have made an extra effort to be compliant.18 Additionally, we were unable to track the records of veterans receiving care through the VA Choice Program for this analysis resulting in a lack of documentation of completed diabetic foot examinations and a lack of internal referrals to VA podiatry.
Another major limitation of this project involved calculating the number of referrals placed to podiatry. On January 1, 2018, about halfway through the program evaluation, a national VA decision enabled veterans to self-refer to podiatry, which may have limited the number of podiatry referrals placed by PCPs. Finally, patients could refuse podiatry referrals. In the 9-month postimplementation period, 57 (64.8%) veterans declined podiatry referrals, according to their CPRS records.
Although, there was an improvement in the accuracy of diabetic foot examinations, ARAs, and appropriate podiatry referrals, the ultimate goal of reducing diabetic foot ulcers and lower limb amputations was not tracked due to the limited timeframe of this analysis. Tracking these endpoints with continuous plan-do-study-act cycles are needed for this ongoing quality improvement project.
Conclusion
The goal of the VAPORHCS Comprehensive Foot Care program is to provide veterans with a program that is predictable, easy and consistent to prevent and treat foot ulcers to reduce the rate of lower limb amputations. It requires multidisciplinary team collaboration for success. Implementation of this new comprehensive program has increased the number of accurate annual foot exams, ARAs and podiatry referrals. Despite these improvements, areas of future improvement include emphasizing patient education and ongoing provider compliance with annual assessments.
Author contributions
MHG proposed the program evaluation project idea. TVQ collected and analyzed the data and wrote the manuscript. MHG oversaw the project and edited the manuscript. TVQ is the guarantor of this project and takes responsibility for the contents of this journal article.
Acknowledgments
The authors thank Tyra Haebe, VAPORHCS Prevention of Amputation in Veterans Everywhere (PAVE) Manager, and the entire VAPORHCS PAVE committee for their support in this program evaluation project.
Individuals with diabetes mellitus (DM), peripheral vascular disease, or end-stage renal disease are at risk for a nontraumatic lower limb amputation.1 Veterans have a high number of risk factors and are especially vulnerable. More than 70% of veterans enrolled in US Department of Veterans Affairs (VA) healthcare are at increased risk for developing DM due to excess weight, poor eating habits, and physical inactivity.2 One in 4 veterans has DM, compared with 1 in 6 in the general population.2
DM can lead to long-term complications including limb amputations. Annually in the US about 73,000 nontraumatic lower limb amputations are performed and > 60% occur among persons with DM.3 Complications from diabetic wounds are the cause of 90% of lower limb amputations, and foot ulcers are the most prevalent complication.4 Diabetic ulcers are slow to heal due to vascular impairments and nerve damage.5 Peripheral vascular disease, a common comorbid condition, contributes to restricted blood flow and can lead to tissue death or gangrene requiring amputation.6
Between 2010 and 2014, VA Portland Healthcare System (VAPORHCS) had one of the highest national amputation rates in VA.7 A clinical chart review found that annual foot examinations and amputation risk assessments (ARAs) were not completed with all at-risk veterans. In 2013, a VA Office of Inspector General (OIG) national report found that more than one-third of veterans enrolled in VA with DM had no documentation of required annual foot exams.8 In 2017, VA released Directive 1410, which outlined the scope of care required to prevent and treat lower limb complications and amputations for veterans at risk for primary or secondary limb loss.1 This national initiative is a comprehensive approach that engages multiprofessional teams to perform routine foot examinations and amputation risk assessments; identify and promptly treat foot ulcers; track, monitor and educate at-risk veterans; and participate in clinical education to enhance staff skills.
To decrease the amputation rate, VAPORHCS redesigned its foot-care program to comply with the national initiative. As is typical in VA, VAPORHCS uses a team-based approach in primary care. The basic 4-member team patient-aligned care team (PACT) consists of a physician or nurse practitioner (NP) primary care provider (PCP), a registered nurse (RN) care manager, a licensed practical nurse (LPN), and a medical staff assistant (MSA) for administrative support. Each PACT cares for about 1,800 veterans. Formerly, LPNs completed the annual diabetic foot exams, and PCPs verified the exams and completed the ARA based on the LPNs’ findings. If patients were moderate risk or high risk, they were referred to podiatry. The VAPORHCS audit found that not all at-risk veterans had both the foot exam and ARA completed, or were referred to podiatry when indicated. There was a need for a process improvement project to develop a seamless program consisting of all recommended foot care components crucial for timely care.
This quality improvement project sought to evaluate the effectiveness of the process changes by examining PCPs’ adoption of, and consistency in completing annual diabetic foot exams and ARAs with veterans. The goals of the project were to evaluate changes in the: (1) Number of accurate diabetic foot exams and amputation risk assessments completed with veterans with DM; (2) Number and timeliness of appropriate referrals to podiatry for an in-depth assessment and treatment of veterans found to be at moderate-to-high risk for lower limb amputations; and (3) Number of administrative text orders entered by PCPs for nurse care managers to offer foot care education and the completion of the education with veterans found to be at normal-to-low risk for lower limb amputations. The institutional review boards of VAPORHCS and Gonzaga University approved the study.
Methods
Established by the American Diabetes Association and endorsed by the American Association of Clinical Endocrinologists, the comprehensive foot exam includes a visual exam, pedal pulse checks, and a sensory exam.9,10 The templated Computerized Patient Record System (CPRS) electronic health record note specifies normal and abnormal parameters of each section. On the same template, the provider assigns an ARA score based on the results of the completed foot exam. Risk scores range from 0 to 3 (0, normal or no risk; 1, low risk, 2; moderate risk; 3, high risk) If the veteran has normal or low risk, the PCP can encourage the veteran to remain at low risk by entering an administrative CPRS text order for the nurse care manager to offer education about daily foot care at the same visit or at a scheduled follow-up visit. This process facilitates nurse care managers to include routine foot care as integral to their usual duties coaching veterans to engage in self-care to manage chronic conditions. If the risk is assessed as moderate or high risk, PCPs are prompted to send a referral to podiatry to repeat the foot exam, verify the ARA score, and provide appropriate foot care treatment and follow-up.
On October 31, 2017, following training on the updated foot exam and ARA template with staff at the 13 VAPORHCS outpatient clinic sites, 2 sites piloted all components of the Comprehensive Foot Care program. An in-person training was completed with PCPs to review the changes of the foot care template in CPRS and to answer their questions about it. PCPs were required to complete both the 3-part foot exam and ARA at least once annually with veterans with DM.
An electronic clinical reminder was built to alert PCPs and PACTs that a veteran was either due or overdue for an exam and risk assessment. VA podiatrists agreed to complete the reminder with veterans under their care. One of the 2 sites was randomly selected for this study. Data were collected from August 1, 2017 to July 31, 2018. Patients were identified from the Diabetes Registry, a database established at VAPORHCS in 2008 to track veterans with DM to ensure quality care.11 Veterans’ personal health identifiers from the registry were used to access their health records to complete chart reviews and assess the completion, accuracy and timeliness of all foot care components.
The Diabetes Registry lists a veterans’ upcoming appointments and tracks their most recent clinic visits; laboratory tests; physical exams; and screening exams for foot, eye, and renal care. Newly diagnosed veterans are uploaded automatically into this registry by tracking all DM-related International Classification of Diseases (ICD-10) codes, hemoglobin A1c (HbA1c) levels ≥ 6.5%, or outpatient prescriptions for insulin or oral hypoglycemic agents.11
Study Design
This quality improvement project evaluated PCPs’ actions in a program process change intended to improve foot care provided with veterans at-risk for nontraumatic lower limb amputations. Audits of CPRS records and the Diabetes Registry determined the results of the practice change. Data on the total number of foot exams, amputation risk scores, appropriate podiatry referrals, administrative orders for nurse coaching, and completed foot care education were collected during the study period. Data collected for the 3-month period preceding the process change established preimplementation comparison vs the postimplementation data. Data were collected at 3, 6, and 9 months after implementation. The foot exams and ARAs were reviewed to determine whether exams and assessments were completed accurately during the pre- and post-implementation timeframes. Incomplete or clearly incorrectly completed documentation were considered inaccurate. For example, it was considered inaccurate if only the foot exam portion was completed in the assessment and the ARA was not.
Data Analysis
Data on the total number of accurately completed foot examinations and ARAs, total number of podiatry referrals, and total number of administrative text orders placed by PCPs, and education completed by nurse care managers were assessed. Statistical significance was evaluated using χ2 and Fisher exact test as appropriate. A Pearson correlation coefficient was used to determine whether there was a statistically significant increase in accurate foot examinations and ARAs as well as total number of podiatry referrals during the study period. Statistical analyses were performed using Stata 14.1 statistical software (College Station, TX).
Results
A total of 1,242 completed diabetic foot examinations were identified from August 1, 2017 to July 31, 2018 using the Diabetes Registry (Table). For the 3 months prior to the change, there were 191 appropriately completed foot examinations and ARAs. This number increased progressively over three 3-month periods (Figure 1). Within the 1-year study period, there was a statistically significant increase in the number of appropriate foot examinations (r = 0.495). PCPs placed 34 podiatry referrals during the prechange period. After the change, the number of appropriate referrals increased statistically significantly in the 3 following 3-month-periods (r = 0.222) (Figure 2).
To determine the accuracy of documentation and ratio of appropriate referrals, the 3-month prech
Notably, at the end of the first year of this evaluation, 119 veterans at the clinic did not show a recorded comprehensive foot examination since receiving a DM diagnosis and 299 veterans were due for an annual examination—a 25.2% gap of veterans without the recommended progression of foot care services. Of those that previously had a recorded foot examination, 51 (17.0%) veterans were found to be ≥ 2 years overdue.
Discussion
DM management requires a comprehensive team-based approach to help monitor for associated complications. At the VA, PACTs are veterans’ initial and primary point of contact for chronic condition management. PACTs have regular opportunities to engage veterans in initial and follow-up care and appropriate self-care. PCPs are critical in placing referrals for specialized care promptly to prevent and minimize complications such as foot ulcers, and ultimately, lower limb amputations.9,10,12
When PCPs assume responsibility for the entire foot examination, they are able to identify problems early.1 Left untreated, foot wounds and ulcers have the potential to grow into serious infections.9 Early risk identification and management can lead to increased patient satisfaction, improved life expectancy, quality of life, and ultimately, lower healthcare costs.12
Multiple studies have shown the clinical importance of foot examinations in preventative care. In one study, researchers found that completing foot examinations, among other early interventions, increased life expectancy and reduced foot complications.13 Diabetic foot management programs involving screening and categorizing patients into low- and high-risk groups had a 47.4% decrease in the incidence of amputations and 37.8% decrease in hospital admissions.14 Amputations were found to be inversely correlated with multidisciplinary foot care programs with reduction of lower limb amputations at 2 years.15 The Centers for Disease Control and Prevention found that comprehensive foot care programs that include a foot examination, ARA, appropriate referrals to specialists, and foot-care education and preventative services can reduce lower limb amputation rates by 45% to 85%.16
With one of the highest amputation rates in VA, VAPORHCS needed an integrated approach to ensure that appropriate foot care occurred regularly with veterans with DM. Prior to the process change, LPNs completed foot examinations and PCPs completed the ARA. Separating these clinical services resulted in few veterans receiving an amputation risk score. Of those with scores, the lack of a standardized program protocol resulted in discrepancies between ARAs from patient to patient as health care providers did not have clear or enough information to select the correct score and make the appropriate referrals. Thus, veterans previously identified as at moderate or high risk also lacked podiatric follow-up care.
The new quality-driven process change corrected the documentation process to nationally accepted standards. The goal was to create a consistent template in the electronic health record for all health care providers. The new template simplifies the documentation process and clarifies the amputation risk score assignment, which allows for proper foot care management. The PCP completes the process from assessment through referral, removing gaps in care and improving efficiency. Although this change was initially met with resistance from PCPs, it led to a significant increase in the number of patients with accurately documented examinations. Similarly, the number of appropriate referrals significantly rose during the study period. The standardized documentation process resulted in improved accurate examinations and ARAs over the past year. The new program also resulted in an increased number of appropriate podiatry referrals for those identified to be at moderate or high risk. This elevation of care is crucial for veterans to receive frequent follow-up visits for preventative care and/or treatment, including surgical modalities to promote limb salvage.
Barriers
This project identified several barriers to the Comprehensive Foot Care program. One major barrier was health care provider resistance to using the new process. For example, VAPORHCS podiatrists are not using the new template with established patients, which requires PCPs to complete the clinical reminder template for quality performance, an additional burden unrelated to clinical care. PCPs that do complete the foot examination/ARA templated note use the podiatrist’s visit note without personally assessing the patient.
PCPs also have been resistant to entering administrative text orders for preventative foot care in normal- or low-risk veterans (4.6% overall), which has resulted in decreased patient education (3.9% overall). Education for normal-risk and low-risk patients is designed to engage veterans in self-care and prevent risk progression, critical to prevention.
It was found that PCPs often did not ask nurses to coach normal- or low-risk veterans on preventative foot care, as suggested by the low rates at which patients were offered education. This is an area we will target with future quality improvement efforts. All patients with DM should have general education about risk factors and appropriate management of them to decrease their risk for complications.9 Preventative foot care education is a critical resource to share with patients during health coaching opportunities to clarify misunderstandings and support change talk when patients are ambivalent or resistant to change. Individual or group-based nurse visits can facilitate better coaching for patients.
At the VA, coaching begins with a conversation about what matters most to the veteran, facilitating the development of a personalized plan based on patients’ values, needs, preferences and goals.9,10,12,17 Coaching allows nurses to assess veterans’ knowledge and willingness to engage in healthy habits; and identify additional resources to help them achieve their goals.
Limitations
There are many limitations to this short quality improvement analysis. For example, only 1 of 2 clinics that piloted the program change was evaluated. In addition, there are 11 other clinics that need additional in-depth education on the program change. Although this analysis was overwhelmingly positive, it may not be as successful at other clinic sites and may be subject to the Hawthorne effect—since the 2 piloted locations knew they were being observed for the quality improvement program and may have made an extra effort to be compliant.18 Additionally, we were unable to track the records of veterans receiving care through the VA Choice Program for this analysis resulting in a lack of documentation of completed diabetic foot examinations and a lack of internal referrals to VA podiatry.
Another major limitation of this project involved calculating the number of referrals placed to podiatry. On January 1, 2018, about halfway through the program evaluation, a national VA decision enabled veterans to self-refer to podiatry, which may have limited the number of podiatry referrals placed by PCPs. Finally, patients could refuse podiatry referrals. In the 9-month postimplementation period, 57 (64.8%) veterans declined podiatry referrals, according to their CPRS records.
Although, there was an improvement in the accuracy of diabetic foot examinations, ARAs, and appropriate podiatry referrals, the ultimate goal of reducing diabetic foot ulcers and lower limb amputations was not tracked due to the limited timeframe of this analysis. Tracking these endpoints with continuous plan-do-study-act cycles are needed for this ongoing quality improvement project.
Conclusion
The goal of the VAPORHCS Comprehensive Foot Care program is to provide veterans with a program that is predictable, easy and consistent to prevent and treat foot ulcers to reduce the rate of lower limb amputations. It requires multidisciplinary team collaboration for success. Implementation of this new comprehensive program has increased the number of accurate annual foot exams, ARAs and podiatry referrals. Despite these improvements, areas of future improvement include emphasizing patient education and ongoing provider compliance with annual assessments.
Author contributions
MHG proposed the program evaluation project idea. TVQ collected and analyzed the data and wrote the manuscript. MHG oversaw the project and edited the manuscript. TVQ is the guarantor of this project and takes responsibility for the contents of this journal article.
Acknowledgments
The authors thank Tyra Haebe, VAPORHCS Prevention of Amputation in Veterans Everywhere (PAVE) Manager, and the entire VAPORHCS PAVE committee for their support in this program evaluation project.
1. US Department of Veterans Affairs, Veterans Health Administration. VHA directive 1410, prevention of amputation in veterans everywhere (PAVE) program. http://vaww.medical surgical.va.gov/podiatry/docs/VHADirective_1410_PAVE.pdf. Published March 31, 2017. Accessed October 11, 2019.
2. US Department of Veterans Affairs. Close to 25 percent of VA patients have diabetes http://www.va.gov/health/NewsFeatures/20111115a.asp. Accessed 14 October 2017
3. Centers for Disease Control and Prevention. National diabetes statistics report, 2017: Estimates of Diabetes and Its Burden in the United States. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Accessed October 11, 2019.
4. Gibson LW, Abbas A: Limb salvage for veterans with diabetes: to care for him who has borne the battle. Crit Care Nurs Clin North Am. 2012;25(1):131-134
5. Boyko EJ, Monteiro-Soares M, Wheeler SGB. “Peripheral arterial disease, foot ulcers, lower extremity amputations, and diabetes.” In: Cowie CC, Casagrande SS, Menke A, et al, eds. Diabetes in America. 3rd ed. Bethesda, MD: National Institutes of Health Publication; 2017:20-21,20-34.
6. National Institute of Health, National Institute of Neurological Disorders and Stroke. Peripheral neuropathy fact sheet. https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Peripheral-Neuropathy-Fact-Sheet. Updated August 13, 2019. Accessed October 11, 2019.
7. US Department of Veterans Affairs, Veterans Health Administration, Support Services Center. Amputation cube, lower amputations 2015. http://vssc.med.va.gov/AlphaIndex. [Nonpublic source, not verified]
8. US Department of Veterans Affairs, Office of Inspector General. Healthcare inspection: Foot care for patients with diabetes and additional risk factors for amputation. https://www.va.gov/oig/pubs/VAOIG-11-00711-74.pdf. Published January 17, 2013. Accessed October 11, 2019.
9. American Diabetes Association. Standards of medical care in diabetes - 2017. Diabetes Care. 2017;40(suppl 1):1-142.
10. Boulton AJM, Armstrong DG, Albert SF, et al. Comprehensive foot examination and risk assessment: a report of the Task Force of the Foot Care Interest Group of the American Diabetes Association, with endorsement by the American Association of Clinical Endocrinologists. Diabetes Care. 2008;31(8):1679-1685.
11. Yang J, McConnachie J, Renfro R, Schreiner S, Tallett S, Winterbottom L. The diabetes registry and future panel management tool https://docplayer.net/19062632-The-diabetes-registry-and.html. Accessed October 11, 2019.
12. National Institute of Health, Centers for Disease Control and Prevention, the National Diabetes Education Program. Working together to manage diabetes: a guide for pharmcy, podiatry, optometry, and dentistry. https://www.cdc.gov/diabetes/ndep/pdfs/ppod-guide.pdf. Accessed October 11, 2019.
13. Ortegon MM, Redekop WK, Niessen LW. Cost-effectiveness of prevention and treatment of the diabetic foot: a Markov analysis. Diabetes Care. 2004;27(4):901-907.
14. Lavery LA, Wunderlich RP, Tredwell JL. Disease management for the diabetic foot: effectiveness of a diabetic foot prevention program to reduce amputations and hospitalizations. Diabetes Res Clin Pract. 2005;70(1):31-37.
15. Paisey RB, Abbott A, Levenson R, et al; South-West Cardiovascular Strategic Clinical Network peer diabetic foot service review team. Diabetes-related major lower limb amputation incidence is strongly related to diabetic foot service provision and improves with enhancement of services: peer review of the south-west of England. Diabet Med. 2017;35(1):53-62.
16. Centers for Disease Control and Prevention. National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States, 2011. https://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf. Published 2011. Accessed October 11, 2019.
17. US Department of Veterans Affairs. Whole health for life. https://www.va.gov/patientcenteredcare/explore/about-whole-health.asp. Updated July 20, 2017. Accessed October 11, 2019.
18. Parsons HM. What happened at Hawthorne? New evidence suggests the Hawthorne effect resulted from operant reinforcement contingencies. Science. 1974;183(4128):922–9322.
1. US Department of Veterans Affairs, Veterans Health Administration. VHA directive 1410, prevention of amputation in veterans everywhere (PAVE) program. http://vaww.medical surgical.va.gov/podiatry/docs/VHADirective_1410_PAVE.pdf. Published March 31, 2017. Accessed October 11, 2019.
2. US Department of Veterans Affairs. Close to 25 percent of VA patients have diabetes http://www.va.gov/health/NewsFeatures/20111115a.asp. Accessed 14 October 2017
3. Centers for Disease Control and Prevention. National diabetes statistics report, 2017: Estimates of Diabetes and Its Burden in the United States. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf. Accessed October 11, 2019.
4. Gibson LW, Abbas A: Limb salvage for veterans with diabetes: to care for him who has borne the battle. Crit Care Nurs Clin North Am. 2012;25(1):131-134
5. Boyko EJ, Monteiro-Soares M, Wheeler SGB. “Peripheral arterial disease, foot ulcers, lower extremity amputations, and diabetes.” In: Cowie CC, Casagrande SS, Menke A, et al, eds. Diabetes in America. 3rd ed. Bethesda, MD: National Institutes of Health Publication; 2017:20-21,20-34.
6. National Institute of Health, National Institute of Neurological Disorders and Stroke. Peripheral neuropathy fact sheet. https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Peripheral-Neuropathy-Fact-Sheet. Updated August 13, 2019. Accessed October 11, 2019.
7. US Department of Veterans Affairs, Veterans Health Administration, Support Services Center. Amputation cube, lower amputations 2015. http://vssc.med.va.gov/AlphaIndex. [Nonpublic source, not verified]
8. US Department of Veterans Affairs, Office of Inspector General. Healthcare inspection: Foot care for patients with diabetes and additional risk factors for amputation. https://www.va.gov/oig/pubs/VAOIG-11-00711-74.pdf. Published January 17, 2013. Accessed October 11, 2019.
9. American Diabetes Association. Standards of medical care in diabetes - 2017. Diabetes Care. 2017;40(suppl 1):1-142.
10. Boulton AJM, Armstrong DG, Albert SF, et al. Comprehensive foot examination and risk assessment: a report of the Task Force of the Foot Care Interest Group of the American Diabetes Association, with endorsement by the American Association of Clinical Endocrinologists. Diabetes Care. 2008;31(8):1679-1685.
11. Yang J, McConnachie J, Renfro R, Schreiner S, Tallett S, Winterbottom L. The diabetes registry and future panel management tool https://docplayer.net/19062632-The-diabetes-registry-and.html. Accessed October 11, 2019.
12. National Institute of Health, Centers for Disease Control and Prevention, the National Diabetes Education Program. Working together to manage diabetes: a guide for pharmcy, podiatry, optometry, and dentistry. https://www.cdc.gov/diabetes/ndep/pdfs/ppod-guide.pdf. Accessed October 11, 2019.
13. Ortegon MM, Redekop WK, Niessen LW. Cost-effectiveness of prevention and treatment of the diabetic foot: a Markov analysis. Diabetes Care. 2004;27(4):901-907.
14. Lavery LA, Wunderlich RP, Tredwell JL. Disease management for the diabetic foot: effectiveness of a diabetic foot prevention program to reduce amputations and hospitalizations. Diabetes Res Clin Pract. 2005;70(1):31-37.
15. Paisey RB, Abbott A, Levenson R, et al; South-West Cardiovascular Strategic Clinical Network peer diabetic foot service review team. Diabetes-related major lower limb amputation incidence is strongly related to diabetic foot service provision and improves with enhancement of services: peer review of the south-west of England. Diabet Med. 2017;35(1):53-62.
16. Centers for Disease Control and Prevention. National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States, 2011. https://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf. Published 2011. Accessed October 11, 2019.
17. US Department of Veterans Affairs. Whole health for life. https://www.va.gov/patientcenteredcare/explore/about-whole-health.asp. Updated July 20, 2017. Accessed October 11, 2019.
18. Parsons HM. What happened at Hawthorne? New evidence suggests the Hawthorne effect resulted from operant reinforcement contingencies. Science. 1974;183(4128):922–9322.
A National Survey of Veterans Affairs Medical Centers’ Cardiology Services (FULL)
The US Department of Veterans Affairs (VA) remains the largest integrated health care system in the US serving 9 million veterans. Two recent studies that compared 30-day mortality and readmission rates between VA and non-VA hospitals among older men with acute myocardial infarction (AMI), and heart failure (HF). The studies found that hospitalization at VA hospitals was associated with lower risk-standardized 30-day all-cause mortality rates for MI and HF when compared with hospitalization at non-VA hospitals.1,2
However, it is unknown whether the delivery of cardiovascular care is optimized in the VA system. For example, in comparisons between generalist-led hospitalized care for MI and HF, several studies have demonstrated that cardiology-led care has been associated with lower rates of mortality.3-5 Although data on the types of cardiac technology and use of cardiac procedures were described previously, we have not found detailed information on the types of inpatient cardiology services provided at VA medical centers nationwide.1,6,7 To develop further improvements in delivery of cardiovascular care within the VA, a better understanding of the types of resources that are currently available within the VA system must be made available. In this article, we present results of a national survey of cardiology services at VA facilities.
Methods
From February to March of 2017, we conducted a comprehensive nation-wide survey of all VA facilities to quantify the availability of cardiology services, excluding cardiothoracic surgical services. The survey questions are listed in the Appendix. The chief of medicine and the chief of cardiology were each e-mailed 3 times at every facility. If no response was received from a facility, we e-mailed the chief of staff 3 times. If there still was no response, the remaining facilities were contacted by phone and study authors (PE and WB) spoke to individuals directly regarding the structure of cardiology services at a facility. Responses were categorized by facility level of complexity. Complexity designation was determined by the VA Central Office (VACO)—level 1 facilities represent the most complex and level 3 facilities are the least complex. VACO also divides facility complexity into sublevels, for example level 1A facilities generally are associated with academic medical centers and provide the highest levels (tertiary or quaternary) of care.8
Results were coded according to a predetermined rubric for how cardiology services are structured (admitting service, consult service, inpatient, outpatient, other) and for how they were staffed (attending only, house staff, or advanced practice providers (APPs). After the first wave of surveys, 2 additional questions were added to the survey tool; these asked about employed vs contracted cardiologist and use of APPs. The results were tabulated and simple percentages calculated to express the prevalence of each structure and staffing model.
The study was reviewed and approved by the University of Utah/Salt Lake City VA Medical Center joint institutional review board and all authors completed human subjects research training.
Results
Study authors initially identified all 168 VA medical center facilities operating in 2017. Initial polling revealed that multiple facilities either were substations or had agreements for cardiology services from larger facilities, with 1 facility having 2 campuses with different levels of service at each. After adjusting for these nuances, the total number of potential respondents was 139. We obtained a response from 122 of the 139 facilities for an overall survey completion rate of 88%. Response rates varied by complexity level (Table 1). The survey received responses from all Level 1A and 1B facilities, 96% from Level 1C facilities; 83% (20/24) from level 2 facilities, and 62% (18/30) from level 3 facilities. (Please note that in the reference document providing detailed descriptions of the VA level of complexity has different numbers for each facility type given that there has been reassignments of the levels since our survey was completed.)8
We were specifically interested in inpatient cardiology services and whether facilities provided only consult services or inpatient services led by a cardiology attending. Having inpatient services does not exclude the availability of consult-liaison services (Table 2).
Higher complexity facilities (1A and 1B) were more likely to have dedicated, cardiology-led inpatient services, while lower complexity facilities relied on a cardiology consult service. Two-thirds of Level 3 facilities did not have inpatient cardiology services available.
Dedicated cardiovascular care unit (CCU) teams were the most common inpatient service provided, present in more than half of all Level 1 facilities and 83% of Level 1A facilities (Table 3). Cardiology-led floor teams were available in 45% and 33% of level 1A and 1B facilities, respectively, but were much less common in Level 1C and Levels 2 and 3 facilities (4%, 10%, 0%, respectively). Only 31% of Level 1 facilities had both a CCU team and a cardiology-led inpatient floor team. Inpatient consulting cardiologists were commonly available at Levels 1 and 2 facilities; however, only 33% of Level 3 facilities had inpatient consulting cardiologists.
Housestaff-managed inpatient services, teams consisting of, but not limited to, medical residents in training, led by a cardiology attending were present in 73% of Level 1 facilities. Interestingly, Level 1B facilities were more likely to have housestaff-led services than were Level 1A facilities (90% and 80% respectively). Inpatient advanced heart failure services were less common and available only in Level 1 facilities. We did not survey the specific details of the other (eg, led by a noncardiology attending physician) models of inpatient cardiology care provided.
Cardiac catheterization (including interventional cardiology and electrophysiology [EP]) services, varied considerably. Ninety percent of Level 1A facilities offered interventional services, compared with only 52% of Level 1C facilities offered interventions. EP services were divided into simple (device only) and complex (ablations). As noted, complex EP services were more common in more complex facilities; for example, 10% of Level 2 facilities offered device placement but none had advanced EP services.
Outpatient services were widely available. Most facilities offered outpatient consultative cardiology services, ranging from 95% (Level 1) to 89% (Level 3) and outpatient cardiology continuity clinics 99% (Level 1) to 72% (Level 3).
Regardless of level of complexity, > 80% of facilities employed cardiologists. Many also used contract cardiologists. No facility utilized only contracted cardiologists. Use of nurse practitioners (NPs) and physician assistants (PAs) to assist with managing inpatient services was relatively common, with 61% of Level 1 facilities using such services.
Discussion
Studies of patient outcomes for various conditions, including cardiac conditions, in the 1990s found that when compared with non-VA health-care systems, patient outcomes in the VA were less favorable.9 During the late 1990s, the VA embraced quality and safety initiatives that have continued to the present time.9,10 Recent studies have found that, in most (but not all) cases, VA patient outcomes are as good as, and in many cases better, than are non-VA patient outcomes.1,10,11 The exact changes that have improved care are not clear, though studies of other health care systems have considered variation in services and costs in relationship to morbidity and mortality outcomes.12-14 In the context of better patient outcomes in VA hospitals, the present study provides insight into the cardiology services available at VA facilities throughout the nation.
Limitations
While this study provides background information that may be useful in comparing cardiology services between VA and non-VA systems, drawing causal relationships may not be warranted. For example, while the literature generally supports the concept of inpatient cardiology services led by an attending cardiologist, a substantial numbers of VA inpatient facilities have not yet adopted this model.4-6 Even among more complex, level 1 facilities, we found that only 31% offered both an inpatient CCU and floor team service led by an attending cardiologist physician. Thus, 69% of Level 1 facilities reported caring for patients with a primary cardiology problem through a noncardiology admitting services (with access to a cardiology consultation service). Additional studies should be conducted that would evaluate patient outcomes in relationship to the types of services available at a given VA medical center. Patient outcomes in relationship to service provision between the VA and non-VA health care systems also are warranted.
This study is limited by its reliance on self-reporting. Although we believe that we reached respondents who were qualified to complete the survey, the accuracy of reporting was not independently validated. Further, we asked questions about the most frequent models of cardiology care but may not have captured more novel methods. In trying to keep the survey time to < 2 minutes, we did not explore other details of cardiology services, such as the availability of a dedicated pharmacist, nor more advanced procedures such as transcatheter aortic valve replacement. Additionally, the present study is a snapshot of cardiology services for a given period, and, as noted above, did not look at patient outcomes. Further research is needed to determine which service provided is most beneficial or feasible in improving patient outcomes, which includes examining the various models of inpatient cardiology-led services for optimal care delivery.
Conclusion
Cardiology services were widely available throughout the VA system. However, the types of services available varied considerably. Predictably, facilities that were more complex generally had more advanced services available. Providing a general overview of how cardiovascular care is being delivered currently across VA systems helps to identify areas for optimization within VA facilities of various complexities with initiatives such as implementation of cardiology-led inpatient services, which may be beneficial in improving patient care outcomes as demonstrated previously in other large healthcare systems.
Acknowledgments
This material is the result of work supported with resources and use of the facilities at the George E. Wahlen Salt Lake City VA Medical Center. We are grateful to all of those who responded to our survey, and the support of the facility leadership. We are thankful for Tasia M. Nash and Tammy Jackson who helped to organize the data, and to Leigh Eleazer for her help in the manuscript preparation and formatting.
1. Nuti SV, Qin L, Rumsfeld JS, et al. Association of admission to Veterans Affairs hospitals vs non-veterans affairs hospitals with mortality and readmission rates among older men hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2016;315(6):582-592.
2. Blay E Jr, DeLancey JO, Hewitt DB, Chung JW, Bilimoria KY. Initial public reporting of quality at Veterans Affairs vs non-Veterans Affairs hospitals. JAMA Intern Med. 2017;177(6):882-885.
3. Hartz A, James PA. A systematic review of studies comparing myocardial infarction mortality for generalists and specialists: lessons for research and health policy. J Am Board Fam Med. 2006;19(3):291-302.
4. Driscoll A, Meagher S, Kennedy R, et al. What is the impact of systems of care for heart failure on patients diagnosed with heart failure: a systematic review. BMC Cardiovasc Disord. 2016;16(1):195.
5. Mitchell P, Marle D, Donkor A, et al; National Heart Failure Audit Steering Group. National heart failure audit: April 2013-March 2014. https://www.nicor.org.uk/wp-content/uploads/2019/02/hfannual13-14-updated.pdf. Published 2014. Accessed October 8, 2019.6. Mirvis DM, Graney MJ. Variations in the use of cardiac procedures in the Veterans Health Administration. Am Heart J. 1999;137(4 pt 1):706-713.
7. Wright SM, Petersen LA, Daley J. Availability of cardiac technology: trends in procedure use and outcomes for patients with acute myocardial infarction. Med Care Res Rev. 1998;55(2):239-254.
8. US Department of Veterans Affairs. Summary of VHA Facility Complexity Model. https://www.vendorportal.ecms.va.gov. [Nonpublic source, not verified]
9. Jha AK, Perlin JB, Kizer KW, Dudley RA. Effect of the transformation of the Veterans Affairs Health Care System on the quality of care. N Engl J Med. 2003;348(22):2218-2227.
10. Atkins D, Clancy C. Advancing high performance in Veterans Affairs health care. JAMA. 2017;318(19):1927-1928.
11. O’Hanlon C, Huang C, Sloss E, et al. Comparing VA and non-VA quality of care: a systematic review. J Gen Intern Med. 2017;32(1):105-121.
12. Stukel TA; Lucas FL, Wennberg DE. Long-term outcomes of regional variations in intensity of invasive vs medical management of medicare patients with acute myocardial infarction. JAMA. 2005;293(11):1329-1337.
13. Krumholz HM, Chen J, Rathore SS, Wang Y, Radford MJ. Regional variation in the treatment and outcomes of myocardial infarction: investigating New England’s advantage. Am Heart J. 2003;146(2):242-249.
14. Petersen LA, Normand SL, Leape LL, McNeil BJ. Regionalization and the underuse of angiography in the Veterans Affairs Health Care System as compared with a fee-for-service system. N Engl J Med. 2003;348(22):2209-2217.
The US Department of Veterans Affairs (VA) remains the largest integrated health care system in the US serving 9 million veterans. Two recent studies that compared 30-day mortality and readmission rates between VA and non-VA hospitals among older men with acute myocardial infarction (AMI), and heart failure (HF). The studies found that hospitalization at VA hospitals was associated with lower risk-standardized 30-day all-cause mortality rates for MI and HF when compared with hospitalization at non-VA hospitals.1,2
However, it is unknown whether the delivery of cardiovascular care is optimized in the VA system. For example, in comparisons between generalist-led hospitalized care for MI and HF, several studies have demonstrated that cardiology-led care has been associated with lower rates of mortality.3-5 Although data on the types of cardiac technology and use of cardiac procedures were described previously, we have not found detailed information on the types of inpatient cardiology services provided at VA medical centers nationwide.1,6,7 To develop further improvements in delivery of cardiovascular care within the VA, a better understanding of the types of resources that are currently available within the VA system must be made available. In this article, we present results of a national survey of cardiology services at VA facilities.
Methods
From February to March of 2017, we conducted a comprehensive nation-wide survey of all VA facilities to quantify the availability of cardiology services, excluding cardiothoracic surgical services. The survey questions are listed in the Appendix. The chief of medicine and the chief of cardiology were each e-mailed 3 times at every facility. If no response was received from a facility, we e-mailed the chief of staff 3 times. If there still was no response, the remaining facilities were contacted by phone and study authors (PE and WB) spoke to individuals directly regarding the structure of cardiology services at a facility. Responses were categorized by facility level of complexity. Complexity designation was determined by the VA Central Office (VACO)—level 1 facilities represent the most complex and level 3 facilities are the least complex. VACO also divides facility complexity into sublevels, for example level 1A facilities generally are associated with academic medical centers and provide the highest levels (tertiary or quaternary) of care.8
Results were coded according to a predetermined rubric for how cardiology services are structured (admitting service, consult service, inpatient, outpatient, other) and for how they were staffed (attending only, house staff, or advanced practice providers (APPs). After the first wave of surveys, 2 additional questions were added to the survey tool; these asked about employed vs contracted cardiologist and use of APPs. The results were tabulated and simple percentages calculated to express the prevalence of each structure and staffing model.
The study was reviewed and approved by the University of Utah/Salt Lake City VA Medical Center joint institutional review board and all authors completed human subjects research training.
Results
Study authors initially identified all 168 VA medical center facilities operating in 2017. Initial polling revealed that multiple facilities either were substations or had agreements for cardiology services from larger facilities, with 1 facility having 2 campuses with different levels of service at each. After adjusting for these nuances, the total number of potential respondents was 139. We obtained a response from 122 of the 139 facilities for an overall survey completion rate of 88%. Response rates varied by complexity level (Table 1). The survey received responses from all Level 1A and 1B facilities, 96% from Level 1C facilities; 83% (20/24) from level 2 facilities, and 62% (18/30) from level 3 facilities. (Please note that in the reference document providing detailed descriptions of the VA level of complexity has different numbers for each facility type given that there has been reassignments of the levels since our survey was completed.)8
We were specifically interested in inpatient cardiology services and whether facilities provided only consult services or inpatient services led by a cardiology attending. Having inpatient services does not exclude the availability of consult-liaison services (Table 2).
Higher complexity facilities (1A and 1B) were more likely to have dedicated, cardiology-led inpatient services, while lower complexity facilities relied on a cardiology consult service. Two-thirds of Level 3 facilities did not have inpatient cardiology services available.
Dedicated cardiovascular care unit (CCU) teams were the most common inpatient service provided, present in more than half of all Level 1 facilities and 83% of Level 1A facilities (Table 3). Cardiology-led floor teams were available in 45% and 33% of level 1A and 1B facilities, respectively, but were much less common in Level 1C and Levels 2 and 3 facilities (4%, 10%, 0%, respectively). Only 31% of Level 1 facilities had both a CCU team and a cardiology-led inpatient floor team. Inpatient consulting cardiologists were commonly available at Levels 1 and 2 facilities; however, only 33% of Level 3 facilities had inpatient consulting cardiologists.
Housestaff-managed inpatient services, teams consisting of, but not limited to, medical residents in training, led by a cardiology attending were present in 73% of Level 1 facilities. Interestingly, Level 1B facilities were more likely to have housestaff-led services than were Level 1A facilities (90% and 80% respectively). Inpatient advanced heart failure services were less common and available only in Level 1 facilities. We did not survey the specific details of the other (eg, led by a noncardiology attending physician) models of inpatient cardiology care provided.
Cardiac catheterization (including interventional cardiology and electrophysiology [EP]) services, varied considerably. Ninety percent of Level 1A facilities offered interventional services, compared with only 52% of Level 1C facilities offered interventions. EP services were divided into simple (device only) and complex (ablations). As noted, complex EP services were more common in more complex facilities; for example, 10% of Level 2 facilities offered device placement but none had advanced EP services.
Outpatient services were widely available. Most facilities offered outpatient consultative cardiology services, ranging from 95% (Level 1) to 89% (Level 3) and outpatient cardiology continuity clinics 99% (Level 1) to 72% (Level 3).
Regardless of level of complexity, > 80% of facilities employed cardiologists. Many also used contract cardiologists. No facility utilized only contracted cardiologists. Use of nurse practitioners (NPs) and physician assistants (PAs) to assist with managing inpatient services was relatively common, with 61% of Level 1 facilities using such services.
Discussion
Studies of patient outcomes for various conditions, including cardiac conditions, in the 1990s found that when compared with non-VA health-care systems, patient outcomes in the VA were less favorable.9 During the late 1990s, the VA embraced quality and safety initiatives that have continued to the present time.9,10 Recent studies have found that, in most (but not all) cases, VA patient outcomes are as good as, and in many cases better, than are non-VA patient outcomes.1,10,11 The exact changes that have improved care are not clear, though studies of other health care systems have considered variation in services and costs in relationship to morbidity and mortality outcomes.12-14 In the context of better patient outcomes in VA hospitals, the present study provides insight into the cardiology services available at VA facilities throughout the nation.
Limitations
While this study provides background information that may be useful in comparing cardiology services between VA and non-VA systems, drawing causal relationships may not be warranted. For example, while the literature generally supports the concept of inpatient cardiology services led by an attending cardiologist, a substantial numbers of VA inpatient facilities have not yet adopted this model.4-6 Even among more complex, level 1 facilities, we found that only 31% offered both an inpatient CCU and floor team service led by an attending cardiologist physician. Thus, 69% of Level 1 facilities reported caring for patients with a primary cardiology problem through a noncardiology admitting services (with access to a cardiology consultation service). Additional studies should be conducted that would evaluate patient outcomes in relationship to the types of services available at a given VA medical center. Patient outcomes in relationship to service provision between the VA and non-VA health care systems also are warranted.
This study is limited by its reliance on self-reporting. Although we believe that we reached respondents who were qualified to complete the survey, the accuracy of reporting was not independently validated. Further, we asked questions about the most frequent models of cardiology care but may not have captured more novel methods. In trying to keep the survey time to < 2 minutes, we did not explore other details of cardiology services, such as the availability of a dedicated pharmacist, nor more advanced procedures such as transcatheter aortic valve replacement. Additionally, the present study is a snapshot of cardiology services for a given period, and, as noted above, did not look at patient outcomes. Further research is needed to determine which service provided is most beneficial or feasible in improving patient outcomes, which includes examining the various models of inpatient cardiology-led services for optimal care delivery.
Conclusion
Cardiology services were widely available throughout the VA system. However, the types of services available varied considerably. Predictably, facilities that were more complex generally had more advanced services available. Providing a general overview of how cardiovascular care is being delivered currently across VA systems helps to identify areas for optimization within VA facilities of various complexities with initiatives such as implementation of cardiology-led inpatient services, which may be beneficial in improving patient care outcomes as demonstrated previously in other large healthcare systems.
Acknowledgments
This material is the result of work supported with resources and use of the facilities at the George E. Wahlen Salt Lake City VA Medical Center. We are grateful to all of those who responded to our survey, and the support of the facility leadership. We are thankful for Tasia M. Nash and Tammy Jackson who helped to organize the data, and to Leigh Eleazer for her help in the manuscript preparation and formatting.
The US Department of Veterans Affairs (VA) remains the largest integrated health care system in the US serving 9 million veterans. Two recent studies that compared 30-day mortality and readmission rates between VA and non-VA hospitals among older men with acute myocardial infarction (AMI), and heart failure (HF). The studies found that hospitalization at VA hospitals was associated with lower risk-standardized 30-day all-cause mortality rates for MI and HF when compared with hospitalization at non-VA hospitals.1,2
However, it is unknown whether the delivery of cardiovascular care is optimized in the VA system. For example, in comparisons between generalist-led hospitalized care for MI and HF, several studies have demonstrated that cardiology-led care has been associated with lower rates of mortality.3-5 Although data on the types of cardiac technology and use of cardiac procedures were described previously, we have not found detailed information on the types of inpatient cardiology services provided at VA medical centers nationwide.1,6,7 To develop further improvements in delivery of cardiovascular care within the VA, a better understanding of the types of resources that are currently available within the VA system must be made available. In this article, we present results of a national survey of cardiology services at VA facilities.
Methods
From February to March of 2017, we conducted a comprehensive nation-wide survey of all VA facilities to quantify the availability of cardiology services, excluding cardiothoracic surgical services. The survey questions are listed in the Appendix. The chief of medicine and the chief of cardiology were each e-mailed 3 times at every facility. If no response was received from a facility, we e-mailed the chief of staff 3 times. If there still was no response, the remaining facilities were contacted by phone and study authors (PE and WB) spoke to individuals directly regarding the structure of cardiology services at a facility. Responses were categorized by facility level of complexity. Complexity designation was determined by the VA Central Office (VACO)—level 1 facilities represent the most complex and level 3 facilities are the least complex. VACO also divides facility complexity into sublevels, for example level 1A facilities generally are associated with academic medical centers and provide the highest levels (tertiary or quaternary) of care.8
Results were coded according to a predetermined rubric for how cardiology services are structured (admitting service, consult service, inpatient, outpatient, other) and for how they were staffed (attending only, house staff, or advanced practice providers (APPs). After the first wave of surveys, 2 additional questions were added to the survey tool; these asked about employed vs contracted cardiologist and use of APPs. The results were tabulated and simple percentages calculated to express the prevalence of each structure and staffing model.
The study was reviewed and approved by the University of Utah/Salt Lake City VA Medical Center joint institutional review board and all authors completed human subjects research training.
Results
Study authors initially identified all 168 VA medical center facilities operating in 2017. Initial polling revealed that multiple facilities either were substations or had agreements for cardiology services from larger facilities, with 1 facility having 2 campuses with different levels of service at each. After adjusting for these nuances, the total number of potential respondents was 139. We obtained a response from 122 of the 139 facilities for an overall survey completion rate of 88%. Response rates varied by complexity level (Table 1). The survey received responses from all Level 1A and 1B facilities, 96% from Level 1C facilities; 83% (20/24) from level 2 facilities, and 62% (18/30) from level 3 facilities. (Please note that in the reference document providing detailed descriptions of the VA level of complexity has different numbers for each facility type given that there has been reassignments of the levels since our survey was completed.)8
We were specifically interested in inpatient cardiology services and whether facilities provided only consult services or inpatient services led by a cardiology attending. Having inpatient services does not exclude the availability of consult-liaison services (Table 2).
Higher complexity facilities (1A and 1B) were more likely to have dedicated, cardiology-led inpatient services, while lower complexity facilities relied on a cardiology consult service. Two-thirds of Level 3 facilities did not have inpatient cardiology services available.
Dedicated cardiovascular care unit (CCU) teams were the most common inpatient service provided, present in more than half of all Level 1 facilities and 83% of Level 1A facilities (Table 3). Cardiology-led floor teams were available in 45% and 33% of level 1A and 1B facilities, respectively, but were much less common in Level 1C and Levels 2 and 3 facilities (4%, 10%, 0%, respectively). Only 31% of Level 1 facilities had both a CCU team and a cardiology-led inpatient floor team. Inpatient consulting cardiologists were commonly available at Levels 1 and 2 facilities; however, only 33% of Level 3 facilities had inpatient consulting cardiologists.
Housestaff-managed inpatient services, teams consisting of, but not limited to, medical residents in training, led by a cardiology attending were present in 73% of Level 1 facilities. Interestingly, Level 1B facilities were more likely to have housestaff-led services than were Level 1A facilities (90% and 80% respectively). Inpatient advanced heart failure services were less common and available only in Level 1 facilities. We did not survey the specific details of the other (eg, led by a noncardiology attending physician) models of inpatient cardiology care provided.
Cardiac catheterization (including interventional cardiology and electrophysiology [EP]) services, varied considerably. Ninety percent of Level 1A facilities offered interventional services, compared with only 52% of Level 1C facilities offered interventions. EP services were divided into simple (device only) and complex (ablations). As noted, complex EP services were more common in more complex facilities; for example, 10% of Level 2 facilities offered device placement but none had advanced EP services.
Outpatient services were widely available. Most facilities offered outpatient consultative cardiology services, ranging from 95% (Level 1) to 89% (Level 3) and outpatient cardiology continuity clinics 99% (Level 1) to 72% (Level 3).
Regardless of level of complexity, > 80% of facilities employed cardiologists. Many also used contract cardiologists. No facility utilized only contracted cardiologists. Use of nurse practitioners (NPs) and physician assistants (PAs) to assist with managing inpatient services was relatively common, with 61% of Level 1 facilities using such services.
Discussion
Studies of patient outcomes for various conditions, including cardiac conditions, in the 1990s found that when compared with non-VA health-care systems, patient outcomes in the VA were less favorable.9 During the late 1990s, the VA embraced quality and safety initiatives that have continued to the present time.9,10 Recent studies have found that, in most (but not all) cases, VA patient outcomes are as good as, and in many cases better, than are non-VA patient outcomes.1,10,11 The exact changes that have improved care are not clear, though studies of other health care systems have considered variation in services and costs in relationship to morbidity and mortality outcomes.12-14 In the context of better patient outcomes in VA hospitals, the present study provides insight into the cardiology services available at VA facilities throughout the nation.
Limitations
While this study provides background information that may be useful in comparing cardiology services between VA and non-VA systems, drawing causal relationships may not be warranted. For example, while the literature generally supports the concept of inpatient cardiology services led by an attending cardiologist, a substantial numbers of VA inpatient facilities have not yet adopted this model.4-6 Even among more complex, level 1 facilities, we found that only 31% offered both an inpatient CCU and floor team service led by an attending cardiologist physician. Thus, 69% of Level 1 facilities reported caring for patients with a primary cardiology problem through a noncardiology admitting services (with access to a cardiology consultation service). Additional studies should be conducted that would evaluate patient outcomes in relationship to the types of services available at a given VA medical center. Patient outcomes in relationship to service provision between the VA and non-VA health care systems also are warranted.
This study is limited by its reliance on self-reporting. Although we believe that we reached respondents who were qualified to complete the survey, the accuracy of reporting was not independently validated. Further, we asked questions about the most frequent models of cardiology care but may not have captured more novel methods. In trying to keep the survey time to < 2 minutes, we did not explore other details of cardiology services, such as the availability of a dedicated pharmacist, nor more advanced procedures such as transcatheter aortic valve replacement. Additionally, the present study is a snapshot of cardiology services for a given period, and, as noted above, did not look at patient outcomes. Further research is needed to determine which service provided is most beneficial or feasible in improving patient outcomes, which includes examining the various models of inpatient cardiology-led services for optimal care delivery.
Conclusion
Cardiology services were widely available throughout the VA system. However, the types of services available varied considerably. Predictably, facilities that were more complex generally had more advanced services available. Providing a general overview of how cardiovascular care is being delivered currently across VA systems helps to identify areas for optimization within VA facilities of various complexities with initiatives such as implementation of cardiology-led inpatient services, which may be beneficial in improving patient care outcomes as demonstrated previously in other large healthcare systems.
Acknowledgments
This material is the result of work supported with resources and use of the facilities at the George E. Wahlen Salt Lake City VA Medical Center. We are grateful to all of those who responded to our survey, and the support of the facility leadership. We are thankful for Tasia M. Nash and Tammy Jackson who helped to organize the data, and to Leigh Eleazer for her help in the manuscript preparation and formatting.
1. Nuti SV, Qin L, Rumsfeld JS, et al. Association of admission to Veterans Affairs hospitals vs non-veterans affairs hospitals with mortality and readmission rates among older men hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2016;315(6):582-592.
2. Blay E Jr, DeLancey JO, Hewitt DB, Chung JW, Bilimoria KY. Initial public reporting of quality at Veterans Affairs vs non-Veterans Affairs hospitals. JAMA Intern Med. 2017;177(6):882-885.
3. Hartz A, James PA. A systematic review of studies comparing myocardial infarction mortality for generalists and specialists: lessons for research and health policy. J Am Board Fam Med. 2006;19(3):291-302.
4. Driscoll A, Meagher S, Kennedy R, et al. What is the impact of systems of care for heart failure on patients diagnosed with heart failure: a systematic review. BMC Cardiovasc Disord. 2016;16(1):195.
5. Mitchell P, Marle D, Donkor A, et al; National Heart Failure Audit Steering Group. National heart failure audit: April 2013-March 2014. https://www.nicor.org.uk/wp-content/uploads/2019/02/hfannual13-14-updated.pdf. Published 2014. Accessed October 8, 2019.6. Mirvis DM, Graney MJ. Variations in the use of cardiac procedures in the Veterans Health Administration. Am Heart J. 1999;137(4 pt 1):706-713.
7. Wright SM, Petersen LA, Daley J. Availability of cardiac technology: trends in procedure use and outcomes for patients with acute myocardial infarction. Med Care Res Rev. 1998;55(2):239-254.
8. US Department of Veterans Affairs. Summary of VHA Facility Complexity Model. https://www.vendorportal.ecms.va.gov. [Nonpublic source, not verified]
9. Jha AK, Perlin JB, Kizer KW, Dudley RA. Effect of the transformation of the Veterans Affairs Health Care System on the quality of care. N Engl J Med. 2003;348(22):2218-2227.
10. Atkins D, Clancy C. Advancing high performance in Veterans Affairs health care. JAMA. 2017;318(19):1927-1928.
11. O’Hanlon C, Huang C, Sloss E, et al. Comparing VA and non-VA quality of care: a systematic review. J Gen Intern Med. 2017;32(1):105-121.
12. Stukel TA; Lucas FL, Wennberg DE. Long-term outcomes of regional variations in intensity of invasive vs medical management of medicare patients with acute myocardial infarction. JAMA. 2005;293(11):1329-1337.
13. Krumholz HM, Chen J, Rathore SS, Wang Y, Radford MJ. Regional variation in the treatment and outcomes of myocardial infarction: investigating New England’s advantage. Am Heart J. 2003;146(2):242-249.
14. Petersen LA, Normand SL, Leape LL, McNeil BJ. Regionalization and the underuse of angiography in the Veterans Affairs Health Care System as compared with a fee-for-service system. N Engl J Med. 2003;348(22):2209-2217.
1. Nuti SV, Qin L, Rumsfeld JS, et al. Association of admission to Veterans Affairs hospitals vs non-veterans affairs hospitals with mortality and readmission rates among older men hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2016;315(6):582-592.
2. Blay E Jr, DeLancey JO, Hewitt DB, Chung JW, Bilimoria KY. Initial public reporting of quality at Veterans Affairs vs non-Veterans Affairs hospitals. JAMA Intern Med. 2017;177(6):882-885.
3. Hartz A, James PA. A systematic review of studies comparing myocardial infarction mortality for generalists and specialists: lessons for research and health policy. J Am Board Fam Med. 2006;19(3):291-302.
4. Driscoll A, Meagher S, Kennedy R, et al. What is the impact of systems of care for heart failure on patients diagnosed with heart failure: a systematic review. BMC Cardiovasc Disord. 2016;16(1):195.
5. Mitchell P, Marle D, Donkor A, et al; National Heart Failure Audit Steering Group. National heart failure audit: April 2013-March 2014. https://www.nicor.org.uk/wp-content/uploads/2019/02/hfannual13-14-updated.pdf. Published 2014. Accessed October 8, 2019.6. Mirvis DM, Graney MJ. Variations in the use of cardiac procedures in the Veterans Health Administration. Am Heart J. 1999;137(4 pt 1):706-713.
7. Wright SM, Petersen LA, Daley J. Availability of cardiac technology: trends in procedure use and outcomes for patients with acute myocardial infarction. Med Care Res Rev. 1998;55(2):239-254.
8. US Department of Veterans Affairs. Summary of VHA Facility Complexity Model. https://www.vendorportal.ecms.va.gov. [Nonpublic source, not verified]
9. Jha AK, Perlin JB, Kizer KW, Dudley RA. Effect of the transformation of the Veterans Affairs Health Care System on the quality of care. N Engl J Med. 2003;348(22):2218-2227.
10. Atkins D, Clancy C. Advancing high performance in Veterans Affairs health care. JAMA. 2017;318(19):1927-1928.
11. O’Hanlon C, Huang C, Sloss E, et al. Comparing VA and non-VA quality of care: a systematic review. J Gen Intern Med. 2017;32(1):105-121.
12. Stukel TA; Lucas FL, Wennberg DE. Long-term outcomes of regional variations in intensity of invasive vs medical management of medicare patients with acute myocardial infarction. JAMA. 2005;293(11):1329-1337.
13. Krumholz HM, Chen J, Rathore SS, Wang Y, Radford MJ. Regional variation in the treatment and outcomes of myocardial infarction: investigating New England’s advantage. Am Heart J. 2003;146(2):242-249.
14. Petersen LA, Normand SL, Leape LL, McNeil BJ. Regionalization and the underuse of angiography in the Veterans Affairs Health Care System as compared with a fee-for-service system. N Engl J Med. 2003;348(22):2209-2217.
A Health Care Provider Intervention to Address Obesity in Patients with Diabetes (FULL)
Obesity is associated with a significant increase in mortality. It increases the risk of type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, and coronary artery disease.1 T2DM is strongly associated with obesity in all ethnic groups.
Medical nutrition therapy and weight loss are very important for DM management.2 This includes providing education about diet modification, increased physical activity, daily calorie intake evaluation, and consistent carbohydrate intake. For patients with T2DM, health care providers (HCPs) should emphasize lowering caloric intake and inducing weight loss for those who are overweight (body mass index [BMI] between 25 and 29.9) and obese (BMI ≥ 30). This can improve glycemic control by decreasing insulin resistance. Initial recommendations for weight loss and physical activity are to lose between 5% and 10% of initial body weight and to accumulate at least 30 minutes of moderate physical activity over the course of most days of the week.3,4
Several formulas are available to estimate baseline caloric intake for weight maintenance. For weight loss of 1 to 2 pounds per week, lowering 500 to 1,000 calories from daily weight maintenance calories serves the goal. The American Diabetes Association (ADA) also suggests that HCPs recommend diet, physical activity, and behavioral therapy designed to achieve > 5% weight loss to overweight and obese patients with T2DM.5
Recognizing the clinical benefits of achieving weight loss in overweight or obese patients with T2DM, we aimed to increase the number of visits in the Endocrine Clinic at Central Arkansas Veterans Healthcare System (CAVHS) in Little Rock that addressed obesity, documented calorie goal for patients who are overweight or obese, and performed an intervention with further education for the patient.
Methods
The study population included veterans with either type 1 DM (T1DM) or T2DM with BMI > 25 on any DM control regimen. We performed a health record review of the eligible patients seen in the CAVHS Endocrine Clinic from June 1, 2016 to July 31, 2016 to determine the baseline percentage of visits that addressed obesity and provided weight loss advice to patients. We obtained a list of patients seen in the clinic during the study period from Strategic Management Service Services at CAVHS. We also obtained information that age, gender, medications, BMI, and last Endocrine clinic HCP assessment from the electronic health record. We reviewed the HCPs notes, including fellows and faculty who were involved in the patients’ treatment, to determine whether their notes documented a BMI > 25 and whether they discussed an intervention for overweight or obesity with the patient. The CAVHS Institutional Review Board reviewed and approved the initiative as a quality improvement study.
Intervention
Our clinic has a defined group of HCPs that we targeted for the intervention. After getting baseline information, during August 2017 we educated these HCPs on the tools available to calculate calorie goal for the patients. We advised the HCPs to use the Mifflin St Jyor equation for estimating energy expenditure and set a goal of initial weight loss between 5% and 7% of body weight. We gave specific instructions and advice to the providers (Table 1). HCPs also received educational material to distribute to patients that provided information on the healthy plate method, discussed how to count calories, and advised them on ADA goals with carbohydrate limitation. We encouraged HCPs to recommend that patients cut between 500 and 1,000 calories daily from their current diet. HCPs also received advice to seek help from clinical dieticians and the VA MOVE! Weight Management Program when appropriate.
Study of Effect of the Intervention
To study the effect of this intervention, we reviewed documentation by HCPs and assessed patient satisfaction. We obtained a list of patients and reviewed HCP notes on patients with BMI > 25 to assess whether providers addressed obesity in November and December 2017. We also evaluated whether HCPs offered a specific intervention to address the problem, such as providing education material to the patient or an estimate of daily calorie goal, or referring them to clinical dietician and/or the MOVE program. Patients received a 5-question survey that assessed their understanding and satisfaction at the end of the visit (Table 2).
Results
Of the 100 charts reviewed prior to intervention, HCPs discussed obesity management with only 6% of patients. After the intervention, we collected data again through chart review of the patients who were overweight or obese and seen for DM in the same clinic during a 2-month period. Of the 100 charts reviewed, we noticed that recognition and management of obesity improved to 60%.
To evaluate the impact of this intervention, patients received a questionnaire at the end of the visit. Nearly all (97%) patients mentioned that the provider discussed weight management during that visit. Most (83%) patients mentioned that weight management was discussed with them during prior visits, while 70% of patients felt their knowledge on working on weight loss had improved. Almost half (46%) were interested in further referral to a dietician or the MOVE program if they did not achieve desired results, but 78% were confident that they could implement the discussed weight management measures.
Discussion
Increased body weight is associated with worsening of DM and can result in poor glycemic control. Achieving weight loss in overweight or obese patients with DM can lead to clinical benefits; however, this is a challenge. In one study, a DM prevention program with lifestyle intervention leading to weight loss significantly reduced the rate of progression from impaired glucose tolerance to DM over a 3-year period and improved cardiovascular risk factors like elevated blood pressure and dyslipidemia.6 A randomized trial of an intensive lifestyle intervention to increase physical activity and decrease caloric intake vs standard DM education in people with T2DM showed a modest weight loss of 8.6% of initial weight at 1 year.7 This weight loss was associated with significant improvement in blood pressure, glycemic control, fasting blood glucose, high-density lipoprotein (HDL) cholesterol, and triglyceride levels and significant reductions in the use of DM, hypertension, and lipid-lowering medications.7 Obesity attributes to dyslipidemia with increased levels of cholesterol, low-density lipoprotein, very low-density lipoprotein, triglycerides, and decreased levels of HDL by about 5%.8 Obesity also is associated with hypertension, coronary heart disease, heart failure, and cardiovascular and all-cause mortality.9
Limitations
Limitations of this study include the small sample size and that multiple HCPs were involved. The nature of intervention might have differed with different HCPs or in a different setting than a VA clinic. In addition, we did not evaluate the effect on weight loss in specific patients as we only reviewed charts to check whether HCPs addressed weight loss. Nevertheless, our intervention was effective because it improved patient and provider awareness. It also gave us the opportunity to create framework for further collaborations and community building. The Endocrinology department at CAVHS is currently collaborating with the MOVE program, which is a part of the nutrition and food services. We hope to have an endocrinologist involved to provide guidance on medication management for obesity.
Conclusion
At CAVHS a simple intervention was instituted to evaluate whether HCPs were discussing weight loss in patients with DM, providing them with information to assess patients’ daily calorie goal, and prompting them for intervention to achieve weight loss. The intervention led to better management of patients with DM and obesity and greater engagement in weight loss from patients.
This project was a team effort. The clinic nurse documented patient’s BMI on the check in slip. HCPs discussed the problem and specific intervention. The clinical dieticians provided focused education for patients. The clerks collected the patient responses to questionnaire. This project also improved communication within the Endocrine Clinic team. Documentation of HCPs pertaining to addressing obesity improved by 54%. Improved patient satisfaction and insight was evident on patient responses to the questionnaire.
We believe that HCP apathy is a major contributor to the problem of obesity. Small steps like these go a long way for further management of obesity. Most VA hospitals have MOVE programs that provide dietary advice and encourage behavioral changes. However, getting patients to commit to these programs is a challenge. Primary care and endocrine clinics are important services that may help with patient awareness.
This project helped us better recognize patients with obesity and provide them with initial counseling and dietary advice. We received help from clinical dieticians and gave patients the option to join MOVE in situations where initial advice did not yield results and for more consistent follow up.
We tried to improve the care for patients with DM who were overweight or obese at CAVHS by prompting HCPs to focus on obesity as a problem and perform interventions to address this problem. The activities carried out and the data collected were used for internal quality improvement and for encouraging further interventions in the care of these patients.
1. Jensen MD, Ryan DH, Apovian CM, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines; Obesity Society. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014;129(25 suppl 2):S102-S138.
2. Evert AB, Boucher JL, Cypress M, et al; American Diabetes Association. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2013;36(11):3821-3842.
3. NHLBI Obesity Education Initiative Expert Panel on the Identification, Evaluation, and Treatment of Obesity in Adults (US). Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report. Bethesda, MD: National Heart, Lung, and Blood Institute; 1998.
4. US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996.
5. American Diabetes Association. 7. Obesity management for the treatment of type 2 diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S65-S72.
6. Knowler WC, Barrett-Connor E, Fowler SE, et al; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403.
7. Look AHEAD Research Group; Pi-Sunyer X, Blackburn G, et al. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial. Diabetes Care. 2007;30(6):1374-1383.
8. Poirier P, Giles TD, Bray GA, et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss. Arterioscler Thromb Vasc Biol. 2006;26(5):968-976.
9. Aune D, Sen A, Norat T, et al. Body mass index, abdominal fatness, and heart failure incidence and mortality: a systematic review and dose-response meta-analysis of prospective studies. Circulation. 2016;133(7):639-649.
Obesity is associated with a significant increase in mortality. It increases the risk of type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, and coronary artery disease.1 T2DM is strongly associated with obesity in all ethnic groups.
Medical nutrition therapy and weight loss are very important for DM management.2 This includes providing education about diet modification, increased physical activity, daily calorie intake evaluation, and consistent carbohydrate intake. For patients with T2DM, health care providers (HCPs) should emphasize lowering caloric intake and inducing weight loss for those who are overweight (body mass index [BMI] between 25 and 29.9) and obese (BMI ≥ 30). This can improve glycemic control by decreasing insulin resistance. Initial recommendations for weight loss and physical activity are to lose between 5% and 10% of initial body weight and to accumulate at least 30 minutes of moderate physical activity over the course of most days of the week.3,4
Several formulas are available to estimate baseline caloric intake for weight maintenance. For weight loss of 1 to 2 pounds per week, lowering 500 to 1,000 calories from daily weight maintenance calories serves the goal. The American Diabetes Association (ADA) also suggests that HCPs recommend diet, physical activity, and behavioral therapy designed to achieve > 5% weight loss to overweight and obese patients with T2DM.5
Recognizing the clinical benefits of achieving weight loss in overweight or obese patients with T2DM, we aimed to increase the number of visits in the Endocrine Clinic at Central Arkansas Veterans Healthcare System (CAVHS) in Little Rock that addressed obesity, documented calorie goal for patients who are overweight or obese, and performed an intervention with further education for the patient.
Methods
The study population included veterans with either type 1 DM (T1DM) or T2DM with BMI > 25 on any DM control regimen. We performed a health record review of the eligible patients seen in the CAVHS Endocrine Clinic from June 1, 2016 to July 31, 2016 to determine the baseline percentage of visits that addressed obesity and provided weight loss advice to patients. We obtained a list of patients seen in the clinic during the study period from Strategic Management Service Services at CAVHS. We also obtained information that age, gender, medications, BMI, and last Endocrine clinic HCP assessment from the electronic health record. We reviewed the HCPs notes, including fellows and faculty who were involved in the patients’ treatment, to determine whether their notes documented a BMI > 25 and whether they discussed an intervention for overweight or obesity with the patient. The CAVHS Institutional Review Board reviewed and approved the initiative as a quality improvement study.
Intervention
Our clinic has a defined group of HCPs that we targeted for the intervention. After getting baseline information, during August 2017 we educated these HCPs on the tools available to calculate calorie goal for the patients. We advised the HCPs to use the Mifflin St Jyor equation for estimating energy expenditure and set a goal of initial weight loss between 5% and 7% of body weight. We gave specific instructions and advice to the providers (Table 1). HCPs also received educational material to distribute to patients that provided information on the healthy plate method, discussed how to count calories, and advised them on ADA goals with carbohydrate limitation. We encouraged HCPs to recommend that patients cut between 500 and 1,000 calories daily from their current diet. HCPs also received advice to seek help from clinical dieticians and the VA MOVE! Weight Management Program when appropriate.
Study of Effect of the Intervention
To study the effect of this intervention, we reviewed documentation by HCPs and assessed patient satisfaction. We obtained a list of patients and reviewed HCP notes on patients with BMI > 25 to assess whether providers addressed obesity in November and December 2017. We also evaluated whether HCPs offered a specific intervention to address the problem, such as providing education material to the patient or an estimate of daily calorie goal, or referring them to clinical dietician and/or the MOVE program. Patients received a 5-question survey that assessed their understanding and satisfaction at the end of the visit (Table 2).
Results
Of the 100 charts reviewed prior to intervention, HCPs discussed obesity management with only 6% of patients. After the intervention, we collected data again through chart review of the patients who were overweight or obese and seen for DM in the same clinic during a 2-month period. Of the 100 charts reviewed, we noticed that recognition and management of obesity improved to 60%.
To evaluate the impact of this intervention, patients received a questionnaire at the end of the visit. Nearly all (97%) patients mentioned that the provider discussed weight management during that visit. Most (83%) patients mentioned that weight management was discussed with them during prior visits, while 70% of patients felt their knowledge on working on weight loss had improved. Almost half (46%) were interested in further referral to a dietician or the MOVE program if they did not achieve desired results, but 78% were confident that they could implement the discussed weight management measures.
Discussion
Increased body weight is associated with worsening of DM and can result in poor glycemic control. Achieving weight loss in overweight or obese patients with DM can lead to clinical benefits; however, this is a challenge. In one study, a DM prevention program with lifestyle intervention leading to weight loss significantly reduced the rate of progression from impaired glucose tolerance to DM over a 3-year period and improved cardiovascular risk factors like elevated blood pressure and dyslipidemia.6 A randomized trial of an intensive lifestyle intervention to increase physical activity and decrease caloric intake vs standard DM education in people with T2DM showed a modest weight loss of 8.6% of initial weight at 1 year.7 This weight loss was associated with significant improvement in blood pressure, glycemic control, fasting blood glucose, high-density lipoprotein (HDL) cholesterol, and triglyceride levels and significant reductions in the use of DM, hypertension, and lipid-lowering medications.7 Obesity attributes to dyslipidemia with increased levels of cholesterol, low-density lipoprotein, very low-density lipoprotein, triglycerides, and decreased levels of HDL by about 5%.8 Obesity also is associated with hypertension, coronary heart disease, heart failure, and cardiovascular and all-cause mortality.9
Limitations
Limitations of this study include the small sample size and that multiple HCPs were involved. The nature of intervention might have differed with different HCPs or in a different setting than a VA clinic. In addition, we did not evaluate the effect on weight loss in specific patients as we only reviewed charts to check whether HCPs addressed weight loss. Nevertheless, our intervention was effective because it improved patient and provider awareness. It also gave us the opportunity to create framework for further collaborations and community building. The Endocrinology department at CAVHS is currently collaborating with the MOVE program, which is a part of the nutrition and food services. We hope to have an endocrinologist involved to provide guidance on medication management for obesity.
Conclusion
At CAVHS a simple intervention was instituted to evaluate whether HCPs were discussing weight loss in patients with DM, providing them with information to assess patients’ daily calorie goal, and prompting them for intervention to achieve weight loss. The intervention led to better management of patients with DM and obesity and greater engagement in weight loss from patients.
This project was a team effort. The clinic nurse documented patient’s BMI on the check in slip. HCPs discussed the problem and specific intervention. The clinical dieticians provided focused education for patients. The clerks collected the patient responses to questionnaire. This project also improved communication within the Endocrine Clinic team. Documentation of HCPs pertaining to addressing obesity improved by 54%. Improved patient satisfaction and insight was evident on patient responses to the questionnaire.
We believe that HCP apathy is a major contributor to the problem of obesity. Small steps like these go a long way for further management of obesity. Most VA hospitals have MOVE programs that provide dietary advice and encourage behavioral changes. However, getting patients to commit to these programs is a challenge. Primary care and endocrine clinics are important services that may help with patient awareness.
This project helped us better recognize patients with obesity and provide them with initial counseling and dietary advice. We received help from clinical dieticians and gave patients the option to join MOVE in situations where initial advice did not yield results and for more consistent follow up.
We tried to improve the care for patients with DM who were overweight or obese at CAVHS by prompting HCPs to focus on obesity as a problem and perform interventions to address this problem. The activities carried out and the data collected were used for internal quality improvement and for encouraging further interventions in the care of these patients.
Obesity is associated with a significant increase in mortality. It increases the risk of type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, and coronary artery disease.1 T2DM is strongly associated with obesity in all ethnic groups.
Medical nutrition therapy and weight loss are very important for DM management.2 This includes providing education about diet modification, increased physical activity, daily calorie intake evaluation, and consistent carbohydrate intake. For patients with T2DM, health care providers (HCPs) should emphasize lowering caloric intake and inducing weight loss for those who are overweight (body mass index [BMI] between 25 and 29.9) and obese (BMI ≥ 30). This can improve glycemic control by decreasing insulin resistance. Initial recommendations for weight loss and physical activity are to lose between 5% and 10% of initial body weight and to accumulate at least 30 minutes of moderate physical activity over the course of most days of the week.3,4
Several formulas are available to estimate baseline caloric intake for weight maintenance. For weight loss of 1 to 2 pounds per week, lowering 500 to 1,000 calories from daily weight maintenance calories serves the goal. The American Diabetes Association (ADA) also suggests that HCPs recommend diet, physical activity, and behavioral therapy designed to achieve > 5% weight loss to overweight and obese patients with T2DM.5
Recognizing the clinical benefits of achieving weight loss in overweight or obese patients with T2DM, we aimed to increase the number of visits in the Endocrine Clinic at Central Arkansas Veterans Healthcare System (CAVHS) in Little Rock that addressed obesity, documented calorie goal for patients who are overweight or obese, and performed an intervention with further education for the patient.
Methods
The study population included veterans with either type 1 DM (T1DM) or T2DM with BMI > 25 on any DM control regimen. We performed a health record review of the eligible patients seen in the CAVHS Endocrine Clinic from June 1, 2016 to July 31, 2016 to determine the baseline percentage of visits that addressed obesity and provided weight loss advice to patients. We obtained a list of patients seen in the clinic during the study period from Strategic Management Service Services at CAVHS. We also obtained information that age, gender, medications, BMI, and last Endocrine clinic HCP assessment from the electronic health record. We reviewed the HCPs notes, including fellows and faculty who were involved in the patients’ treatment, to determine whether their notes documented a BMI > 25 and whether they discussed an intervention for overweight or obesity with the patient. The CAVHS Institutional Review Board reviewed and approved the initiative as a quality improvement study.
Intervention
Our clinic has a defined group of HCPs that we targeted for the intervention. After getting baseline information, during August 2017 we educated these HCPs on the tools available to calculate calorie goal for the patients. We advised the HCPs to use the Mifflin St Jyor equation for estimating energy expenditure and set a goal of initial weight loss between 5% and 7% of body weight. We gave specific instructions and advice to the providers (Table 1). HCPs also received educational material to distribute to patients that provided information on the healthy plate method, discussed how to count calories, and advised them on ADA goals with carbohydrate limitation. We encouraged HCPs to recommend that patients cut between 500 and 1,000 calories daily from their current diet. HCPs also received advice to seek help from clinical dieticians and the VA MOVE! Weight Management Program when appropriate.
Study of Effect of the Intervention
To study the effect of this intervention, we reviewed documentation by HCPs and assessed patient satisfaction. We obtained a list of patients and reviewed HCP notes on patients with BMI > 25 to assess whether providers addressed obesity in November and December 2017. We also evaluated whether HCPs offered a specific intervention to address the problem, such as providing education material to the patient or an estimate of daily calorie goal, or referring them to clinical dietician and/or the MOVE program. Patients received a 5-question survey that assessed their understanding and satisfaction at the end of the visit (Table 2).
Results
Of the 100 charts reviewed prior to intervention, HCPs discussed obesity management with only 6% of patients. After the intervention, we collected data again through chart review of the patients who were overweight or obese and seen for DM in the same clinic during a 2-month period. Of the 100 charts reviewed, we noticed that recognition and management of obesity improved to 60%.
To evaluate the impact of this intervention, patients received a questionnaire at the end of the visit. Nearly all (97%) patients mentioned that the provider discussed weight management during that visit. Most (83%) patients mentioned that weight management was discussed with them during prior visits, while 70% of patients felt their knowledge on working on weight loss had improved. Almost half (46%) were interested in further referral to a dietician or the MOVE program if they did not achieve desired results, but 78% were confident that they could implement the discussed weight management measures.
Discussion
Increased body weight is associated with worsening of DM and can result in poor glycemic control. Achieving weight loss in overweight or obese patients with DM can lead to clinical benefits; however, this is a challenge. In one study, a DM prevention program with lifestyle intervention leading to weight loss significantly reduced the rate of progression from impaired glucose tolerance to DM over a 3-year period and improved cardiovascular risk factors like elevated blood pressure and dyslipidemia.6 A randomized trial of an intensive lifestyle intervention to increase physical activity and decrease caloric intake vs standard DM education in people with T2DM showed a modest weight loss of 8.6% of initial weight at 1 year.7 This weight loss was associated with significant improvement in blood pressure, glycemic control, fasting blood glucose, high-density lipoprotein (HDL) cholesterol, and triglyceride levels and significant reductions in the use of DM, hypertension, and lipid-lowering medications.7 Obesity attributes to dyslipidemia with increased levels of cholesterol, low-density lipoprotein, very low-density lipoprotein, triglycerides, and decreased levels of HDL by about 5%.8 Obesity also is associated with hypertension, coronary heart disease, heart failure, and cardiovascular and all-cause mortality.9
Limitations
Limitations of this study include the small sample size and that multiple HCPs were involved. The nature of intervention might have differed with different HCPs or in a different setting than a VA clinic. In addition, we did not evaluate the effect on weight loss in specific patients as we only reviewed charts to check whether HCPs addressed weight loss. Nevertheless, our intervention was effective because it improved patient and provider awareness. It also gave us the opportunity to create framework for further collaborations and community building. The Endocrinology department at CAVHS is currently collaborating with the MOVE program, which is a part of the nutrition and food services. We hope to have an endocrinologist involved to provide guidance on medication management for obesity.
Conclusion
At CAVHS a simple intervention was instituted to evaluate whether HCPs were discussing weight loss in patients with DM, providing them with information to assess patients’ daily calorie goal, and prompting them for intervention to achieve weight loss. The intervention led to better management of patients with DM and obesity and greater engagement in weight loss from patients.
This project was a team effort. The clinic nurse documented patient’s BMI on the check in slip. HCPs discussed the problem and specific intervention. The clinical dieticians provided focused education for patients. The clerks collected the patient responses to questionnaire. This project also improved communication within the Endocrine Clinic team. Documentation of HCPs pertaining to addressing obesity improved by 54%. Improved patient satisfaction and insight was evident on patient responses to the questionnaire.
We believe that HCP apathy is a major contributor to the problem of obesity. Small steps like these go a long way for further management of obesity. Most VA hospitals have MOVE programs that provide dietary advice and encourage behavioral changes. However, getting patients to commit to these programs is a challenge. Primary care and endocrine clinics are important services that may help with patient awareness.
This project helped us better recognize patients with obesity and provide them with initial counseling and dietary advice. We received help from clinical dieticians and gave patients the option to join MOVE in situations where initial advice did not yield results and for more consistent follow up.
We tried to improve the care for patients with DM who were overweight or obese at CAVHS by prompting HCPs to focus on obesity as a problem and perform interventions to address this problem. The activities carried out and the data collected were used for internal quality improvement and for encouraging further interventions in the care of these patients.
1. Jensen MD, Ryan DH, Apovian CM, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines; Obesity Society. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014;129(25 suppl 2):S102-S138.
2. Evert AB, Boucher JL, Cypress M, et al; American Diabetes Association. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2013;36(11):3821-3842.
3. NHLBI Obesity Education Initiative Expert Panel on the Identification, Evaluation, and Treatment of Obesity in Adults (US). Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report. Bethesda, MD: National Heart, Lung, and Blood Institute; 1998.
4. US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996.
5. American Diabetes Association. 7. Obesity management for the treatment of type 2 diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S65-S72.
6. Knowler WC, Barrett-Connor E, Fowler SE, et al; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403.
7. Look AHEAD Research Group; Pi-Sunyer X, Blackburn G, et al. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial. Diabetes Care. 2007;30(6):1374-1383.
8. Poirier P, Giles TD, Bray GA, et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss. Arterioscler Thromb Vasc Biol. 2006;26(5):968-976.
9. Aune D, Sen A, Norat T, et al. Body mass index, abdominal fatness, and heart failure incidence and mortality: a systematic review and dose-response meta-analysis of prospective studies. Circulation. 2016;133(7):639-649.
1. Jensen MD, Ryan DH, Apovian CM, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines; Obesity Society. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation. 2014;129(25 suppl 2):S102-S138.
2. Evert AB, Boucher JL, Cypress M, et al; American Diabetes Association. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2013;36(11):3821-3842.
3. NHLBI Obesity Education Initiative Expert Panel on the Identification, Evaluation, and Treatment of Obesity in Adults (US). Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report. Bethesda, MD: National Heart, Lung, and Blood Institute; 1998.
4. US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996.
5. American Diabetes Association. 7. Obesity management for the treatment of type 2 diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care. 2018;41(Suppl 1):S65-S72.
6. Knowler WC, Barrett-Connor E, Fowler SE, et al; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403.
7. Look AHEAD Research Group; Pi-Sunyer X, Blackburn G, et al. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial. Diabetes Care. 2007;30(6):1374-1383.
8. Poirier P, Giles TD, Bray GA, et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss. Arterioscler Thromb Vasc Biol. 2006;26(5):968-976.
9. Aune D, Sen A, Norat T, et al. Body mass index, abdominal fatness, and heart failure incidence and mortality: a systematic review and dose-response meta-analysis of prospective studies. Circulation. 2016;133(7):639-649.
Limited Use of Outpatient Stress Testing in Young Patients With Atypical Chest Pain (FULL)
Low prevalence of coronary artery disease within this population suggests that younger patients may not require stress testing for chest pain evaluations as long as pretest likelihood is low.
The decision to perform stress testing in the evaluation of chest pain is often based on the pretest likelihood of coronary artery disease (CAD).1-7 Cardiac risk scores, which incorporate smoking status, blood pressure, diabetes mellitus, and cholesterol levels, also may provide further risk stratification.8-11 Assuming that the prevalence of CAD increases with age, young adults could be deemed low risk, not warranting cardiac screening.12
Professional society guidelines from the American College of Cardiology/American Heart Association and American College of Physicians4,5 recommend stress testing as the initial diagnostic test for CAD in symptomatic patients; additionally, the guidelines also suggest that screening stress tests may confer primary prevention benefit in intermediate-risk asymptomatic patients.9,13 Exercise treadmill testing is considered the initial modality of choice, given its technical ease and lower cost, compared with stress echocardiography.14
Previously published reports have shown the limited use of stress testing to screen young asymptomatic adults.15-17 Because this patient demographic typically has a low pretest likelihood of CAD, positive stress tests are often false-positive results.7,18 The consequence of false-positive testing may be unnecessary additional cardiac testing, potentially leading to more patient harm than benefit.18,19 For active-duty service members, false-positive testing also has the potential to affect worldwide deployability and/or sea duty status while further risk stratification is performed; as a result, mission readiness may be impacted.Although the number of clinic visits for chest pain has declined, there has been a discordant increase in the rates of stress testing in the US.20-22 Additionally, the rate of stress testing among young adults, specifically in the 25- to 34-year age group, has increased in recent years. Given the rising use of stress tests in the young patient population, the clinical use of stress testing needs to be reassessed.
Although much of the literature has already demonstrated the low value of stress testing in young asymptomatic adults, no data currently exist regarding its outpatient use in evaluating young symptomatic patients. The military represents a predominantly young cross-section of the general population suitable for exploring this topic. Using a cohort of active-duty service members, we aimed to determine the use of outpatient stress testing in evaluating young patients with atypical chest pain.
Methods
The US Department of Defense (DoD) Military Health System Database Repository (MDR) and Comprehensive Ambulatory Professional Encounter Record (CAPER) were the data sources for this study. The MDR contains continually updated, longitudinal electronic medical records (EMRs) for nearly 1.4 million active-duty service members and is composed of administrative, medical, pharmacy, and clinical data. The Naval Medical Center Portsmouth (NMCP) Institutional Review Board approved this study.
Study Cohort
We performed chart reviews of service members aged 18 to 35 years who received cardiac stress testing at NMCP, an academic tertiary care center, within 30 days after an office visit for atypical chest pain between October 1, 2010, and September 30, 2015. Atypical chest pain was defined as any outpatient claim with ICD-9 code, 786.5x, in the primary diagnosis field (Table 1).4 
Demographics and cardiac risk factors (ie, hypertension, hyperlipidemia, diabetes mellitus, and smoking status) were assessed prior to index chest pain evaluations and defined via ICD-9 codes within outpatient records.
Cardiac Testing Outcomes
Patients were initially categorized by the results of baseline electrocardiograms (ECG) and index stress tests (ie, exercise treadmill or stress echocardiography, exercise or Lexiscan myocardial perfusion imaging, dobutamine stress echocardiography). Positive tests were defined as those having electrical or structural ischemic changes. Chronotropic changes were infrequent and nonpathologic and were not counted. Patient endpoints were either additional cardiac testing or negative index stress test without additional testing.
Statistical Analysis
The agreement between both baseline ECG and index stress test as well as index stress test and additional cardiac testing were analyzed using McNemar test and matched-pair odds ratios (ORs) with corresponding 95% CIs. Analyses were stratified by demographics and cardiac risk factors to assess for potential confounding. Analyses were performed using SAS version 9.4 (Cary, NC).
Results
A total of 1,036 patients were evaluated for atypical chest pain and had index stress testing between October 1, 2010 and September 30, 2015. The study cohort was 69% male with a mean (SD) age of 27.3 (4.7) years. More than 60% of the cohort was older than aged > 25 years. 
In the initial testing cohort, exercise treadmill test (59.3%) and exercise echocardiogram (37.1%) were the most common stress testing modalities. The mean (SD) metabolic equivalents (METS) achieved among individuals who performed exercise stress testing was 13.9 (2.8). There were 65 patients who had a positive baseline ECG/negative index stress test, 958 patients had a negative ECG/negative index test, and 8 patients had a negative ECG/positive index test. 
There were 102 patients (10%) who performed additional cardiac testing. Among this subgroup, 13 patients (1.3%) had additional testing for further evaluation of a positive index stress test (Table 4) and 89 patients (8.6%) had testing for continuing atypical chest pain despite a negative index stress test. 
Coronary computed tomography angiography (CCTA) demonstrated nonobstructive CAD in 3 patients (0.3%) within the study cohort. There was no obstructive CAD identified in our cohort. Two patients had negative left heart catheterizations (LHC). One of these patients had a negative LHC and a negative Lexiscan after a CCTA showed CAD; all 3 of these additional tests were performed for evaluation of continued chest pain despite negative index stress testing. The positive predictive value of cardiac stress testing for nonobstructive CAD in this low-risk population was 15.4% (2 of 13). Stratification by demographics, CAD risk factors, and cardiac test results revealed no presence of confounding factors during analyses.
Discussion
In this retrospective, observational study of 1,036 young patients with atypical chest pain who had stress testing, there was relatively strong agreement between baseline ECG and index stress test results. Individuals also were 8 times more likely to have positive baseline ECGs and negative stress testing than having the opposite finding. Additional cardiac testing similarly demonstrated congruency with index stress testing and showed the propensity for false-positive stress tests. Further testing with CCTA demonstrated minimal nonobstructive CAD in < 1% of the study cohort and 2 LHC were negative. Despite the low prevalence of CAD and apparent low diagnostic use of stress testing in our young cohort, symptomatic service members still require stress testing to determine deployment suitability.
The low yield of outpatient stress testing in our young population is rooted in Bayes’ theorem, which highlights the importance of pretest likelihood in the diagnosis of CAD.7,23 Because our cohort had a low prevalence and low pretest likelihood of CAD, positive index stress tests were often false-positive results and consequently did not increase the posttest likelihood of CAD, resulting in low positive predictive value. Additional cardiac testing had limited clinical value in our cohort. The 3 cases of nonobstructive CAD were unlikely to be pathologic given the minimal degree of observed stenosis and the 2 LHC did not require revascularization. These results are similar to those shown by Christman and colleagues and Mudrick and colleagues, which highlighted the low yield of additional cardiac studies and low rate of revascularization among symptomatic patients without known cardiac disease, respectively.18,19
This is the first study, to our knowledge, to quantitatively demonstrate the low use of outpatient stress testing for young adults with atypical chest pain. Previous studies that assessed stress testing for young patients with chest pain in acute settings such as emergency departments and chest pain observation units, similarly demonstrated minimal yield of routine diagnostic testing.23,24 This further highlights the premise that outpatient and even emergent-setting stress testing in low cardiac risk individuals may be of limited value and not always necessary.
Limitations
There were several study limitations. As a single-center, cross-sectional review, we may not be able to extrapolate our findings to the general population. However, given the low prevalence of CAD in young adults, stress testing would likely have limited value regardless of the sample distribution; so it may be possible to extend our findings beyond our cohort. Also, neither baseline ECG nor index stress test (irrespective of modality) could be given a diagnostic value in predicting ischemia alone; doing so would require comparison with the gold standard—heart catheterization. Although referral bias has been associated with diagnostic performance of stress testing, we did not adjust for this phenomenon.25 Given the higher average metabolic equivalents achieved in our cohort, this potential bias likely did not affect diagnostic performance.
Conclusion
There was low diagnostic use of outpatient stress testing and additional cardiac testing for CAD among young patients with atypical chest pain. The limited value of cardiac stress testing is likely a function of the low CAD prevalence within this population, suggesting that younger patients may not necessarily require stress testing for chest pain evaluations as long as pretest likelihood is low. Despite our results, we maintain that the decision to perform stress testing should still be guided by clinical judgment, but perhaps our findings may alleviate physicians’ concerns over the urgency of when to refer low-risk patients for testing. Although we are cautious in inferring our findings to the general population, the similarity it shares with those from other published reports may suggest its applicability beyond our study cohort.
1. Fowler-Brown A, Pignone M, Pletcher M, et al. Exercise tolerance testing to screen for coronary heart disease: a systematic review for the technical support for the U.S. Preventive Services Task Force. Ann Intern Med. 2004;140(7):W9-W24.
2. Gibbons RJ, Balady GJ, Bricker JT, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Committee to Update the 1997 Exercise Testing Guidelines. ACC/AHA 2002 guideline update for exercise testing: summary article. A report of the American College of Cardiology/American Heart Association Task Force on practice guidelines (Committee to Update the 1997 Exercise Testing Guidelines). J Am Coll Cardiol. 2002;40(8):1531-1540.
3. Chou R, Arora B, Dana T, Fu R, Miranda Walker M, Humphrey L. Screening Asymptomatic Adults for Coronary Heart Disease With Resting or Exercise Electrocardiography: Systematic Review to Update the 2004 U.S. Preventive Services Task Force recommendation. Evidence Synthesis No. 88. AHRQ Publication No. 11-05158-EF-1. Rockville, MD: Agency for Healthcare Research and Quality; September 2011.
4. Fihn S, Gardin J, Abrams J, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J Am Coll Cardiology. 2012;60(24):e44-e164.
5. Gibbons RJ, Balady GJ, Beasley JW, et al. ACC/AHA guidelines for exercise testing: executive summary. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Exercise Testing). Circulation. 1997;96(1):345-354.
6. Greenland P, Alpert JS, Beller GA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults. A report of the American College of Cardiology Foundation/ American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56(25):e50-e103.
7. Diamond G, Forrester J. Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. N Engl J Med. 1979;300(24):1350-1358.
8. Goff D, Lloyd-Jones D, Bennett G, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25)(suppl 2):S49-S73.
9. Greenland P, Gaziano J. Selecting asymptomatic patients for coronary computed tomography or electrocardiographic exercise testing. N Engl J Med. 2003;349(5):465-473.
10. Shah N, Soon K, Wong C, Kellu AM. Screening for asymptomatic coronary heart disease in the young ‘at risk’ population: who and how? Int J Cardiol Heart Vasc. 2014;6:60-65.
11. Morise A, Evans M, Jalisi F, Shetty R, Stauffer M. A pretest prognostic score to assess patients undergoing exercise or pharmacological stress testing. Heart. 2007;93(2):200-204.
12. Amsterdam EA, Kirk JD, Bluemke DA, et al; American Heart Association Exercise, Cardiac Rehabilitation, and Prevention Committee of the Council on Clinical Cardiology, Council on Cardiovascular Nursing, and Interdisciplinary Council on Quality of Care and Outcomes Research. Testing of low-risk patients presenting to the emergency department with chest pain: a scientific statement from the American Heart Association. Circulation. 2010;122(17):1756-1776.
13. Livschitz S, Sharabi Y, Yushin J, et al. Limited clinical value of exercise stress test for the screening of coronary artery disease in young, asymptomatic adult men. Am J Cardiol. 2000;86(4):462-464.
14. Miller T. Stress testing: the case for the standard treadmill test. Curr Opin Cardiol. 2011;26(5):363-369.
15. La Gerche A, Baggish A, Knuuti J, et al. Cardiac imaging and stress testing asymptomatic athletes to identify those at risk of sudden cardiac death. JACC Cardiovasc Imaging. 2013;6(9):993-1007.
16. Lauer M, Froelicher ES, Williams M, Kligfield P; American Heart Association Council on Clinical Cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention. Exercise testing in asymptomatic adults: a statement for professionals from the American Heart Association Council on Clinical Cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention. Circulation. 2005;112(5):771-776.
17. Sammito S, Gundlach N, Bockelmann I. Prevalence of cardiac arrhythmia under stress conditions in occupational health assessments of young military servicemen and servicewomen. Mil Med. 2016;181(4):369-372.
18. Mudrick DW, Cowper PA, Shah BR, et al. Downstream procedures and outcomes after stress testing for chest pain without known coronary artery disease in the United States. Am Heart J. 2012;163(3):454-461.
19. Christman MP, Bittencourt MS, Hulten E, et al. Yield of downstream tests after exercise treadmill testing. J Am Coll Cardiol. 2014;63(13):1264-1274.
20. Will J, Loustalot F, Hong Y. National trends in visits to physician offices and outpatient clinics for angina 1995 to 2010. Circ Cardiovasc Qual Outcomes. 2014;7(1):110-117.
21. Kini V, McCarthy F, Dayoub E, et al. Cardiac stress test trends among US patients younger than 65 years, 2005-2012. JAMA Cardiol. 2016;1(9):1038-1042.
22. Ladapo JA, Blecker S, Douglas PS. Physician decision making and trends in the use of cardiac stress testing in the United States: an analysis of repeated cross-sectional data. Ann Intern Med. 2014;161(7):482-490.
23. Winchester DE, Brandt J, Schmidt C, Schmidt C, Allen B, Payton T, Amsterdam EA. Diagnostic yield of routine noninvasive cardiovascular testing in low-risk acute chest pain patients. Am J Cardiol. 2015;116(2):204-207.
24. Hermann L, Weingart SD, Duvall W, Henzlova MJ. The limited utility of routine cardiac stress testing in emergency department chest pain patients younger than 40 years. Ann Emerg Med. 2009;54(1):12-16.
25. Ladapo JA, Blecker S, Elashoff MR, et al. Clinical implications of referral bias in the diagnostic performance of exercise testing for coronary artery disease. J Am Heart Assoc. 2013;2(6):e000505.
Low prevalence of coronary artery disease within this population suggests that younger patients may not require stress testing for chest pain evaluations as long as pretest likelihood is low.
Low prevalence of coronary artery disease within this population suggests that younger patients may not require stress testing for chest pain evaluations as long as pretest likelihood is low.
The decision to perform stress testing in the evaluation of chest pain is often based on the pretest likelihood of coronary artery disease (CAD).1-7 Cardiac risk scores, which incorporate smoking status, blood pressure, diabetes mellitus, and cholesterol levels, also may provide further risk stratification.8-11 Assuming that the prevalence of CAD increases with age, young adults could be deemed low risk, not warranting cardiac screening.12
Professional society guidelines from the American College of Cardiology/American Heart Association and American College of Physicians4,5 recommend stress testing as the initial diagnostic test for CAD in symptomatic patients; additionally, the guidelines also suggest that screening stress tests may confer primary prevention benefit in intermediate-risk asymptomatic patients.9,13 Exercise treadmill testing is considered the initial modality of choice, given its technical ease and lower cost, compared with stress echocardiography.14
Previously published reports have shown the limited use of stress testing to screen young asymptomatic adults.15-17 Because this patient demographic typically has a low pretest likelihood of CAD, positive stress tests are often false-positive results.7,18 The consequence of false-positive testing may be unnecessary additional cardiac testing, potentially leading to more patient harm than benefit.18,19 For active-duty service members, false-positive testing also has the potential to affect worldwide deployability and/or sea duty status while further risk stratification is performed; as a result, mission readiness may be impacted.Although the number of clinic visits for chest pain has declined, there has been a discordant increase in the rates of stress testing in the US.20-22 Additionally, the rate of stress testing among young adults, specifically in the 25- to 34-year age group, has increased in recent years. Given the rising use of stress tests in the young patient population, the clinical use of stress testing needs to be reassessed.
Although much of the literature has already demonstrated the low value of stress testing in young asymptomatic adults, no data currently exist regarding its outpatient use in evaluating young symptomatic patients. The military represents a predominantly young cross-section of the general population suitable for exploring this topic. Using a cohort of active-duty service members, we aimed to determine the use of outpatient stress testing in evaluating young patients with atypical chest pain.
Methods
The US Department of Defense (DoD) Military Health System Database Repository (MDR) and Comprehensive Ambulatory Professional Encounter Record (CAPER) were the data sources for this study. The MDR contains continually updated, longitudinal electronic medical records (EMRs) for nearly 1.4 million active-duty service members and is composed of administrative, medical, pharmacy, and clinical data. The Naval Medical Center Portsmouth (NMCP) Institutional Review Board approved this study.
Study Cohort
We performed chart reviews of service members aged 18 to 35 years who received cardiac stress testing at NMCP, an academic tertiary care center, within 30 days after an office visit for atypical chest pain between October 1, 2010, and September 30, 2015. Atypical chest pain was defined as any outpatient claim with ICD-9 code, 786.5x, in the primary diagnosis field (Table 1).4
Demographics and cardiac risk factors (ie, hypertension, hyperlipidemia, diabetes mellitus, and smoking status) were assessed prior to index chest pain evaluations and defined via ICD-9 codes within outpatient records.
Cardiac Testing Outcomes
Patients were initially categorized by the results of baseline electrocardiograms (ECG) and index stress tests (ie, exercise treadmill or stress echocardiography, exercise or Lexiscan myocardial perfusion imaging, dobutamine stress echocardiography). Positive tests were defined as those having electrical or structural ischemic changes. Chronotropic changes were infrequent and nonpathologic and were not counted. Patient endpoints were either additional cardiac testing or negative index stress test without additional testing.
Statistical Analysis
The agreement between both baseline ECG and index stress test as well as index stress test and additional cardiac testing were analyzed using McNemar test and matched-pair odds ratios (ORs) with corresponding 95% CIs. Analyses were stratified by demographics and cardiac risk factors to assess for potential confounding. Analyses were performed using SAS version 9.4 (Cary, NC).
Results
A total of 1,036 patients were evaluated for atypical chest pain and had index stress testing between October 1, 2010 and September 30, 2015. The study cohort was 69% male with a mean (SD) age of 27.3 (4.7) years. More than 60% of the cohort was older than aged > 25 years.
In the initial testing cohort, exercise treadmill test (59.3%) and exercise echocardiogram (37.1%) were the most common stress testing modalities. The mean (SD) metabolic equivalents (METS) achieved among individuals who performed exercise stress testing was 13.9 (2.8). There were 65 patients who had a positive baseline ECG/negative index stress test, 958 patients had a negative ECG/negative index test, and 8 patients had a negative ECG/positive index test.
There were 102 patients (10%) who performed additional cardiac testing. Among this subgroup, 13 patients (1.3%) had additional testing for further evaluation of a positive index stress test (Table 4) and 89 patients (8.6%) had testing for continuing atypical chest pain despite a negative index stress test.
Coronary computed tomography angiography (CCTA) demonstrated nonobstructive CAD in 3 patients (0.3%) within the study cohort. There was no obstructive CAD identified in our cohort. Two patients had negative left heart catheterizations (LHC). One of these patients had a negative LHC and a negative Lexiscan after a CCTA showed CAD; all 3 of these additional tests were performed for evaluation of continued chest pain despite negative index stress testing. The positive predictive value of cardiac stress testing for nonobstructive CAD in this low-risk population was 15.4% (2 of 13). Stratification by demographics, CAD risk factors, and cardiac test results revealed no presence of confounding factors during analyses.
Discussion
In this retrospective, observational study of 1,036 young patients with atypical chest pain who had stress testing, there was relatively strong agreement between baseline ECG and index stress test results. Individuals also were 8 times more likely to have positive baseline ECGs and negative stress testing than having the opposite finding. Additional cardiac testing similarly demonstrated congruency with index stress testing and showed the propensity for false-positive stress tests. Further testing with CCTA demonstrated minimal nonobstructive CAD in < 1% of the study cohort and 2 LHC were negative. Despite the low prevalence of CAD and apparent low diagnostic use of stress testing in our young cohort, symptomatic service members still require stress testing to determine deployment suitability.
The low yield of outpatient stress testing in our young population is rooted in Bayes’ theorem, which highlights the importance of pretest likelihood in the diagnosis of CAD.7,23 Because our cohort had a low prevalence and low pretest likelihood of CAD, positive index stress tests were often false-positive results and consequently did not increase the posttest likelihood of CAD, resulting in low positive predictive value. Additional cardiac testing had limited clinical value in our cohort. The 3 cases of nonobstructive CAD were unlikely to be pathologic given the minimal degree of observed stenosis and the 2 LHC did not require revascularization. These results are similar to those shown by Christman and colleagues and Mudrick and colleagues, which highlighted the low yield of additional cardiac studies and low rate of revascularization among symptomatic patients without known cardiac disease, respectively.18,19
This is the first study, to our knowledge, to quantitatively demonstrate the low use of outpatient stress testing for young adults with atypical chest pain. Previous studies that assessed stress testing for young patients with chest pain in acute settings such as emergency departments and chest pain observation units, similarly demonstrated minimal yield of routine diagnostic testing.23,24 This further highlights the premise that outpatient and even emergent-setting stress testing in low cardiac risk individuals may be of limited value and not always necessary.
Limitations
There were several study limitations. As a single-center, cross-sectional review, we may not be able to extrapolate our findings to the general population. However, given the low prevalence of CAD in young adults, stress testing would likely have limited value regardless of the sample distribution; so it may be possible to extend our findings beyond our cohort. Also, neither baseline ECG nor index stress test (irrespective of modality) could be given a diagnostic value in predicting ischemia alone; doing so would require comparison with the gold standard—heart catheterization. Although referral bias has been associated with diagnostic performance of stress testing, we did not adjust for this phenomenon.25 Given the higher average metabolic equivalents achieved in our cohort, this potential bias likely did not affect diagnostic performance.
Conclusion
There was low diagnostic use of outpatient stress testing and additional cardiac testing for CAD among young patients with atypical chest pain. The limited value of cardiac stress testing is likely a function of the low CAD prevalence within this population, suggesting that younger patients may not necessarily require stress testing for chest pain evaluations as long as pretest likelihood is low. Despite our results, we maintain that the decision to perform stress testing should still be guided by clinical judgment, but perhaps our findings may alleviate physicians’ concerns over the urgency of when to refer low-risk patients for testing. Although we are cautious in inferring our findings to the general population, the similarity it shares with those from other published reports may suggest its applicability beyond our study cohort.
The decision to perform stress testing in the evaluation of chest pain is often based on the pretest likelihood of coronary artery disease (CAD).1-7 Cardiac risk scores, which incorporate smoking status, blood pressure, diabetes mellitus, and cholesterol levels, also may provide further risk stratification.8-11 Assuming that the prevalence of CAD increases with age, young adults could be deemed low risk, not warranting cardiac screening.12
Professional society guidelines from the American College of Cardiology/American Heart Association and American College of Physicians4,5 recommend stress testing as the initial diagnostic test for CAD in symptomatic patients; additionally, the guidelines also suggest that screening stress tests may confer primary prevention benefit in intermediate-risk asymptomatic patients.9,13 Exercise treadmill testing is considered the initial modality of choice, given its technical ease and lower cost, compared with stress echocardiography.14
Previously published reports have shown the limited use of stress testing to screen young asymptomatic adults.15-17 Because this patient demographic typically has a low pretest likelihood of CAD, positive stress tests are often false-positive results.7,18 The consequence of false-positive testing may be unnecessary additional cardiac testing, potentially leading to more patient harm than benefit.18,19 For active-duty service members, false-positive testing also has the potential to affect worldwide deployability and/or sea duty status while further risk stratification is performed; as a result, mission readiness may be impacted.Although the number of clinic visits for chest pain has declined, there has been a discordant increase in the rates of stress testing in the US.20-22 Additionally, the rate of stress testing among young adults, specifically in the 25- to 34-year age group, has increased in recent years. Given the rising use of stress tests in the young patient population, the clinical use of stress testing needs to be reassessed.
Although much of the literature has already demonstrated the low value of stress testing in young asymptomatic adults, no data currently exist regarding its outpatient use in evaluating young symptomatic patients. The military represents a predominantly young cross-section of the general population suitable for exploring this topic. Using a cohort of active-duty service members, we aimed to determine the use of outpatient stress testing in evaluating young patients with atypical chest pain.
Methods
The US Department of Defense (DoD) Military Health System Database Repository (MDR) and Comprehensive Ambulatory Professional Encounter Record (CAPER) were the data sources for this study. The MDR contains continually updated, longitudinal electronic medical records (EMRs) for nearly 1.4 million active-duty service members and is composed of administrative, medical, pharmacy, and clinical data. The Naval Medical Center Portsmouth (NMCP) Institutional Review Board approved this study.
Study Cohort
We performed chart reviews of service members aged 18 to 35 years who received cardiac stress testing at NMCP, an academic tertiary care center, within 30 days after an office visit for atypical chest pain between October 1, 2010, and September 30, 2015. Atypical chest pain was defined as any outpatient claim with ICD-9 code, 786.5x, in the primary diagnosis field (Table 1).4
Demographics and cardiac risk factors (ie, hypertension, hyperlipidemia, diabetes mellitus, and smoking status) were assessed prior to index chest pain evaluations and defined via ICD-9 codes within outpatient records.
Cardiac Testing Outcomes
Patients were initially categorized by the results of baseline electrocardiograms (ECG) and index stress tests (ie, exercise treadmill or stress echocardiography, exercise or Lexiscan myocardial perfusion imaging, dobutamine stress echocardiography). Positive tests were defined as those having electrical or structural ischemic changes. Chronotropic changes were infrequent and nonpathologic and were not counted. Patient endpoints were either additional cardiac testing or negative index stress test without additional testing.
Statistical Analysis
The agreement between both baseline ECG and index stress test as well as index stress test and additional cardiac testing were analyzed using McNemar test and matched-pair odds ratios (ORs) with corresponding 95% CIs. Analyses were stratified by demographics and cardiac risk factors to assess for potential confounding. Analyses were performed using SAS version 9.4 (Cary, NC).
Results
A total of 1,036 patients were evaluated for atypical chest pain and had index stress testing between October 1, 2010 and September 30, 2015. The study cohort was 69% male with a mean (SD) age of 27.3 (4.7) years. More than 60% of the cohort was older than aged > 25 years.
In the initial testing cohort, exercise treadmill test (59.3%) and exercise echocardiogram (37.1%) were the most common stress testing modalities. The mean (SD) metabolic equivalents (METS) achieved among individuals who performed exercise stress testing was 13.9 (2.8). There were 65 patients who had a positive baseline ECG/negative index stress test, 958 patients had a negative ECG/negative index test, and 8 patients had a negative ECG/positive index test.
There were 102 patients (10%) who performed additional cardiac testing. Among this subgroup, 13 patients (1.3%) had additional testing for further evaluation of a positive index stress test (Table 4) and 89 patients (8.6%) had testing for continuing atypical chest pain despite a negative index stress test.
Coronary computed tomography angiography (CCTA) demonstrated nonobstructive CAD in 3 patients (0.3%) within the study cohort. There was no obstructive CAD identified in our cohort. Two patients had negative left heart catheterizations (LHC). One of these patients had a negative LHC and a negative Lexiscan after a CCTA showed CAD; all 3 of these additional tests were performed for evaluation of continued chest pain despite negative index stress testing. The positive predictive value of cardiac stress testing for nonobstructive CAD in this low-risk population was 15.4% (2 of 13). Stratification by demographics, CAD risk factors, and cardiac test results revealed no presence of confounding factors during analyses.
Discussion
In this retrospective, observational study of 1,036 young patients with atypical chest pain who had stress testing, there was relatively strong agreement between baseline ECG and index stress test results. Individuals also were 8 times more likely to have positive baseline ECGs and negative stress testing than having the opposite finding. Additional cardiac testing similarly demonstrated congruency with index stress testing and showed the propensity for false-positive stress tests. Further testing with CCTA demonstrated minimal nonobstructive CAD in < 1% of the study cohort and 2 LHC were negative. Despite the low prevalence of CAD and apparent low diagnostic use of stress testing in our young cohort, symptomatic service members still require stress testing to determine deployment suitability.
The low yield of outpatient stress testing in our young population is rooted in Bayes’ theorem, which highlights the importance of pretest likelihood in the diagnosis of CAD.7,23 Because our cohort had a low prevalence and low pretest likelihood of CAD, positive index stress tests were often false-positive results and consequently did not increase the posttest likelihood of CAD, resulting in low positive predictive value. Additional cardiac testing had limited clinical value in our cohort. The 3 cases of nonobstructive CAD were unlikely to be pathologic given the minimal degree of observed stenosis and the 2 LHC did not require revascularization. These results are similar to those shown by Christman and colleagues and Mudrick and colleagues, which highlighted the low yield of additional cardiac studies and low rate of revascularization among symptomatic patients without known cardiac disease, respectively.18,19
This is the first study, to our knowledge, to quantitatively demonstrate the low use of outpatient stress testing for young adults with atypical chest pain. Previous studies that assessed stress testing for young patients with chest pain in acute settings such as emergency departments and chest pain observation units, similarly demonstrated minimal yield of routine diagnostic testing.23,24 This further highlights the premise that outpatient and even emergent-setting stress testing in low cardiac risk individuals may be of limited value and not always necessary.
Limitations
There were several study limitations. As a single-center, cross-sectional review, we may not be able to extrapolate our findings to the general population. However, given the low prevalence of CAD in young adults, stress testing would likely have limited value regardless of the sample distribution; so it may be possible to extend our findings beyond our cohort. Also, neither baseline ECG nor index stress test (irrespective of modality) could be given a diagnostic value in predicting ischemia alone; doing so would require comparison with the gold standard—heart catheterization. Although referral bias has been associated with diagnostic performance of stress testing, we did not adjust for this phenomenon.25 Given the higher average metabolic equivalents achieved in our cohort, this potential bias likely did not affect diagnostic performance.
Conclusion
There was low diagnostic use of outpatient stress testing and additional cardiac testing for CAD among young patients with atypical chest pain. The limited value of cardiac stress testing is likely a function of the low CAD prevalence within this population, suggesting that younger patients may not necessarily require stress testing for chest pain evaluations as long as pretest likelihood is low. Despite our results, we maintain that the decision to perform stress testing should still be guided by clinical judgment, but perhaps our findings may alleviate physicians’ concerns over the urgency of when to refer low-risk patients for testing. Although we are cautious in inferring our findings to the general population, the similarity it shares with those from other published reports may suggest its applicability beyond our study cohort.
1. Fowler-Brown A, Pignone M, Pletcher M, et al. Exercise tolerance testing to screen for coronary heart disease: a systematic review for the technical support for the U.S. Preventive Services Task Force. Ann Intern Med. 2004;140(7):W9-W24.
2. Gibbons RJ, Balady GJ, Bricker JT, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Committee to Update the 1997 Exercise Testing Guidelines. ACC/AHA 2002 guideline update for exercise testing: summary article. A report of the American College of Cardiology/American Heart Association Task Force on practice guidelines (Committee to Update the 1997 Exercise Testing Guidelines). J Am Coll Cardiol. 2002;40(8):1531-1540.
3. Chou R, Arora B, Dana T, Fu R, Miranda Walker M, Humphrey L. Screening Asymptomatic Adults for Coronary Heart Disease With Resting or Exercise Electrocardiography: Systematic Review to Update the 2004 U.S. Preventive Services Task Force recommendation. Evidence Synthesis No. 88. AHRQ Publication No. 11-05158-EF-1. Rockville, MD: Agency for Healthcare Research and Quality; September 2011.
4. Fihn S, Gardin J, Abrams J, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J Am Coll Cardiology. 2012;60(24):e44-e164.
5. Gibbons RJ, Balady GJ, Beasley JW, et al. ACC/AHA guidelines for exercise testing: executive summary. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Exercise Testing). Circulation. 1997;96(1):345-354.
6. Greenland P, Alpert JS, Beller GA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults. A report of the American College of Cardiology Foundation/ American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56(25):e50-e103.
7. Diamond G, Forrester J. Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. N Engl J Med. 1979;300(24):1350-1358.
8. Goff D, Lloyd-Jones D, Bennett G, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25)(suppl 2):S49-S73.
9. Greenland P, Gaziano J. Selecting asymptomatic patients for coronary computed tomography or electrocardiographic exercise testing. N Engl J Med. 2003;349(5):465-473.
10. Shah N, Soon K, Wong C, Kellu AM. Screening for asymptomatic coronary heart disease in the young ‘at risk’ population: who and how? Int J Cardiol Heart Vasc. 2014;6:60-65.
11. Morise A, Evans M, Jalisi F, Shetty R, Stauffer M. A pretest prognostic score to assess patients undergoing exercise or pharmacological stress testing. Heart. 2007;93(2):200-204.
12. Amsterdam EA, Kirk JD, Bluemke DA, et al; American Heart Association Exercise, Cardiac Rehabilitation, and Prevention Committee of the Council on Clinical Cardiology, Council on Cardiovascular Nursing, and Interdisciplinary Council on Quality of Care and Outcomes Research. Testing of low-risk patients presenting to the emergency department with chest pain: a scientific statement from the American Heart Association. Circulation. 2010;122(17):1756-1776.
13. Livschitz S, Sharabi Y, Yushin J, et al. Limited clinical value of exercise stress test for the screening of coronary artery disease in young, asymptomatic adult men. Am J Cardiol. 2000;86(4):462-464.
14. Miller T. Stress testing: the case for the standard treadmill test. Curr Opin Cardiol. 2011;26(5):363-369.
15. La Gerche A, Baggish A, Knuuti J, et al. Cardiac imaging and stress testing asymptomatic athletes to identify those at risk of sudden cardiac death. JACC Cardiovasc Imaging. 2013;6(9):993-1007.
16. Lauer M, Froelicher ES, Williams M, Kligfield P; American Heart Association Council on Clinical Cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention. Exercise testing in asymptomatic adults: a statement for professionals from the American Heart Association Council on Clinical Cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention. Circulation. 2005;112(5):771-776.
17. Sammito S, Gundlach N, Bockelmann I. Prevalence of cardiac arrhythmia under stress conditions in occupational health assessments of young military servicemen and servicewomen. Mil Med. 2016;181(4):369-372.
18. Mudrick DW, Cowper PA, Shah BR, et al. Downstream procedures and outcomes after stress testing for chest pain without known coronary artery disease in the United States. Am Heart J. 2012;163(3):454-461.
19. Christman MP, Bittencourt MS, Hulten E, et al. Yield of downstream tests after exercise treadmill testing. J Am Coll Cardiol. 2014;63(13):1264-1274.
20. Will J, Loustalot F, Hong Y. National trends in visits to physician offices and outpatient clinics for angina 1995 to 2010. Circ Cardiovasc Qual Outcomes. 2014;7(1):110-117.
21. Kini V, McCarthy F, Dayoub E, et al. Cardiac stress test trends among US patients younger than 65 years, 2005-2012. JAMA Cardiol. 2016;1(9):1038-1042.
22. Ladapo JA, Blecker S, Douglas PS. Physician decision making and trends in the use of cardiac stress testing in the United States: an analysis of repeated cross-sectional data. Ann Intern Med. 2014;161(7):482-490.
23. Winchester DE, Brandt J, Schmidt C, Schmidt C, Allen B, Payton T, Amsterdam EA. Diagnostic yield of routine noninvasive cardiovascular testing in low-risk acute chest pain patients. Am J Cardiol. 2015;116(2):204-207.
24. Hermann L, Weingart SD, Duvall W, Henzlova MJ. The limited utility of routine cardiac stress testing in emergency department chest pain patients younger than 40 years. Ann Emerg Med. 2009;54(1):12-16.
25. Ladapo JA, Blecker S, Elashoff MR, et al. Clinical implications of referral bias in the diagnostic performance of exercise testing for coronary artery disease. J Am Heart Assoc. 2013;2(6):e000505.
1. Fowler-Brown A, Pignone M, Pletcher M, et al. Exercise tolerance testing to screen for coronary heart disease: a systematic review for the technical support for the U.S. Preventive Services Task Force. Ann Intern Med. 2004;140(7):W9-W24.
2. Gibbons RJ, Balady GJ, Bricker JT, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Committee to Update the 1997 Exercise Testing Guidelines. ACC/AHA 2002 guideline update for exercise testing: summary article. A report of the American College of Cardiology/American Heart Association Task Force on practice guidelines (Committee to Update the 1997 Exercise Testing Guidelines). J Am Coll Cardiol. 2002;40(8):1531-1540.
3. Chou R, Arora B, Dana T, Fu R, Miranda Walker M, Humphrey L. Screening Asymptomatic Adults for Coronary Heart Disease With Resting or Exercise Electrocardiography: Systematic Review to Update the 2004 U.S. Preventive Services Task Force recommendation. Evidence Synthesis No. 88. AHRQ Publication No. 11-05158-EF-1. Rockville, MD: Agency for Healthcare Research and Quality; September 2011.
4. Fihn S, Gardin J, Abrams J, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J Am Coll Cardiology. 2012;60(24):e44-e164.
5. Gibbons RJ, Balady GJ, Beasley JW, et al. ACC/AHA guidelines for exercise testing: executive summary. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Exercise Testing). Circulation. 1997;96(1):345-354.
6. Greenland P, Alpert JS, Beller GA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults. A report of the American College of Cardiology Foundation/ American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56(25):e50-e103.
7. Diamond G, Forrester J. Analysis of probability as an aid in the clinical diagnosis of coronary artery disease. N Engl J Med. 1979;300(24):1350-1358.
8. Goff D, Lloyd-Jones D, Bennett G, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25)(suppl 2):S49-S73.
9. Greenland P, Gaziano J. Selecting asymptomatic patients for coronary computed tomography or electrocardiographic exercise testing. N Engl J Med. 2003;349(5):465-473.
10. Shah N, Soon K, Wong C, Kellu AM. Screening for asymptomatic coronary heart disease in the young ‘at risk’ population: who and how? Int J Cardiol Heart Vasc. 2014;6:60-65.
11. Morise A, Evans M, Jalisi F, Shetty R, Stauffer M. A pretest prognostic score to assess patients undergoing exercise or pharmacological stress testing. Heart. 2007;93(2):200-204.
12. Amsterdam EA, Kirk JD, Bluemke DA, et al; American Heart Association Exercise, Cardiac Rehabilitation, and Prevention Committee of the Council on Clinical Cardiology, Council on Cardiovascular Nursing, and Interdisciplinary Council on Quality of Care and Outcomes Research. Testing of low-risk patients presenting to the emergency department with chest pain: a scientific statement from the American Heart Association. Circulation. 2010;122(17):1756-1776.
13. Livschitz S, Sharabi Y, Yushin J, et al. Limited clinical value of exercise stress test for the screening of coronary artery disease in young, asymptomatic adult men. Am J Cardiol. 2000;86(4):462-464.
14. Miller T. Stress testing: the case for the standard treadmill test. Curr Opin Cardiol. 2011;26(5):363-369.
15. La Gerche A, Baggish A, Knuuti J, et al. Cardiac imaging and stress testing asymptomatic athletes to identify those at risk of sudden cardiac death. JACC Cardiovasc Imaging. 2013;6(9):993-1007.
16. Lauer M, Froelicher ES, Williams M, Kligfield P; American Heart Association Council on Clinical Cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention. Exercise testing in asymptomatic adults: a statement for professionals from the American Heart Association Council on Clinical Cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention. Circulation. 2005;112(5):771-776.
17. Sammito S, Gundlach N, Bockelmann I. Prevalence of cardiac arrhythmia under stress conditions in occupational health assessments of young military servicemen and servicewomen. Mil Med. 2016;181(4):369-372.
18. Mudrick DW, Cowper PA, Shah BR, et al. Downstream procedures and outcomes after stress testing for chest pain without known coronary artery disease in the United States. Am Heart J. 2012;163(3):454-461.
19. Christman MP, Bittencourt MS, Hulten E, et al. Yield of downstream tests after exercise treadmill testing. J Am Coll Cardiol. 2014;63(13):1264-1274.
20. Will J, Loustalot F, Hong Y. National trends in visits to physician offices and outpatient clinics for angina 1995 to 2010. Circ Cardiovasc Qual Outcomes. 2014;7(1):110-117.
21. Kini V, McCarthy F, Dayoub E, et al. Cardiac stress test trends among US patients younger than 65 years, 2005-2012. JAMA Cardiol. 2016;1(9):1038-1042.
22. Ladapo JA, Blecker S, Douglas PS. Physician decision making and trends in the use of cardiac stress testing in the United States: an analysis of repeated cross-sectional data. Ann Intern Med. 2014;161(7):482-490.
23. Winchester DE, Brandt J, Schmidt C, Schmidt C, Allen B, Payton T, Amsterdam EA. Diagnostic yield of routine noninvasive cardiovascular testing in low-risk acute chest pain patients. Am J Cardiol. 2015;116(2):204-207.
24. Hermann L, Weingart SD, Duvall W, Henzlova MJ. The limited utility of routine cardiac stress testing in emergency department chest pain patients younger than 40 years. Ann Emerg Med. 2009;54(1):12-16.
25. Ladapo JA, Blecker S, Elashoff MR, et al. Clinical implications of referral bias in the diagnostic performance of exercise testing for coronary artery disease. J Am Heart Assoc. 2013;2(6):e000505.
Evaluating a Veterans Affairs Home-Based Primary Care Population for Patients at High Risk of Osteoporosis
Osteoporosis is a disease characterized by the loss of bone density.1 Bone is normally porous and is in a state of flux due to changes in regeneration caused by osteoclast or osteoblast activity. However, age and other factors can accelerate loss in bone density and lead to decreased bone strength and an increased risk of fracture. In men, bone mineral density (BMD) can begin to decline as early as age 30 to 40 years. By age 80 years, 25% of total bone mass may be lost.2
Of the 44 million Americans with low BMD or osteoporosis, 20% are men.1 This group accounts for up to 40% of all osteoporotic fractures. About 1 in 4 men aged ≥ 50 years may experience a lifetime fracture. Fractures may lead to chronic pain, disability, increased dependence, and potentially death. These complications cause expenditures upward of $4.1 billion annually in North America alone.3,4 About 80,000 US men will experience a hip fracture each year, one-third of whom will die within that year. This constitutes a mortality rate 2 to 3 times higher than that of women. Osteoporosis often goes undiagnosed and untreated due to a lack of symptoms until a fracture occurs, underlining the potential benefit of preemptive screening.
In 2007, Shekell and colleagues outlined how the US Department of Veterans Affairs (VA) screened men for osteoporosis.5 At the time, 95% of the VA population was male, though it has since dropped to 91%.6 Shekell and colleagues estimated that about 200,0000 to 400,0000 male veterans had osteoporosis.5 Osteoporotic risk factors deemed specific to veterans were excessive alcohol use, spinal cord injury and lack of weight-bearing exercise, prolonged corticosteroid use, and androgen deprivation therapy in prostate cancer. Different screening techniques were assessed, and the VA recommended the Osteoporosis Self-Assessment Tool (OST).5 Many organizations have developed clinical guidance, including who should be screened; however, screening for men remains a controversial area due to a lack of any strong recommendations (Table 1).
Endocrine Society screening guidelines for men are the most specific: testing BMD in men aged ≥ 70 years, or if aged 50 to 69 years with an additional risk factor (eg, low body weight, smoking, chronic obstructive pulmonary disease, chronic steroid use).1 The Fracture Risk Assessment tool (FRAX) score is often cited as a common screening tool. It is a free online questionnaire that provides a 10-year probability risk of hip or major osteoporotic fracture.11 However, this tool is limited by age, weight, and the assumption that all questions are answered accurately. Some of the information required includes the presence of a number of risk factors, such as alcohol use, glucocorticoids, and medical history of rheumatoid arthritis, among others (Table 2). The OST score, on the other hand, is a calculation that does not take into account other risk factors (Figure 1). This tool categorizes the patient into low, moderate, or high risk for osteoporosis.8
In a study of 4,000 men aged ≥ 70 years,
A 2017 VA Office of Rural Health study examined the utility of OST to screen referred patients aged > 50 years to receive DEXA scans in patient aligned care team (PACT) clinics at 3 different VA locations.13 The study excluded patients who had been screened previously or treated for osteoporosis, were receiving hospice care; 1 site excluded patients aged > 88 years. Two of the sites also reviewed the patient’s medications to screen for agents that may contribute to increased fracture risk. Veterans identified as high risk were referred for education and offered a DEXA scan and treatment. In total, 867 veterans were screened; 19% (168) were deemed high risk, and 6% (53) underwent DEXA scans. The study noted that only 15 patients had reportable DEXA scans and 10 were positive for bone disease.
As there has been documented success in the PACT setting in implementing standardized protocols for screening and treating veterans, it is reasonable to extend the concept into other VA services. The home-based primary care (HBPC) population is especially vulnerable due to the age of patients, limited weight-bearing exercise to improve bone strength, and limited access to DEXA scans due to difficulty traveling outside of the home. Despite these issues, a goal of the HBPC service is to provide continual care for veterans and improve their health so they may return to the community setting. As a result, patients are followed frequently, providing many opportunities for interventions. This study aims to determine the proportion of HBPC patients who are at high risk for osteoporosis and can receive a DEXA scan for evaluation.
Methods
This study was a retrospective chart analysis using descriptive statistics. It was reviewed and approved by the institutional review board at Captain James A. Lovell Federal Health Care Center (FHCC). Patients were included in the study if they were enrolled in the HBPC program at FHCC. Patients were excluded if they were receiving hospice or palliative care, had a limited life expectancy per the HBPC provider, or had a diagnosis of osteoporosis that was being managed by a VA endocrinologist, rheumatologist, or non-VA provider.
The study was conducted from February 1, 2018, through November 30, 2018. All chart reviews were done through the FHCC electronic health record. A minimum of 80 and maximum of 150 charts were reviewed as this was the typical patient volume in the HBPC program. Basic demographic information was collected and analyzed by calculating FRAX and OST scores. With the results, patients were classified as low or high risk of developing osteoporosis, and whether a DEXA scan should be recommended.
Results
After chart review, 83 patients were enrolled in the FHCC HBPC program during the study period. Out of these, 5 patients were excluded due to hospice or palliative care status, limited life expectancy, or had their osteoporosis managed by another non-HBPC provider. As a result, 78 patients were analyzed to determine their risk of osteoporosis (Figure 2). Most of the patients were white males with a median age of 82 years. A majority of the patients did not have any current or previous treatment with bisphosphonates, 77% had normal vitamin D levels, and only 13% (10) were current smokers; of the male patients only 21% (15) had a previous DEXA scan (Table 3).
The FRAX and OST scores for each male patient were calculated (Table 4). Half the patients were low risk for osteoporosis. Just 20% (14) of the patients were at high risk for osteoporosis, and only 6 of those had DEXA scans. However, if expanding the criteria to OST scores of < 2, then only 24% (10) received DEXA scans. When calculating FRAX scores, 30% (21) had ≥ 9.3% for major osteoporotic fracture risk, and only 19% (4) had received a DEXA scan.
Discussion
Based on the collected data, many of the male HBPC patients have not had an evaluation for osteoporosis despite being in a high-risk population and meeting some of the screening guidelines by various organizations.1 Based on Diem and colleagues and the 2007 VA report, utilizing OST scores could help capture a subset of patients that would be referred for DEXA scans.5,12 Of the 60% (42) of patients that met OST scores of < 2, 76% (32) of them could have been referred for DEXA scans for osteoporosis evaluation. However, at the time of publication of this article, 50% (16) of the patients have been discharged from the service without interventions. Of the remaining 16 patients, only 2 were referred for a DEXA scan, and 1 patient had confirmed osteoporosis. Currently, these results have been reviewed by the HBPC provider, and plans are in place for DEXA scan referrals for the remaining patients. In addition, for new patients admitted to the program and during annual reviews, the plan is to use OST scores to help screen for osteoporosis.
Limitations
The HBPC population is often in flux due to discharges as patients pass away, become eligible for long-term care, advance to hospice or palliative care status, or see an improvement in their condition to transition back into the community. Along with patients who are bed-bound, have poor prognosis, and barriers to access (eg, transportation issues), interventions for DEXA scan referrals are often not clinically indicated. During calculations of the FRAX score, documentation is often missing from a patient’s medical chart, making it difficult to answer all questions on the questionnaire. This does increase the utility of the OST score as the calculation is much easier and does not rely on other osteoporotic factors. Despite these restrictions for offering DEXA scans, the HBPC service has a high standard of excellence in preventing falls, a major contributor to fractures. Physical therapy services are readily available, nursing visits are frequent and as clinically indicated, vitamin D levels are maintained within normal limits via supplementation, and medication management is performed at least quarterly among other interventions.
Conclusions
The retrospective chart review of patients in the HBPC program suggests that there may be a lack of standardized screening for osteoporosis in the male patient population. As seen within the data, there is great potential for interventions as many of the patients would be candidates for screening based on the OST score. The tool is easy to use and readily accessible to all health care providers and staff. By increasing screening of eligible patients, it also increases the identification of those who would benefit from osteoporosis treatment. While the HBPC population has access limitations (eg, homebound, limited life expectancy), the implementation of a protocol and extension of concepts from this study can be extrapolated into other PACT clinics at VA facilities. Osteoporosis in the male population is often overlooked, but screening procedures can help reduce health care expenditures.
1. Watts NB, Adler RA, Bilezikian JP, et al; Endocrine Society. Osteoporosis in men: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2012;97(6):1802-1822.
2. Holt G, Smith R, Duncan K, Hutchison JD, Gregori A. Gender differences in epidemiology and outcome after hip fracture: evidence from the Scottish Hip Fracture Audit. J Bone Joint Surg Br. 2008;90(4):480-483.
3. Ackman JM, Lata PF, Schuna AA, Elliott ME. Bone health evaluation in a veteran population: a need for the Fracture Risk Assessment tool (FRAX). Ann Pharmacother. 2014;48(10):1288-1293.
4. International Osteoporosis Foundation. Osteoporosis in men: why change needs to happen. http://share.iofbone-health.org/WOD/2014/thematic-report/WOD14-Report.pdf. Published 2014. Accessed September 16, 2019.
5. Shekell P, Munjas B, Liu H, et al. Screening Men for Osteoporosis: Who & How. Evidence-based Synthesis Program. Washington, DC: Department of Veterans Affairs; 2007.
6. US Department of Veterans Affairs, National Center for Veterans Analysis and Statistics. Veteran population. https://www.va.gov/vetdata/Veteran_Population.asp. Accessed September 16, 2019.
7. Rao SS, Budhwar N, Ashfaque A. Osteoporosis in men. Am Fam Physician. 2010;82(5):503-508.
8. US Preventive Services Task Force, Curry SJ, Krist AH, et al. Screening for osteoporosis to prevent fractures: US Preventive Services Task Force recommendation statement. JAMA. 2018;319(24):2521-2531.
9. Viswanathan M, Reddy S, Berkman N, et al. Screening to prevent osteoporotic fractures updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2018;319(24):2532-2551.
10. Cosman F, de Beur SJ, LeBoff MS, et al; National Osteoporosis Foundation. Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2014;25(10):2359-2381.
11. Centre for Metabolic Bone Diseases, University of Sheffield, UK. FRAX Fracture Risk Assessment Tool. http://www.sheffield.ac.uk/FRAX/tool.aspx?country=9. Accessed September 16, 2019.
12. Diem SJ, Peters KW, Gourlay ML, et al; Osteoporotic Fractures in Men Research Group. Screening for osteoporosis in older men: operating characteristics of proposed strategies for selecting men for BMD testing. J Gen Intern Med. 2017;32(11):1235-1241.
13. US Department of Veterans Affairs, Office of Rural Health. Osteoporosis risk assessment using Osteoporosis Self-Assessment Tool (OST) and other interventions at rural facilities. https://www.ruralhealth.va.gov/docs/promise/2017_02_01_OST_Issue%20Brief_v2.pdf. Published February 7, 2019. Accessed September 16, 2019.
Osteoporosis is a disease characterized by the loss of bone density.1 Bone is normally porous and is in a state of flux due to changes in regeneration caused by osteoclast or osteoblast activity. However, age and other factors can accelerate loss in bone density and lead to decreased bone strength and an increased risk of fracture. In men, bone mineral density (BMD) can begin to decline as early as age 30 to 40 years. By age 80 years, 25% of total bone mass may be lost.2
Of the 44 million Americans with low BMD or osteoporosis, 20% are men.1 This group accounts for up to 40% of all osteoporotic fractures. About 1 in 4 men aged ≥ 50 years may experience a lifetime fracture. Fractures may lead to chronic pain, disability, increased dependence, and potentially death. These complications cause expenditures upward of $4.1 billion annually in North America alone.3,4 About 80,000 US men will experience a hip fracture each year, one-third of whom will die within that year. This constitutes a mortality rate 2 to 3 times higher than that of women. Osteoporosis often goes undiagnosed and untreated due to a lack of symptoms until a fracture occurs, underlining the potential benefit of preemptive screening.
In 2007, Shekell and colleagues outlined how the US Department of Veterans Affairs (VA) screened men for osteoporosis.5 At the time, 95% of the VA population was male, though it has since dropped to 91%.6 Shekell and colleagues estimated that about 200,0000 to 400,0000 male veterans had osteoporosis.5 Osteoporotic risk factors deemed specific to veterans were excessive alcohol use, spinal cord injury and lack of weight-bearing exercise, prolonged corticosteroid use, and androgen deprivation therapy in prostate cancer. Different screening techniques were assessed, and the VA recommended the Osteoporosis Self-Assessment Tool (OST).5 Many organizations have developed clinical guidance, including who should be screened; however, screening for men remains a controversial area due to a lack of any strong recommendations (Table 1).
Endocrine Society screening guidelines for men are the most specific: testing BMD in men aged ≥ 70 years, or if aged 50 to 69 years with an additional risk factor (eg, low body weight, smoking, chronic obstructive pulmonary disease, chronic steroid use).1 The Fracture Risk Assessment tool (FRAX) score is often cited as a common screening tool. It is a free online questionnaire that provides a 10-year probability risk of hip or major osteoporotic fracture.11 However, this tool is limited by age, weight, and the assumption that all questions are answered accurately. Some of the information required includes the presence of a number of risk factors, such as alcohol use, glucocorticoids, and medical history of rheumatoid arthritis, among others (Table 2). The OST score, on the other hand, is a calculation that does not take into account other risk factors (Figure 1). This tool categorizes the patient into low, moderate, or high risk for osteoporosis.8
In a study of 4,000 men aged ≥ 70 years,
A 2017 VA Office of Rural Health study examined the utility of OST to screen referred patients aged > 50 years to receive DEXA scans in patient aligned care team (PACT) clinics at 3 different VA locations.13 The study excluded patients who had been screened previously or treated for osteoporosis, were receiving hospice care; 1 site excluded patients aged > 88 years. Two of the sites also reviewed the patient’s medications to screen for agents that may contribute to increased fracture risk. Veterans identified as high risk were referred for education and offered a DEXA scan and treatment. In total, 867 veterans were screened; 19% (168) were deemed high risk, and 6% (53) underwent DEXA scans. The study noted that only 15 patients had reportable DEXA scans and 10 were positive for bone disease.
As there has been documented success in the PACT setting in implementing standardized protocols for screening and treating veterans, it is reasonable to extend the concept into other VA services. The home-based primary care (HBPC) population is especially vulnerable due to the age of patients, limited weight-bearing exercise to improve bone strength, and limited access to DEXA scans due to difficulty traveling outside of the home. Despite these issues, a goal of the HBPC service is to provide continual care for veterans and improve their health so they may return to the community setting. As a result, patients are followed frequently, providing many opportunities for interventions. This study aims to determine the proportion of HBPC patients who are at high risk for osteoporosis and can receive a DEXA scan for evaluation.
Methods
This study was a retrospective chart analysis using descriptive statistics. It was reviewed and approved by the institutional review board at Captain James A. Lovell Federal Health Care Center (FHCC). Patients were included in the study if they were enrolled in the HBPC program at FHCC. Patients were excluded if they were receiving hospice or palliative care, had a limited life expectancy per the HBPC provider, or had a diagnosis of osteoporosis that was being managed by a VA endocrinologist, rheumatologist, or non-VA provider.
The study was conducted from February 1, 2018, through November 30, 2018. All chart reviews were done through the FHCC electronic health record. A minimum of 80 and maximum of 150 charts were reviewed as this was the typical patient volume in the HBPC program. Basic demographic information was collected and analyzed by calculating FRAX and OST scores. With the results, patients were classified as low or high risk of developing osteoporosis, and whether a DEXA scan should be recommended.
Results
After chart review, 83 patients were enrolled in the FHCC HBPC program during the study period. Out of these, 5 patients were excluded due to hospice or palliative care status, limited life expectancy, or had their osteoporosis managed by another non-HBPC provider. As a result, 78 patients were analyzed to determine their risk of osteoporosis (Figure 2). Most of the patients were white males with a median age of 82 years. A majority of the patients did not have any current or previous treatment with bisphosphonates, 77% had normal vitamin D levels, and only 13% (10) were current smokers; of the male patients only 21% (15) had a previous DEXA scan (Table 3).
The FRAX and OST scores for each male patient were calculated (Table 4). Half the patients were low risk for osteoporosis. Just 20% (14) of the patients were at high risk for osteoporosis, and only 6 of those had DEXA scans. However, if expanding the criteria to OST scores of < 2, then only 24% (10) received DEXA scans. When calculating FRAX scores, 30% (21) had ≥ 9.3% for major osteoporotic fracture risk, and only 19% (4) had received a DEXA scan.
Discussion
Based on the collected data, many of the male HBPC patients have not had an evaluation for osteoporosis despite being in a high-risk population and meeting some of the screening guidelines by various organizations.1 Based on Diem and colleagues and the 2007 VA report, utilizing OST scores could help capture a subset of patients that would be referred for DEXA scans.5,12 Of the 60% (42) of patients that met OST scores of < 2, 76% (32) of them could have been referred for DEXA scans for osteoporosis evaluation. However, at the time of publication of this article, 50% (16) of the patients have been discharged from the service without interventions. Of the remaining 16 patients, only 2 were referred for a DEXA scan, and 1 patient had confirmed osteoporosis. Currently, these results have been reviewed by the HBPC provider, and plans are in place for DEXA scan referrals for the remaining patients. In addition, for new patients admitted to the program and during annual reviews, the plan is to use OST scores to help screen for osteoporosis.
Limitations
The HBPC population is often in flux due to discharges as patients pass away, become eligible for long-term care, advance to hospice or palliative care status, or see an improvement in their condition to transition back into the community. Along with patients who are bed-bound, have poor prognosis, and barriers to access (eg, transportation issues), interventions for DEXA scan referrals are often not clinically indicated. During calculations of the FRAX score, documentation is often missing from a patient’s medical chart, making it difficult to answer all questions on the questionnaire. This does increase the utility of the OST score as the calculation is much easier and does not rely on other osteoporotic factors. Despite these restrictions for offering DEXA scans, the HBPC service has a high standard of excellence in preventing falls, a major contributor to fractures. Physical therapy services are readily available, nursing visits are frequent and as clinically indicated, vitamin D levels are maintained within normal limits via supplementation, and medication management is performed at least quarterly among other interventions.
Conclusions
The retrospective chart review of patients in the HBPC program suggests that there may be a lack of standardized screening for osteoporosis in the male patient population. As seen within the data, there is great potential for interventions as many of the patients would be candidates for screening based on the OST score. The tool is easy to use and readily accessible to all health care providers and staff. By increasing screening of eligible patients, it also increases the identification of those who would benefit from osteoporosis treatment. While the HBPC population has access limitations (eg, homebound, limited life expectancy), the implementation of a protocol and extension of concepts from this study can be extrapolated into other PACT clinics at VA facilities. Osteoporosis in the male population is often overlooked, but screening procedures can help reduce health care expenditures.
Osteoporosis is a disease characterized by the loss of bone density.1 Bone is normally porous and is in a state of flux due to changes in regeneration caused by osteoclast or osteoblast activity. However, age and other factors can accelerate loss in bone density and lead to decreased bone strength and an increased risk of fracture. In men, bone mineral density (BMD) can begin to decline as early as age 30 to 40 years. By age 80 years, 25% of total bone mass may be lost.2
Of the 44 million Americans with low BMD or osteoporosis, 20% are men.1 This group accounts for up to 40% of all osteoporotic fractures. About 1 in 4 men aged ≥ 50 years may experience a lifetime fracture. Fractures may lead to chronic pain, disability, increased dependence, and potentially death. These complications cause expenditures upward of $4.1 billion annually in North America alone.3,4 About 80,000 US men will experience a hip fracture each year, one-third of whom will die within that year. This constitutes a mortality rate 2 to 3 times higher than that of women. Osteoporosis often goes undiagnosed and untreated due to a lack of symptoms until a fracture occurs, underlining the potential benefit of preemptive screening.
In 2007, Shekell and colleagues outlined how the US Department of Veterans Affairs (VA) screened men for osteoporosis.5 At the time, 95% of the VA population was male, though it has since dropped to 91%.6 Shekell and colleagues estimated that about 200,0000 to 400,0000 male veterans had osteoporosis.5 Osteoporotic risk factors deemed specific to veterans were excessive alcohol use, spinal cord injury and lack of weight-bearing exercise, prolonged corticosteroid use, and androgen deprivation therapy in prostate cancer. Different screening techniques were assessed, and the VA recommended the Osteoporosis Self-Assessment Tool (OST).5 Many organizations have developed clinical guidance, including who should be screened; however, screening for men remains a controversial area due to a lack of any strong recommendations (Table 1).
Endocrine Society screening guidelines for men are the most specific: testing BMD in men aged ≥ 70 years, or if aged 50 to 69 years with an additional risk factor (eg, low body weight, smoking, chronic obstructive pulmonary disease, chronic steroid use).1 The Fracture Risk Assessment tool (FRAX) score is often cited as a common screening tool. It is a free online questionnaire that provides a 10-year probability risk of hip or major osteoporotic fracture.11 However, this tool is limited by age, weight, and the assumption that all questions are answered accurately. Some of the information required includes the presence of a number of risk factors, such as alcohol use, glucocorticoids, and medical history of rheumatoid arthritis, among others (Table 2). The OST score, on the other hand, is a calculation that does not take into account other risk factors (Figure 1). This tool categorizes the patient into low, moderate, or high risk for osteoporosis.8
In a study of 4,000 men aged ≥ 70 years,
A 2017 VA Office of Rural Health study examined the utility of OST to screen referred patients aged > 50 years to receive DEXA scans in patient aligned care team (PACT) clinics at 3 different VA locations.13 The study excluded patients who had been screened previously or treated for osteoporosis, were receiving hospice care; 1 site excluded patients aged > 88 years. Two of the sites also reviewed the patient’s medications to screen for agents that may contribute to increased fracture risk. Veterans identified as high risk were referred for education and offered a DEXA scan and treatment. In total, 867 veterans were screened; 19% (168) were deemed high risk, and 6% (53) underwent DEXA scans. The study noted that only 15 patients had reportable DEXA scans and 10 were positive for bone disease.
As there has been documented success in the PACT setting in implementing standardized protocols for screening and treating veterans, it is reasonable to extend the concept into other VA services. The home-based primary care (HBPC) population is especially vulnerable due to the age of patients, limited weight-bearing exercise to improve bone strength, and limited access to DEXA scans due to difficulty traveling outside of the home. Despite these issues, a goal of the HBPC service is to provide continual care for veterans and improve their health so they may return to the community setting. As a result, patients are followed frequently, providing many opportunities for interventions. This study aims to determine the proportion of HBPC patients who are at high risk for osteoporosis and can receive a DEXA scan for evaluation.
Methods
This study was a retrospective chart analysis using descriptive statistics. It was reviewed and approved by the institutional review board at Captain James A. Lovell Federal Health Care Center (FHCC). Patients were included in the study if they were enrolled in the HBPC program at FHCC. Patients were excluded if they were receiving hospice or palliative care, had a limited life expectancy per the HBPC provider, or had a diagnosis of osteoporosis that was being managed by a VA endocrinologist, rheumatologist, or non-VA provider.
The study was conducted from February 1, 2018, through November 30, 2018. All chart reviews were done through the FHCC electronic health record. A minimum of 80 and maximum of 150 charts were reviewed as this was the typical patient volume in the HBPC program. Basic demographic information was collected and analyzed by calculating FRAX and OST scores. With the results, patients were classified as low or high risk of developing osteoporosis, and whether a DEXA scan should be recommended.
Results
After chart review, 83 patients were enrolled in the FHCC HBPC program during the study period. Out of these, 5 patients were excluded due to hospice or palliative care status, limited life expectancy, or had their osteoporosis managed by another non-HBPC provider. As a result, 78 patients were analyzed to determine their risk of osteoporosis (Figure 2). Most of the patients were white males with a median age of 82 years. A majority of the patients did not have any current or previous treatment with bisphosphonates, 77% had normal vitamin D levels, and only 13% (10) were current smokers; of the male patients only 21% (15) had a previous DEXA scan (Table 3).
The FRAX and OST scores for each male patient were calculated (Table 4). Half the patients were low risk for osteoporosis. Just 20% (14) of the patients were at high risk for osteoporosis, and only 6 of those had DEXA scans. However, if expanding the criteria to OST scores of < 2, then only 24% (10) received DEXA scans. When calculating FRAX scores, 30% (21) had ≥ 9.3% for major osteoporotic fracture risk, and only 19% (4) had received a DEXA scan.
Discussion
Based on the collected data, many of the male HBPC patients have not had an evaluation for osteoporosis despite being in a high-risk population and meeting some of the screening guidelines by various organizations.1 Based on Diem and colleagues and the 2007 VA report, utilizing OST scores could help capture a subset of patients that would be referred for DEXA scans.5,12 Of the 60% (42) of patients that met OST scores of < 2, 76% (32) of them could have been referred for DEXA scans for osteoporosis evaluation. However, at the time of publication of this article, 50% (16) of the patients have been discharged from the service without interventions. Of the remaining 16 patients, only 2 were referred for a DEXA scan, and 1 patient had confirmed osteoporosis. Currently, these results have been reviewed by the HBPC provider, and plans are in place for DEXA scan referrals for the remaining patients. In addition, for new patients admitted to the program and during annual reviews, the plan is to use OST scores to help screen for osteoporosis.
Limitations
The HBPC population is often in flux due to discharges as patients pass away, become eligible for long-term care, advance to hospice or palliative care status, or see an improvement in their condition to transition back into the community. Along with patients who are bed-bound, have poor prognosis, and barriers to access (eg, transportation issues), interventions for DEXA scan referrals are often not clinically indicated. During calculations of the FRAX score, documentation is often missing from a patient’s medical chart, making it difficult to answer all questions on the questionnaire. This does increase the utility of the OST score as the calculation is much easier and does not rely on other osteoporotic factors. Despite these restrictions for offering DEXA scans, the HBPC service has a high standard of excellence in preventing falls, a major contributor to fractures. Physical therapy services are readily available, nursing visits are frequent and as clinically indicated, vitamin D levels are maintained within normal limits via supplementation, and medication management is performed at least quarterly among other interventions.
Conclusions
The retrospective chart review of patients in the HBPC program suggests that there may be a lack of standardized screening for osteoporosis in the male patient population. As seen within the data, there is great potential for interventions as many of the patients would be candidates for screening based on the OST score. The tool is easy to use and readily accessible to all health care providers and staff. By increasing screening of eligible patients, it also increases the identification of those who would benefit from osteoporosis treatment. While the HBPC population has access limitations (eg, homebound, limited life expectancy), the implementation of a protocol and extension of concepts from this study can be extrapolated into other PACT clinics at VA facilities. Osteoporosis in the male population is often overlooked, but screening procedures can help reduce health care expenditures.
1. Watts NB, Adler RA, Bilezikian JP, et al; Endocrine Society. Osteoporosis in men: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2012;97(6):1802-1822.
2. Holt G, Smith R, Duncan K, Hutchison JD, Gregori A. Gender differences in epidemiology and outcome after hip fracture: evidence from the Scottish Hip Fracture Audit. J Bone Joint Surg Br. 2008;90(4):480-483.
3. Ackman JM, Lata PF, Schuna AA, Elliott ME. Bone health evaluation in a veteran population: a need for the Fracture Risk Assessment tool (FRAX). Ann Pharmacother. 2014;48(10):1288-1293.
4. International Osteoporosis Foundation. Osteoporosis in men: why change needs to happen. http://share.iofbone-health.org/WOD/2014/thematic-report/WOD14-Report.pdf. Published 2014. Accessed September 16, 2019.
5. Shekell P, Munjas B, Liu H, et al. Screening Men for Osteoporosis: Who & How. Evidence-based Synthesis Program. Washington, DC: Department of Veterans Affairs; 2007.
6. US Department of Veterans Affairs, National Center for Veterans Analysis and Statistics. Veteran population. https://www.va.gov/vetdata/Veteran_Population.asp. Accessed September 16, 2019.
7. Rao SS, Budhwar N, Ashfaque A. Osteoporosis in men. Am Fam Physician. 2010;82(5):503-508.
8. US Preventive Services Task Force, Curry SJ, Krist AH, et al. Screening for osteoporosis to prevent fractures: US Preventive Services Task Force recommendation statement. JAMA. 2018;319(24):2521-2531.
9. Viswanathan M, Reddy S, Berkman N, et al. Screening to prevent osteoporotic fractures updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2018;319(24):2532-2551.
10. Cosman F, de Beur SJ, LeBoff MS, et al; National Osteoporosis Foundation. Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2014;25(10):2359-2381.
11. Centre for Metabolic Bone Diseases, University of Sheffield, UK. FRAX Fracture Risk Assessment Tool. http://www.sheffield.ac.uk/FRAX/tool.aspx?country=9. Accessed September 16, 2019.
12. Diem SJ, Peters KW, Gourlay ML, et al; Osteoporotic Fractures in Men Research Group. Screening for osteoporosis in older men: operating characteristics of proposed strategies for selecting men for BMD testing. J Gen Intern Med. 2017;32(11):1235-1241.
13. US Department of Veterans Affairs, Office of Rural Health. Osteoporosis risk assessment using Osteoporosis Self-Assessment Tool (OST) and other interventions at rural facilities. https://www.ruralhealth.va.gov/docs/promise/2017_02_01_OST_Issue%20Brief_v2.pdf. Published February 7, 2019. Accessed September 16, 2019.
1. Watts NB, Adler RA, Bilezikian JP, et al; Endocrine Society. Osteoporosis in men: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2012;97(6):1802-1822.
2. Holt G, Smith R, Duncan K, Hutchison JD, Gregori A. Gender differences in epidemiology and outcome after hip fracture: evidence from the Scottish Hip Fracture Audit. J Bone Joint Surg Br. 2008;90(4):480-483.
3. Ackman JM, Lata PF, Schuna AA, Elliott ME. Bone health evaluation in a veteran population: a need for the Fracture Risk Assessment tool (FRAX). Ann Pharmacother. 2014;48(10):1288-1293.
4. International Osteoporosis Foundation. Osteoporosis in men: why change needs to happen. http://share.iofbone-health.org/WOD/2014/thematic-report/WOD14-Report.pdf. Published 2014. Accessed September 16, 2019.
5. Shekell P, Munjas B, Liu H, et al. Screening Men for Osteoporosis: Who & How. Evidence-based Synthesis Program. Washington, DC: Department of Veterans Affairs; 2007.
6. US Department of Veterans Affairs, National Center for Veterans Analysis and Statistics. Veteran population. https://www.va.gov/vetdata/Veteran_Population.asp. Accessed September 16, 2019.
7. Rao SS, Budhwar N, Ashfaque A. Osteoporosis in men. Am Fam Physician. 2010;82(5):503-508.
8. US Preventive Services Task Force, Curry SJ, Krist AH, et al. Screening for osteoporosis to prevent fractures: US Preventive Services Task Force recommendation statement. JAMA. 2018;319(24):2521-2531.
9. Viswanathan M, Reddy S, Berkman N, et al. Screening to prevent osteoporotic fractures updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2018;319(24):2532-2551.
10. Cosman F, de Beur SJ, LeBoff MS, et al; National Osteoporosis Foundation. Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2014;25(10):2359-2381.
11. Centre for Metabolic Bone Diseases, University of Sheffield, UK. FRAX Fracture Risk Assessment Tool. http://www.sheffield.ac.uk/FRAX/tool.aspx?country=9. Accessed September 16, 2019.
12. Diem SJ, Peters KW, Gourlay ML, et al; Osteoporotic Fractures in Men Research Group. Screening for osteoporosis in older men: operating characteristics of proposed strategies for selecting men for BMD testing. J Gen Intern Med. 2017;32(11):1235-1241.
13. US Department of Veterans Affairs, Office of Rural Health. Osteoporosis risk assessment using Osteoporosis Self-Assessment Tool (OST) and other interventions at rural facilities. https://www.ruralhealth.va.gov/docs/promise/2017_02_01_OST_Issue%20Brief_v2.pdf. Published February 7, 2019. Accessed September 16, 2019.
Using Voogle to Search Within Patient Records in the VA Corporate Data Warehouse
Digitalization of patient-specific information over the past 2 decades has dramatically altered health care delivery. Nonetheless, this technology has yet to live up to its promise of improving patient outcomes, in part due to data storage challenges as well as the emphasis on data entry to support administrative and financial goals of the institution.1-4 Substantially less emphasis has been placed on the retrieval of information required for accurate diagnosis.
A new search engine, Voogle, is now available through Microsoft Internet Explorer (Redmond, WA) to all providers in the US Department of Veterans Affairs (VA) on any intranet-enabled computer behind the VA firewall. Voogle facilitates rapid query-based search and retrieval of patient-specific data in the VA Corporate Data Warehouse (CDW).
Case Example
A veteran presented requesting consideration for implantation of a new device for obstructive sleep apnea. Guidelines for implantation of the new device specify a narrow therapeutic window, so determination of his apnea-hypopnea index (AHI) was critical. The patient had received care at more than 20 VA facilities and knew the approximate year the test had been performed at a non-VA facility.
A health care provider (HCP) using Voogle from his VA computer indexed all Veterans Information Systems and Technology Architecture (VistA) notes for the desired date range. The indexing of > 200 notes was completed in seconds. The HCP opened the indexed records with Voogle and entered a query for “sleep apnea,” which displayed multiple instances of the term within the patient record notes. A VA HCP had previously entered the data from the outside sleep study into a note shortly after the study.
This information was found immediately by sorting the indexed notes by date. The total time required by Voogle to find and display the critical information from the sleep study entered at a different VA more than a dozen years earlier was about 1 minute. These data provided the information needed for decision making at the time of the current patient encounter, without which repeat (and unnecessary) testing would have been required.
Information Overload
Electronic health records (EHRs) such as VistA, upload, store, collate, and present data in near real-time across multiple locations. Although the availability of these data can potentially reduce the risk of error due to missing critical information, its sheer volume limits its utility for point-of-care decision making. Much patient-specific text data found in clinical notes are recorded for administrative, financial, and business purposes rather than to support patient care decision making.1-3 The majority of data documents processes of care rather than HCP observations, assessment of current status, or plans for care. Much of this text is inserted into templates, consists of imported structured data elements, and may contain repeated copy-and-paste free text.
Data uploaded to the CDW are aggregated from multiple hospitals, each with its own “instance” of VistA. Often the CDW contains thousands of text notes for a single patient. This volume of text may conceal critical historical information needed for patient care mixed with a plethora of duplicated or extraneous text entered to satisfy administrative requirements. The effects of information overload and poor system usability have been studied extensively in other disciplines, but this science has largely not been incorporated into EHR design.1,3,4
A position paper published recently by the American College of Physicians notes that physician cognitive work is adversely impacted by the incorporation of nonclinical information into the EHR for use by other administrative and financial functions.2
Information Chaos
Beasley and colleagues noted that information in an EHR needed for optimal care may be unavailable, inadequate, scattered, conflicting, lost, or inaccurate, a condition they term information chaos.5 Smith and colleagues reported that decision making in 1 of 7 primary care visits was impaired by missing critical information. Surveyed HCPs estimated that 44% of patients with missing information may receive compromised care as a result, including delayed or erroneous diagnosis and increased costs due to duplication of diagnostic testing.6
Even when technically available, the usability of patient-specific data needed for accurate diagnosis is compromised if the HCP cannot find the information. In most systems data storage paradigms mirror database design rather than provider cognitive models. Ultimately, the design of current EHR interaction paradigms squanders precious cognitive resources and time, particularly during patient encounters, leaving little available for the cognitive tasks necessary for accurate diagnosis and treatment decisions.1,3,4,7
VA Corporate Data Warehouse
VistA was implemented as a decentralized system with 130 instances, each of which is a freestanding EHR. However, as all systems share common data structures, the data can be combined from multiple instances when needed. The VA established a CDW more than 15 years ago in order to collate information from multiple sites to support operations as well as to seek new insights. The CDW currently updates nightly from all 130 EHR instances and is the only location in which patient information from all treating sites is combined. Voogle can access the CDW through the Veterans Informatics and Computing Infrastructure (VINCI), which is a mirror of the CDW databases and was established as a secure research environment.
The CDW contains information on 25 million veterans, with about 15 terabytes of text data. Approximately 4 billion data points, including 1 million text notes, are accrued nightly. The Integrated Control Number (ICN), a unique patient identifier, is assigned to each CDW record and is cross-indexed in the master patient index. All CDW data are tied to the ICN, facilitating access to and attribution of all patient data from all VA sites. Voogle relies on this identifier to build indexed files, or domains (which are document collections), of requested specific patient information to support its search algorithm.
Structured Data
Most of the data accrued in an EHR are structured data (such as laboratory test results and vital signs) and stored in a defined database framework. Voogle uses iFind (Intersystems Inc, Cambridge, MA) to index, count, and then search for requested information within structured data fields.
Unstructured Text
In contrast to structured data, text notes are stored as documents that are retrievable by patient, author, date, clinic, as well as numerous other fields. Unstructured (free) text notes are more information rich than either structured data or templated notes since their narrative format more closely parallels providers’ cognitive processes.1,7 The value of the narrative becomes even more critical in understanding complex clinical scenarios with multiple interacting disease processes. Narratives emphasize important details, reducing cognitive overload by reducing the salience of detail the author deems to be less critical. Narrative notes simultaneously assure availability through the use of unstandardized language, often including specialty and disease-specific abbreviations.1 Information needed for decision making in the illustrative case in this report was present only in HCP-entered free-text notes, as the structured data from which the free text was derived were not available.
Search
The introduction of search engines can be considered one of the major technologic disruptors of the 21st century.8 However, this advance has not yet made significant inroads into health care, despite advances in other domains. As of 2019, EHR users are still required to be familiar with the system’s data and menu structure in order to find needed information (or enter orders, code visits, or any of a number of tasks). Anecdotally, one of the authors (David Eibling) observed that the most common question from his trainees is “How do you . . .?” referring not to the care of the patient but rather to interaction with the EHR.
What is needed is a simple query-based application that finds the data on request. In addition to Voogle, other advances are being made in this arena such as the EMERSE, medical record search engine (project-emerse.org). Voogle was released to VA providers in 2017 and is available through the Internet Explorer browser on VA computers with VA intranet access. The goal of Voogle is to reduce HCP cognitive load by reducing the time and effort needed to seek relevant information for the care of a specific patient.
Natural Language Processing
Linguistic analysis of text seeking to understand its meaning constitutes a rapidly expanding field, with current heavy emphasis on the role of artificial intelligence and machine learning.1 Advances in processing both structured data and free-text notes in the health care domain is in its infancy, despite the investment of considerable resources. Undoubtedly, advances in this arena will dramatically change provider cognitive work in the next decades.
VistA is coded in MUMPS (Massachusetts General Hospital Utility Multi-Programming System, also known as M), which has been in use for more than 50 years. Voogle employs iKnow, a novel natural language processing (NLP) application that resides in Caché (Intersystems, Boston, MA), the vendor-supported MUMPS infrastructure VistA uses to perform text analysis. iKnow does not attempt to interpret the meaning of text as do other common NLP applications, but instead relies on the expert user to interpret the meaning of the analyzed text. iKnow initially divides sentences into relations (usually verbs) and concepts, and then generates an index of these entities. The efficiency of iKnow results in very rapid indexing—often several thousand notes (not an uncommon number) can be indexed in 20 to 30 seconds. iKnow responds to a user query by searching for specific terms or similar terms within the indexed text, and then displays these terms within the original source documents, similar to well-known commercial search engines. Structured data are indexed by the iFind program simultaneously with free-text indexing (Figure 1).
Security
Maintaining high levels of security of Health Insurance Portability and Accountability (HIPAA)-compliant information in an online application such as Voogle is critical to ensure trust of veterans and HCPs. All patient data accessed by Voogle reside within the secure firewall-protected VINCI environment. All moving information is protected with high-level encryption protocols (transport layer security [TLS]), and data at rest are also encrypted. As the application is online, no data are stored on the accessing device. Voogle uses a secure Microsoft Windows logon using VA Active Directory coupled with VistA authorization to regulate who can see the data and use the application. All access is audited, not only for “sensitive patients,” but also for specific data types. Users are reminded of this Voogle attribute on the home screen.
Accessing Voogle
Voogle is available on the VA intranet to all authorized users at https://voogle.vha.med.va.gov/voogle. To assure high-level security the application can only be accessed with the Internet Explorer browser using established user identification protocols to avoid unauthorized access or duplicative log-in tasks.
Indexing
Indexing is user-driven and is required prior to patient selection and term query. The user is prompted for a patient identifier and a date range. The CDW unique patient identifier is used for all internal processing. However, a social security number look-up table is incorporated to facilitate patient selection. The date field defaults to 3 years but can be extended to approximately the year 2000.
Queries
Entering the patient name in Lastname, Firstname (no space) format will yield a list of indexed patients. All access is audited in order to deter unauthorized queries. Data from a demonstration patient are displayed in Figures 2, 3, 4, 5,
and 6.
Structured Data Searches
Structured data categories that contain the queried term, as well as a term count, are displayed after the “Structured Data” toggle is selected (Figure 2). After the desired category (Figure 2: “Outpatient Rx”) is selected, Voogle accesses the data file and displays it as a grid (medication list, Figure 3). Filter and sort functions enable display of specific medications, drug classes, or date ranges (Figure 4).
Display of Terms Within Text Notes
Selecting a term from the drop-down list (Figure 5) opens a grid with the term highlighted in a snippet of text (Figure 6). Opening the document displays the context of the term, along with negation terms (ie, not, denies, no, etc) in red font if present. Voogle, unlike other NLP tools that attempt to interpret medical notes, relies on interpretation by the HCP user. Duplicate note fragments will be displayed in multiple notes, often across multiple screens, vividly demonstrating the pervasive use of the copy-and-paste text-entry strategy. Voogle satisfies 2 of the 4 recommendations of the recent report on copy-and-paste by Tsou and colleagues.9 The Voogle text display grid identifies copy-and-pasted text as well as establishes the provenance of the text (by sorting on the date column). Text can be copied from Voogle into an active Computerized Patient Record System (CPRS) note if needed for active patient care. Reindexing the following day and then repeating the search will demonstrate the newly copied-and-pasted text appended to the sequence.
Limitations
Voogle is unable to access all VA patient data currently. There are a dozen or so clinical domains that are indexed by Voogle that include prescriptions, problem lists, health factors, and others. More domains can be added with minimal effort and would then be available for fast search. The most critical deficiency is its inability to access, index, or query text reports stored as images within VistA Imaging. This includes nearly all reports from outside HCPs, emergency department visits or discharge summaries from unlinked hospitals, anesthesia reports, intensive care unit flow sheets, electrocardiograms, as well as numerous other text reports such as pulmonary function reports or sleep studies. Information that is transcribed by the provider into VistA as text (as in the case presented) is available within the CDW and can be found and displayed by Voogle search.
Voogle requires that the user initiates the indexing process prior to initiating the search process. Although Voogle defaults to 3 years prior to the current date, the user can specify a start date extending to close to the year 2000. The volume of data flowing into the CDW precludes automatic indexing of all patient data, as well as automatic updating of previously indexed data. We have explored the feasibility of queueing scheduled appointments for the following day, and although the strategy shows some promise, avoiding conflict with user-requested on-demand indexing remains challenging.
The current VA network architecture updates the CDW every night, resulting in up to a 24-hour delay in data availability. However, this delay should be reduced to several minutes after implementation of real-time data feeds accompanying the coming transition to a new EHR platform.
Conclusions
The recent introduction of the Joint Legacy Viewer (JLV) to the VA EHR desktop has enhanced the breadth of patient-specific information available to any VHA clinician, with recent enhancements providing access to some community care notes from outside HCPs. Voogle builds on this capability by enabling rapid search of text notes and structured data from multiple VA sites, over an extended time frame, and perhaps entered by hundreds of authors, as demonstrated in the case example. Formal usability and workload studies have not been performed; however, anecdotal reports indicate the application dramatically reduces the time required to search for critical information needed for care of complex patients who have been treated in multiple different VA hospitals and clinics.
The Voogle paradigm of leveraging patient information stored within a large enterprise-wide data warehouse through NLP techniques may be applicable to other systems as well, and warrants exploration. We believe that replacing traditional data search paradigms that require knowledge of data structure with a true query-based paradigm is a potential game changer for health information systems. Ultimately this strategy may help provide an antidote for the information chaos impacting HCP cognition. Moreover, reducing HCP cognitive load and time on task may lessen overall health care costs, reduce provider burn-out, and improve the quality of care received by patients.
Near real-time data feeds and adding additional clinical domains will potentially provide other benefits to patient care. For example, the authors plan to investigate whether sampling incoming data may assist with behind-the-scenes continuous monitoring of indicators of patient status to facilitate early warning of impending physiologic collapse.10 Other possible applications could include real-time scans for biosurveillance or other population screening requirements.
Acknowledgments
The authors express their sincere appreciation to Leslie DeYoung for documentation and Justin Wilson who constructed much of the graphical user interface for the Voogle application and design. Without their expertise, passion, and commitment the application would not be available as it is now.
1. Wachter RM. The Digital Doctor: Hope, Hype and Harm at the Dawn of the Computer Age New York: McGraw-Hill Education; 2017.
2. Erickson SM, Rockwern B, Koltov M, McLean RM; Medical Practice and Quality Committee of the American College of Physicians. Putting patients first by reducing administrative tasks in health care: a position paper of the American College of Physicians. Ann Intern Med. 2017;166(9):659-661.
3. Woods DD, Patterson ES, Roth EM. Can we ever escape from data overload? A cognitive systems diagnosis. Cogn Technol Work. 2002;4(1):22-36.
4. Gupta A, Harrod M, Quinn M, et al. Mind the overlap: how system problems contribute to cognitive failure and diagnostic errors. Diagnosis (Berl). 2018;5(3):151-156.
5. Beasley JW, Wetterneck TB, Temte J, et al. Information chaos in primary care: implications for physician performance and patient safety. J Am Board Fam Med. 2011;24(6):745-751.
6. Smith PC, Araya-Guerra R, Bublitz C, et al. Missing clinical information during primary care visits. JAMA. 2005;293(5):565-571.
7. Papadakos PJ, Berman E, eds. Distracted Doctoring: Returning to Patient-Centered Care in the Digital Age. New York: Springer International Publishing; 2017.
8. Battelle J. Search: How Google and its Rivals Rewrote the Rules of Business and Transformed Our Culture. New York: Penguin Group; 2005.
9. Tsou AY, Lehmann CU, Michel J, Solomon R, Possanza L, Gandhi T. Safe practices for copy and paste in the EHR. Systematic review, recommendations, and novel model for health IT collaboration. Appl Clin Inform. 2017;8(1):12-34.
10. Rothman MJ, Rothman SI, Beals J 4th. Development and validation of a continuous measure of patient condition using the electronic medical record. J Biomed Inform. 2013;46(5):837-848.
Digitalization of patient-specific information over the past 2 decades has dramatically altered health care delivery. Nonetheless, this technology has yet to live up to its promise of improving patient outcomes, in part due to data storage challenges as well as the emphasis on data entry to support administrative and financial goals of the institution.1-4 Substantially less emphasis has been placed on the retrieval of information required for accurate diagnosis.
A new search engine, Voogle, is now available through Microsoft Internet Explorer (Redmond, WA) to all providers in the US Department of Veterans Affairs (VA) on any intranet-enabled computer behind the VA firewall. Voogle facilitates rapid query-based search and retrieval of patient-specific data in the VA Corporate Data Warehouse (CDW).
Case Example
A veteran presented requesting consideration for implantation of a new device for obstructive sleep apnea. Guidelines for implantation of the new device specify a narrow therapeutic window, so determination of his apnea-hypopnea index (AHI) was critical. The patient had received care at more than 20 VA facilities and knew the approximate year the test had been performed at a non-VA facility.
A health care provider (HCP) using Voogle from his VA computer indexed all Veterans Information Systems and Technology Architecture (VistA) notes for the desired date range. The indexing of > 200 notes was completed in seconds. The HCP opened the indexed records with Voogle and entered a query for “sleep apnea,” which displayed multiple instances of the term within the patient record notes. A VA HCP had previously entered the data from the outside sleep study into a note shortly after the study.
This information was found immediately by sorting the indexed notes by date. The total time required by Voogle to find and display the critical information from the sleep study entered at a different VA more than a dozen years earlier was about 1 minute. These data provided the information needed for decision making at the time of the current patient encounter, without which repeat (and unnecessary) testing would have been required.
Information Overload
Electronic health records (EHRs) such as VistA, upload, store, collate, and present data in near real-time across multiple locations. Although the availability of these data can potentially reduce the risk of error due to missing critical information, its sheer volume limits its utility for point-of-care decision making. Much patient-specific text data found in clinical notes are recorded for administrative, financial, and business purposes rather than to support patient care decision making.1-3 The majority of data documents processes of care rather than HCP observations, assessment of current status, or plans for care. Much of this text is inserted into templates, consists of imported structured data elements, and may contain repeated copy-and-paste free text.
Data uploaded to the CDW are aggregated from multiple hospitals, each with its own “instance” of VistA. Often the CDW contains thousands of text notes for a single patient. This volume of text may conceal critical historical information needed for patient care mixed with a plethora of duplicated or extraneous text entered to satisfy administrative requirements. The effects of information overload and poor system usability have been studied extensively in other disciplines, but this science has largely not been incorporated into EHR design.1,3,4
A position paper published recently by the American College of Physicians notes that physician cognitive work is adversely impacted by the incorporation of nonclinical information into the EHR for use by other administrative and financial functions.2
Information Chaos
Beasley and colleagues noted that information in an EHR needed for optimal care may be unavailable, inadequate, scattered, conflicting, lost, or inaccurate, a condition they term information chaos.5 Smith and colleagues reported that decision making in 1 of 7 primary care visits was impaired by missing critical information. Surveyed HCPs estimated that 44% of patients with missing information may receive compromised care as a result, including delayed or erroneous diagnosis and increased costs due to duplication of diagnostic testing.6
Even when technically available, the usability of patient-specific data needed for accurate diagnosis is compromised if the HCP cannot find the information. In most systems data storage paradigms mirror database design rather than provider cognitive models. Ultimately, the design of current EHR interaction paradigms squanders precious cognitive resources and time, particularly during patient encounters, leaving little available for the cognitive tasks necessary for accurate diagnosis and treatment decisions.1,3,4,7
VA Corporate Data Warehouse
VistA was implemented as a decentralized system with 130 instances, each of which is a freestanding EHR. However, as all systems share common data structures, the data can be combined from multiple instances when needed. The VA established a CDW more than 15 years ago in order to collate information from multiple sites to support operations as well as to seek new insights. The CDW currently updates nightly from all 130 EHR instances and is the only location in which patient information from all treating sites is combined. Voogle can access the CDW through the Veterans Informatics and Computing Infrastructure (VINCI), which is a mirror of the CDW databases and was established as a secure research environment.
The CDW contains information on 25 million veterans, with about 15 terabytes of text data. Approximately 4 billion data points, including 1 million text notes, are accrued nightly. The Integrated Control Number (ICN), a unique patient identifier, is assigned to each CDW record and is cross-indexed in the master patient index. All CDW data are tied to the ICN, facilitating access to and attribution of all patient data from all VA sites. Voogle relies on this identifier to build indexed files, or domains (which are document collections), of requested specific patient information to support its search algorithm.
Structured Data
Most of the data accrued in an EHR are structured data (such as laboratory test results and vital signs) and stored in a defined database framework. Voogle uses iFind (Intersystems Inc, Cambridge, MA) to index, count, and then search for requested information within structured data fields.
Unstructured Text
In contrast to structured data, text notes are stored as documents that are retrievable by patient, author, date, clinic, as well as numerous other fields. Unstructured (free) text notes are more information rich than either structured data or templated notes since their narrative format more closely parallels providers’ cognitive processes.1,7 The value of the narrative becomes even more critical in understanding complex clinical scenarios with multiple interacting disease processes. Narratives emphasize important details, reducing cognitive overload by reducing the salience of detail the author deems to be less critical. Narrative notes simultaneously assure availability through the use of unstandardized language, often including specialty and disease-specific abbreviations.1 Information needed for decision making in the illustrative case in this report was present only in HCP-entered free-text notes, as the structured data from which the free text was derived were not available.
Search
The introduction of search engines can be considered one of the major technologic disruptors of the 21st century.8 However, this advance has not yet made significant inroads into health care, despite advances in other domains. As of 2019, EHR users are still required to be familiar with the system’s data and menu structure in order to find needed information (or enter orders, code visits, or any of a number of tasks). Anecdotally, one of the authors (David Eibling) observed that the most common question from his trainees is “How do you . . .?” referring not to the care of the patient but rather to interaction with the EHR.
What is needed is a simple query-based application that finds the data on request. In addition to Voogle, other advances are being made in this arena such as the EMERSE, medical record search engine (project-emerse.org). Voogle was released to VA providers in 2017 and is available through the Internet Explorer browser on VA computers with VA intranet access. The goal of Voogle is to reduce HCP cognitive load by reducing the time and effort needed to seek relevant information for the care of a specific patient.
Natural Language Processing
Linguistic analysis of text seeking to understand its meaning constitutes a rapidly expanding field, with current heavy emphasis on the role of artificial intelligence and machine learning.1 Advances in processing both structured data and free-text notes in the health care domain is in its infancy, despite the investment of considerable resources. Undoubtedly, advances in this arena will dramatically change provider cognitive work in the next decades.
VistA is coded in MUMPS (Massachusetts General Hospital Utility Multi-Programming System, also known as M), which has been in use for more than 50 years. Voogle employs iKnow, a novel natural language processing (NLP) application that resides in Caché (Intersystems, Boston, MA), the vendor-supported MUMPS infrastructure VistA uses to perform text analysis. iKnow does not attempt to interpret the meaning of text as do other common NLP applications, but instead relies on the expert user to interpret the meaning of the analyzed text. iKnow initially divides sentences into relations (usually verbs) and concepts, and then generates an index of these entities. The efficiency of iKnow results in very rapid indexing—often several thousand notes (not an uncommon number) can be indexed in 20 to 30 seconds. iKnow responds to a user query by searching for specific terms or similar terms within the indexed text, and then displays these terms within the original source documents, similar to well-known commercial search engines. Structured data are indexed by the iFind program simultaneously with free-text indexing (Figure 1).
Security
Maintaining high levels of security of Health Insurance Portability and Accountability (HIPAA)-compliant information in an online application such as Voogle is critical to ensure trust of veterans and HCPs. All patient data accessed by Voogle reside within the secure firewall-protected VINCI environment. All moving information is protected with high-level encryption protocols (transport layer security [TLS]), and data at rest are also encrypted. As the application is online, no data are stored on the accessing device. Voogle uses a secure Microsoft Windows logon using VA Active Directory coupled with VistA authorization to regulate who can see the data and use the application. All access is audited, not only for “sensitive patients,” but also for specific data types. Users are reminded of this Voogle attribute on the home screen.
Accessing Voogle
Voogle is available on the VA intranet to all authorized users at https://voogle.vha.med.va.gov/voogle. To assure high-level security the application can only be accessed with the Internet Explorer browser using established user identification protocols to avoid unauthorized access or duplicative log-in tasks.
Indexing
Indexing is user-driven and is required prior to patient selection and term query. The user is prompted for a patient identifier and a date range. The CDW unique patient identifier is used for all internal processing. However, a social security number look-up table is incorporated to facilitate patient selection. The date field defaults to 3 years but can be extended to approximately the year 2000.
Queries
Entering the patient name in Lastname, Firstname (no space) format will yield a list of indexed patients. All access is audited in order to deter unauthorized queries. Data from a demonstration patient are displayed in Figures 2, 3, 4, 5,
and 6.
Structured Data Searches
Structured data categories that contain the queried term, as well as a term count, are displayed after the “Structured Data” toggle is selected (Figure 2). After the desired category (Figure 2: “Outpatient Rx”) is selected, Voogle accesses the data file and displays it as a grid (medication list, Figure 3). Filter and sort functions enable display of specific medications, drug classes, or date ranges (Figure 4).
Display of Terms Within Text Notes
Selecting a term from the drop-down list (Figure 5) opens a grid with the term highlighted in a snippet of text (Figure 6). Opening the document displays the context of the term, along with negation terms (ie, not, denies, no, etc) in red font if present. Voogle, unlike other NLP tools that attempt to interpret medical notes, relies on interpretation by the HCP user. Duplicate note fragments will be displayed in multiple notes, often across multiple screens, vividly demonstrating the pervasive use of the copy-and-paste text-entry strategy. Voogle satisfies 2 of the 4 recommendations of the recent report on copy-and-paste by Tsou and colleagues.9 The Voogle text display grid identifies copy-and-pasted text as well as establishes the provenance of the text (by sorting on the date column). Text can be copied from Voogle into an active Computerized Patient Record System (CPRS) note if needed for active patient care. Reindexing the following day and then repeating the search will demonstrate the newly copied-and-pasted text appended to the sequence.
Limitations
Voogle is unable to access all VA patient data currently. There are a dozen or so clinical domains that are indexed by Voogle that include prescriptions, problem lists, health factors, and others. More domains can be added with minimal effort and would then be available for fast search. The most critical deficiency is its inability to access, index, or query text reports stored as images within VistA Imaging. This includes nearly all reports from outside HCPs, emergency department visits or discharge summaries from unlinked hospitals, anesthesia reports, intensive care unit flow sheets, electrocardiograms, as well as numerous other text reports such as pulmonary function reports or sleep studies. Information that is transcribed by the provider into VistA as text (as in the case presented) is available within the CDW and can be found and displayed by Voogle search.
Voogle requires that the user initiates the indexing process prior to initiating the search process. Although Voogle defaults to 3 years prior to the current date, the user can specify a start date extending to close to the year 2000. The volume of data flowing into the CDW precludes automatic indexing of all patient data, as well as automatic updating of previously indexed data. We have explored the feasibility of queueing scheduled appointments for the following day, and although the strategy shows some promise, avoiding conflict with user-requested on-demand indexing remains challenging.
The current VA network architecture updates the CDW every night, resulting in up to a 24-hour delay in data availability. However, this delay should be reduced to several minutes after implementation of real-time data feeds accompanying the coming transition to a new EHR platform.
Conclusions
The recent introduction of the Joint Legacy Viewer (JLV) to the VA EHR desktop has enhanced the breadth of patient-specific information available to any VHA clinician, with recent enhancements providing access to some community care notes from outside HCPs. Voogle builds on this capability by enabling rapid search of text notes and structured data from multiple VA sites, over an extended time frame, and perhaps entered by hundreds of authors, as demonstrated in the case example. Formal usability and workload studies have not been performed; however, anecdotal reports indicate the application dramatically reduces the time required to search for critical information needed for care of complex patients who have been treated in multiple different VA hospitals and clinics.
The Voogle paradigm of leveraging patient information stored within a large enterprise-wide data warehouse through NLP techniques may be applicable to other systems as well, and warrants exploration. We believe that replacing traditional data search paradigms that require knowledge of data structure with a true query-based paradigm is a potential game changer for health information systems. Ultimately this strategy may help provide an antidote for the information chaos impacting HCP cognition. Moreover, reducing HCP cognitive load and time on task may lessen overall health care costs, reduce provider burn-out, and improve the quality of care received by patients.
Near real-time data feeds and adding additional clinical domains will potentially provide other benefits to patient care. For example, the authors plan to investigate whether sampling incoming data may assist with behind-the-scenes continuous monitoring of indicators of patient status to facilitate early warning of impending physiologic collapse.10 Other possible applications could include real-time scans for biosurveillance or other population screening requirements.
Acknowledgments
The authors express their sincere appreciation to Leslie DeYoung for documentation and Justin Wilson who constructed much of the graphical user interface for the Voogle application and design. Without their expertise, passion, and commitment the application would not be available as it is now.
Digitalization of patient-specific information over the past 2 decades has dramatically altered health care delivery. Nonetheless, this technology has yet to live up to its promise of improving patient outcomes, in part due to data storage challenges as well as the emphasis on data entry to support administrative and financial goals of the institution.1-4 Substantially less emphasis has been placed on the retrieval of information required for accurate diagnosis.
A new search engine, Voogle, is now available through Microsoft Internet Explorer (Redmond, WA) to all providers in the US Department of Veterans Affairs (VA) on any intranet-enabled computer behind the VA firewall. Voogle facilitates rapid query-based search and retrieval of patient-specific data in the VA Corporate Data Warehouse (CDW).
Case Example
A veteran presented requesting consideration for implantation of a new device for obstructive sleep apnea. Guidelines for implantation of the new device specify a narrow therapeutic window, so determination of his apnea-hypopnea index (AHI) was critical. The patient had received care at more than 20 VA facilities and knew the approximate year the test had been performed at a non-VA facility.
A health care provider (HCP) using Voogle from his VA computer indexed all Veterans Information Systems and Technology Architecture (VistA) notes for the desired date range. The indexing of > 200 notes was completed in seconds. The HCP opened the indexed records with Voogle and entered a query for “sleep apnea,” which displayed multiple instances of the term within the patient record notes. A VA HCP had previously entered the data from the outside sleep study into a note shortly after the study.
This information was found immediately by sorting the indexed notes by date. The total time required by Voogle to find and display the critical information from the sleep study entered at a different VA more than a dozen years earlier was about 1 minute. These data provided the information needed for decision making at the time of the current patient encounter, without which repeat (and unnecessary) testing would have been required.
Information Overload
Electronic health records (EHRs) such as VistA, upload, store, collate, and present data in near real-time across multiple locations. Although the availability of these data can potentially reduce the risk of error due to missing critical information, its sheer volume limits its utility for point-of-care decision making. Much patient-specific text data found in clinical notes are recorded for administrative, financial, and business purposes rather than to support patient care decision making.1-3 The majority of data documents processes of care rather than HCP observations, assessment of current status, or plans for care. Much of this text is inserted into templates, consists of imported structured data elements, and may contain repeated copy-and-paste free text.
Data uploaded to the CDW are aggregated from multiple hospitals, each with its own “instance” of VistA. Often the CDW contains thousands of text notes for a single patient. This volume of text may conceal critical historical information needed for patient care mixed with a plethora of duplicated or extraneous text entered to satisfy administrative requirements. The effects of information overload and poor system usability have been studied extensively in other disciplines, but this science has largely not been incorporated into EHR design.1,3,4
A position paper published recently by the American College of Physicians notes that physician cognitive work is adversely impacted by the incorporation of nonclinical information into the EHR for use by other administrative and financial functions.2
Information Chaos
Beasley and colleagues noted that information in an EHR needed for optimal care may be unavailable, inadequate, scattered, conflicting, lost, or inaccurate, a condition they term information chaos.5 Smith and colleagues reported that decision making in 1 of 7 primary care visits was impaired by missing critical information. Surveyed HCPs estimated that 44% of patients with missing information may receive compromised care as a result, including delayed or erroneous diagnosis and increased costs due to duplication of diagnostic testing.6
Even when technically available, the usability of patient-specific data needed for accurate diagnosis is compromised if the HCP cannot find the information. In most systems data storage paradigms mirror database design rather than provider cognitive models. Ultimately, the design of current EHR interaction paradigms squanders precious cognitive resources and time, particularly during patient encounters, leaving little available for the cognitive tasks necessary for accurate diagnosis and treatment decisions.1,3,4,7
VA Corporate Data Warehouse
VistA was implemented as a decentralized system with 130 instances, each of which is a freestanding EHR. However, as all systems share common data structures, the data can be combined from multiple instances when needed. The VA established a CDW more than 15 years ago in order to collate information from multiple sites to support operations as well as to seek new insights. The CDW currently updates nightly from all 130 EHR instances and is the only location in which patient information from all treating sites is combined. Voogle can access the CDW through the Veterans Informatics and Computing Infrastructure (VINCI), which is a mirror of the CDW databases and was established as a secure research environment.
The CDW contains information on 25 million veterans, with about 15 terabytes of text data. Approximately 4 billion data points, including 1 million text notes, are accrued nightly. The Integrated Control Number (ICN), a unique patient identifier, is assigned to each CDW record and is cross-indexed in the master patient index. All CDW data are tied to the ICN, facilitating access to and attribution of all patient data from all VA sites. Voogle relies on this identifier to build indexed files, or domains (which are document collections), of requested specific patient information to support its search algorithm.
Structured Data
Most of the data accrued in an EHR are structured data (such as laboratory test results and vital signs) and stored in a defined database framework. Voogle uses iFind (Intersystems Inc, Cambridge, MA) to index, count, and then search for requested information within structured data fields.
Unstructured Text
In contrast to structured data, text notes are stored as documents that are retrievable by patient, author, date, clinic, as well as numerous other fields. Unstructured (free) text notes are more information rich than either structured data or templated notes since their narrative format more closely parallels providers’ cognitive processes.1,7 The value of the narrative becomes even more critical in understanding complex clinical scenarios with multiple interacting disease processes. Narratives emphasize important details, reducing cognitive overload by reducing the salience of detail the author deems to be less critical. Narrative notes simultaneously assure availability through the use of unstandardized language, often including specialty and disease-specific abbreviations.1 Information needed for decision making in the illustrative case in this report was present only in HCP-entered free-text notes, as the structured data from which the free text was derived were not available.
Search
The introduction of search engines can be considered one of the major technologic disruptors of the 21st century.8 However, this advance has not yet made significant inroads into health care, despite advances in other domains. As of 2019, EHR users are still required to be familiar with the system’s data and menu structure in order to find needed information (or enter orders, code visits, or any of a number of tasks). Anecdotally, one of the authors (David Eibling) observed that the most common question from his trainees is “How do you . . .?” referring not to the care of the patient but rather to interaction with the EHR.
What is needed is a simple query-based application that finds the data on request. In addition to Voogle, other advances are being made in this arena such as the EMERSE, medical record search engine (project-emerse.org). Voogle was released to VA providers in 2017 and is available through the Internet Explorer browser on VA computers with VA intranet access. The goal of Voogle is to reduce HCP cognitive load by reducing the time and effort needed to seek relevant information for the care of a specific patient.
Natural Language Processing
Linguistic analysis of text seeking to understand its meaning constitutes a rapidly expanding field, with current heavy emphasis on the role of artificial intelligence and machine learning.1 Advances in processing both structured data and free-text notes in the health care domain is in its infancy, despite the investment of considerable resources. Undoubtedly, advances in this arena will dramatically change provider cognitive work in the next decades.
VistA is coded in MUMPS (Massachusetts General Hospital Utility Multi-Programming System, also known as M), which has been in use for more than 50 years. Voogle employs iKnow, a novel natural language processing (NLP) application that resides in Caché (Intersystems, Boston, MA), the vendor-supported MUMPS infrastructure VistA uses to perform text analysis. iKnow does not attempt to interpret the meaning of text as do other common NLP applications, but instead relies on the expert user to interpret the meaning of the analyzed text. iKnow initially divides sentences into relations (usually verbs) and concepts, and then generates an index of these entities. The efficiency of iKnow results in very rapid indexing—often several thousand notes (not an uncommon number) can be indexed in 20 to 30 seconds. iKnow responds to a user query by searching for specific terms or similar terms within the indexed text, and then displays these terms within the original source documents, similar to well-known commercial search engines. Structured data are indexed by the iFind program simultaneously with free-text indexing (Figure 1).
Security
Maintaining high levels of security of Health Insurance Portability and Accountability (HIPAA)-compliant information in an online application such as Voogle is critical to ensure trust of veterans and HCPs. All patient data accessed by Voogle reside within the secure firewall-protected VINCI environment. All moving information is protected with high-level encryption protocols (transport layer security [TLS]), and data at rest are also encrypted. As the application is online, no data are stored on the accessing device. Voogle uses a secure Microsoft Windows logon using VA Active Directory coupled with VistA authorization to regulate who can see the data and use the application. All access is audited, not only for “sensitive patients,” but also for specific data types. Users are reminded of this Voogle attribute on the home screen.
Accessing Voogle
Voogle is available on the VA intranet to all authorized users at https://voogle.vha.med.va.gov/voogle. To assure high-level security the application can only be accessed with the Internet Explorer browser using established user identification protocols to avoid unauthorized access or duplicative log-in tasks.
Indexing
Indexing is user-driven and is required prior to patient selection and term query. The user is prompted for a patient identifier and a date range. The CDW unique patient identifier is used for all internal processing. However, a social security number look-up table is incorporated to facilitate patient selection. The date field defaults to 3 years but can be extended to approximately the year 2000.
Queries
Entering the patient name in Lastname, Firstname (no space) format will yield a list of indexed patients. All access is audited in order to deter unauthorized queries. Data from a demonstration patient are displayed in Figures 2, 3, 4, 5,
and 6.
Structured Data Searches
Structured data categories that contain the queried term, as well as a term count, are displayed after the “Structured Data” toggle is selected (Figure 2). After the desired category (Figure 2: “Outpatient Rx”) is selected, Voogle accesses the data file and displays it as a grid (medication list, Figure 3). Filter and sort functions enable display of specific medications, drug classes, or date ranges (Figure 4).
Display of Terms Within Text Notes
Selecting a term from the drop-down list (Figure 5) opens a grid with the term highlighted in a snippet of text (Figure 6). Opening the document displays the context of the term, along with negation terms (ie, not, denies, no, etc) in red font if present. Voogle, unlike other NLP tools that attempt to interpret medical notes, relies on interpretation by the HCP user. Duplicate note fragments will be displayed in multiple notes, often across multiple screens, vividly demonstrating the pervasive use of the copy-and-paste text-entry strategy. Voogle satisfies 2 of the 4 recommendations of the recent report on copy-and-paste by Tsou and colleagues.9 The Voogle text display grid identifies copy-and-pasted text as well as establishes the provenance of the text (by sorting on the date column). Text can be copied from Voogle into an active Computerized Patient Record System (CPRS) note if needed for active patient care. Reindexing the following day and then repeating the search will demonstrate the newly copied-and-pasted text appended to the sequence.
Limitations
Voogle is unable to access all VA patient data currently. There are a dozen or so clinical domains that are indexed by Voogle that include prescriptions, problem lists, health factors, and others. More domains can be added with minimal effort and would then be available for fast search. The most critical deficiency is its inability to access, index, or query text reports stored as images within VistA Imaging. This includes nearly all reports from outside HCPs, emergency department visits or discharge summaries from unlinked hospitals, anesthesia reports, intensive care unit flow sheets, electrocardiograms, as well as numerous other text reports such as pulmonary function reports or sleep studies. Information that is transcribed by the provider into VistA as text (as in the case presented) is available within the CDW and can be found and displayed by Voogle search.
Voogle requires that the user initiates the indexing process prior to initiating the search process. Although Voogle defaults to 3 years prior to the current date, the user can specify a start date extending to close to the year 2000. The volume of data flowing into the CDW precludes automatic indexing of all patient data, as well as automatic updating of previously indexed data. We have explored the feasibility of queueing scheduled appointments for the following day, and although the strategy shows some promise, avoiding conflict with user-requested on-demand indexing remains challenging.
The current VA network architecture updates the CDW every night, resulting in up to a 24-hour delay in data availability. However, this delay should be reduced to several minutes after implementation of real-time data feeds accompanying the coming transition to a new EHR platform.
Conclusions
The recent introduction of the Joint Legacy Viewer (JLV) to the VA EHR desktop has enhanced the breadth of patient-specific information available to any VHA clinician, with recent enhancements providing access to some community care notes from outside HCPs. Voogle builds on this capability by enabling rapid search of text notes and structured data from multiple VA sites, over an extended time frame, and perhaps entered by hundreds of authors, as demonstrated in the case example. Formal usability and workload studies have not been performed; however, anecdotal reports indicate the application dramatically reduces the time required to search for critical information needed for care of complex patients who have been treated in multiple different VA hospitals and clinics.
The Voogle paradigm of leveraging patient information stored within a large enterprise-wide data warehouse through NLP techniques may be applicable to other systems as well, and warrants exploration. We believe that replacing traditional data search paradigms that require knowledge of data structure with a true query-based paradigm is a potential game changer for health information systems. Ultimately this strategy may help provide an antidote for the information chaos impacting HCP cognition. Moreover, reducing HCP cognitive load and time on task may lessen overall health care costs, reduce provider burn-out, and improve the quality of care received by patients.
Near real-time data feeds and adding additional clinical domains will potentially provide other benefits to patient care. For example, the authors plan to investigate whether sampling incoming data may assist with behind-the-scenes continuous monitoring of indicators of patient status to facilitate early warning of impending physiologic collapse.10 Other possible applications could include real-time scans for biosurveillance or other population screening requirements.
Acknowledgments
The authors express their sincere appreciation to Leslie DeYoung for documentation and Justin Wilson who constructed much of the graphical user interface for the Voogle application and design. Without their expertise, passion, and commitment the application would not be available as it is now.
1. Wachter RM. The Digital Doctor: Hope, Hype and Harm at the Dawn of the Computer Age New York: McGraw-Hill Education; 2017.
2. Erickson SM, Rockwern B, Koltov M, McLean RM; Medical Practice and Quality Committee of the American College of Physicians. Putting patients first by reducing administrative tasks in health care: a position paper of the American College of Physicians. Ann Intern Med. 2017;166(9):659-661.
3. Woods DD, Patterson ES, Roth EM. Can we ever escape from data overload? A cognitive systems diagnosis. Cogn Technol Work. 2002;4(1):22-36.
4. Gupta A, Harrod M, Quinn M, et al. Mind the overlap: how system problems contribute to cognitive failure and diagnostic errors. Diagnosis (Berl). 2018;5(3):151-156.
5. Beasley JW, Wetterneck TB, Temte J, et al. Information chaos in primary care: implications for physician performance and patient safety. J Am Board Fam Med. 2011;24(6):745-751.
6. Smith PC, Araya-Guerra R, Bublitz C, et al. Missing clinical information during primary care visits. JAMA. 2005;293(5):565-571.
7. Papadakos PJ, Berman E, eds. Distracted Doctoring: Returning to Patient-Centered Care in the Digital Age. New York: Springer International Publishing; 2017.
8. Battelle J. Search: How Google and its Rivals Rewrote the Rules of Business and Transformed Our Culture. New York: Penguin Group; 2005.
9. Tsou AY, Lehmann CU, Michel J, Solomon R, Possanza L, Gandhi T. Safe practices for copy and paste in the EHR. Systematic review, recommendations, and novel model for health IT collaboration. Appl Clin Inform. 2017;8(1):12-34.
10. Rothman MJ, Rothman SI, Beals J 4th. Development and validation of a continuous measure of patient condition using the electronic medical record. J Biomed Inform. 2013;46(5):837-848.
1. Wachter RM. The Digital Doctor: Hope, Hype and Harm at the Dawn of the Computer Age New York: McGraw-Hill Education; 2017.
2. Erickson SM, Rockwern B, Koltov M, McLean RM; Medical Practice and Quality Committee of the American College of Physicians. Putting patients first by reducing administrative tasks in health care: a position paper of the American College of Physicians. Ann Intern Med. 2017;166(9):659-661.
3. Woods DD, Patterson ES, Roth EM. Can we ever escape from data overload? A cognitive systems diagnosis. Cogn Technol Work. 2002;4(1):22-36.
4. Gupta A, Harrod M, Quinn M, et al. Mind the overlap: how system problems contribute to cognitive failure and diagnostic errors. Diagnosis (Berl). 2018;5(3):151-156.
5. Beasley JW, Wetterneck TB, Temte J, et al. Information chaos in primary care: implications for physician performance and patient safety. J Am Board Fam Med. 2011;24(6):745-751.
6. Smith PC, Araya-Guerra R, Bublitz C, et al. Missing clinical information during primary care visits. JAMA. 2005;293(5):565-571.
7. Papadakos PJ, Berman E, eds. Distracted Doctoring: Returning to Patient-Centered Care in the Digital Age. New York: Springer International Publishing; 2017.
8. Battelle J. Search: How Google and its Rivals Rewrote the Rules of Business and Transformed Our Culture. New York: Penguin Group; 2005.
9. Tsou AY, Lehmann CU, Michel J, Solomon R, Possanza L, Gandhi T. Safe practices for copy and paste in the EHR. Systematic review, recommendations, and novel model for health IT collaboration. Appl Clin Inform. 2017;8(1):12-34.
10. Rothman MJ, Rothman SI, Beals J 4th. Development and validation of a continuous measure of patient condition using the electronic medical record. J Biomed Inform. 2013;46(5):837-848.