Original Research

Bundled payments represent one of the most prominent value-based payment arrangements nationwide. Under this payment approach, hospitals assume responsibility for quality and costs across discrete episodes of care. Hospitals that maintain quality while achieving cost reductions are eligible for financial incentives, whereas those that do not are subject to financial penalties.

To date, the largest completed bundled payment program nationwide is Medicare’s Bundled Payments for Care Improvement (BPCI) initiative. Among four different participation models in BPCI, hospital enrollment was greatest in Model 2, in which episodes spanned from hospitalization through 90 days of post–acute care. The overall results from BPCI Model 2 have been positive: hospitals participating in both common surgical episodes, such as joint replacement surgery, and medical episodes, such as acute myocardial infarction (AMI) and congestive heart failure (CHF), have demonstrated long-term financial savings with stable quality performance.^1,2

Safety net hospitals that disproportionately serve low-income patients may fare differently than other hospitals under bundled payment models. At baseline, these hospitals typically have fewer financial resources, which may limit their ability to implement measures to standardize care during hospitalization (eg, clinical pathways) or after discharge (eg, postdischarge programs and other strategies to reduce readmissions).³ Efforts to redesign care may be further complicated by greater clinical complexity and social and structural determinants of health among patients seeking care at safety net hospitals. Given the well-known interactions between social determinants and health conditions, these factors are highly relevant for patients hospitalized at safety net hospitals for acute medical events or exacerbations of chronic conditions.

Existing evidence has shown that safety net hospitals have not performed as well as other hospitals in other value-based reforms.^4-8 In the context of bundled payments for joint replacement surgery, safety net hospitals have been less likely to achieve financial savings but more likely to receive penalties.^9-11 Moreover, the savings achieved by safety net hospitals have been smaller than those achieved by non–safety net hospitals.¹²

Despite these concerning findings, there are few data about how safety net hospitals have fared under bundled payments for common medical conditions. To address this critical knowledge gap, we evaluated the effect of hospital safety net status on the association between BPCI Model 2 participation and changes in outcomes for medical condition episodes.

This study was approved by the University of Pennsylvania Institutional Review Board with a waiver of informed consent.

Data

We used 100% Medicare claims data from 2011 to 2016 for patients receiving care at hospitals participating in BPCI Model 2 for one of four common medical condition episodes: AMI, pneumonia, CHF, and chronic obstructive pulmonary disease (COPD). A 20% random national sample was used for patients hospitalized at nonparticipant hospitals. Publicly available data from the Centers for Medicare & Medicaid Services (CMS) were used to identify hospital enrollment in BPCI Model 2, while data from the 2017 CMS Impact File were used to quantify each hospital’s disproportionate patient percentage (DPP), which reflects the proportion of Medicaid and low-income Medicare beneficiaries served and determines a hospital’s eligibility to earn disproportionate share hospital payments.

Data from the 2011 American Hospital Association Annual Survey were used to capture hospital characteristics, such as number of beds, teaching status, and profit status, while data from the Medicare provider of service, beneficiary summary, and accountable care organization files were used to capture additional hospital characteristics and market characteristics, such as population size and Medicare Advantage penetration. The Medicare Provider Enrollment, Chain, and Ownership System file was used to identify and remove BPCI episodes from physician group practices. State-level data about area deprivation index—a census tract–based measure that incorporates factors such as income, education, employment, and housing quality to describe socioeconomic disadvantage among neighborhoods—were used to define socioeconomically disadvantaged areas as those in the top 20% of area deprivation index statewide.¹³ Markets were defined using hospital referral regions.¹⁴

Study Periods and Hospital Groups

Our analysis spanned the period between January 1, 2011, and December 31, 2016. We separated this period into a baseline period (January 2011–September 2013) prior to the start of BPCI and a subsequent BPCI period (October 2013–December 2016).

We defined any hospitals participating in BPCI Model 2 across this period for any of the four included medical condition episodes as BPCI hospitals. Because hospitals were able to enter or exit BPCI over time, and enrollment data were provided by CMS as quarterly participation files, we were able to identify dates of entry into or exit from BPCI over time by hospital-condition pairs. Hospitals were considered BPCI hospitals until the end of the study period, regardless of subsequent exit.

We defined non-BPCI hospitals as those that never participated in the program and had 10 or more admissions in the BPCI period for the included medical condition episodes. We used this approach to minimize potential bias arising from BPCI entry and exit over time.

Across both BPCI and non-BPCI hospital groups, we followed prior methods and defined safety net hospitals based on a hospital’s DPP.¹⁵ Specifically, safety net hospitals were those in the top quartile of DPP among all hospitals nationwide, and hospitals in the other three quartiles were defined as non–safety net hospitals.^9,12

Study Sample and Episode Construction

Our study sample included Medicare fee-for-service beneficiaries admitted to BPCI and non-BPCI hospitals for any of the four medical conditions of interest. We adhered to BPCI program rules, which defined each episode type based on a set of Medicare Severity Diagnosis Related Group (MS-DRG) codes (eg, myocardial infarction episodes were defined as MS-DRGs 280-282). From this sample, we excluded beneficiaries with end-stage renal disease or insurance coverage through Medicare Advantage, as well as beneficiaries who died during the index hospital admission, had any non–Inpatient Prospective Payment System claims, or lacked continuous primary Medicare fee-for-service coverage either during the episode or in the 12 months preceding it.

We constructed 90-day medical condition episodes that began with hospital admission and spanned 90 days after hospital discharge. To avoid bias arising from CMS rules related to precedence (rules for handling how overlapping episodes are assigned to hospitals), we followed prior methods and constructed naturally occurring episodes by assigning overlapping ones to the earlier hospital admission.^2,16 From this set of episodes, we identified those for AMI, CHF, COPD, and pneumonia.

Exposure and Covariate Variables

Our study exposure was the interaction between hospital safety net status and hospital BPCI participation, which captured whether the association between BPCI participation and outcomes varied by safety net status (eg, whether differential changes in an outcome related to BPCI participation were different for safety net and non–safety net hospitals in the program). BPCI participation was defined using a time-varying indicator of BPCI participation to distinguish between episodes occurring under the program (ie, after a hospital began participating) or before participation in it. Covariates were chosen based on prior studies and included patient variables such as age, sex, Elixhauser comorbidities, frailty, and Medicare/Medicaid dual-eligibility status.^17-23 Additionally, our analysis included market variables such as population size and Medicare Advantage penetration.

Outcome Variables

The prespecified primary study outcome was standardized 90-day postdischarge spending. This outcome was chosen owing to the lack of variation in standardized index hospitalization spending given the MS-DRG system and prior work suggesting that bundled payment participants instead targeted changes to postdischarge utilization and spending.² Secondary outcomes included 90-day unplanned readmission rates, 90-day postdischarge mortality rates, discharge to institutional post–acute care providers (defined as either skilled nursing facilities [SNFs] or inpatient rehabilitation facilities), discharge home with home health agency services, and—among patients discharged to SNFs—SNF length of stay (LOS), measured in number of days.

Statistical Analysis

We described the characteristics of patients and hospitals in our samples. In adjusted analyses, we used a series of difference-in-differences (DID) generalized linear models to conduct a heterogeneity analysis evaluating whether the relationship between hospital BPCI participation and medical condition episode outcomes varied based on hospital safety net status.

In these models, the DID estimator was a time-varying indicator of hospital BPCI participation (equal to 1 for episodes occurring during the BPCI period at BPCI hospitals after they initiated participation; 0 otherwise) together with hospital and quarter-time fixed effects. To examine differences in the association between BPCI and episode outcomes by hospital safety net status—that is, whether there was heterogeneity in the outcome changes between safety net and non–safety net hospitals participating in BPCI—our models also included an interaction term between hospital safety net status and the time-varying BPCI participation term (Appendix Methods). In this approach, BPCI safety net and BPCI non–safety net hospitals were compared with non-BPCI hospitals as the comparison group. The comparisons were chosen to yield the most policy-salient findings, since Medicare evaluated hospitals in BPCI, whether safety net or not, by comparing their performance to nonparticipating hospitals, whether safety net or not.

All models controlled for patient and time-varying market characteristics and included hospital fixed effects (to account for time-invariant hospital market characteristics) and MS-DRG fixed effects. All outcomes were evaluated using models with identity links and normal distributions (ie, ordinary least squares). These variables and models were applied to data from the baseline period to examine consistency with the parallel trends assumption. Overall, Wald tests did not indicate divergent baseline period trends in outcomes between BPCI and non-BPCI hospitals (Appendix Figure 1) or BPCI safety net versus BPCI non–safety net hospitals (Appendix Figure 2).

We conducted sensitivity analyses to evaluate the robustness of our results. First, instead of comparing differential changes at BPCI safety net vs BPCI non–safety net hospitals (ie, evaluating safety net status among BPCI hospitals), we evaluated changes at BPCI safety net vs non-BPCI safety net hospitals compared with changes at BPCI non–safety net vs non-BPCI non–safety net hospitals (ie, marginal differences in the changes associated with BPCI participation among safety net vs non–safety net hospitals). Because safety net hospitals in BPCI were compared with nonparticipating safety net hospitals, and non–safety net hospitals in BPCI were compared with nonparticipating non–safety net hospitals, this set of analyses helped address potential concerns about unobservable differences between safety net and non–safety net organizations and their potential impact on our findings.

Second, we used an alternative, BPCI-specific definition for safety net hospitals: instead of defining safety net status based on all hospitals nationwide, we defined it only among BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all BPCI hospitals) and non-BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all non-BPCI hospitals). Third, we repeated our main analyses using models with standard errors clustered at the hospital level and without hospital fixed effects. Fourth, we repeated analysis using models with alternative nonlinear link functions and outcome distributions and without hospital fixed effects.

Statistical tests were two-tailed and considered significant at α = .05 for the primary outcome. Statistical analyses were conducted using SAS 9.4 (SAS Institute, Inc.).

Our sample consisted of 3066 hospitals nationwide that collectively provided medical condition episode care to a total of 1,611,848 Medicare fee-for-service beneficiaries. This sample included 238 BPCI hospitals and 2769 non-BPCI hospitals (Table 1, Appendix Table 1).

Among BPCI hospitals, 63 were safety net and 175 were non–safety net hospitals. Compared with non–safety net hospitals, safety net hospitals tended to be larger and were more likely to be urban teaching hospitals. Safety net hospitals also tended to be located in areas with larger populations, more low-income individuals, and greater Medicare Advantage penetration.

In both the baseline and BPCI periods, there were differences in several characteristics for patients admitted to safety net vs non–safety net hospitals (Table 2; Appendix Table 2). Among BPCI hospitals, in both periods, patients admitted at safety net hospitals were younger and more likely to be Black, be Medicare/Medicaid dual eligible, and report having a disability than patients admitted to non–safety net hospitals. Patients admitted to safety net hospitals were also more likely to reside in socioeconomically disadvantaged areas.

Safety Net Status Among BPCI Hospitals

In the baseline period (Appendix Table 3), postdischarge spending was slightly greater among patients admitted to BPCI safety net hospitals ($18,817) than those admitted to BPCI non–safety net hospitals ($18,335). There were also small differences in secondary outcomes between the BPCI safety net and non−safety net groups.

In adjusted analyses evaluating heterogeneity in the effect of BPCI participation between safety net and non–safety net hospitals (Figure 1), differential changes in postdischarge spending between baseline and BPCI participation periods did not differ between safety net and non–safety net hospitals participating in BPCI (aDID, $40; 95% CI, –$254 to $335; P = .79).

Sensitivity Analysis

Analyses of BPCI participation among safety net vs non–safety net hospitals nationwide yielded results that were similar to those from our main analyses (Appendix Figures 4, 5, and 6). Compared with BPCI participation among non–safety net hospitals, participation among safety net hospitals was associated with a differential increase from baseline to BPCI periods in discharge to institutional post–acute care providers (aDID, 1.07 percentage points; 95% CI, 0.47-1.67 percentage points; P < .001), but no differential changes between baseline and BPCI periods in postdischarge spending (aDID, –$199;95% CI, –$461 to $63; P = .14), SNF LOS (aDID, –0.22 days; 95% CI, –0.54 to 0.09 days; P = .16), or other secondary outcomes.

Replicating our main analyses using an alternative, BPCI-specific definition of safety net hospitals yielded similar results overall (Appendix Table 4; Appendix Figures 7, 8, and 9). There were no differential changes between baseline and BPCI periods in postdischarge spending between BPCI safety net and BPCI non–safety net hospitals (aDID, $111; 95% CI, –$189 to $411; P = .47). Results for secondary outcomes were also qualitatively similar to results from main analyses, with the exception that among BPCI hospitals, safety net hospitals had a differentially higher SNF LOS than non–safety net hospitals between baseline and BPCI periods (aDID, 0.38 days; 95% CI, 0.02-0.74 days; P = .04).

Compared with results from our main analysis, findings were qualitatively similar overall in analyses using models with hospital-clustered standard errors and without hospital fixed effects (Appendix Figures 10, 11, and 12) as well as models with alternative link functions and outcome distributions and without hospital fixed effects (Appendix Figures 13, 14, and 15).

This analysis builds on prior work by evaluating how hospital safety net status affected the known association between bundled payment participation and decreased spending and stable quality for medical condition episodes. Although safety net status did not appear to affect those relationships, it did affect the relationship between participation and post–acute care utilization. These results have three main implications.

First, our results suggest that policymakers should continue engaging safety net hospitals in medical condition bundled payments while monitoring for unintended consequences. Our findings with regard to spending provide some reassurance that safety net hospitals can potentially achieve savings while maintaining quality under bundled payments, similar to other types of hospitals. However, the differences in patient populations and post–acute care utilization patterns suggest that policymakers should continue to carefully monitor for disparities based on hospital safety net status and consider implementing measures that have been used in other payment reforms to support safety net organizations. Such measures could involve providing customized technical assistance or evaluating performance using “peer groups” that compare performance among safety net hospitals alone rather than among all hospitals.^24,25

Second, our findings underscore potential challenges that safety net hospitals may face when attempting to redesign care. For instance, among hospitals accepting bundled payments for medical conditions, successful strategies in BPCI have often included maintaining the proportion of patients discharged to institutional post–acute care providers while reducing SNF LOS.² However, in our study, discharge to institutional post–acute care providers actually increased among safety net hospitals relative to other hospitals while SNF LOS did not decrease. Additionally, while other hospitals in bundled payments have exhibited differentially greater discharge home with home health services, we found that safety net hospitals did not. These represent areas for future work, particularly because little is known about how safety net hospitals coordinate post–acute care (eg, the extent to which safety net hospitals integrate with post–acute care providers or coordinate home-based care for vulnerable patient populations).

Third, study results offer insight into potential challenges to practice changes. Compared with other hospitals, safety net hospitals in our analysis provided medical condition episode care to more Black, Medicare/Medicaid dual-eligible, and disabled patients, as well as individuals living in socioeconomically disadvantaged areas. Collectively, these groups may face more challenging socioeconomic circumstances or existing disparities. The combination of these factors and limited financial resources at safety net hospitals could complicate their ability to manage transitions of care after hospitalization by shifting discharge away from high-intensity institutional post–acute care facilities.

Our analysis has limitations. First, given the observational study design, findings are subject to residual confounding and selection bias. For instance, findings related to post–acute care utilization could have been influenced by unobservable changes in market supply and other factors. However, we mitigated these risks using a quasi-experimental methodology that also directly accounted for multiple patient, hospital, and market characteristics and also used fixed effects to account for unobserved heterogeneity. Second, in studying BPCI Model 2, we evaluated one model within one bundled payment program. However, BPCI Model 2 encompassed a wide range of medical conditions, and both this scope and program design have served as the direct basis for subsequent bundled payment models, such as the ongoing BPCI Advanced and other forthcoming programs.²⁶ Third, while our analysis evaluated multiple aspects of patient complexity, individuals may be “high risk” owing to several clinical and social determinants. Future work should evaluate different features of patient risk and how they affect outcomes under payment models such as bundled payments.

Safety net status appeared to affect the relationship between bundled payment participation and post–acute care utilization, but not episode spending. These findings suggest that policymakers could support safety net hospitals within bundled payment programs and consider safety net status when evaluating them.

Bundled payments represent one of the most prominent value-based payment arrangements nationwide. Under this payment approach, hospitals assume responsibility for quality and costs across discrete episodes of care. Hospitals that maintain quality while achieving cost reductions are eligible for financial incentives, whereas those that do not are subject to financial penalties.

To date, the largest completed bundled payment program nationwide is Medicare’s Bundled Payments for Care Improvement (BPCI) initiative. Among four different participation models in BPCI, hospital enrollment was greatest in Model 2, in which episodes spanned from hospitalization through 90 days of post–acute care. The overall results from BPCI Model 2 have been positive: hospitals participating in both common surgical episodes, such as joint replacement surgery, and medical episodes, such as acute myocardial infarction (AMI) and congestive heart failure (CHF), have demonstrated long-term financial savings with stable quality performance.^1,2

Safety net hospitals that disproportionately serve low-income patients may fare differently than other hospitals under bundled payment models. At baseline, these hospitals typically have fewer financial resources, which may limit their ability to implement measures to standardize care during hospitalization (eg, clinical pathways) or after discharge (eg, postdischarge programs and other strategies to reduce readmissions).³ Efforts to redesign care may be further complicated by greater clinical complexity and social and structural determinants of health among patients seeking care at safety net hospitals. Given the well-known interactions between social determinants and health conditions, these factors are highly relevant for patients hospitalized at safety net hospitals for acute medical events or exacerbations of chronic conditions.

Existing evidence has shown that safety net hospitals have not performed as well as other hospitals in other value-based reforms.^4-8 In the context of bundled payments for joint replacement surgery, safety net hospitals have been less likely to achieve financial savings but more likely to receive penalties.^9-11 Moreover, the savings achieved by safety net hospitals have been smaller than those achieved by non–safety net hospitals.¹²

Despite these concerning findings, there are few data about how safety net hospitals have fared under bundled payments for common medical conditions. To address this critical knowledge gap, we evaluated the effect of hospital safety net status on the association between BPCI Model 2 participation and changes in outcomes for medical condition episodes.

This study was approved by the University of Pennsylvania Institutional Review Board with a waiver of informed consent.

Data

We used 100% Medicare claims data from 2011 to 2016 for patients receiving care at hospitals participating in BPCI Model 2 for one of four common medical condition episodes: AMI, pneumonia, CHF, and chronic obstructive pulmonary disease (COPD). A 20% random national sample was used for patients hospitalized at nonparticipant hospitals. Publicly available data from the Centers for Medicare & Medicaid Services (CMS) were used to identify hospital enrollment in BPCI Model 2, while data from the 2017 CMS Impact File were used to quantify each hospital’s disproportionate patient percentage (DPP), which reflects the proportion of Medicaid and low-income Medicare beneficiaries served and determines a hospital’s eligibility to earn disproportionate share hospital payments.

Data from the 2011 American Hospital Association Annual Survey were used to capture hospital characteristics, such as number of beds, teaching status, and profit status, while data from the Medicare provider of service, beneficiary summary, and accountable care organization files were used to capture additional hospital characteristics and market characteristics, such as population size and Medicare Advantage penetration. The Medicare Provider Enrollment, Chain, and Ownership System file was used to identify and remove BPCI episodes from physician group practices. State-level data about area deprivation index—a census tract–based measure that incorporates factors such as income, education, employment, and housing quality to describe socioeconomic disadvantage among neighborhoods—were used to define socioeconomically disadvantaged areas as those in the top 20% of area deprivation index statewide.¹³ Markets were defined using hospital referral regions.¹⁴

Study Periods and Hospital Groups

Our analysis spanned the period between January 1, 2011, and December 31, 2016. We separated this period into a baseline period (January 2011–September 2013) prior to the start of BPCI and a subsequent BPCI period (October 2013–December 2016).

We defined any hospitals participating in BPCI Model 2 across this period for any of the four included medical condition episodes as BPCI hospitals. Because hospitals were able to enter or exit BPCI over time, and enrollment data were provided by CMS as quarterly participation files, we were able to identify dates of entry into or exit from BPCI over time by hospital-condition pairs. Hospitals were considered BPCI hospitals until the end of the study period, regardless of subsequent exit.

We defined non-BPCI hospitals as those that never participated in the program and had 10 or more admissions in the BPCI period for the included medical condition episodes. We used this approach to minimize potential bias arising from BPCI entry and exit over time.

Across both BPCI and non-BPCI hospital groups, we followed prior methods and defined safety net hospitals based on a hospital’s DPP.¹⁵ Specifically, safety net hospitals were those in the top quartile of DPP among all hospitals nationwide, and hospitals in the other three quartiles were defined as non–safety net hospitals.^9,12

Study Sample and Episode Construction

Our study sample included Medicare fee-for-service beneficiaries admitted to BPCI and non-BPCI hospitals for any of the four medical conditions of interest. We adhered to BPCI program rules, which defined each episode type based on a set of Medicare Severity Diagnosis Related Group (MS-DRG) codes (eg, myocardial infarction episodes were defined as MS-DRGs 280-282). From this sample, we excluded beneficiaries with end-stage renal disease or insurance coverage through Medicare Advantage, as well as beneficiaries who died during the index hospital admission, had any non–Inpatient Prospective Payment System claims, or lacked continuous primary Medicare fee-for-service coverage either during the episode or in the 12 months preceding it.

We constructed 90-day medical condition episodes that began with hospital admission and spanned 90 days after hospital discharge. To avoid bias arising from CMS rules related to precedence (rules for handling how overlapping episodes are assigned to hospitals), we followed prior methods and constructed naturally occurring episodes by assigning overlapping ones to the earlier hospital admission.^2,16 From this set of episodes, we identified those for AMI, CHF, COPD, and pneumonia.

Exposure and Covariate Variables

Our study exposure was the interaction between hospital safety net status and hospital BPCI participation, which captured whether the association between BPCI participation and outcomes varied by safety net status (eg, whether differential changes in an outcome related to BPCI participation were different for safety net and non–safety net hospitals in the program). BPCI participation was defined using a time-varying indicator of BPCI participation to distinguish between episodes occurring under the program (ie, after a hospital began participating) or before participation in it. Covariates were chosen based on prior studies and included patient variables such as age, sex, Elixhauser comorbidities, frailty, and Medicare/Medicaid dual-eligibility status.^17-23 Additionally, our analysis included market variables such as population size and Medicare Advantage penetration.

Outcome Variables

The prespecified primary study outcome was standardized 90-day postdischarge spending. This outcome was chosen owing to the lack of variation in standardized index hospitalization spending given the MS-DRG system and prior work suggesting that bundled payment participants instead targeted changes to postdischarge utilization and spending.² Secondary outcomes included 90-day unplanned readmission rates, 90-day postdischarge mortality rates, discharge to institutional post–acute care providers (defined as either skilled nursing facilities [SNFs] or inpatient rehabilitation facilities), discharge home with home health agency services, and—among patients discharged to SNFs—SNF length of stay (LOS), measured in number of days.

Statistical Analysis

We described the characteristics of patients and hospitals in our samples. In adjusted analyses, we used a series of difference-in-differences (DID) generalized linear models to conduct a heterogeneity analysis evaluating whether the relationship between hospital BPCI participation and medical condition episode outcomes varied based on hospital safety net status.

In these models, the DID estimator was a time-varying indicator of hospital BPCI participation (equal to 1 for episodes occurring during the BPCI period at BPCI hospitals after they initiated participation; 0 otherwise) together with hospital and quarter-time fixed effects. To examine differences in the association between BPCI and episode outcomes by hospital safety net status—that is, whether there was heterogeneity in the outcome changes between safety net and non–safety net hospitals participating in BPCI—our models also included an interaction term between hospital safety net status and the time-varying BPCI participation term (Appendix Methods). In this approach, BPCI safety net and BPCI non–safety net hospitals were compared with non-BPCI hospitals as the comparison group. The comparisons were chosen to yield the most policy-salient findings, since Medicare evaluated hospitals in BPCI, whether safety net or not, by comparing their performance to nonparticipating hospitals, whether safety net or not.

All models controlled for patient and time-varying market characteristics and included hospital fixed effects (to account for time-invariant hospital market characteristics) and MS-DRG fixed effects. All outcomes were evaluated using models with identity links and normal distributions (ie, ordinary least squares). These variables and models were applied to data from the baseline period to examine consistency with the parallel trends assumption. Overall, Wald tests did not indicate divergent baseline period trends in outcomes between BPCI and non-BPCI hospitals (Appendix Figure 1) or BPCI safety net versus BPCI non–safety net hospitals (Appendix Figure 2).

We conducted sensitivity analyses to evaluate the robustness of our results. First, instead of comparing differential changes at BPCI safety net vs BPCI non–safety net hospitals (ie, evaluating safety net status among BPCI hospitals), we evaluated changes at BPCI safety net vs non-BPCI safety net hospitals compared with changes at BPCI non–safety net vs non-BPCI non–safety net hospitals (ie, marginal differences in the changes associated with BPCI participation among safety net vs non–safety net hospitals). Because safety net hospitals in BPCI were compared with nonparticipating safety net hospitals, and non–safety net hospitals in BPCI were compared with nonparticipating non–safety net hospitals, this set of analyses helped address potential concerns about unobservable differences between safety net and non–safety net organizations and their potential impact on our findings.

Second, we used an alternative, BPCI-specific definition for safety net hospitals: instead of defining safety net status based on all hospitals nationwide, we defined it only among BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all BPCI hospitals) and non-BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all non-BPCI hospitals). Third, we repeated our main analyses using models with standard errors clustered at the hospital level and without hospital fixed effects. Fourth, we repeated analysis using models with alternative nonlinear link functions and outcome distributions and without hospital fixed effects.

Statistical tests were two-tailed and considered significant at α = .05 for the primary outcome. Statistical analyses were conducted using SAS 9.4 (SAS Institute, Inc.).

Our sample consisted of 3066 hospitals nationwide that collectively provided medical condition episode care to a total of 1,611,848 Medicare fee-for-service beneficiaries. This sample included 238 BPCI hospitals and 2769 non-BPCI hospitals (Table 1, Appendix Table 1).

Among BPCI hospitals, 63 were safety net and 175 were non–safety net hospitals. Compared with non–safety net hospitals, safety net hospitals tended to be larger and were more likely to be urban teaching hospitals. Safety net hospitals also tended to be located in areas with larger populations, more low-income individuals, and greater Medicare Advantage penetration.

In both the baseline and BPCI periods, there were differences in several characteristics for patients admitted to safety net vs non–safety net hospitals (Table 2; Appendix Table 2). Among BPCI hospitals, in both periods, patients admitted at safety net hospitals were younger and more likely to be Black, be Medicare/Medicaid dual eligible, and report having a disability than patients admitted to non–safety net hospitals. Patients admitted to safety net hospitals were also more likely to reside in socioeconomically disadvantaged areas.

Safety Net Status Among BPCI Hospitals

In the baseline period (Appendix Table 3), postdischarge spending was slightly greater among patients admitted to BPCI safety net hospitals ($18,817) than those admitted to BPCI non–safety net hospitals ($18,335). There were also small differences in secondary outcomes between the BPCI safety net and non−safety net groups.

In adjusted analyses evaluating heterogeneity in the effect of BPCI participation between safety net and non–safety net hospitals (Figure 1), differential changes in postdischarge spending between baseline and BPCI participation periods did not differ between safety net and non–safety net hospitals participating in BPCI (aDID, $40; 95% CI, –$254 to $335; P = .79).

Sensitivity Analysis

Analyses of BPCI participation among safety net vs non–safety net hospitals nationwide yielded results that were similar to those from our main analyses (Appendix Figures 4, 5, and 6). Compared with BPCI participation among non–safety net hospitals, participation among safety net hospitals was associated with a differential increase from baseline to BPCI periods in discharge to institutional post–acute care providers (aDID, 1.07 percentage points; 95% CI, 0.47-1.67 percentage points; P < .001), but no differential changes between baseline and BPCI periods in postdischarge spending (aDID, –$199;95% CI, –$461 to $63; P = .14), SNF LOS (aDID, –0.22 days; 95% CI, –0.54 to 0.09 days; P = .16), or other secondary outcomes.

Replicating our main analyses using an alternative, BPCI-specific definition of safety net hospitals yielded similar results overall (Appendix Table 4; Appendix Figures 7, 8, and 9). There were no differential changes between baseline and BPCI periods in postdischarge spending between BPCI safety net and BPCI non–safety net hospitals (aDID, $111; 95% CI, –$189 to $411; P = .47). Results for secondary outcomes were also qualitatively similar to results from main analyses, with the exception that among BPCI hospitals, safety net hospitals had a differentially higher SNF LOS than non–safety net hospitals between baseline and BPCI periods (aDID, 0.38 days; 95% CI, 0.02-0.74 days; P = .04).

Compared with results from our main analysis, findings were qualitatively similar overall in analyses using models with hospital-clustered standard errors and without hospital fixed effects (Appendix Figures 10, 11, and 12) as well as models with alternative link functions and outcome distributions and without hospital fixed effects (Appendix Figures 13, 14, and 15).

This analysis builds on prior work by evaluating how hospital safety net status affected the known association between bundled payment participation and decreased spending and stable quality for medical condition episodes. Although safety net status did not appear to affect those relationships, it did affect the relationship between participation and post–acute care utilization. These results have three main implications.

First, our results suggest that policymakers should continue engaging safety net hospitals in medical condition bundled payments while monitoring for unintended consequences. Our findings with regard to spending provide some reassurance that safety net hospitals can potentially achieve savings while maintaining quality under bundled payments, similar to other types of hospitals. However, the differences in patient populations and post–acute care utilization patterns suggest that policymakers should continue to carefully monitor for disparities based on hospital safety net status and consider implementing measures that have been used in other payment reforms to support safety net organizations. Such measures could involve providing customized technical assistance or evaluating performance using “peer groups” that compare performance among safety net hospitals alone rather than among all hospitals.^24,25

Second, our findings underscore potential challenges that safety net hospitals may face when attempting to redesign care. For instance, among hospitals accepting bundled payments for medical conditions, successful strategies in BPCI have often included maintaining the proportion of patients discharged to institutional post–acute care providers while reducing SNF LOS.² However, in our study, discharge to institutional post–acute care providers actually increased among safety net hospitals relative to other hospitals while SNF LOS did not decrease. Additionally, while other hospitals in bundled payments have exhibited differentially greater discharge home with home health services, we found that safety net hospitals did not. These represent areas for future work, particularly because little is known about how safety net hospitals coordinate post–acute care (eg, the extent to which safety net hospitals integrate with post–acute care providers or coordinate home-based care for vulnerable patient populations).

Third, study results offer insight into potential challenges to practice changes. Compared with other hospitals, safety net hospitals in our analysis provided medical condition episode care to more Black, Medicare/Medicaid dual-eligible, and disabled patients, as well as individuals living in socioeconomically disadvantaged areas. Collectively, these groups may face more challenging socioeconomic circumstances or existing disparities. The combination of these factors and limited financial resources at safety net hospitals could complicate their ability to manage transitions of care after hospitalization by shifting discharge away from high-intensity institutional post–acute care facilities.

Our analysis has limitations. First, given the observational study design, findings are subject to residual confounding and selection bias. For instance, findings related to post–acute care utilization could have been influenced by unobservable changes in market supply and other factors. However, we mitigated these risks using a quasi-experimental methodology that also directly accounted for multiple patient, hospital, and market characteristics and also used fixed effects to account for unobserved heterogeneity. Second, in studying BPCI Model 2, we evaluated one model within one bundled payment program. However, BPCI Model 2 encompassed a wide range of medical conditions, and both this scope and program design have served as the direct basis for subsequent bundled payment models, such as the ongoing BPCI Advanced and other forthcoming programs.²⁶ Third, while our analysis evaluated multiple aspects of patient complexity, individuals may be “high risk” owing to several clinical and social determinants. Future work should evaluate different features of patient risk and how they affect outcomes under payment models such as bundled payments.

Safety net status appeared to affect the relationship between bundled payment participation and post–acute care utilization, but not episode spending. These findings suggest that policymakers could support safety net hospitals within bundled payment programs and consider safety net status when evaluating them.

Bundled payments represent one of the most prominent value-based payment arrangements nationwide. Under this payment approach, hospitals assume responsibility for quality and costs across discrete episodes of care. Hospitals that maintain quality while achieving cost reductions are eligible for financial incentives, whereas those that do not are subject to financial penalties.

To date, the largest completed bundled payment program nationwide is Medicare’s Bundled Payments for Care Improvement (BPCI) initiative. Among four different participation models in BPCI, hospital enrollment was greatest in Model 2, in which episodes spanned from hospitalization through 90 days of post–acute care. The overall results from BPCI Model 2 have been positive: hospitals participating in both common surgical episodes, such as joint replacement surgery, and medical episodes, such as acute myocardial infarction (AMI) and congestive heart failure (CHF), have demonstrated long-term financial savings with stable quality performance.^1,2

Safety net hospitals that disproportionately serve low-income patients may fare differently than other hospitals under bundled payment models. At baseline, these hospitals typically have fewer financial resources, which may limit their ability to implement measures to standardize care during hospitalization (eg, clinical pathways) or after discharge (eg, postdischarge programs and other strategies to reduce readmissions).³ Efforts to redesign care may be further complicated by greater clinical complexity and social and structural determinants of health among patients seeking care at safety net hospitals. Given the well-known interactions between social determinants and health conditions, these factors are highly relevant for patients hospitalized at safety net hospitals for acute medical events or exacerbations of chronic conditions.

Existing evidence has shown that safety net hospitals have not performed as well as other hospitals in other value-based reforms.^4-8 In the context of bundled payments for joint replacement surgery, safety net hospitals have been less likely to achieve financial savings but more likely to receive penalties.^9-11 Moreover, the savings achieved by safety net hospitals have been smaller than those achieved by non–safety net hospitals.¹²

Despite these concerning findings, there are few data about how safety net hospitals have fared under bundled payments for common medical conditions. To address this critical knowledge gap, we evaluated the effect of hospital safety net status on the association between BPCI Model 2 participation and changes in outcomes for medical condition episodes.

This study was approved by the University of Pennsylvania Institutional Review Board with a waiver of informed consent.

Data

We used 100% Medicare claims data from 2011 to 2016 for patients receiving care at hospitals participating in BPCI Model 2 for one of four common medical condition episodes: AMI, pneumonia, CHF, and chronic obstructive pulmonary disease (COPD). A 20% random national sample was used for patients hospitalized at nonparticipant hospitals. Publicly available data from the Centers for Medicare & Medicaid Services (CMS) were used to identify hospital enrollment in BPCI Model 2, while data from the 2017 CMS Impact File were used to quantify each hospital’s disproportionate patient percentage (DPP), which reflects the proportion of Medicaid and low-income Medicare beneficiaries served and determines a hospital’s eligibility to earn disproportionate share hospital payments.

Data from the 2011 American Hospital Association Annual Survey were used to capture hospital characteristics, such as number of beds, teaching status, and profit status, while data from the Medicare provider of service, beneficiary summary, and accountable care organization files were used to capture additional hospital characteristics and market characteristics, such as population size and Medicare Advantage penetration. The Medicare Provider Enrollment, Chain, and Ownership System file was used to identify and remove BPCI episodes from physician group practices. State-level data about area deprivation index—a census tract–based measure that incorporates factors such as income, education, employment, and housing quality to describe socioeconomic disadvantage among neighborhoods—were used to define socioeconomically disadvantaged areas as those in the top 20% of area deprivation index statewide.¹³ Markets were defined using hospital referral regions.¹⁴

Study Periods and Hospital Groups

Our analysis spanned the period between January 1, 2011, and December 31, 2016. We separated this period into a baseline period (January 2011–September 2013) prior to the start of BPCI and a subsequent BPCI period (October 2013–December 2016).

We defined any hospitals participating in BPCI Model 2 across this period for any of the four included medical condition episodes as BPCI hospitals. Because hospitals were able to enter or exit BPCI over time, and enrollment data were provided by CMS as quarterly participation files, we were able to identify dates of entry into or exit from BPCI over time by hospital-condition pairs. Hospitals were considered BPCI hospitals until the end of the study period, regardless of subsequent exit.

We defined non-BPCI hospitals as those that never participated in the program and had 10 or more admissions in the BPCI period for the included medical condition episodes. We used this approach to minimize potential bias arising from BPCI entry and exit over time.

Across both BPCI and non-BPCI hospital groups, we followed prior methods and defined safety net hospitals based on a hospital’s DPP.¹⁵ Specifically, safety net hospitals were those in the top quartile of DPP among all hospitals nationwide, and hospitals in the other three quartiles were defined as non–safety net hospitals.^9,12

Study Sample and Episode Construction

Our study sample included Medicare fee-for-service beneficiaries admitted to BPCI and non-BPCI hospitals for any of the four medical conditions of interest. We adhered to BPCI program rules, which defined each episode type based on a set of Medicare Severity Diagnosis Related Group (MS-DRG) codes (eg, myocardial infarction episodes were defined as MS-DRGs 280-282). From this sample, we excluded beneficiaries with end-stage renal disease or insurance coverage through Medicare Advantage, as well as beneficiaries who died during the index hospital admission, had any non–Inpatient Prospective Payment System claims, or lacked continuous primary Medicare fee-for-service coverage either during the episode or in the 12 months preceding it.

We constructed 90-day medical condition episodes that began with hospital admission and spanned 90 days after hospital discharge. To avoid bias arising from CMS rules related to precedence (rules for handling how overlapping episodes are assigned to hospitals), we followed prior methods and constructed naturally occurring episodes by assigning overlapping ones to the earlier hospital admission.^2,16 From this set of episodes, we identified those for AMI, CHF, COPD, and pneumonia.

Exposure and Covariate Variables

Our study exposure was the interaction between hospital safety net status and hospital BPCI participation, which captured whether the association between BPCI participation and outcomes varied by safety net status (eg, whether differential changes in an outcome related to BPCI participation were different for safety net and non–safety net hospitals in the program). BPCI participation was defined using a time-varying indicator of BPCI participation to distinguish between episodes occurring under the program (ie, after a hospital began participating) or before participation in it. Covariates were chosen based on prior studies and included patient variables such as age, sex, Elixhauser comorbidities, frailty, and Medicare/Medicaid dual-eligibility status.^17-23 Additionally, our analysis included market variables such as population size and Medicare Advantage penetration.

Outcome Variables

The prespecified primary study outcome was standardized 90-day postdischarge spending. This outcome was chosen owing to the lack of variation in standardized index hospitalization spending given the MS-DRG system and prior work suggesting that bundled payment participants instead targeted changes to postdischarge utilization and spending.² Secondary outcomes included 90-day unplanned readmission rates, 90-day postdischarge mortality rates, discharge to institutional post–acute care providers (defined as either skilled nursing facilities [SNFs] or inpatient rehabilitation facilities), discharge home with home health agency services, and—among patients discharged to SNFs—SNF length of stay (LOS), measured in number of days.

Statistical Analysis

We described the characteristics of patients and hospitals in our samples. In adjusted analyses, we used a series of difference-in-differences (DID) generalized linear models to conduct a heterogeneity analysis evaluating whether the relationship between hospital BPCI participation and medical condition episode outcomes varied based on hospital safety net status.

In these models, the DID estimator was a time-varying indicator of hospital BPCI participation (equal to 1 for episodes occurring during the BPCI period at BPCI hospitals after they initiated participation; 0 otherwise) together with hospital and quarter-time fixed effects. To examine differences in the association between BPCI and episode outcomes by hospital safety net status—that is, whether there was heterogeneity in the outcome changes between safety net and non–safety net hospitals participating in BPCI—our models also included an interaction term between hospital safety net status and the time-varying BPCI participation term (Appendix Methods). In this approach, BPCI safety net and BPCI non–safety net hospitals were compared with non-BPCI hospitals as the comparison group. The comparisons were chosen to yield the most policy-salient findings, since Medicare evaluated hospitals in BPCI, whether safety net or not, by comparing their performance to nonparticipating hospitals, whether safety net or not.

All models controlled for patient and time-varying market characteristics and included hospital fixed effects (to account for time-invariant hospital market characteristics) and MS-DRG fixed effects. All outcomes were evaluated using models with identity links and normal distributions (ie, ordinary least squares). These variables and models were applied to data from the baseline period to examine consistency with the parallel trends assumption. Overall, Wald tests did not indicate divergent baseline period trends in outcomes between BPCI and non-BPCI hospitals (Appendix Figure 1) or BPCI safety net versus BPCI non–safety net hospitals (Appendix Figure 2).

We conducted sensitivity analyses to evaluate the robustness of our results. First, instead of comparing differential changes at BPCI safety net vs BPCI non–safety net hospitals (ie, evaluating safety net status among BPCI hospitals), we evaluated changes at BPCI safety net vs non-BPCI safety net hospitals compared with changes at BPCI non–safety net vs non-BPCI non–safety net hospitals (ie, marginal differences in the changes associated with BPCI participation among safety net vs non–safety net hospitals). Because safety net hospitals in BPCI were compared with nonparticipating safety net hospitals, and non–safety net hospitals in BPCI were compared with nonparticipating non–safety net hospitals, this set of analyses helped address potential concerns about unobservable differences between safety net and non–safety net organizations and their potential impact on our findings.

Second, we used an alternative, BPCI-specific definition for safety net hospitals: instead of defining safety net status based on all hospitals nationwide, we defined it only among BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all BPCI hospitals) and non-BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all non-BPCI hospitals). Third, we repeated our main analyses using models with standard errors clustered at the hospital level and without hospital fixed effects. Fourth, we repeated analysis using models with alternative nonlinear link functions and outcome distributions and without hospital fixed effects.

Statistical tests were two-tailed and considered significant at α = .05 for the primary outcome. Statistical analyses were conducted using SAS 9.4 (SAS Institute, Inc.).

Our sample consisted of 3066 hospitals nationwide that collectively provided medical condition episode care to a total of 1,611,848 Medicare fee-for-service beneficiaries. This sample included 238 BPCI hospitals and 2769 non-BPCI hospitals (Table 1, Appendix Table 1).

Among BPCI hospitals, 63 were safety net and 175 were non–safety net hospitals. Compared with non–safety net hospitals, safety net hospitals tended to be larger and were more likely to be urban teaching hospitals. Safety net hospitals also tended to be located in areas with larger populations, more low-income individuals, and greater Medicare Advantage penetration.

In both the baseline and BPCI periods, there were differences in several characteristics for patients admitted to safety net vs non–safety net hospitals (Table 2; Appendix Table 2). Among BPCI hospitals, in both periods, patients admitted at safety net hospitals were younger and more likely to be Black, be Medicare/Medicaid dual eligible, and report having a disability than patients admitted to non–safety net hospitals. Patients admitted to safety net hospitals were also more likely to reside in socioeconomically disadvantaged areas.

Safety Net Status Among BPCI Hospitals

In the baseline period (Appendix Table 3), postdischarge spending was slightly greater among patients admitted to BPCI safety net hospitals ($18,817) than those admitted to BPCI non–safety net hospitals ($18,335). There were also small differences in secondary outcomes between the BPCI safety net and non−safety net groups.

In adjusted analyses evaluating heterogeneity in the effect of BPCI participation between safety net and non–safety net hospitals (Figure 1), differential changes in postdischarge spending between baseline and BPCI participation periods did not differ between safety net and non–safety net hospitals participating in BPCI (aDID, $40; 95% CI, –$254 to $335; P = .79).

Sensitivity Analysis

Analyses of BPCI participation among safety net vs non–safety net hospitals nationwide yielded results that were similar to those from our main analyses (Appendix Figures 4, 5, and 6). Compared with BPCI participation among non–safety net hospitals, participation among safety net hospitals was associated with a differential increase from baseline to BPCI periods in discharge to institutional post–acute care providers (aDID, 1.07 percentage points; 95% CI, 0.47-1.67 percentage points; P < .001), but no differential changes between baseline and BPCI periods in postdischarge spending (aDID, –$199;95% CI, –$461 to $63; P = .14), SNF LOS (aDID, –0.22 days; 95% CI, –0.54 to 0.09 days; P = .16), or other secondary outcomes.

Replicating our main analyses using an alternative, BPCI-specific definition of safety net hospitals yielded similar results overall (Appendix Table 4; Appendix Figures 7, 8, and 9). There were no differential changes between baseline and BPCI periods in postdischarge spending between BPCI safety net and BPCI non–safety net hospitals (aDID, $111; 95% CI, –$189 to $411; P = .47). Results for secondary outcomes were also qualitatively similar to results from main analyses, with the exception that among BPCI hospitals, safety net hospitals had a differentially higher SNF LOS than non–safety net hospitals between baseline and BPCI periods (aDID, 0.38 days; 95% CI, 0.02-0.74 days; P = .04).

Compared with results from our main analysis, findings were qualitatively similar overall in analyses using models with hospital-clustered standard errors and without hospital fixed effects (Appendix Figures 10, 11, and 12) as well as models with alternative link functions and outcome distributions and without hospital fixed effects (Appendix Figures 13, 14, and 15).

This analysis builds on prior work by evaluating how hospital safety net status affected the known association between bundled payment participation and decreased spending and stable quality for medical condition episodes. Although safety net status did not appear to affect those relationships, it did affect the relationship between participation and post–acute care utilization. These results have three main implications.

First, our results suggest that policymakers should continue engaging safety net hospitals in medical condition bundled payments while monitoring for unintended consequences. Our findings with regard to spending provide some reassurance that safety net hospitals can potentially achieve savings while maintaining quality under bundled payments, similar to other types of hospitals. However, the differences in patient populations and post–acute care utilization patterns suggest that policymakers should continue to carefully monitor for disparities based on hospital safety net status and consider implementing measures that have been used in other payment reforms to support safety net organizations. Such measures could involve providing customized technical assistance or evaluating performance using “peer groups” that compare performance among safety net hospitals alone rather than among all hospitals.^24,25

Second, our findings underscore potential challenges that safety net hospitals may face when attempting to redesign care. For instance, among hospitals accepting bundled payments for medical conditions, successful strategies in BPCI have often included maintaining the proportion of patients discharged to institutional post–acute care providers while reducing SNF LOS.² However, in our study, discharge to institutional post–acute care providers actually increased among safety net hospitals relative to other hospitals while SNF LOS did not decrease. Additionally, while other hospitals in bundled payments have exhibited differentially greater discharge home with home health services, we found that safety net hospitals did not. These represent areas for future work, particularly because little is known about how safety net hospitals coordinate post–acute care (eg, the extent to which safety net hospitals integrate with post–acute care providers or coordinate home-based care for vulnerable patient populations).

Third, study results offer insight into potential challenges to practice changes. Compared with other hospitals, safety net hospitals in our analysis provided medical condition episode care to more Black, Medicare/Medicaid dual-eligible, and disabled patients, as well as individuals living in socioeconomically disadvantaged areas. Collectively, these groups may face more challenging socioeconomic circumstances or existing disparities. The combination of these factors and limited financial resources at safety net hospitals could complicate their ability to manage transitions of care after hospitalization by shifting discharge away from high-intensity institutional post–acute care facilities.

Our analysis has limitations. First, given the observational study design, findings are subject to residual confounding and selection bias. For instance, findings related to post–acute care utilization could have been influenced by unobservable changes in market supply and other factors. However, we mitigated these risks using a quasi-experimental methodology that also directly accounted for multiple patient, hospital, and market characteristics and also used fixed effects to account for unobserved heterogeneity. Second, in studying BPCI Model 2, we evaluated one model within one bundled payment program. However, BPCI Model 2 encompassed a wide range of medical conditions, and both this scope and program design have served as the direct basis for subsequent bundled payment models, such as the ongoing BPCI Advanced and other forthcoming programs.²⁶ Third, while our analysis evaluated multiple aspects of patient complexity, individuals may be “high risk” owing to several clinical and social determinants. Future work should evaluate different features of patient risk and how they affect outcomes under payment models such as bundled payments.

Safety net status appeared to affect the relationship between bundled payment participation and post–acute care utilization, but not episode spending. These findings suggest that policymakers could support safety net hospitals within bundled payment programs and consider safety net status when evaluating them.

In 2016, 17.6 million deaths occurred globally due to cardiovascular disease (CVD) with coronary artery disease (CAD) and ischemic stroke as top contributors.¹ Elevated low-density lipoprotein cholesterol (LDL-C) has been linked to greater risk of atherosclerotic cardiovascular disease (ASCVD); therefore, LDL-C reduction is imperative to decrease risk of cardiovascular (CV) morbidity and mortality.² Since 1987, statin therapy has been the mainstay of treatment for hypercholesterolemia, and current practice guidelines recommend statins as first-line therapy given demonstrated reductions in LDL-C and CV mortality reduction in robust clinical trials.^2-4 Although generally safe and well tolerated, muscle-related adverse events (AEs) limit optimal use of statins in up to 20% of individuals who have an indication for statin therapy.⁵ As a consequence, these patients receive suboptimal statin doses or no statin therapy and are at a higher risk for ASCVD.⁵

Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly lower LDL-C when used as monotherapy or in combination with statins and/or other lipid-lowering therapies.⁵ These agents are currently approved by the US Food and Drug Administration as an adjunct to diet with or without other lipid-lowering therapies for the management of primary hypercholesterolemia (including heterozygous familial hypercholesterolemia), homozygous familial hypercholesterolemia (evolocumab only), and for use in patients with established CVD unable to achieve their lipid-lowering goals with maximally tolerated statin doses and ezetimibe.⁴ With the ability to reduce LDL-C by up to 65%, PCSK9 inhibitors offer an alternative option for LDL-C and potentially CV risk reduction in statin-intolerant patients.⁵

Alirocumab, the formulary preferred PCSK9 inhibitor at the Michael E. DeBakey Veterans Affairs Medical Center (MEDVAMC) in Houston, Texas, has been increasingly used in high-risk statin-intolerant veterans. The primary objective of this case series was to assess LDL-C reduction associated with alirocumab use in statin-intolerant veterans at the MEDVAMC. The secondary objective was to assess the incidence of CV events. This study was approved by the MEDVAMC Quality Assurance and Regulatory Affairs committee.

Methods

In this single-center case series, a retrospective chart review was conducted to identify statin-intolerant veterans who were initiated on treatment with alirocumab for LDL-C and/or CV risk reduction between June 2017 and May 2019. Adult veterans with a diagnosis of primary hypercholesterolemia (including heterozygous familial hypercholesterolemia) and/or CAD with documented statin intolerance were included in the study. Statin intolerance was defined in accordance with the National Lipid Association (NLA) definition as aninability to tolerate ≥ 2 statins with a trial of at least 1 statin at its lowest daily dose.⁵ Veterans who previously received treatment with evolocumab, those prescribed concurrent statin therapies, and those missing follow-up lipid panels at 24 weeks were excluded from the study. To assess LDL-C reduction, LDL-C at baseline was compared with LDL-C at 4 and 24 weeks. Incident CV events before and after alirocumab initiation were documented. The US Department of Veteran Affairs (VA) Computerized Patient Record System was used to collect patient data.

Data Collection, Measures, and Analysis

Electronic health records of all eligible patients who received alirocumab were reviewed, and basic demographics (patient age, sex, and race/ethnicity) as well as medical characteristics at baseline were collected. To confirm statin intolerance, each veteran’s history of statin use and use of additional lipid-lowering agents was documented. CV history was measured with an index of categorical measures for hypertension, confirmed CAD, hyperlipidemia, heart failure, arrhythmias, peripheral artery disease, stroke, diabetes mellitus, and hypothyroidism. Additionally, concomitant medications, such as aspirin, P2Y₁₂ inhibitors, β-blockers, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers that patients were taking also were collected. Each veteran’s lipid panel at baseline, and at 4 and 24 weeks posttreatment initiation, also was extracted. Continuous variables were summarized with means (SD), and categorical variables were summarized with frequencies and proportions. The paired Wilcoxon signed rank test was used to compare LDL-C at 4 and 24 weeks after alirocumab initiation with patients’ baseline LDL-C.

Results

Between June 2017 and May 2019, 122 veterans were initiated on alirocumab. Of these veterans, 98 were excluded: 35 concurrently received statin therapy, 33 missed follow-up lipid panels, 21 had previously received evolocumab, 6 failed to meet the NLA definition for statin intolerance, 2 did not fill active alirocumab prescriptions, and 1 had an incalculable LDL-C with a baseline triglyceride level of 3079 mg/dL. This resulted in 24 veterans included in the analysis.

Most participants were male (87.5%) and White veterans (79.2%) with a mean (SD) age of 66.0 (8.4) years and mean (SD) baseline LDL-C of 161.9 (74.3) mg/dL. At baseline, 21 veterans had a history of primary hyperlipidemia, 19 had a history of CAD, and 2 had a history of heterozygous familial hypercholesterolemia. Of the 24 patients included, the most trialed statins before alirocumab initiation were atorvastatin (95.8%), simvastatin (79.2%), rosuvastatin (79.2%), and pravastatin (62.5%) (Table).

LDL-C Reduction

Veterans were initially treated with alirocumab 75 mg administered subcutaneously every 2 weeks; however, 11 veterans required a dose increase to 150 mg every 2 weeks. At treatment week 4, the median LDL-C reduction was 78.5 mg/dL (IQR, 28.0-107.3; P < .01), and at treatment week 24, the median LDL-C reduction was 55.6 mg/dL (IQR, 18.6-85.3; P < .01). This equated to median LDL-C reductions from baseline of 48.5% at week 4 and 34.3% at week 24. A total of 3 veterans experienced LDL-C increases following initiation of alirocumab. At week 4, 9 veterans were noted to have an LDL-C reduction > 50%, 7 veterans had an LDL-C reduction between 30% and 50%, and 5 veterans had an LDL-C reduction of < 30%. At week 24, 6 had an LDL-C reduction > 50%, 9 veterans had an LDL-C reduction between 30% and 50%, and 6 had a LDL-C reduction < 30%.

Cardiovascular Events

Before alirocumab initiation, 22 CV events and interventions were reported in 16 veterans: 12 percutaneous coronary interventions, 5 coronary artery bypass surgeries (CABG), 4 myocardial infarctions, and 1 transient ischemic attack. One month following alirocumab initiation, 1 veteran underwent a CABG after a non-ST-elevation myocardial infarction (NSTEMI).

Safety and Tolerability

Alirocumab was discontinued in 5 veterans due to 4 cases of intolerance (reported memory loss, lethargy, myalgias, and body aches with dyspnea) and 1 case of persistent LDL-C of < 40 mg/dL. Alirocumab was discontinued after 1 year in 2 patients (persistent LDL-C < 40 mg/dL and reported memory loss) and after 6 months in the veteran who reported lethargy. Alirocumab was discontinued after 4 months in the veteran with myalgias and within 2 months in the veteran with body aches and dyspnea. No other AEs were reported.

Discussion

The Efficacy and Safety of Alirocumab vs Ezetimibe in Statin-Intolerant Veterans With a Statin Rechallenge Arm trial is the first clinical trial to examine the efficacy and safety of alirocumab use in statin-intolerant patients. In the trial, 314 patients were randomized to receive alirocumab, ezetimibe, or an atorvastatin rechallenge.⁶ At 24 weeks, alirocumab reduced mean (SE) LDL-C by 45.0% (2.2%) vs 14.6% (2.2%) with ezetimibe (mean difference 30.4% [3.1%], P < .01).⁶ Fewer skeletal-muscle-related events also were noted with alirocumab vs atorvastatin (hazard ratio, 0.61; 95% CI, 0.38-0.99; P = .04).⁶

In this case series, an LDL-C reduction of > 50% was observed in 9 veterans (42.9%) following 4 weeks of treatment; however, LDL-C reduction of > 50% compared with baseline was sustained in only 6 veterans (28.6%) at week 24. Additionally, LDL-C increases from baseline were observed in 3 veterans; the reasoning for the observed increase was unclear, but this may have been due to nonadherence and dietary factors.⁴ Although a majority of patients saw a significant and clinically meaningful reduction in LDL-C, the group of patients with an increase in the same may have benefitted from targeted intervention to improve medication and dietary adherence. PCSK9 inhibitor resistance also may have contributed to an increase in LDL-C during treatment.⁷

Of the 24 patients included, 4 reported AEs resulted in therapy discontinuation. Memory impairment, a rare AE of alirocumab, was reported 1 year following alirocumab initiation. Additionally, lethargy was reported after 6 months of treatment. Myalgia also was reported in a veteran 4 months following treatment, and 1 veteran experienced body aches and dyspnea < 2 months following treatment. The most common AEs associated with alirocumab, as noted in previous safety and efficacy clinical trials, included: nasopharyngitis, injection site reaction, influenza, urinary tract infection, and myalgias.⁸ Many of these more common AEs may be subclinical and underreported. This small case series, however, detected 4 events severe enough to lead to therapy discontinuation. Although this sample is not representative of all statin-intolerant patients who receive treatment with alirocumab, our findings suggest the need for patient education on potential AEs before therapy initiation and clinician monitoring at follow-up visits.

The ODYSSEY OUTCOMES trial established a CV benefit associated with alirocumab; however, patients included had a recent acute coronary syndrome event and were receiving a high-intensity statin.⁹ This case series is unique in that before alirocumab initiation, 22 CV events/interventions were reported in the sample of 24 patients. After therapy initiation, 1 patient underwent a CABG after an NSTEMI in the month following initiation. This suggests that cardiac complications are possible after PCSK-9 initiation; however, little information can be gained from 1 patient. Nevertheless, early therapy failure should be investigated in the context of real-world use in statin-intolerant patients. This is a complex task, however, given the difficulties of achieving a balanced study design. Statin intolerance is a clear source of selection bias into treatment with alirocumab as patients in this population have already initiated and failed statin therapy. The prevalence of prior CV events and the time-dependent association between prior and future CV events stand as another complex confounder. Although there is a clear and pressing need to understand the risks and benefits of PCSK9 therapy in statin-intolerant patients, future research in this area will need to cautiously address these important sources of bias.

Overall, the results of this case series support LDL-C reduction associated with alirocumab in the absence of statin therapy. Despite favorable results, use of alirocumab may be limited by cost and its subcutaneous route of administration. Bempedoic acid, an oral, once-daily lipid-lowering agent poses an alternative to PCSK9 inhibitors, but further data regarding CV outcomes with this agent is needed.^10,11 Robust randomized controlled trials also are needed to evaluate CV outcomes for alirocumab use in statin-intolerant veterans.

Limitations

Only 24 veterans were included in the study, reflecting 20% of the charts reviewed (80% exclusion rate), and in this small sample, only 1 CV event was observed. Both of these serve as threats to external validity. As the study information was extracted from chart review, the results may be limited by coding or historical bias. Medical information from outside institutions may be missing from medical records. Additionally, results may be skewed by possible documentation errors. Furthermore, the period between previous CV events and alirocumab initiation is unclear as event dates were often not recorded if treatment was received at an outside institution.

Due to the short follow-up period, the case series is limited in its assessment of CV outcomes and safety outcomes. Larger studies over an extended period are needed to assess CV outcomes and safety of alirocumab use in statin-intolerant patients. Also, medication adherence was not assessed. Given the impact of medication adherence on LDL-C reduction, it is unclear what role medication adherence played in the LDL-C reduction observed in this study.⁴

Conclusions

Alirocumab use in 24 statin-intolerant veterans resulted in a significant reduction in LDL-C at 4 and 24 weeks after initiation. In addition, 1 CV event/intervention was observed following alirocumab initiation, although this should be interpreted with caution due to the retrospective nature of this case series, small sample size, and short follow-up period. Large, long-term studies would better evaluate the CV benefit associated with alirocumab therapy in a veteran population.

In 2016, 17.6 million deaths occurred globally due to cardiovascular disease (CVD) with coronary artery disease (CAD) and ischemic stroke as top contributors.¹ Elevated low-density lipoprotein cholesterol (LDL-C) has been linked to greater risk of atherosclerotic cardiovascular disease (ASCVD); therefore, LDL-C reduction is imperative to decrease risk of cardiovascular (CV) morbidity and mortality.² Since 1987, statin therapy has been the mainstay of treatment for hypercholesterolemia, and current practice guidelines recommend statins as first-line therapy given demonstrated reductions in LDL-C and CV mortality reduction in robust clinical trials.^2-4 Although generally safe and well tolerated, muscle-related adverse events (AEs) limit optimal use of statins in up to 20% of individuals who have an indication for statin therapy.⁵ As a consequence, these patients receive suboptimal statin doses or no statin therapy and are at a higher risk for ASCVD.⁵

Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly lower LDL-C when used as monotherapy or in combination with statins and/or other lipid-lowering therapies.⁵ These agents are currently approved by the US Food and Drug Administration as an adjunct to diet with or without other lipid-lowering therapies for the management of primary hypercholesterolemia (including heterozygous familial hypercholesterolemia), homozygous familial hypercholesterolemia (evolocumab only), and for use in patients with established CVD unable to achieve their lipid-lowering goals with maximally tolerated statin doses and ezetimibe.⁴ With the ability to reduce LDL-C by up to 65%, PCSK9 inhibitors offer an alternative option for LDL-C and potentially CV risk reduction in statin-intolerant patients.⁵

Alirocumab, the formulary preferred PCSK9 inhibitor at the Michael E. DeBakey Veterans Affairs Medical Center (MEDVAMC) in Houston, Texas, has been increasingly used in high-risk statin-intolerant veterans. The primary objective of this case series was to assess LDL-C reduction associated with alirocumab use in statin-intolerant veterans at the MEDVAMC. The secondary objective was to assess the incidence of CV events. This study was approved by the MEDVAMC Quality Assurance and Regulatory Affairs committee.

Methods

In this single-center case series, a retrospective chart review was conducted to identify statin-intolerant veterans who were initiated on treatment with alirocumab for LDL-C and/or CV risk reduction between June 2017 and May 2019. Adult veterans with a diagnosis of primary hypercholesterolemia (including heterozygous familial hypercholesterolemia) and/or CAD with documented statin intolerance were included in the study. Statin intolerance was defined in accordance with the National Lipid Association (NLA) definition as aninability to tolerate ≥ 2 statins with a trial of at least 1 statin at its lowest daily dose.⁵ Veterans who previously received treatment with evolocumab, those prescribed concurrent statin therapies, and those missing follow-up lipid panels at 24 weeks were excluded from the study. To assess LDL-C reduction, LDL-C at baseline was compared with LDL-C at 4 and 24 weeks. Incident CV events before and after alirocumab initiation were documented. The US Department of Veteran Affairs (VA) Computerized Patient Record System was used to collect patient data.

Data Collection, Measures, and Analysis

Electronic health records of all eligible patients who received alirocumab were reviewed, and basic demographics (patient age, sex, and race/ethnicity) as well as medical characteristics at baseline were collected. To confirm statin intolerance, each veteran’s history of statin use and use of additional lipid-lowering agents was documented. CV history was measured with an index of categorical measures for hypertension, confirmed CAD, hyperlipidemia, heart failure, arrhythmias, peripheral artery disease, stroke, diabetes mellitus, and hypothyroidism. Additionally, concomitant medications, such as aspirin, P2Y₁₂ inhibitors, β-blockers, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers that patients were taking also were collected. Each veteran’s lipid panel at baseline, and at 4 and 24 weeks posttreatment initiation, also was extracted. Continuous variables were summarized with means (SD), and categorical variables were summarized with frequencies and proportions. The paired Wilcoxon signed rank test was used to compare LDL-C at 4 and 24 weeks after alirocumab initiation with patients’ baseline LDL-C.

Results

Between June 2017 and May 2019, 122 veterans were initiated on alirocumab. Of these veterans, 98 were excluded: 35 concurrently received statin therapy, 33 missed follow-up lipid panels, 21 had previously received evolocumab, 6 failed to meet the NLA definition for statin intolerance, 2 did not fill active alirocumab prescriptions, and 1 had an incalculable LDL-C with a baseline triglyceride level of 3079 mg/dL. This resulted in 24 veterans included in the analysis.

Most participants were male (87.5%) and White veterans (79.2%) with a mean (SD) age of 66.0 (8.4) years and mean (SD) baseline LDL-C of 161.9 (74.3) mg/dL. At baseline, 21 veterans had a history of primary hyperlipidemia, 19 had a history of CAD, and 2 had a history of heterozygous familial hypercholesterolemia. Of the 24 patients included, the most trialed statins before alirocumab initiation were atorvastatin (95.8%), simvastatin (79.2%), rosuvastatin (79.2%), and pravastatin (62.5%) (Table).

LDL-C Reduction

Veterans were initially treated with alirocumab 75 mg administered subcutaneously every 2 weeks; however, 11 veterans required a dose increase to 150 mg every 2 weeks. At treatment week 4, the median LDL-C reduction was 78.5 mg/dL (IQR, 28.0-107.3; P < .01), and at treatment week 24, the median LDL-C reduction was 55.6 mg/dL (IQR, 18.6-85.3; P < .01). This equated to median LDL-C reductions from baseline of 48.5% at week 4 and 34.3% at week 24. A total of 3 veterans experienced LDL-C increases following initiation of alirocumab. At week 4, 9 veterans were noted to have an LDL-C reduction > 50%, 7 veterans had an LDL-C reduction between 30% and 50%, and 5 veterans had an LDL-C reduction of < 30%. At week 24, 6 had an LDL-C reduction > 50%, 9 veterans had an LDL-C reduction between 30% and 50%, and 6 had a LDL-C reduction < 30%.

Cardiovascular Events

Before alirocumab initiation, 22 CV events and interventions were reported in 16 veterans: 12 percutaneous coronary interventions, 5 coronary artery bypass surgeries (CABG), 4 myocardial infarctions, and 1 transient ischemic attack. One month following alirocumab initiation, 1 veteran underwent a CABG after a non-ST-elevation myocardial infarction (NSTEMI).

Safety and Tolerability

Alirocumab was discontinued in 5 veterans due to 4 cases of intolerance (reported memory loss, lethargy, myalgias, and body aches with dyspnea) and 1 case of persistent LDL-C of < 40 mg/dL. Alirocumab was discontinued after 1 year in 2 patients (persistent LDL-C < 40 mg/dL and reported memory loss) and after 6 months in the veteran who reported lethargy. Alirocumab was discontinued after 4 months in the veteran with myalgias and within 2 months in the veteran with body aches and dyspnea. No other AEs were reported.

Discussion

The Efficacy and Safety of Alirocumab vs Ezetimibe in Statin-Intolerant Veterans With a Statin Rechallenge Arm trial is the first clinical trial to examine the efficacy and safety of alirocumab use in statin-intolerant patients. In the trial, 314 patients were randomized to receive alirocumab, ezetimibe, or an atorvastatin rechallenge.⁶ At 24 weeks, alirocumab reduced mean (SE) LDL-C by 45.0% (2.2%) vs 14.6% (2.2%) with ezetimibe (mean difference 30.4% [3.1%], P < .01).⁶ Fewer skeletal-muscle-related events also were noted with alirocumab vs atorvastatin (hazard ratio, 0.61; 95% CI, 0.38-0.99; P = .04).⁶

In this case series, an LDL-C reduction of > 50% was observed in 9 veterans (42.9%) following 4 weeks of treatment; however, LDL-C reduction of > 50% compared with baseline was sustained in only 6 veterans (28.6%) at week 24. Additionally, LDL-C increases from baseline were observed in 3 veterans; the reasoning for the observed increase was unclear, but this may have been due to nonadherence and dietary factors.⁴ Although a majority of patients saw a significant and clinically meaningful reduction in LDL-C, the group of patients with an increase in the same may have benefitted from targeted intervention to improve medication and dietary adherence. PCSK9 inhibitor resistance also may have contributed to an increase in LDL-C during treatment.⁷

Of the 24 patients included, 4 reported AEs resulted in therapy discontinuation. Memory impairment, a rare AE of alirocumab, was reported 1 year following alirocumab initiation. Additionally, lethargy was reported after 6 months of treatment. Myalgia also was reported in a veteran 4 months following treatment, and 1 veteran experienced body aches and dyspnea < 2 months following treatment. The most common AEs associated with alirocumab, as noted in previous safety and efficacy clinical trials, included: nasopharyngitis, injection site reaction, influenza, urinary tract infection, and myalgias.⁸ Many of these more common AEs may be subclinical and underreported. This small case series, however, detected 4 events severe enough to lead to therapy discontinuation. Although this sample is not representative of all statin-intolerant patients who receive treatment with alirocumab, our findings suggest the need for patient education on potential AEs before therapy initiation and clinician monitoring at follow-up visits.

The ODYSSEY OUTCOMES trial established a CV benefit associated with alirocumab; however, patients included had a recent acute coronary syndrome event and were receiving a high-intensity statin.⁹ This case series is unique in that before alirocumab initiation, 22 CV events/interventions were reported in the sample of 24 patients. After therapy initiation, 1 patient underwent a CABG after an NSTEMI in the month following initiation. This suggests that cardiac complications are possible after PCSK-9 initiation; however, little information can be gained from 1 patient. Nevertheless, early therapy failure should be investigated in the context of real-world use in statin-intolerant patients. This is a complex task, however, given the difficulties of achieving a balanced study design. Statin intolerance is a clear source of selection bias into treatment with alirocumab as patients in this population have already initiated and failed statin therapy. The prevalence of prior CV events and the time-dependent association between prior and future CV events stand as another complex confounder. Although there is a clear and pressing need to understand the risks and benefits of PCSK9 therapy in statin-intolerant patients, future research in this area will need to cautiously address these important sources of bias.

Overall, the results of this case series support LDL-C reduction associated with alirocumab in the absence of statin therapy. Despite favorable results, use of alirocumab may be limited by cost and its subcutaneous route of administration. Bempedoic acid, an oral, once-daily lipid-lowering agent poses an alternative to PCSK9 inhibitors, but further data regarding CV outcomes with this agent is needed.^10,11 Robust randomized controlled trials also are needed to evaluate CV outcomes for alirocumab use in statin-intolerant veterans.

Limitations

Only 24 veterans were included in the study, reflecting 20% of the charts reviewed (80% exclusion rate), and in this small sample, only 1 CV event was observed. Both of these serve as threats to external validity. As the study information was extracted from chart review, the results may be limited by coding or historical bias. Medical information from outside institutions may be missing from medical records. Additionally, results may be skewed by possible documentation errors. Furthermore, the period between previous CV events and alirocumab initiation is unclear as event dates were often not recorded if treatment was received at an outside institution.

Due to the short follow-up period, the case series is limited in its assessment of CV outcomes and safety outcomes. Larger studies over an extended period are needed to assess CV outcomes and safety of alirocumab use in statin-intolerant patients. Also, medication adherence was not assessed. Given the impact of medication adherence on LDL-C reduction, it is unclear what role medication adherence played in the LDL-C reduction observed in this study.⁴

Conclusions

Alirocumab use in 24 statin-intolerant veterans resulted in a significant reduction in LDL-C at 4 and 24 weeks after initiation. In addition, 1 CV event/intervention was observed following alirocumab initiation, although this should be interpreted with caution due to the retrospective nature of this case series, small sample size, and short follow-up period. Large, long-term studies would better evaluate the CV benefit associated with alirocumab therapy in a veteran population.

In 2016, 17.6 million deaths occurred globally due to cardiovascular disease (CVD) with coronary artery disease (CAD) and ischemic stroke as top contributors.¹ Elevated low-density lipoprotein cholesterol (LDL-C) has been linked to greater risk of atherosclerotic cardiovascular disease (ASCVD); therefore, LDL-C reduction is imperative to decrease risk of cardiovascular (CV) morbidity and mortality.² Since 1987, statin therapy has been the mainstay of treatment for hypercholesterolemia, and current practice guidelines recommend statins as first-line therapy given demonstrated reductions in LDL-C and CV mortality reduction in robust clinical trials.^2-4 Although generally safe and well tolerated, muscle-related adverse events (AEs) limit optimal use of statins in up to 20% of individuals who have an indication for statin therapy.⁵ As a consequence, these patients receive suboptimal statin doses or no statin therapy and are at a higher risk for ASCVD.⁵

Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have been shown to significantly lower LDL-C when used as monotherapy or in combination with statins and/or other lipid-lowering therapies.⁵ These agents are currently approved by the US Food and Drug Administration as an adjunct to diet with or without other lipid-lowering therapies for the management of primary hypercholesterolemia (including heterozygous familial hypercholesterolemia), homozygous familial hypercholesterolemia (evolocumab only), and for use in patients with established CVD unable to achieve their lipid-lowering goals with maximally tolerated statin doses and ezetimibe.⁴ With the ability to reduce LDL-C by up to 65%, PCSK9 inhibitors offer an alternative option for LDL-C and potentially CV risk reduction in statin-intolerant patients.⁵

Alirocumab, the formulary preferred PCSK9 inhibitor at the Michael E. DeBakey Veterans Affairs Medical Center (MEDVAMC) in Houston, Texas, has been increasingly used in high-risk statin-intolerant veterans. The primary objective of this case series was to assess LDL-C reduction associated with alirocumab use in statin-intolerant veterans at the MEDVAMC. The secondary objective was to assess the incidence of CV events. This study was approved by the MEDVAMC Quality Assurance and Regulatory Affairs committee.

Methods

In this single-center case series, a retrospective chart review was conducted to identify statin-intolerant veterans who were initiated on treatment with alirocumab for LDL-C and/or CV risk reduction between June 2017 and May 2019. Adult veterans with a diagnosis of primary hypercholesterolemia (including heterozygous familial hypercholesterolemia) and/or CAD with documented statin intolerance were included in the study. Statin intolerance was defined in accordance with the National Lipid Association (NLA) definition as aninability to tolerate ≥ 2 statins with a trial of at least 1 statin at its lowest daily dose.⁵ Veterans who previously received treatment with evolocumab, those prescribed concurrent statin therapies, and those missing follow-up lipid panels at 24 weeks were excluded from the study. To assess LDL-C reduction, LDL-C at baseline was compared with LDL-C at 4 and 24 weeks. Incident CV events before and after alirocumab initiation were documented. The US Department of Veteran Affairs (VA) Computerized Patient Record System was used to collect patient data.

Data Collection, Measures, and Analysis

Electronic health records of all eligible patients who received alirocumab were reviewed, and basic demographics (patient age, sex, and race/ethnicity) as well as medical characteristics at baseline were collected. To confirm statin intolerance, each veteran’s history of statin use and use of additional lipid-lowering agents was documented. CV history was measured with an index of categorical measures for hypertension, confirmed CAD, hyperlipidemia, heart failure, arrhythmias, peripheral artery disease, stroke, diabetes mellitus, and hypothyroidism. Additionally, concomitant medications, such as aspirin, P2Y₁₂ inhibitors, β-blockers, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers that patients were taking also were collected. Each veteran’s lipid panel at baseline, and at 4 and 24 weeks posttreatment initiation, also was extracted. Continuous variables were summarized with means (SD), and categorical variables were summarized with frequencies and proportions. The paired Wilcoxon signed rank test was used to compare LDL-C at 4 and 24 weeks after alirocumab initiation with patients’ baseline LDL-C.

Results

Between June 2017 and May 2019, 122 veterans were initiated on alirocumab. Of these veterans, 98 were excluded: 35 concurrently received statin therapy, 33 missed follow-up lipid panels, 21 had previously received evolocumab, 6 failed to meet the NLA definition for statin intolerance, 2 did not fill active alirocumab prescriptions, and 1 had an incalculable LDL-C with a baseline triglyceride level of 3079 mg/dL. This resulted in 24 veterans included in the analysis.

Most participants were male (87.5%) and White veterans (79.2%) with a mean (SD) age of 66.0 (8.4) years and mean (SD) baseline LDL-C of 161.9 (74.3) mg/dL. At baseline, 21 veterans had a history of primary hyperlipidemia, 19 had a history of CAD, and 2 had a history of heterozygous familial hypercholesterolemia. Of the 24 patients included, the most trialed statins before alirocumab initiation were atorvastatin (95.8%), simvastatin (79.2%), rosuvastatin (79.2%), and pravastatin (62.5%) (Table).

LDL-C Reduction

Veterans were initially treated with alirocumab 75 mg administered subcutaneously every 2 weeks; however, 11 veterans required a dose increase to 150 mg every 2 weeks. At treatment week 4, the median LDL-C reduction was 78.5 mg/dL (IQR, 28.0-107.3; P < .01), and at treatment week 24, the median LDL-C reduction was 55.6 mg/dL (IQR, 18.6-85.3; P < .01). This equated to median LDL-C reductions from baseline of 48.5% at week 4 and 34.3% at week 24. A total of 3 veterans experienced LDL-C increases following initiation of alirocumab. At week 4, 9 veterans were noted to have an LDL-C reduction > 50%, 7 veterans had an LDL-C reduction between 30% and 50%, and 5 veterans had an LDL-C reduction of < 30%. At week 24, 6 had an LDL-C reduction > 50%, 9 veterans had an LDL-C reduction between 30% and 50%, and 6 had a LDL-C reduction < 30%.

Cardiovascular Events

Before alirocumab initiation, 22 CV events and interventions were reported in 16 veterans: 12 percutaneous coronary interventions, 5 coronary artery bypass surgeries (CABG), 4 myocardial infarctions, and 1 transient ischemic attack. One month following alirocumab initiation, 1 veteran underwent a CABG after a non-ST-elevation myocardial infarction (NSTEMI).

Safety and Tolerability

Alirocumab was discontinued in 5 veterans due to 4 cases of intolerance (reported memory loss, lethargy, myalgias, and body aches with dyspnea) and 1 case of persistent LDL-C of < 40 mg/dL. Alirocumab was discontinued after 1 year in 2 patients (persistent LDL-C < 40 mg/dL and reported memory loss) and after 6 months in the veteran who reported lethargy. Alirocumab was discontinued after 4 months in the veteran with myalgias and within 2 months in the veteran with body aches and dyspnea. No other AEs were reported.

Discussion

The Efficacy and Safety of Alirocumab vs Ezetimibe in Statin-Intolerant Veterans With a Statin Rechallenge Arm trial is the first clinical trial to examine the efficacy and safety of alirocumab use in statin-intolerant patients. In the trial, 314 patients were randomized to receive alirocumab, ezetimibe, or an atorvastatin rechallenge.⁶ At 24 weeks, alirocumab reduced mean (SE) LDL-C by 45.0% (2.2%) vs 14.6% (2.2%) with ezetimibe (mean difference 30.4% [3.1%], P < .01).⁶ Fewer skeletal-muscle-related events also were noted with alirocumab vs atorvastatin (hazard ratio, 0.61; 95% CI, 0.38-0.99; P = .04).⁶

In this case series, an LDL-C reduction of > 50% was observed in 9 veterans (42.9%) following 4 weeks of treatment; however, LDL-C reduction of > 50% compared with baseline was sustained in only 6 veterans (28.6%) at week 24. Additionally, LDL-C increases from baseline were observed in 3 veterans; the reasoning for the observed increase was unclear, but this may have been due to nonadherence and dietary factors.⁴ Although a majority of patients saw a significant and clinically meaningful reduction in LDL-C, the group of patients with an increase in the same may have benefitted from targeted intervention to improve medication and dietary adherence. PCSK9 inhibitor resistance also may have contributed to an increase in LDL-C during treatment.⁷

Of the 24 patients included, 4 reported AEs resulted in therapy discontinuation. Memory impairment, a rare AE of alirocumab, was reported 1 year following alirocumab initiation. Additionally, lethargy was reported after 6 months of treatment. Myalgia also was reported in a veteran 4 months following treatment, and 1 veteran experienced body aches and dyspnea < 2 months following treatment. The most common AEs associated with alirocumab, as noted in previous safety and efficacy clinical trials, included: nasopharyngitis, injection site reaction, influenza, urinary tract infection, and myalgias.⁸ Many of these more common AEs may be subclinical and underreported. This small case series, however, detected 4 events severe enough to lead to therapy discontinuation. Although this sample is not representative of all statin-intolerant patients who receive treatment with alirocumab, our findings suggest the need for patient education on potential AEs before therapy initiation and clinician monitoring at follow-up visits.

The ODYSSEY OUTCOMES trial established a CV benefit associated with alirocumab; however, patients included had a recent acute coronary syndrome event and were receiving a high-intensity statin.⁹ This case series is unique in that before alirocumab initiation, 22 CV events/interventions were reported in the sample of 24 patients. After therapy initiation, 1 patient underwent a CABG after an NSTEMI in the month following initiation. This suggests that cardiac complications are possible after PCSK-9 initiation; however, little information can be gained from 1 patient. Nevertheless, early therapy failure should be investigated in the context of real-world use in statin-intolerant patients. This is a complex task, however, given the difficulties of achieving a balanced study design. Statin intolerance is a clear source of selection bias into treatment with alirocumab as patients in this population have already initiated and failed statin therapy. The prevalence of prior CV events and the time-dependent association between prior and future CV events stand as another complex confounder. Although there is a clear and pressing need to understand the risks and benefits of PCSK9 therapy in statin-intolerant patients, future research in this area will need to cautiously address these important sources of bias.

Overall, the results of this case series support LDL-C reduction associated with alirocumab in the absence of statin therapy. Despite favorable results, use of alirocumab may be limited by cost and its subcutaneous route of administration. Bempedoic acid, an oral, once-daily lipid-lowering agent poses an alternative to PCSK9 inhibitors, but further data regarding CV outcomes with this agent is needed.^10,11 Robust randomized controlled trials also are needed to evaluate CV outcomes for alirocumab use in statin-intolerant veterans.

Limitations

Only 24 veterans were included in the study, reflecting 20% of the charts reviewed (80% exclusion rate), and in this small sample, only 1 CV event was observed. Both of these serve as threats to external validity. As the study information was extracted from chart review, the results may be limited by coding or historical bias. Medical information from outside institutions may be missing from medical records. Additionally, results may be skewed by possible documentation errors. Furthermore, the period between previous CV events and alirocumab initiation is unclear as event dates were often not recorded if treatment was received at an outside institution.

Due to the short follow-up period, the case series is limited in its assessment of CV outcomes and safety outcomes. Larger studies over an extended period are needed to assess CV outcomes and safety of alirocumab use in statin-intolerant patients. Also, medication adherence was not assessed. Given the impact of medication adherence on LDL-C reduction, it is unclear what role medication adherence played in the LDL-C reduction observed in this study.⁴

Conclusions

Alirocumab use in 24 statin-intolerant veterans resulted in a significant reduction in LDL-C at 4 and 24 weeks after initiation. In addition, 1 CV event/intervention was observed following alirocumab initiation, although this should be interpreted with caution due to the retrospective nature of this case series, small sample size, and short follow-up period. Large, long-term studies would better evaluate the CV benefit associated with alirocumab therapy in a veteran population.

Nearly 25% of patients served in the US Department of Veterans Affairs (VA) have been diagnosed with type 2 diabetes mellitus (T2DM), although the prevalence among adults in the United States is 9%.¹ Patients with DM typically monitor their blood glucose using intermittent fingerstick self-testing. Continuous glucose monitoring (CGM) might offer a more comprehensive picture of glucose control to improve disease management. Within the VA, criteria for CGM use varies among facilities, but generally veterans prescribed at least 3 daily insulin injections and 4 daily blood glucose checks qualify.²

CGM therapy has been extensively researched for type 1 DM (T1DM); however, outcomes of CGM use among older adults with T2DM have not been fully evaluated. In a 2018 review of randomized clinical trials evaluating CGM use, 17 trials examined only patients with T1DM (2009 participants), 4 included only patients with T2DM patients (547 patients), 3 evaluated patients with T1DM or T2DM (655 patients), and 3 included women with gestational diabetes (585 patients).³ Of 27 studies that included change in hemoglobin A_1c (HbA_1c) as an endpoint, 15 found a statistically significant reduction in HbA_1c for the CGM group. Four trials evaluated CGM use in adults with T2DM and 3 found no difference in HbA_1c overall. However, 1 study found a difference in HbA_1c only in individuals aged < 65 years, and another study found a greater improvement in the CGM group (approximately 0.5%).^4,5 These mixed results indicate a need for further subgroup analysis in specific populations to determine the optimal use of CGM in adults with T2DM. Although this study was not designed to measure changes in hypoglycemic episodes or the relative efficacy of different CGM products, it establishes a baseline from which to conduct additional research.

Our primary objective was to determine change in HbA_1c in each patient from the year before CGM initiation to the year after. Secondary objectives included changes in blood pressure (BP), weight, and diabetes-related hospital and clinic visits during the same time frame. We also completed subanalysis comparing primary outcomes in engaged or adherent patients compared with the entire study group. This study was completed as a quality improvement project with approval from the Lexington Veterans Affairs Health Care System in Kentucky information security office and was exempted from institutional review board review.

Methods

This project was a retrospective evaluation using the VA database of patient records. Rather than using a control group, our study used a pre–post model to determine the impact of CGM for each patient. For the primary outcome, average HbA_1c values were calculated for the year before and year after CGM initiation. Hemoglobin and hematocrit values were included if reported within 3 months of the HbA_1c values to ensure validity of HbA_1c results. Average HbA_1c was 13.37 g/dL (range, 10.5-17.3), and average hematocrit was 43.3% (range, 36-52). Change in average HbA_1c was recorded for each patient. Based on research by Taylor and colleagues, a change in HbA_1c of 0.8% was considered clinically significant for this project.⁶

Mean BP and weight were calculated for the years before and after CGM initiation. Only values for routine clinic visits were included; values taken during an acute health incident, inpatient stay, infusion clinic appointments, or home readings were excluded. Changes were recorded for each patient. Patient encounter notes were used to determine the number of DM-related hospital, emergency department, and clinic visits, such as nephrology, podiatry, vascular medicine, or infectious disease clinic or inpatient encounters during the study period. Routine endocrinology or primary care visits were not included, and patient care notes were consulted to ensure that the encounters were related to a DM complication. The change in number of visits was calculated for each patient.

Adherence was defined as patients receiving active medication management, documented treatment regimen adherence, and > 4 annual endocrinology clinic visits. Active medication management was defined as having > 1 dosage or medication change for oral or noninsulin antihyperglycemics, initiation, or adjustment of insulin dosages according to the patient records. Treatment adherence was determined based on medication reconciliation notes and refill request history. Only endocrinology clinic visits at VA outpatient clinics were included.

Study Population

A sample of 166 patients was needed to detect an HbA_1c change of 0.8 per power analysis. The normal approximation method using the z statistic was used, with 2-tailed α = 0.05, β = 0.05, E = 0.8, and S = 1.2. We randomly selected 175 patients among all individuals with an active prescription for CGM in 2018 and 2019, who had a diagnosis of T2DM, and were managed by VA endocrinology clinics (including endocrine clinics, diabetes clinics, and patient aligned care team clinics) at 87 VA medical centers. Patients with types of DM other than T2DM were excluded, as well as those with a diagnosed hemoglobinopathy or HbA_1c < 10 g/dL. The adherent subgroup included 40 patients of the 175 sample population (Table 1).

Results

Both the total population and the adherent subgroup showed reduction in HbA_1c, the primary endpoint. The complete population showed a HbA_1c change of –0.3 (95% CI, –0.4 to –0.2), and the adherent subgroup had a change of –1.3 (95% CI, –1.5 to –1.2). The total survey population had a mean change in weight of –1.9 lb (–0.9 kg) (95% CI, –3.7 to –0.1) and the adherent subgroup had an average change of –8.0 lb (–3.6 kg) (95% CI, –12.3 to –3.8). Average systolic BP changes were –0.1 mm Hg (95% CI, –1.6 to 1.5) in the total population and +3.3 mm Hg (95% CI, –0.01 to 6.22) in the adherent subgroup. A decrease in total encounters for DM complications was observed in the population (–0.3 total encounters per patient, 95% CI, –0.5 to –0.2) and the adherent subgroup (–0.6 total encounters per patient, 95% CI, –1.0 to –0.1) (Table 2).

Before the study, 107 (61.1%) patients were taking oral or noninsulin DM medication only, 4 (2.3%) were on insulin only, and 64 (36.6%) were prescribed both insulin and oral/noninsulin antihyperglycemics. Noninsulin and oral antihyperglycemic regimens included combinations of biguanide, dipeptidyl peptidase- 4 inhibitor, sodium-glucose cotransporter-2 inhibitor, sulfonylurea, meglitinide, β-glucosidase inhibitor, glucagon-like peptide-1 (GLP-1) analog, and thiazolidinedione drug classes. Nearly 70% (122) had no reported changes in DM treatment beyond dosage titrations. Among these patients, 18 (10.3%) were on an insulin pump for the duration of the study. Among the 53 (30.3%) patients who had changes in treatment, 31 (17.7%) transitioned from insulin injections to an insulin pump, 13 (7.4%) changed from 1 insulin injection to another (ie, addition of long-acting insulin, transition to u500 insulin, changing from 1 insulin category or brand to another), 8 (4.6%) began an oral/noninsulin antihyperglycemic, 4 (2.3%) began insulin injections, 13 (7.4%) discontinued noninsulin or oral antihyperglycemics, and 2 (1.1%) discontinued insulin during the study period.

Data showed that 113 (64.5%) patients had no changes in antihypertensives. The remaining 62 (35.4%) had the following adjustments: 14 (8%) increased dose of current medication(s), 9 (5.1%) decreased dose of current medication(s), 8 (4.6%) discontinued all antihypertensive medications, 10 (5.7%) switched to a different antihypertensive class, and 16 (9.1%) added additional antihypertensive medication(s) to their existing regimen during the study period.

Patients in the study group used 7 different types of CGM sensors. Chart review revealed that 84 (47.7%) patients used Medtronic devices, with 26 (14.8%) using first-generation Guardian sensors, 50 (28.4%) using Enlite sensors, and 8 (4.5) using Guardian 3 sensors. We found that 81 (46.0%) veterans were prescribed Dexcom devices, with 5 (2.8%) using SEVEN PLUS sensors, 68 (38.6%) using G4-5 sensors, and 8 (4.5%) using G6 sensors. The remaining 10 (5.7%) patients were using Freestyle Libre sensors during the study period.

Discussion

CGM did not correspond with clinically significant reductions in HbA_1c. However, veterans with increased health care engagement were likely to achieve clinically significant HbA_1c improvements. The veterans in the adherent subgroup had a higher baseline HbA_1c, which could be because of a variety of factors mentioned in patient care notes, including insulin resistance, poor dietary habits, and exercise regimen nonadherence. These patients might have had more room to improve their glycemic control without concern of hypoglycemia, and their higher baseline HbA_1c could have provided increased motivation for improving their health during the study period.

Adherent patients also had a greater reduction in weight and hospital or clinic visits with CGM compared with the total population. These veterans’ increased involvement in their health care might have led to better dietary and exercise adherence, which would have decreased insulin dosing and contributed to weight loss. Only 1 patient in the adherent subgroup initiated a GLP-1 agonist during the study period, making it unlikely that medication changes had a significant impact on weight loss in the subgroup analysis. This improvement in overall health status might have contributed to the reduction in hospital or clinic visits observed in this population.

Average systolic BP data decreased minimally in the total survey population and increased in the adherent subgroup over the course of the study. These results were determined to be statistically significant. Changes in systolic BP readings were minimal, indicating that it is unlikely that these changes contributed meaningfully to the patients’ overall health status.

Although not related to the study objectives, the adherent population required less antihypertensive adjustments with similar BP control. This could be explained by improved overall health or better adherence and engagement in therapy. The results of this project show that despite limited medication changes, T2DM management improved among adherent patients using CGM. The general study population, which was more likely to have documented nonadherence with treatment or clinic appointments, had minimal benefit. CGM technology in the T2DM veteran population is more likely to have significant clinical benefit in patients who are adherent with their medication regimens and follow-up appointments compared with the larger study population.

The results of this study are in line with previous studies on CGM use in the T2DM patient population. We agree with the previously published research that CGM alone does not have a meaningful impact on HbA_1c reduction. Our study population also was older than those in previous studies, adding to the Haak and colleagues conclusion that patients aged < 65 years might have better outcomes with CGM.⁴

Limitations

Limitations of this study include retrospective design, a small sample size, and solely focusing on T2DM. As a retrospective study, we cannot rule out the influence of outside factors, such as participation in a non-VA weight loss program. This study lacked the power to assess the impact of the different CGM brands. The study did not include data on severe hypoglycemic or hyperglycemic episodes as veterans might have needed emergent care at non-VA facilities. Future research will evaluate the impact of CGM on symptomatic and severe hypoglycemic episodes and use of insulin vs oral or noninsulin antihyperglycemics and the comparative efficacy of different CGM brands among veterans.

Conclusions

CGM did not correspond with clinically significant reductions in HbA_1c. However, veterans with increased health care engagement were likely to achieve clinically significant HbA_1c improvements. Adherent patients also had more reduction in weight and hospital or clinic visits with CGM compared with the total population. These veterans’ increased involvement in their health care might have led to better dietary and exercise adherence, which would have decreased insulin dosing and contributed to weight loss.

Nearly 25% of patients served in the US Department of Veterans Affairs (VA) have been diagnosed with type 2 diabetes mellitus (T2DM), although the prevalence among adults in the United States is 9%.¹ Patients with DM typically monitor their blood glucose using intermittent fingerstick self-testing. Continuous glucose monitoring (CGM) might offer a more comprehensive picture of glucose control to improve disease management. Within the VA, criteria for CGM use varies among facilities, but generally veterans prescribed at least 3 daily insulin injections and 4 daily blood glucose checks qualify.²

CGM therapy has been extensively researched for type 1 DM (T1DM); however, outcomes of CGM use among older adults with T2DM have not been fully evaluated. In a 2018 review of randomized clinical trials evaluating CGM use, 17 trials examined only patients with T1DM (2009 participants), 4 included only patients with T2DM patients (547 patients), 3 evaluated patients with T1DM or T2DM (655 patients), and 3 included women with gestational diabetes (585 patients).³ Of 27 studies that included change in hemoglobin A_1c (HbA_1c) as an endpoint, 15 found a statistically significant reduction in HbA_1c for the CGM group. Four trials evaluated CGM use in adults with T2DM and 3 found no difference in HbA_1c overall. However, 1 study found a difference in HbA_1c only in individuals aged < 65 years, and another study found a greater improvement in the CGM group (approximately 0.5%).^4,5 These mixed results indicate a need for further subgroup analysis in specific populations to determine the optimal use of CGM in adults with T2DM. Although this study was not designed to measure changes in hypoglycemic episodes or the relative efficacy of different CGM products, it establishes a baseline from which to conduct additional research.

Our primary objective was to determine change in HbA_1c in each patient from the year before CGM initiation to the year after. Secondary objectives included changes in blood pressure (BP), weight, and diabetes-related hospital and clinic visits during the same time frame. We also completed subanalysis comparing primary outcomes in engaged or adherent patients compared with the entire study group. This study was completed as a quality improvement project with approval from the Lexington Veterans Affairs Health Care System in Kentucky information security office and was exempted from institutional review board review.

Methods

This project was a retrospective evaluation using the VA database of patient records. Rather than using a control group, our study used a pre–post model to determine the impact of CGM for each patient. For the primary outcome, average HbA_1c values were calculated for the year before and year after CGM initiation. Hemoglobin and hematocrit values were included if reported within 3 months of the HbA_1c values to ensure validity of HbA_1c results. Average HbA_1c was 13.37 g/dL (range, 10.5-17.3), and average hematocrit was 43.3% (range, 36-52). Change in average HbA_1c was recorded for each patient. Based on research by Taylor and colleagues, a change in HbA_1c of 0.8% was considered clinically significant for this project.⁶

Mean BP and weight were calculated for the years before and after CGM initiation. Only values for routine clinic visits were included; values taken during an acute health incident, inpatient stay, infusion clinic appointments, or home readings were excluded. Changes were recorded for each patient. Patient encounter notes were used to determine the number of DM-related hospital, emergency department, and clinic visits, such as nephrology, podiatry, vascular medicine, or infectious disease clinic or inpatient encounters during the study period. Routine endocrinology or primary care visits were not included, and patient care notes were consulted to ensure that the encounters were related to a DM complication. The change in number of visits was calculated for each patient.

Adherence was defined as patients receiving active medication management, documented treatment regimen adherence, and > 4 annual endocrinology clinic visits. Active medication management was defined as having > 1 dosage or medication change for oral or noninsulin antihyperglycemics, initiation, or adjustment of insulin dosages according to the patient records. Treatment adherence was determined based on medication reconciliation notes and refill request history. Only endocrinology clinic visits at VA outpatient clinics were included.

Study Population

A sample of 166 patients was needed to detect an HbA_1c change of 0.8 per power analysis. The normal approximation method using the z statistic was used, with 2-tailed α = 0.05, β = 0.05, E = 0.8, and S = 1.2. We randomly selected 175 patients among all individuals with an active prescription for CGM in 2018 and 2019, who had a diagnosis of T2DM, and were managed by VA endocrinology clinics (including endocrine clinics, diabetes clinics, and patient aligned care team clinics) at 87 VA medical centers. Patients with types of DM other than T2DM were excluded, as well as those with a diagnosed hemoglobinopathy or HbA_1c < 10 g/dL. The adherent subgroup included 40 patients of the 175 sample population (Table 1).

Results

Both the total population and the adherent subgroup showed reduction in HbA_1c, the primary endpoint. The complete population showed a HbA_1c change of –0.3 (95% CI, –0.4 to –0.2), and the adherent subgroup had a change of –1.3 (95% CI, –1.5 to –1.2). The total survey population had a mean change in weight of –1.9 lb (–0.9 kg) (95% CI, –3.7 to –0.1) and the adherent subgroup had an average change of –8.0 lb (–3.6 kg) (95% CI, –12.3 to –3.8). Average systolic BP changes were –0.1 mm Hg (95% CI, –1.6 to 1.5) in the total population and +3.3 mm Hg (95% CI, –0.01 to 6.22) in the adherent subgroup. A decrease in total encounters for DM complications was observed in the population (–0.3 total encounters per patient, 95% CI, –0.5 to –0.2) and the adherent subgroup (–0.6 total encounters per patient, 95% CI, –1.0 to –0.1) (Table 2).

Before the study, 107 (61.1%) patients were taking oral or noninsulin DM medication only, 4 (2.3%) were on insulin only, and 64 (36.6%) were prescribed both insulin and oral/noninsulin antihyperglycemics. Noninsulin and oral antihyperglycemic regimens included combinations of biguanide, dipeptidyl peptidase- 4 inhibitor, sodium-glucose cotransporter-2 inhibitor, sulfonylurea, meglitinide, β-glucosidase inhibitor, glucagon-like peptide-1 (GLP-1) analog, and thiazolidinedione drug classes. Nearly 70% (122) had no reported changes in DM treatment beyond dosage titrations. Among these patients, 18 (10.3%) were on an insulin pump for the duration of the study. Among the 53 (30.3%) patients who had changes in treatment, 31 (17.7%) transitioned from insulin injections to an insulin pump, 13 (7.4%) changed from 1 insulin injection to another (ie, addition of long-acting insulin, transition to u500 insulin, changing from 1 insulin category or brand to another), 8 (4.6%) began an oral/noninsulin antihyperglycemic, 4 (2.3%) began insulin injections, 13 (7.4%) discontinued noninsulin or oral antihyperglycemics, and 2 (1.1%) discontinued insulin during the study period.

Data showed that 113 (64.5%) patients had no changes in antihypertensives. The remaining 62 (35.4%) had the following adjustments: 14 (8%) increased dose of current medication(s), 9 (5.1%) decreased dose of current medication(s), 8 (4.6%) discontinued all antihypertensive medications, 10 (5.7%) switched to a different antihypertensive class, and 16 (9.1%) added additional antihypertensive medication(s) to their existing regimen during the study period.

Patients in the study group used 7 different types of CGM sensors. Chart review revealed that 84 (47.7%) patients used Medtronic devices, with 26 (14.8%) using first-generation Guardian sensors, 50 (28.4%) using Enlite sensors, and 8 (4.5) using Guardian 3 sensors. We found that 81 (46.0%) veterans were prescribed Dexcom devices, with 5 (2.8%) using SEVEN PLUS sensors, 68 (38.6%) using G4-5 sensors, and 8 (4.5%) using G6 sensors. The remaining 10 (5.7%) patients were using Freestyle Libre sensors during the study period.

Discussion

CGM did not correspond with clinically significant reductions in HbA_1c. However, veterans with increased health care engagement were likely to achieve clinically significant HbA_1c improvements. The veterans in the adherent subgroup had a higher baseline HbA_1c, which could be because of a variety of factors mentioned in patient care notes, including insulin resistance, poor dietary habits, and exercise regimen nonadherence. These patients might have had more room to improve their glycemic control without concern of hypoglycemia, and their higher baseline HbA_1c could have provided increased motivation for improving their health during the study period.

Adherent patients also had a greater reduction in weight and hospital or clinic visits with CGM compared with the total population. These veterans’ increased involvement in their health care might have led to better dietary and exercise adherence, which would have decreased insulin dosing and contributed to weight loss. Only 1 patient in the adherent subgroup initiated a GLP-1 agonist during the study period, making it unlikely that medication changes had a significant impact on weight loss in the subgroup analysis. This improvement in overall health status might have contributed to the reduction in hospital or clinic visits observed in this population.

Average systolic BP data decreased minimally in the total survey population and increased in the adherent subgroup over the course of the study. These results were determined to be statistically significant. Changes in systolic BP readings were minimal, indicating that it is unlikely that these changes contributed meaningfully to the patients’ overall health status.

Although not related to the study objectives, the adherent population required less antihypertensive adjustments with similar BP control. This could be explained by improved overall health or better adherence and engagement in therapy. The results of this project show that despite limited medication changes, T2DM management improved among adherent patients using CGM. The general study population, which was more likely to have documented nonadherence with treatment or clinic appointments, had minimal benefit. CGM technology in the T2DM veteran population is more likely to have significant clinical benefit in patients who are adherent with their medication regimens and follow-up appointments compared with the larger study population.

The results of this study are in line with previous studies on CGM use in the T2DM patient population. We agree with the previously published research that CGM alone does not have a meaningful impact on HbA_1c reduction. Our study population also was older than those in previous studies, adding to the Haak and colleagues conclusion that patients aged < 65 years might have better outcomes with CGM.⁴

Limitations

Limitations of this study include retrospective design, a small sample size, and solely focusing on T2DM. As a retrospective study, we cannot rule out the influence of outside factors, such as participation in a non-VA weight loss program. This study lacked the power to assess the impact of the different CGM brands. The study did not include data on severe hypoglycemic or hyperglycemic episodes as veterans might have needed emergent care at non-VA facilities. Future research will evaluate the impact of CGM on symptomatic and severe hypoglycemic episodes and use of insulin vs oral or noninsulin antihyperglycemics and the comparative efficacy of different CGM brands among veterans.

Conclusions

CGM did not correspond with clinically significant reductions in HbA_1c. However, veterans with increased health care engagement were likely to achieve clinically significant HbA_1c improvements. Adherent patients also had more reduction in weight and hospital or clinic visits with CGM compared with the total population. These veterans’ increased involvement in their health care might have led to better dietary and exercise adherence, which would have decreased insulin dosing and contributed to weight loss.

Nearly 25% of patients served in the US Department of Veterans Affairs (VA) have been diagnosed with type 2 diabetes mellitus (T2DM), although the prevalence among adults in the United States is 9%.¹ Patients with DM typically monitor their blood glucose using intermittent fingerstick self-testing. Continuous glucose monitoring (CGM) might offer a more comprehensive picture of glucose control to improve disease management. Within the VA, criteria for CGM use varies among facilities, but generally veterans prescribed at least 3 daily insulin injections and 4 daily blood glucose checks qualify.²

CGM therapy has been extensively researched for type 1 DM (T1DM); however, outcomes of CGM use among older adults with T2DM have not been fully evaluated. In a 2018 review of randomized clinical trials evaluating CGM use, 17 trials examined only patients with T1DM (2009 participants), 4 included only patients with T2DM patients (547 patients), 3 evaluated patients with T1DM or T2DM (655 patients), and 3 included women with gestational diabetes (585 patients).³ Of 27 studies that included change in hemoglobin A_1c (HbA_1c) as an endpoint, 15 found a statistically significant reduction in HbA_1c for the CGM group. Four trials evaluated CGM use in adults with T2DM and 3 found no difference in HbA_1c overall. However, 1 study found a difference in HbA_1c only in individuals aged < 65 years, and another study found a greater improvement in the CGM group (approximately 0.5%).^4,5 These mixed results indicate a need for further subgroup analysis in specific populations to determine the optimal use of CGM in adults with T2DM. Although this study was not designed to measure changes in hypoglycemic episodes or the relative efficacy of different CGM products, it establishes a baseline from which to conduct additional research.

Our primary objective was to determine change in HbA_1c in each patient from the year before CGM initiation to the year after. Secondary objectives included changes in blood pressure (BP), weight, and diabetes-related hospital and clinic visits during the same time frame. We also completed subanalysis comparing primary outcomes in engaged or adherent patients compared with the entire study group. This study was completed as a quality improvement project with approval from the Lexington Veterans Affairs Health Care System in Kentucky information security office and was exempted from institutional review board review.

Methods

This project was a retrospective evaluation using the VA database of patient records. Rather than using a control group, our study used a pre–post model to determine the impact of CGM for each patient. For the primary outcome, average HbA_1c values were calculated for the year before and year after CGM initiation. Hemoglobin and hematocrit values were included if reported within 3 months of the HbA_1c values to ensure validity of HbA_1c results. Average HbA_1c was 13.37 g/dL (range, 10.5-17.3), and average hematocrit was 43.3% (range, 36-52). Change in average HbA_1c was recorded for each patient. Based on research by Taylor and colleagues, a change in HbA_1c of 0.8% was considered clinically significant for this project.⁶

Mean BP and weight were calculated for the years before and after CGM initiation. Only values for routine clinic visits were included; values taken during an acute health incident, inpatient stay, infusion clinic appointments, or home readings were excluded. Changes were recorded for each patient. Patient encounter notes were used to determine the number of DM-related hospital, emergency department, and clinic visits, such as nephrology, podiatry, vascular medicine, or infectious disease clinic or inpatient encounters during the study period. Routine endocrinology or primary care visits were not included, and patient care notes were consulted to ensure that the encounters were related to a DM complication. The change in number of visits was calculated for each patient.

Adherence was defined as patients receiving active medication management, documented treatment regimen adherence, and > 4 annual endocrinology clinic visits. Active medication management was defined as having > 1 dosage or medication change for oral or noninsulin antihyperglycemics, initiation, or adjustment of insulin dosages according to the patient records. Treatment adherence was determined based on medication reconciliation notes and refill request history. Only endocrinology clinic visits at VA outpatient clinics were included.

Study Population

A sample of 166 patients was needed to detect an HbA_1c change of 0.8 per power analysis. The normal approximation method using the z statistic was used, with 2-tailed α = 0.05, β = 0.05, E = 0.8, and S = 1.2. We randomly selected 175 patients among all individuals with an active prescription for CGM in 2018 and 2019, who had a diagnosis of T2DM, and were managed by VA endocrinology clinics (including endocrine clinics, diabetes clinics, and patient aligned care team clinics) at 87 VA medical centers. Patients with types of DM other than T2DM were excluded, as well as those with a diagnosed hemoglobinopathy or HbA_1c < 10 g/dL. The adherent subgroup included 40 patients of the 175 sample population (Table 1).

Results

Both the total population and the adherent subgroup showed reduction in HbA_1c, the primary endpoint. The complete population showed a HbA_1c change of –0.3 (95% CI, –0.4 to –0.2), and the adherent subgroup had a change of –1.3 (95% CI, –1.5 to –1.2). The total survey population had a mean change in weight of –1.9 lb (–0.9 kg) (95% CI, –3.7 to –0.1) and the adherent subgroup had an average change of –8.0 lb (–3.6 kg) (95% CI, –12.3 to –3.8). Average systolic BP changes were –0.1 mm Hg (95% CI, –1.6 to 1.5) in the total population and +3.3 mm Hg (95% CI, –0.01 to 6.22) in the adherent subgroup. A decrease in total encounters for DM complications was observed in the population (–0.3 total encounters per patient, 95% CI, –0.5 to –0.2) and the adherent subgroup (–0.6 total encounters per patient, 95% CI, –1.0 to –0.1) (Table 2).

Before the study, 107 (61.1%) patients were taking oral or noninsulin DM medication only, 4 (2.3%) were on insulin only, and 64 (36.6%) were prescribed both insulin and oral/noninsulin antihyperglycemics. Noninsulin and oral antihyperglycemic regimens included combinations of biguanide, dipeptidyl peptidase- 4 inhibitor, sodium-glucose cotransporter-2 inhibitor, sulfonylurea, meglitinide, β-glucosidase inhibitor, glucagon-like peptide-1 (GLP-1) analog, and thiazolidinedione drug classes. Nearly 70% (122) had no reported changes in DM treatment beyond dosage titrations. Among these patients, 18 (10.3%) were on an insulin pump for the duration of the study. Among the 53 (30.3%) patients who had changes in treatment, 31 (17.7%) transitioned from insulin injections to an insulin pump, 13 (7.4%) changed from 1 insulin injection to another (ie, addition of long-acting insulin, transition to u500 insulin, changing from 1 insulin category or brand to another), 8 (4.6%) began an oral/noninsulin antihyperglycemic, 4 (2.3%) began insulin injections, 13 (7.4%) discontinued noninsulin or oral antihyperglycemics, and 2 (1.1%) discontinued insulin during the study period.

Data showed that 113 (64.5%) patients had no changes in antihypertensives. The remaining 62 (35.4%) had the following adjustments: 14 (8%) increased dose of current medication(s), 9 (5.1%) decreased dose of current medication(s), 8 (4.6%) discontinued all antihypertensive medications, 10 (5.7%) switched to a different antihypertensive class, and 16 (9.1%) added additional antihypertensive medication(s) to their existing regimen during the study period.

Patients in the study group used 7 different types of CGM sensors. Chart review revealed that 84 (47.7%) patients used Medtronic devices, with 26 (14.8%) using first-generation Guardian sensors, 50 (28.4%) using Enlite sensors, and 8 (4.5) using Guardian 3 sensors. We found that 81 (46.0%) veterans were prescribed Dexcom devices, with 5 (2.8%) using SEVEN PLUS sensors, 68 (38.6%) using G4-5 sensors, and 8 (4.5%) using G6 sensors. The remaining 10 (5.7%) patients were using Freestyle Libre sensors during the study period.

Discussion

CGM did not correspond with clinically significant reductions in HbA_1c. However, veterans with increased health care engagement were likely to achieve clinically significant HbA_1c improvements. The veterans in the adherent subgroup had a higher baseline HbA_1c, which could be because of a variety of factors mentioned in patient care notes, including insulin resistance, poor dietary habits, and exercise regimen nonadherence. These patients might have had more room to improve their glycemic control without concern of hypoglycemia, and their higher baseline HbA_1c could have provided increased motivation for improving their health during the study period.

Adherent patients also had a greater reduction in weight and hospital or clinic visits with CGM compared with the total population. These veterans’ increased involvement in their health care might have led to better dietary and exercise adherence, which would have decreased insulin dosing and contributed to weight loss. Only 1 patient in the adherent subgroup initiated a GLP-1 agonist during the study period, making it unlikely that medication changes had a significant impact on weight loss in the subgroup analysis. This improvement in overall health status might have contributed to the reduction in hospital or clinic visits observed in this population.

Average systolic BP data decreased minimally in the total survey population and increased in the adherent subgroup over the course of the study. These results were determined to be statistically significant. Changes in systolic BP readings were minimal, indicating that it is unlikely that these changes contributed meaningfully to the patients’ overall health status.

Although not related to the study objectives, the adherent population required less antihypertensive adjustments with similar BP control. This could be explained by improved overall health or better adherence and engagement in therapy. The results of this project show that despite limited medication changes, T2DM management improved among adherent patients using CGM. The general study population, which was more likely to have documented nonadherence with treatment or clinic appointments, had minimal benefit. CGM technology in the T2DM veteran population is more likely to have significant clinical benefit in patients who are adherent with their medication regimens and follow-up appointments compared with the larger study population.

The results of this study are in line with previous studies on CGM use in the T2DM patient population. We agree with the previously published research that CGM alone does not have a meaningful impact on HbA_1c reduction. Our study population also was older than those in previous studies, adding to the Haak and colleagues conclusion that patients aged < 65 years might have better outcomes with CGM.⁴

Limitations

Limitations of this study include retrospective design, a small sample size, and solely focusing on T2DM. As a retrospective study, we cannot rule out the influence of outside factors, such as participation in a non-VA weight loss program. This study lacked the power to assess the impact of the different CGM brands. The study did not include data on severe hypoglycemic or hyperglycemic episodes as veterans might have needed emergent care at non-VA facilities. Future research will evaluate the impact of CGM on symptomatic and severe hypoglycemic episodes and use of insulin vs oral or noninsulin antihyperglycemics and the comparative efficacy of different CGM brands among veterans.

Conclusions

CGM did not correspond with clinically significant reductions in HbA_1c. However, veterans with increased health care engagement were likely to achieve clinically significant HbA_1c improvements. Adherent patients also had more reduction in weight and hospital or clinic visits with CGM compared with the total population. These veterans’ increased involvement in their health care might have led to better dietary and exercise adherence, which would have decreased insulin dosing and contributed to weight loss.

Attention should be paid to changing a tolerated medication to another within its class. Many drugs approved by the US Food and Drug Administration (FDA), have equivalent therapeutic properties as existing drugs. Rarely do such medications share the same potency and adverse effect (AE) profile.

Case Presentation

A 77-year-old man presented to the emergency department (ED) at the Raymond G. Murphy Medical Center in Albuquerque, New Mexico, with a 1-month history of progressive muscle weakness, which was so severe that he required assistance rising from chairs. The symptoms began when he switched from atorvastatin 40 mg daily to rosuvastatin 40 mg daily. A nephrology consultation was requested for an elevated plasma creatinine.

The patient reported strict adherence to his prescribed medications. In the days following the switch to rosuvastatin, he noticed that his urine turned black. He described the color as “like burnt coffee.” The color gradually cleared before his ED presentation. The patient stopped taking rosuvastatin the day prior to presentation and noted improvement of his symptoms. Review of symptoms was significant for lower extremity paresthesia and numbness the day he started rosuvastatin. He had no symptoms of decompensated heart failure and no recent exacerbations requiring alteration of his diuretic regimen.

The patient’s medical history was significant for traumatic brain injury with complex partial seizures, carpal tunnel syndrome, dyslipidemia, coronary artery disease with percutaneous intervention to the right coronary artery in the late 1990s, atrial fibrillation and ventricular tachycardia, status post implantable cardioverter defibrillator, heart failure with reduced ejection fraction (25%) attributed to ischemic cardiomyopathy, hypertension, lower urinary tract symptoms/prostatism, and previous bladder cancer. In the mid-1960s, the patient served in the US Army and had been deployed to South Korea. After the service, he worked for the local city government. He was retired for about 15 years. He reported no tobacco, alcohol, or recreational drug use and no tattoos. He did not require prior blood or blood product transfusions. None of his family members—parents, siblings, or children—had any history of kidney disease.

The patient’s outpatient medications included levetiracetam 750 mg twice daily, melatonin 9 mg at night, menthol 16%/methyl-salicylate 30% topically up to 4 times per day as needed, aspirin 81 mg once daily, fish oil 1000 mg twice daily, amiodarone 400 mg twice daily, hydralazine 20 mg 3 times daily, isosorbide mononitrate 60 mg daily, metoprolol succinate 100 mg daily, and tamsulosin 0.4 mg at night. His vital signs were stable: afebrile (97.5 ºF), normocardic (74 beats per minute), normotensive (118/78 mm Hg), and normoxic (98% on room air). On examination, he appeared elderly, somewhat frail, and chronically ill but in no acute distress. Affect was pleasant and appropriate, attention was high, and his thought process was logical. He had sparse, grey scalp hair. Extraocular movements were intact. Oral mucosa was pink and moist. His back was nontender, and there was no costovertebral tenderness bilaterally. The patient was in no respiratory distress, with a slightly hyperresonant chest to percussion bilaterally, very faint inspiratory basilar crepitant rales (that cleared with repeat inspiration), and was otherwise clear to auscultation throughout. An outline of an implanted pacemaker was evident on the chest under his left clavicle, with a laterally displaced apical impulse. The rate was normal and the rhythm was regular. Upper extremities demonstrated papyraceous skin but without cyanosis, clubbing, or edema. Radial pulses were slightly diminished. He had no lower extremity edema.

His laboratory values are provided in Table 1. Kidney function was stable months prior to admission. Of note, the blood urea nitrogen and plasma creatinine were increased from his baseline up to 47 and 5.89 mg/dL, respectively. The serum glutamic-oxaloacetic transaminase and serum glutamic pyruvic transaminase were 1051 U/L and 408 U/L, respectively. Plasma amylase and lipase levels also were elevated, 230 U/L and 892 U/L, respectively. Creatine kinase was 41,099 U/L. Urinalysis demonstrated a specific gravity of 1.017, pH of 5, and a large amount of blood (92 red blood cells/high power field).

Discussion

Statin-class drugs inhibit the HMG-CoA reductase (Table 2). Upregulation of low-density lipoprotein cholesterol (LDL-C) receptors in the liver result in increased LDL-C uptake and cholesterol catabolism.¹ Prescribed inhibitors of the HMG-CoA reductase—statins—are known to reduce mortality due to cardiovascular disease (CVD). Much like any other pharmaceutical agent with any measurable potency, HMG-CoA inhibitors can have AEs. Statin therapy has been associated with pancreatitis.² Muscle toxicity is a complication of HMG-CoA reductase inhibitors, and statin-associated symptoms are a leading cause of nonadherence.³ Rosuvastatin had higher AE and drug reactions compared with that of atorvastatin and pitavastatin (35.6%, 8.7%, and 22.2%, respectively) in clinical trials for approval.⁴ We have reported concomitant adermatopathic dermatomyositis with statin-induced myopathy in a 48-year-old man from simvastatin (40 to 80 mg daily).¹

Toxin-induced myopathy should be considered early in the differential diagnosis of weakness.⁵ All HMG-CoA inhibitors have been associated with acute kidney injury, particularly at high doses and also are known to induce myopathies, sometimes with inclusion bodies.¹ Muscle-related AEs correlate with the potency of an HMG-CoA reductase inhibitor according to an analysis using the FDA AE Reporting System (AERS).⁶ Myalgia and rhabdomyolysis are well-known AEs of this class of medications. Furthermore, type II muscle atrophy—particularly in the proximal limb muscles—has been reported.⁵ Patients may have difficulty rising from chairs.¹ Rosuvastatin had the strongest signal for muscular AEs (eg, myalgia, rhabdomyolysis, increased creatine phosphokinase level) from an FDA analysis of AERS.⁷

Rosuvastatin is the only HMG-CoA reductase inhibitor that causes dose-dependent increases in proteinuria and hematuria (Figure 4).⁸ Rosuvastatin at a 5-mg dose may induce 4 times the proteinuria as a placebo. Typically, other statins potentially reduce proteinuria (without hematuria). Proteinuria may be induced by rosuvastatin even at low doses.⁸ Proteinuria is attributed to how rosuvastatin impacts proximal tubular function.⁹ The drug is transported into the proximal tubule by the organic anion transporter-3. Acute kidney injury has been associated with several statins, including rosuvastatin.^7,10 This may be associated with denuded tubular epithelia, active urinary sediment, acute tubular toxicity, vacuolated epithelial cells, and tubular cell casts. Unlike atorvastatin, the increase in proteinuria and hematuria also is dose dependent.

In patients with renal insufficiency (short of end-stage renal disease [ESRD]), most statins other than rosuvastatin are well tolerated and recommended for reduction of overall and CVD mortality risk. However, these benefits seem to diminish once ESRD is reached. Atorvastatin did not impact CVD mortality in patients with type 2 diabetes mellitus (T2DM) and ESRD (despite decreasing LDL-C).¹¹ The AURORA study randomized 10 mg of statin vs placebo in 2776 maintenance dialysis patients aged 50 to 80 years. Rosuvastatin lowered the LDL-C but did not affect all-cause mortality (13.5 vs 14.0 events per 100 patient-years). Patients randomized to rosuvastatin had more than twice as many unclassified strokes (9 vs 4). Rosuvastatin, although efficacious in reducing LDL-C, had no impact on CVD mortality, nonfatal myocardial infarction, or nonfatal stroke.¹² Post hoc analysis demonstrated that in patients with T2DM with ESRD the hazard ratio for hemorrhagic stroke was 5.2.¹³

Rosuvastatin ranked lower than lovastatin, pravastatin, simvastatin, atorvastatin, and fluvastatin with respect to reduction of all-cause mortality in trials of participants with or without prior coronary artery disease.¹⁴ AEs, such as rhabdomyolysis, proteinuria, nephropathy, renal failure, liver, and muscle toxicity are higher with rosuvastatin than other medications in its class.¹⁵

Conclusions

For patients with existing CVD, standard clinical practice is to encourage increased and regular physical activity, cholesterol-lowering diets, weight loss, and smoking cessation. Hypertension should be treated. Glycemia should be well controlled in the setting of T2DM. β-blockers may be beneficial in those with histories of myocardial infarction or heart failure with reduced systolic function. Statins are a valuable tool in the treatment of dyslipidemia.

Statin-induced muscle symptoms are a major reason for discontinuation and nonadherence.¹⁶ Statin-induced myalgia, myositis, and myopathy have been used interchangeably.¹⁷ Rhabdomyolysis, myalgia, increased creatine kinase, statin myopathy, and immune-mediated necrotizing myopathy are among the clinical phenotypes caused by statins.¹⁷ There are 33,695 serious cases—1808 deaths—reported with rosuvastatin in the FDA AERS as of June 30, 2021. Myalgia, pain in extremity, muscle spasms, pain, and arthralgia top the list of AEs. When statin-induced symptoms occur, adherence is rarely improved by dismissive clinicians.¹⁸

Drugs in the same class often have common therapeutic properties. Potencies and AE profiles are seldom uniform. The decision to add or change the brand of medication within a class should be balanced with considerations for the indication, duplications, simplification, AEs, appropriate dosage, and drug-drug interactions.

Acknowledgments

Brent Wagner is funded by a US Department of Veterans Affairs Merit Award (I01 BX001958), a National Institutes of Health R01 grant (DK-102085), Dialysis Clinic, Inc., and partially supported by the University of New Mexico Brain and Behavioral Health Institute (BBHI 2018-1008, 2020-21-002) and in part by the University of New Mexico’s Signature Program in Cardiovascular and Metabolic Disease (CVMD); and the University of New Mexico School of Medicine Research Allocation Committee (C-2459-RAC, New Mexico Medical Trust). Brent Wagner is an Associate Member to the University of New Mexico Health Sciences Center Autophagy, Inflammation, and Metabolism Center of Biomedical Research Excellence (AIM CoBRE) supported by NIH grant P20GM121176.

Funding

National Institutes of Health Grant R01 DK-102085, Dialysis Clinic Inc., VA Merit Award I01 BX001958, Center for Integrated Nanotechnologies User Agreement 2019AU0120, Brain & Behavioral Health Institute (grants 2018-1008, 2020-21-002), University of New Mexico’s Signature Program in Cardiovascular and Metabolic Disease (CVMD), the University of New Mexico School of Medicine Research Allocation Committee (C-2459-RAC, New Mexico Medical Trust) and a metabolomics voucher from the AIM Center (NIH P20GM121176).

Attention should be paid to changing a tolerated medication to another within its class. Many drugs approved by the US Food and Drug Administration (FDA), have equivalent therapeutic properties as existing drugs. Rarely do such medications share the same potency and adverse effect (AE) profile.

Case Presentation

A 77-year-old man presented to the emergency department (ED) at the Raymond G. Murphy Medical Center in Albuquerque, New Mexico, with a 1-month history of progressive muscle weakness, which was so severe that he required assistance rising from chairs. The symptoms began when he switched from atorvastatin 40 mg daily to rosuvastatin 40 mg daily. A nephrology consultation was requested for an elevated plasma creatinine.

The patient reported strict adherence to his prescribed medications. In the days following the switch to rosuvastatin, he noticed that his urine turned black. He described the color as “like burnt coffee.” The color gradually cleared before his ED presentation. The patient stopped taking rosuvastatin the day prior to presentation and noted improvement of his symptoms. Review of symptoms was significant for lower extremity paresthesia and numbness the day he started rosuvastatin. He had no symptoms of decompensated heart failure and no recent exacerbations requiring alteration of his diuretic regimen.

The patient’s medical history was significant for traumatic brain injury with complex partial seizures, carpal tunnel syndrome, dyslipidemia, coronary artery disease with percutaneous intervention to the right coronary artery in the late 1990s, atrial fibrillation and ventricular tachycardia, status post implantable cardioverter defibrillator, heart failure with reduced ejection fraction (25%) attributed to ischemic cardiomyopathy, hypertension, lower urinary tract symptoms/prostatism, and previous bladder cancer. In the mid-1960s, the patient served in the US Army and had been deployed to South Korea. After the service, he worked for the local city government. He was retired for about 15 years. He reported no tobacco, alcohol, or recreational drug use and no tattoos. He did not require prior blood or blood product transfusions. None of his family members—parents, siblings, or children—had any history of kidney disease.

The patient’s outpatient medications included levetiracetam 750 mg twice daily, melatonin 9 mg at night, menthol 16%/methyl-salicylate 30% topically up to 4 times per day as needed, aspirin 81 mg once daily, fish oil 1000 mg twice daily, amiodarone 400 mg twice daily, hydralazine 20 mg 3 times daily, isosorbide mononitrate 60 mg daily, metoprolol succinate 100 mg daily, and tamsulosin 0.4 mg at night. His vital signs were stable: afebrile (97.5 ºF), normocardic (74 beats per minute), normotensive (118/78 mm Hg), and normoxic (98% on room air). On examination, he appeared elderly, somewhat frail, and chronically ill but in no acute distress. Affect was pleasant and appropriate, attention was high, and his thought process was logical. He had sparse, grey scalp hair. Extraocular movements were intact. Oral mucosa was pink and moist. His back was nontender, and there was no costovertebral tenderness bilaterally. The patient was in no respiratory distress, with a slightly hyperresonant chest to percussion bilaterally, very faint inspiratory basilar crepitant rales (that cleared with repeat inspiration), and was otherwise clear to auscultation throughout. An outline of an implanted pacemaker was evident on the chest under his left clavicle, with a laterally displaced apical impulse. The rate was normal and the rhythm was regular. Upper extremities demonstrated papyraceous skin but without cyanosis, clubbing, or edema. Radial pulses were slightly diminished. He had no lower extremity edema.

His laboratory values are provided in Table 1. Kidney function was stable months prior to admission. Of note, the blood urea nitrogen and plasma creatinine were increased from his baseline up to 47 and 5.89 mg/dL, respectively. The serum glutamic-oxaloacetic transaminase and serum glutamic pyruvic transaminase were 1051 U/L and 408 U/L, respectively. Plasma amylase and lipase levels also were elevated, 230 U/L and 892 U/L, respectively. Creatine kinase was 41,099 U/L. Urinalysis demonstrated a specific gravity of 1.017, pH of 5, and a large amount of blood (92 red blood cells/high power field).

Discussion

Statin-class drugs inhibit the HMG-CoA reductase (Table 2). Upregulation of low-density lipoprotein cholesterol (LDL-C) receptors in the liver result in increased LDL-C uptake and cholesterol catabolism.¹ Prescribed inhibitors of the HMG-CoA reductase—statins—are known to reduce mortality due to cardiovascular disease (CVD). Much like any other pharmaceutical agent with any measurable potency, HMG-CoA inhibitors can have AEs. Statin therapy has been associated with pancreatitis.² Muscle toxicity is a complication of HMG-CoA reductase inhibitors, and statin-associated symptoms are a leading cause of nonadherence.³ Rosuvastatin had higher AE and drug reactions compared with that of atorvastatin and pitavastatin (35.6%, 8.7%, and 22.2%, respectively) in clinical trials for approval.⁴ We have reported concomitant adermatopathic dermatomyositis with statin-induced myopathy in a 48-year-old man from simvastatin (40 to 80 mg daily).¹

Toxin-induced myopathy should be considered early in the differential diagnosis of weakness.⁵ All HMG-CoA inhibitors have been associated with acute kidney injury, particularly at high doses and also are known to induce myopathies, sometimes with inclusion bodies.¹ Muscle-related AEs correlate with the potency of an HMG-CoA reductase inhibitor according to an analysis using the FDA AE Reporting System (AERS).⁶ Myalgia and rhabdomyolysis are well-known AEs of this class of medications. Furthermore, type II muscle atrophy—particularly in the proximal limb muscles—has been reported.⁵ Patients may have difficulty rising from chairs.¹ Rosuvastatin had the strongest signal for muscular AEs (eg, myalgia, rhabdomyolysis, increased creatine phosphokinase level) from an FDA analysis of AERS.⁷

Rosuvastatin is the only HMG-CoA reductase inhibitor that causes dose-dependent increases in proteinuria and hematuria (Figure 4).⁸ Rosuvastatin at a 5-mg dose may induce 4 times the proteinuria as a placebo. Typically, other statins potentially reduce proteinuria (without hematuria). Proteinuria may be induced by rosuvastatin even at low doses.⁸ Proteinuria is attributed to how rosuvastatin impacts proximal tubular function.⁹ The drug is transported into the proximal tubule by the organic anion transporter-3. Acute kidney injury has been associated with several statins, including rosuvastatin.^7,10 This may be associated with denuded tubular epithelia, active urinary sediment, acute tubular toxicity, vacuolated epithelial cells, and tubular cell casts. Unlike atorvastatin, the increase in proteinuria and hematuria also is dose dependent.

In patients with renal insufficiency (short of end-stage renal disease [ESRD]), most statins other than rosuvastatin are well tolerated and recommended for reduction of overall and CVD mortality risk. However, these benefits seem to diminish once ESRD is reached. Atorvastatin did not impact CVD mortality in patients with type 2 diabetes mellitus (T2DM) and ESRD (despite decreasing LDL-C).¹¹ The AURORA study randomized 10 mg of statin vs placebo in 2776 maintenance dialysis patients aged 50 to 80 years. Rosuvastatin lowered the LDL-C but did not affect all-cause mortality (13.5 vs 14.0 events per 100 patient-years). Patients randomized to rosuvastatin had more than twice as many unclassified strokes (9 vs 4). Rosuvastatin, although efficacious in reducing LDL-C, had no impact on CVD mortality, nonfatal myocardial infarction, or nonfatal stroke.¹² Post hoc analysis demonstrated that in patients with T2DM with ESRD the hazard ratio for hemorrhagic stroke was 5.2.¹³

Rosuvastatin ranked lower than lovastatin, pravastatin, simvastatin, atorvastatin, and fluvastatin with respect to reduction of all-cause mortality in trials of participants with or without prior coronary artery disease.¹⁴ AEs, such as rhabdomyolysis, proteinuria, nephropathy, renal failure, liver, and muscle toxicity are higher with rosuvastatin than other medications in its class.¹⁵

Conclusions

For patients with existing CVD, standard clinical practice is to encourage increased and regular physical activity, cholesterol-lowering diets, weight loss, and smoking cessation. Hypertension should be treated. Glycemia should be well controlled in the setting of T2DM. β-blockers may be beneficial in those with histories of myocardial infarction or heart failure with reduced systolic function. Statins are a valuable tool in the treatment of dyslipidemia.

Statin-induced muscle symptoms are a major reason for discontinuation and nonadherence.¹⁶ Statin-induced myalgia, myositis, and myopathy have been used interchangeably.¹⁷ Rhabdomyolysis, myalgia, increased creatine kinase, statin myopathy, and immune-mediated necrotizing myopathy are among the clinical phenotypes caused by statins.¹⁷ There are 33,695 serious cases—1808 deaths—reported with rosuvastatin in the FDA AERS as of June 30, 2021. Myalgia, pain in extremity, muscle spasms, pain, and arthralgia top the list of AEs. When statin-induced symptoms occur, adherence is rarely improved by dismissive clinicians.¹⁸

Drugs in the same class often have common therapeutic properties. Potencies and AE profiles are seldom uniform. The decision to add or change the brand of medication within a class should be balanced with considerations for the indication, duplications, simplification, AEs, appropriate dosage, and drug-drug interactions.

Acknowledgments

Brent Wagner is funded by a US Department of Veterans Affairs Merit Award (I01 BX001958), a National Institutes of Health R01 grant (DK-102085), Dialysis Clinic, Inc., and partially supported by the University of New Mexico Brain and Behavioral Health Institute (BBHI 2018-1008, 2020-21-002) and in part by the University of New Mexico’s Signature Program in Cardiovascular and Metabolic Disease (CVMD); and the University of New Mexico School of Medicine Research Allocation Committee (C-2459-RAC, New Mexico Medical Trust). Brent Wagner is an Associate Member to the University of New Mexico Health Sciences Center Autophagy, Inflammation, and Metabolism Center of Biomedical Research Excellence (AIM CoBRE) supported by NIH grant P20GM121176.

Funding

National Institutes of Health Grant R01 DK-102085, Dialysis Clinic Inc., VA Merit Award I01 BX001958, Center for Integrated Nanotechnologies User Agreement 2019AU0120, Brain & Behavioral Health Institute (grants 2018-1008, 2020-21-002), University of New Mexico’s Signature Program in Cardiovascular and Metabolic Disease (CVMD), the University of New Mexico School of Medicine Research Allocation Committee (C-2459-RAC, New Mexico Medical Trust) and a metabolomics voucher from the AIM Center (NIH P20GM121176).

Attention should be paid to changing a tolerated medication to another within its class. Many drugs approved by the US Food and Drug Administration (FDA), have equivalent therapeutic properties as existing drugs. Rarely do such medications share the same potency and adverse effect (AE) profile.

Case Presentation

A 77-year-old man presented to the emergency department (ED) at the Raymond G. Murphy Medical Center in Albuquerque, New Mexico, with a 1-month history of progressive muscle weakness, which was so severe that he required assistance rising from chairs. The symptoms began when he switched from atorvastatin 40 mg daily to rosuvastatin 40 mg daily. A nephrology consultation was requested for an elevated plasma creatinine.

The patient reported strict adherence to his prescribed medications. In the days following the switch to rosuvastatin, he noticed that his urine turned black. He described the color as “like burnt coffee.” The color gradually cleared before his ED presentation. The patient stopped taking rosuvastatin the day prior to presentation and noted improvement of his symptoms. Review of symptoms was significant for lower extremity paresthesia and numbness the day he started rosuvastatin. He had no symptoms of decompensated heart failure and no recent exacerbations requiring alteration of his diuretic regimen.

The patient’s medical history was significant for traumatic brain injury with complex partial seizures, carpal tunnel syndrome, dyslipidemia, coronary artery disease with percutaneous intervention to the right coronary artery in the late 1990s, atrial fibrillation and ventricular tachycardia, status post implantable cardioverter defibrillator, heart failure with reduced ejection fraction (25%) attributed to ischemic cardiomyopathy, hypertension, lower urinary tract symptoms/prostatism, and previous bladder cancer. In the mid-1960s, the patient served in the US Army and had been deployed to South Korea. After the service, he worked for the local city government. He was retired for about 15 years. He reported no tobacco, alcohol, or recreational drug use and no tattoos. He did not require prior blood or blood product transfusions. None of his family members—parents, siblings, or children—had any history of kidney disease.

The patient’s outpatient medications included levetiracetam 750 mg twice daily, melatonin 9 mg at night, menthol 16%/methyl-salicylate 30% topically up to 4 times per day as needed, aspirin 81 mg once daily, fish oil 1000 mg twice daily, amiodarone 400 mg twice daily, hydralazine 20 mg 3 times daily, isosorbide mononitrate 60 mg daily, metoprolol succinate 100 mg daily, and tamsulosin 0.4 mg at night. His vital signs were stable: afebrile (97.5 ºF), normocardic (74 beats per minute), normotensive (118/78 mm Hg), and normoxic (98% on room air). On examination, he appeared elderly, somewhat frail, and chronically ill but in no acute distress. Affect was pleasant and appropriate, attention was high, and his thought process was logical. He had sparse, grey scalp hair. Extraocular movements were intact. Oral mucosa was pink and moist. His back was nontender, and there was no costovertebral tenderness bilaterally. The patient was in no respiratory distress, with a slightly hyperresonant chest to percussion bilaterally, very faint inspiratory basilar crepitant rales (that cleared with repeat inspiration), and was otherwise clear to auscultation throughout. An outline of an implanted pacemaker was evident on the chest under his left clavicle, with a laterally displaced apical impulse. The rate was normal and the rhythm was regular. Upper extremities demonstrated papyraceous skin but without cyanosis, clubbing, or edema. Radial pulses were slightly diminished. He had no lower extremity edema.

His laboratory values are provided in Table 1. Kidney function was stable months prior to admission. Of note, the blood urea nitrogen and plasma creatinine were increased from his baseline up to 47 and 5.89 mg/dL, respectively. The serum glutamic-oxaloacetic transaminase and serum glutamic pyruvic transaminase were 1051 U/L and 408 U/L, respectively. Plasma amylase and lipase levels also were elevated, 230 U/L and 892 U/L, respectively. Creatine kinase was 41,099 U/L. Urinalysis demonstrated a specific gravity of 1.017, pH of 5, and a large amount of blood (92 red blood cells/high power field).

Discussion

Statin-class drugs inhibit the HMG-CoA reductase (Table 2). Upregulation of low-density lipoprotein cholesterol (LDL-C) receptors in the liver result in increased LDL-C uptake and cholesterol catabolism.¹ Prescribed inhibitors of the HMG-CoA reductase—statins—are known to reduce mortality due to cardiovascular disease (CVD). Much like any other pharmaceutical agent with any measurable potency, HMG-CoA inhibitors can have AEs. Statin therapy has been associated with pancreatitis.² Muscle toxicity is a complication of HMG-CoA reductase inhibitors, and statin-associated symptoms are a leading cause of nonadherence.³ Rosuvastatin had higher AE and drug reactions compared with that of atorvastatin and pitavastatin (35.6%, 8.7%, and 22.2%, respectively) in clinical trials for approval.⁴ We have reported concomitant adermatopathic dermatomyositis with statin-induced myopathy in a 48-year-old man from simvastatin (40 to 80 mg daily).¹

Toxin-induced myopathy should be considered early in the differential diagnosis of weakness.⁵ All HMG-CoA inhibitors have been associated with acute kidney injury, particularly at high doses and also are known to induce myopathies, sometimes with inclusion bodies.¹ Muscle-related AEs correlate with the potency of an HMG-CoA reductase inhibitor according to an analysis using the FDA AE Reporting System (AERS).⁶ Myalgia and rhabdomyolysis are well-known AEs of this class of medications. Furthermore, type II muscle atrophy—particularly in the proximal limb muscles—has been reported.⁵ Patients may have difficulty rising from chairs.¹ Rosuvastatin had the strongest signal for muscular AEs (eg, myalgia, rhabdomyolysis, increased creatine phosphokinase level) from an FDA analysis of AERS.⁷

Rosuvastatin is the only HMG-CoA reductase inhibitor that causes dose-dependent increases in proteinuria and hematuria (Figure 4).⁸ Rosuvastatin at a 5-mg dose may induce 4 times the proteinuria as a placebo. Typically, other statins potentially reduce proteinuria (without hematuria). Proteinuria may be induced by rosuvastatin even at low doses.⁸ Proteinuria is attributed to how rosuvastatin impacts proximal tubular function.⁹ The drug is transported into the proximal tubule by the organic anion transporter-3. Acute kidney injury has been associated with several statins, including rosuvastatin.^7,10 This may be associated with denuded tubular epithelia, active urinary sediment, acute tubular toxicity, vacuolated epithelial cells, and tubular cell casts. Unlike atorvastatin, the increase in proteinuria and hematuria also is dose dependent.

In patients with renal insufficiency (short of end-stage renal disease [ESRD]), most statins other than rosuvastatin are well tolerated and recommended for reduction of overall and CVD mortality risk. However, these benefits seem to diminish once ESRD is reached. Atorvastatin did not impact CVD mortality in patients with type 2 diabetes mellitus (T2DM) and ESRD (despite decreasing LDL-C).¹¹ The AURORA study randomized 10 mg of statin vs placebo in 2776 maintenance dialysis patients aged 50 to 80 years. Rosuvastatin lowered the LDL-C but did not affect all-cause mortality (13.5 vs 14.0 events per 100 patient-years). Patients randomized to rosuvastatin had more than twice as many unclassified strokes (9 vs 4). Rosuvastatin, although efficacious in reducing LDL-C, had no impact on CVD mortality, nonfatal myocardial infarction, or nonfatal stroke.¹² Post hoc analysis demonstrated that in patients with T2DM with ESRD the hazard ratio for hemorrhagic stroke was 5.2.¹³

Rosuvastatin ranked lower than lovastatin, pravastatin, simvastatin, atorvastatin, and fluvastatin with respect to reduction of all-cause mortality in trials of participants with or without prior coronary artery disease.¹⁴ AEs, such as rhabdomyolysis, proteinuria, nephropathy, renal failure, liver, and muscle toxicity are higher with rosuvastatin than other medications in its class.¹⁵

Conclusions

For patients with existing CVD, standard clinical practice is to encourage increased and regular physical activity, cholesterol-lowering diets, weight loss, and smoking cessation. Hypertension should be treated. Glycemia should be well controlled in the setting of T2DM. β-blockers may be beneficial in those with histories of myocardial infarction or heart failure with reduced systolic function. Statins are a valuable tool in the treatment of dyslipidemia.

Statin-induced muscle symptoms are a major reason for discontinuation and nonadherence.¹⁶ Statin-induced myalgia, myositis, and myopathy have been used interchangeably.¹⁷ Rhabdomyolysis, myalgia, increased creatine kinase, statin myopathy, and immune-mediated necrotizing myopathy are among the clinical phenotypes caused by statins.¹⁷ There are 33,695 serious cases—1808 deaths—reported with rosuvastatin in the FDA AERS as of June 30, 2021. Myalgia, pain in extremity, muscle spasms, pain, and arthralgia top the list of AEs. When statin-induced symptoms occur, adherence is rarely improved by dismissive clinicians.¹⁸

Drugs in the same class often have common therapeutic properties. Potencies and AE profiles are seldom uniform. The decision to add or change the brand of medication within a class should be balanced with considerations for the indication, duplications, simplification, AEs, appropriate dosage, and drug-drug interactions.

Acknowledgments

Brent Wagner is funded by a US Department of Veterans Affairs Merit Award (I01 BX001958), a National Institutes of Health R01 grant (DK-102085), Dialysis Clinic, Inc., and partially supported by the University of New Mexico Brain and Behavioral Health Institute (BBHI 2018-1008, 2020-21-002) and in part by the University of New Mexico’s Signature Program in Cardiovascular and Metabolic Disease (CVMD); and the University of New Mexico School of Medicine Research Allocation Committee (C-2459-RAC, New Mexico Medical Trust). Brent Wagner is an Associate Member to the University of New Mexico Health Sciences Center Autophagy, Inflammation, and Metabolism Center of Biomedical Research Excellence (AIM CoBRE) supported by NIH grant P20GM121176.

Funding

National Institutes of Health Grant R01 DK-102085, Dialysis Clinic Inc., VA Merit Award I01 BX001958, Center for Integrated Nanotechnologies User Agreement 2019AU0120, Brain & Behavioral Health Institute (grants 2018-1008, 2020-21-002), University of New Mexico’s Signature Program in Cardiovascular and Metabolic Disease (CVMD), the University of New Mexico School of Medicine Research Allocation Committee (C-2459-RAC, New Mexico Medical Trust) and a metabolomics voucher from the AIM Center (NIH P20GM121176).

In 1997, Elizabeth McGlynn wrote, “Measuring quality is no simple task.”¹ We are reminded of this seminal Health Affairs article at a very pertinent point—as health care practice progresses, measuring the impact of performance improvement initiatives on clinical care delivery remains integral to monitoring overall effectiveness of quality. Mortality outcomes are a major focus of quality.

Inpatient mortality within the Veterans Health Administration (VHA) was measured as actual number of deaths (unadjusted mortality), and adjusted mortality was calculated using the standardized mortality ratio (SMR). SMR included actual number of deaths during hospitalization or within 1 day of hospital discharge divided by predicted number of deaths using a risk-adjusted formula and was calculated separately for acute level of care (LOC) and the intensive care unit (ICU). Using risk-adjusted SMR, if an observed/expected ratio was > 1.0, there were more inpatient deaths than expected; if < 1.0, fewer inpatient deaths occurred than predicted; and if 1.0, observed number of inpatient deaths was equivalent to expected number of deaths.²

Mortality reduction is a complex area of performance improvement. Health care facilities often focus their efforts on the biggest mortality contributors. According to Dantes and Epstein, sepsis results in about 265,000 deaths annually in the United States.³ Reinhart and colleagues demonstrated that sepsis is a worldwide issue resulting in approximately 30 million cases and 6 million deaths annually.⁴ Furthermore, Kumar and colleagues have noted that when sepsis progresses to septic shock, survival decreases by almost 8% for each hour delay in sepsis identification and treatment.⁵

Improvements in sepsis management have been multifaceted. The Surviving Sepsis Campaign guidelines created sepsis treatment bundles to guide early diagnosis/treatment of sepsis.⁶ In addition to awareness and sepsis care bundles, a plethora of informatics solutions within electronic health record (EHR) systems have demonstrated improved sepsis care.^7-16 Various approaches to early diagnosis and management of sepsis have been collectively referred to as an early warning sepsis system (EWSS).

An EWSS typically contains automated decision support tools that are integrated in the EHR and meant to assist health care professionals with clinical workflow decision-making. Automated decision support tools within the EHR have a variety of functions, such as clinical care reminders and alerts.¹⁷

Sepsis screening tools function as a form of automated decision support and may be incorporated into the EHR to support the EWSS. Although sepsis screening tools vary, they frequently include a combination of data involving vital signs, laboratory values and/or physical examination findings, such as mental status evaluation.The Modified Early Warning Signs (MEWS) + Sepsis Recognition Score (SRS) is one example of a sepsis screening tool.^7,16

At Malcom Randall Veterans Affairs Medical Center (MRVAMC) in Gainesville, Florida, we identified a quality improvement project opportunity to improve sepsis care in the emergency department (ED) and inpatient wards using the VHA EHR system, the Computerized Patient Record System (CPRS), which is supported by the Veterans Information Systems and Technology Architecture (VistA).¹⁸ A VistA/CPRS EWSS was developed using Lean Six Sigma DMAIC (define, measure, analyze, improve, and control) methodology.¹⁹ During the improve stage, informatics solutions were applied and included a combination of EHR interventions, such as template design, an order set, and clinical reminders. Clinical reminders have a wide variety of use, such as reminders for clinical tasks and as automated decision support within clinical workflows using Boolean logic.

To the best of our knowledge, there has been no published application of an EWSS within VistA/CPRS. In this study, we outline the strategic development of an EWSS in VistA/CPRS that assisted clinical staff with identification and treatment of sepsis; improved documentation of sepsis when present; and associated with improvement in unadjusted and adjusted inpatient mortality.

Methods 

According to policy activities that constitute research at MRVAMC, no institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

The North Florida/South Georgia Veterans Health System (NF/SGVHS) includes MRVAMC, a large academic hospital with rotating residents/fellows and multiple specialty care services. MRVAMC comprised 144 beds on the medicine/surgery wards; 48 beds in the psychiatry unit; 18 intermediate LOC beds; and 27 ICU beds. The MRVAMC SMR was identified as an improvement opportunity during fiscal year (FY) 2017 (Table 1). Its adjusted mortality for acute LOC demonstrated an observed/expected ratio of > 1.0 suggesting more inpatient deaths were observed than expected. The number of deaths (unadjusted mortality) on acute LOC at MRVAMC was noted to be rising during the first 3 quarters of FY 2017. A deeper examination of data by Pyramid Analytics (www.pyramidanalytics.com) discovered that sepsis was the primary driver for inpatient mortality on acute LOC at MRVAMC. Our goal was to reduce inpatient sepsis-related mortality via development of an EWSS that leveraged VistA/CPRS to improve early identification and treatment of sepsis in the ED and inpatient wards.

Emergency Department

Given the importance of recognizing sepsis early, the sepsis team focused on improvement opportunities at the initial point of patient contact: ED triage. The goal was to incorporate automated VistA/CPRS decision support to assist clinicians with identifying sepsis in triage using MEWS, which was chosen to optimize immediate hospital-wide buy-in. Clinical staff were already familiar with MEWS, which was in use on the inpatient wards.

Flow through the ED and availability of resources differed from the wards. Hence, modification to MEWS on the wards was necessary to fit clinical workflow in the ED. Temperature, heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), mental status, and white blood cell count (WBC) factored into a MEWS + SRS score on the wards (Table 2). For the ED, MEWS included temperature, HR, RR and SBP, but excluded mental status and WBC. Mental status assessment was excluded due to technical infeasibility (while vital signs could be automatically calculated in real time for a MEWS score, that was not possible for mental status changes). WBC was excluded from the ED as laboratory test results would not be available in triage.

MEWS + SRS scores were calculated in VistA by using clinical reminders. Clinical reminder logic included a series of conditional statements based on various combinations of MEWS + SRS clinical data entered in the EHR. When ED triage vital signs data were entered in CPRS, clinical data were stored and processed according to clinical reminder logic in VistA and displayed to the user in CPRS. While MEWS of ≥ 5 triggered a sepsis alert on the wards, the ≥ 4 threshold was used in the ED given mental status and WBC were excluded from calculations in triage (eAppendix 1 available at doi:10.12788/fp.0194).

Once a sepsis alert was triggered in triage for MEWS ≥ 4, ED nursing staff prioritized bed location and expedited staffing with an ED attending physician for early assessment. The ED attending then performed an assessment to confirm whether sepsis was present and direct early treatment. Although every patient who triggered a sepsis alert in triage did not meet clinical findings of sepsis, patients with MEWS ≥ 4 were frequently ill and required timely intervention.

If an ED attending physician agreed with a sepsis diagnosis, the physician had access to a sepsis workup and treatment order set in CPRS (eAppendix 2 available at doi:10.12788/fp.0194). The sepsis order set incorporated recommendations from the Surviving Sepsis Campaign guidelines and included orders for 2 large-bore peripheral IV lines; aggressive fluid resuscitation (30 mL/kg) for patients with clinical findings of hypoperfusion; broad-spectrum antibiotics; and frequent ordering of laboratory tests and imaging during initial sepsis workup.⁶ Vancomycin and cefepime were selected as routine broad-spectrum antibiotics in the order set when sepsis was suspected based on local antimicrobial stewardship and safety-efficacy profiles. For example, Luther and colleagues demonstrated that cefepime has lower rates of acute kidney injury when combined with vancomycin vs vancomycin + piperacillin-tazobactam.²⁰ If a β-lactam antibiotic could not be used due to a patient’s drug allergy history, aztreonam was available as an alternative option.

The design of the order set also functioned as a communication interface with clinical pharmacists. Given the large volume of antibiotics ordered in the ED, it was difficult for pharmacists to prioritize antibiotic order verification. While stat orders convey high priority, they often lack specificity. When antibiotic orders were selected from the sepsis order set, comments were already included that stated: “STAT. First dose for sepsis protocol” (eAppendix 3 available at doi:10.12788/fp.0194). This standardized communication conveyed a sense of urgency and a collective understanding that patients with suspected sepsis required timely order verification and administration of antibiotics.

Hospital Ward

Mental status and WBC were included on the wards to monitor for possible signs of sepsis, using MEWS + SRS, which was routinely monitored by nursing every 4 to 8 hours. When MEWS + SRS was ≥ 5 points, ward nursing staff called a sepsis alert.^7,16 Early response team (ERT) members received telephone notifications of the alert. ERT staff proceeded with immediate evaluation and treatment at the bedside along with determination for most appropriate LOC. The ERT members included an ICU physician and nurse; respiratory therapist; and nursing supervisor/bed flow coordinator. During bedside evaluation, if the ERT or primary team agreed with a sepsis diagnosis, the ERT or primary team used the sepsis order set to ensure standardized procedures. Stat orders generated through the sepsis order set pathway conveyed a sense of urgency and need for immediate order verification and administration of antibiotics.

In addition to clinical care process improvement, accurate documentation also was emphasized in the EWSS. When a sepsis alert was called, a clinician from the primary team was expected to complete a standardized progress note, which communicated clinical findings, a treatment plan, and captured severity of illness (eAppendix 4 available at doi:10.12788/fp.0194). It included sections for subjective, objective, assessment, and plan. In addition, data objects were created for vital signs and common laboratory findings that retrieved important clinical data from VistA and inserted it into the CPRS note.²¹

Nursing staff on the wards were expected to communicate results with the primary team for clinical decision making when a patient had a MEWS + SRS of 3 to 4. A sepsis alert may have been called at the discretion of clinical team members but was not required if the score was < 5. Additionally, vital signs were expected to be checked by the nursing staff on the wards at least every 4 hours for closer monitoring.

Sepsis Review Meetings

Weekly meetings were scheduled to review sepsis cases to assess diagnosis, treatment, and documentation entered in the patient record. The team conducting sepsis reviews comprised the chief of staff, chief of quality management, director of patient safety, physician utilization management advisor, chief resident in quality and patient safety (CRQS), and inpatient pharmacy supervisor. In addition, ad hoc physicians and nurses from different specialty areas, such as infectious diseases, hospitalist section, ICU, and the ED participated on request for subject matter expertise when needed. At the conclusion of weekly sepsis meetings, sepsis team members provided feedback to the clinical staff for continuous improvement purposes.

Results

Before implementation of an EWSS at NF/SGVHS, a plan was devised to increase awareness and educate staff on sepsis-related mortality in late FY 2017. Awareness and education about sepsis-related mortality was organized at physician, nursing, and pharmacy leadership clinical staff meetings. Posters about early warning signs of sepsis also were displayed on the nursing units for educational purposes and to convey the importance of early recognition/treatment of sepsis. In addition, the CRQS was the quality leader for house staff and led sepsis campaign change efforts for residents/fellows. An immediate improvement in unadjusted mortality at MRVAMC was noted with initial sepsis awareness and education. From FY 2017, quarter 3 to FY 2018, quarter 1, the number of acute LOC inpatient deaths decreased from 48 to 28, a 42% reduction in unadjusted mortality at MRVAMC (Figure 1). Additionally, the acute LOC SMR improved from 1.20 during FY 2017, quarter 3 down to as low as 0.71 during FY 2018, quarter 1 (Figure 2).

The number of MRVAMC inpatient deaths increased from 28 in FY 2018, quarter 1 to 45 in FY 2018, quarter 3. While acute LOC showed improvement in unadjusted mortality after sepsis education/awareness, it was felt continuous improvement could not be sustained with education alone. An EWSS was designed and implemented within the EHR system in FY 2018. Following implementation of EWSS and reeducating staff on early recognition and treatment of sepsis, acute LOC inpatient deaths decreased from 45 in FY 2018, quarter 3 through FY 2019 where unadjusted mortality was as low as 27 during FY 2019, quarter 4. The MRVAMC acute LOC SMR was consistently < 1.0 from FY 2018, quarter 4 through FY 2019, quarter 4.

In addition to the observed decrease in acute LOC inpatient deaths and improved SMR, the number of ERT alerts and sepsis alerts on the inpatient wards were monitored from FY 2017 through FY 2019. ERT alerts listed in Table 3 were nonspecific and initiated by nursing staff on the wards where a patient’s clinical status was identified as worsening while sepsis alerts were specific ERT alerts called by the ward nursing staff due to concerns for sepsis. The inpatient wards included inpatient medicine, surgery, and psychiatry acute care and the intermediate level of care unit while outpatient clinical areas of treatment, intensive care units, stroke alerts, and STEMI alerts were excluded.

Discussion

Implementation of the EWSS was associated with improved unadjusted mortality and adjusted mortality for acute LOC at MRVAMC. Although variation exists with application of EWSS at other medical centers, there was similarity with improved sepsis outcomes reported at other health care systems after EWSS implementation.^7-16

Improved unadjusted mortality and adjusted mortality for acute LOC at MRVAMC was likely due to multiple contributing factors. First, during design and implementation of the EWSS, project work was interdisciplinary with input from physicians, nurses, and pharmacists from multiple specialties (ie, ED, ICU, and the medicine service); quality management and data analysis specialists; and clinical informatics. Second, facility commitment to improving early recognition and treatment of sepsis from leadership level down to front-line staff was evident. Weekly sepsis meetings with the NF/SGVHS chief of staff helped to sustain EWSS efforts and to identify additional improvement opportunities. Third, integrated informatics solutions within the EHR helped identify early sepsis and minimized human error as well as assisted with coordination of sepsis care across services. Fourth, the focus was on both early identification and treatment of sepsis in the ED and hospital wards. Although it cannot be deduced whether there was causation between reduced inpatient mortality and an increased number of nonspecific ERT alerts+ sepsis alerts on the inpatient wards after EWSS implementation, inpatient deaths decreased and SMR improved. Finally, the EWSS emphasized both the importance of evidence-based clinical care of sepsis and standardized documentation to appropriately capture clinical severity of illness.

Limitations

This program has limitations. The EWSS was studied at a single VHA facility. Veteran demographics and local epidemiology may limit conclusion of outcomes to an individual VHA facility located in a specific geographical region. Additional research is necessary to demonstrate reproducibility and determine whether applicable to other VHA facilities and community care settings.

SMR is a risk-adjusted formula developed by the VHA Inpatient Evaluation Center, which included numerous factors such as diagnosis, comorbid conditions, age, marital status, procedures, source of admission, specific laboratory values, medical or surgical diagnosis-related group, ICU stays, immunosuppressive status, and a COVID-19 positive indicator (added after this study). Further research is needed to evaluate sepsis-related outcomes using the EWSS during the COVID-19 pandemic.

EWSS in the literature have demonstrated various approaches to early identification and treatment of sepsis and have used different sepsis screening tools.²² Evidence suggests that the MEWS + SRS sepsis screening tool may result in false-positive screenings.^23-27 Additional research into the specificity of this sepsis screening tool is needed. Ward nursing staff were encouraged to initiate automatic sepsis alerts when MEWS + SRS was ≥ 5; however, this still depended on human factors. Because sepsis alerts are software-specific and others were incompatible with the VHA EHR, it was necessary to design our own EWSS.

Despite improvement with MRVAMC acute LOC unadjusted and adjusted mortality with our EWSS, we did not identify any actual improvement in earlier antibiotic administration times once sepsis was recognized. While accurate documentation regarding degree of sepsis improved, a MRVAMC clinical documentation improvement program was expanded in FY 2018. Therefore, it is difficult to demonstrate causation related to improved sepsis documentation with template changes alone. While sepsis alerts on the inpatient wards were variable since EWSS implementation, nonspecific ERT alerts increased. It is unclear whether some sepsis alerts were called as nonspecific ERT alerts, making it impossible to know the true number of sepsis alerts.

MRVAMC experienced an increase in nurse turnover during FY 2018 and as a teaching hospital had frequent rotating residents and fellows new to processes/protocols. These factors may have contributed to variations in unadjusted mortality. Also the decrease in inpatient mortality and improvement in SMR on acute LOC could have been the result of factors other than the EWSS and the effect of education alone may have been at least as good as that of the EWSS intervention.

Conclusions

Education along with the possible implementation of an EWSS at NF/SGVHS was associated with a decrease in the number of inpatient deaths on MRVAMC’s acute LOC wards from as high as 48 in FY 2017, quarter 3 to as low as 27 in FY 2019, quarter 4 resulting in as large of an improvement as a 44% reduction in unadjusted mortality from FY 2017 to FY 2019. In addition, MRVAMC’s acute LOC SMR improved from > 1.0 to < 1.0, demonstrating fewer inpatient mortalities than predicted from FY 2017 to FY 2019.

This multifaceted interventional strategy may be effectively applied at other VHA health care facilities that use the same EHR system. Next steps may include determining the specificity of MEWS + SRS as a sepsis screening tool; studying outcomes of MRVAMC’s EWSS during the COVID-19 era; and conducting a multicentered study on this EWSS across multiple VHA facilities.

In 1997, Elizabeth McGlynn wrote, “Measuring quality is no simple task.”¹ We are reminded of this seminal Health Affairs article at a very pertinent point—as health care practice progresses, measuring the impact of performance improvement initiatives on clinical care delivery remains integral to monitoring overall effectiveness of quality. Mortality outcomes are a major focus of quality.

Inpatient mortality within the Veterans Health Administration (VHA) was measured as actual number of deaths (unadjusted mortality), and adjusted mortality was calculated using the standardized mortality ratio (SMR). SMR included actual number of deaths during hospitalization or within 1 day of hospital discharge divided by predicted number of deaths using a risk-adjusted formula and was calculated separately for acute level of care (LOC) and the intensive care unit (ICU). Using risk-adjusted SMR, if an observed/expected ratio was > 1.0, there were more inpatient deaths than expected; if < 1.0, fewer inpatient deaths occurred than predicted; and if 1.0, observed number of inpatient deaths was equivalent to expected number of deaths.²

Mortality reduction is a complex area of performance improvement. Health care facilities often focus their efforts on the biggest mortality contributors. According to Dantes and Epstein, sepsis results in about 265,000 deaths annually in the United States.³ Reinhart and colleagues demonstrated that sepsis is a worldwide issue resulting in approximately 30 million cases and 6 million deaths annually.⁴ Furthermore, Kumar and colleagues have noted that when sepsis progresses to septic shock, survival decreases by almost 8% for each hour delay in sepsis identification and treatment.⁵

Improvements in sepsis management have been multifaceted. The Surviving Sepsis Campaign guidelines created sepsis treatment bundles to guide early diagnosis/treatment of sepsis.⁶ In addition to awareness and sepsis care bundles, a plethora of informatics solutions within electronic health record (EHR) systems have demonstrated improved sepsis care.^7-16 Various approaches to early diagnosis and management of sepsis have been collectively referred to as an early warning sepsis system (EWSS).

An EWSS typically contains automated decision support tools that are integrated in the EHR and meant to assist health care professionals with clinical workflow decision-making. Automated decision support tools within the EHR have a variety of functions, such as clinical care reminders and alerts.¹⁷

Sepsis screening tools function as a form of automated decision support and may be incorporated into the EHR to support the EWSS. Although sepsis screening tools vary, they frequently include a combination of data involving vital signs, laboratory values and/or physical examination findings, such as mental status evaluation.The Modified Early Warning Signs (MEWS) + Sepsis Recognition Score (SRS) is one example of a sepsis screening tool.^7,16

At Malcom Randall Veterans Affairs Medical Center (MRVAMC) in Gainesville, Florida, we identified a quality improvement project opportunity to improve sepsis care in the emergency department (ED) and inpatient wards using the VHA EHR system, the Computerized Patient Record System (CPRS), which is supported by the Veterans Information Systems and Technology Architecture (VistA).¹⁸ A VistA/CPRS EWSS was developed using Lean Six Sigma DMAIC (define, measure, analyze, improve, and control) methodology.¹⁹ During the improve stage, informatics solutions were applied and included a combination of EHR interventions, such as template design, an order set, and clinical reminders. Clinical reminders have a wide variety of use, such as reminders for clinical tasks and as automated decision support within clinical workflows using Boolean logic.

To the best of our knowledge, there has been no published application of an EWSS within VistA/CPRS. In this study, we outline the strategic development of an EWSS in VistA/CPRS that assisted clinical staff with identification and treatment of sepsis; improved documentation of sepsis when present; and associated with improvement in unadjusted and adjusted inpatient mortality.

Methods 

According to policy activities that constitute research at MRVAMC, no institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

The North Florida/South Georgia Veterans Health System (NF/SGVHS) includes MRVAMC, a large academic hospital with rotating residents/fellows and multiple specialty care services. MRVAMC comprised 144 beds on the medicine/surgery wards; 48 beds in the psychiatry unit; 18 intermediate LOC beds; and 27 ICU beds. The MRVAMC SMR was identified as an improvement opportunity during fiscal year (FY) 2017 (Table 1). Its adjusted mortality for acute LOC demonstrated an observed/expected ratio of > 1.0 suggesting more inpatient deaths were observed than expected. The number of deaths (unadjusted mortality) on acute LOC at MRVAMC was noted to be rising during the first 3 quarters of FY 2017. A deeper examination of data by Pyramid Analytics (www.pyramidanalytics.com) discovered that sepsis was the primary driver for inpatient mortality on acute LOC at MRVAMC. Our goal was to reduce inpatient sepsis-related mortality via development of an EWSS that leveraged VistA/CPRS to improve early identification and treatment of sepsis in the ED and inpatient wards.

Emergency Department

Given the importance of recognizing sepsis early, the sepsis team focused on improvement opportunities at the initial point of patient contact: ED triage. The goal was to incorporate automated VistA/CPRS decision support to assist clinicians with identifying sepsis in triage using MEWS, which was chosen to optimize immediate hospital-wide buy-in. Clinical staff were already familiar with MEWS, which was in use on the inpatient wards.

Flow through the ED and availability of resources differed from the wards. Hence, modification to MEWS on the wards was necessary to fit clinical workflow in the ED. Temperature, heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), mental status, and white blood cell count (WBC) factored into a MEWS + SRS score on the wards (Table 2). For the ED, MEWS included temperature, HR, RR and SBP, but excluded mental status and WBC. Mental status assessment was excluded due to technical infeasibility (while vital signs could be automatically calculated in real time for a MEWS score, that was not possible for mental status changes). WBC was excluded from the ED as laboratory test results would not be available in triage.

MEWS + SRS scores were calculated in VistA by using clinical reminders. Clinical reminder logic included a series of conditional statements based on various combinations of MEWS + SRS clinical data entered in the EHR. When ED triage vital signs data were entered in CPRS, clinical data were stored and processed according to clinical reminder logic in VistA and displayed to the user in CPRS. While MEWS of ≥ 5 triggered a sepsis alert on the wards, the ≥ 4 threshold was used in the ED given mental status and WBC were excluded from calculations in triage (eAppendix 1 available at doi:10.12788/fp.0194).

Once a sepsis alert was triggered in triage for MEWS ≥ 4, ED nursing staff prioritized bed location and expedited staffing with an ED attending physician for early assessment. The ED attending then performed an assessment to confirm whether sepsis was present and direct early treatment. Although every patient who triggered a sepsis alert in triage did not meet clinical findings of sepsis, patients with MEWS ≥ 4 were frequently ill and required timely intervention.

If an ED attending physician agreed with a sepsis diagnosis, the physician had access to a sepsis workup and treatment order set in CPRS (eAppendix 2 available at doi:10.12788/fp.0194). The sepsis order set incorporated recommendations from the Surviving Sepsis Campaign guidelines and included orders for 2 large-bore peripheral IV lines; aggressive fluid resuscitation (30 mL/kg) for patients with clinical findings of hypoperfusion; broad-spectrum antibiotics; and frequent ordering of laboratory tests and imaging during initial sepsis workup.⁶ Vancomycin and cefepime were selected as routine broad-spectrum antibiotics in the order set when sepsis was suspected based on local antimicrobial stewardship and safety-efficacy profiles. For example, Luther and colleagues demonstrated that cefepime has lower rates of acute kidney injury when combined with vancomycin vs vancomycin + piperacillin-tazobactam.²⁰ If a β-lactam antibiotic could not be used due to a patient’s drug allergy history, aztreonam was available as an alternative option.

The design of the order set also functioned as a communication interface with clinical pharmacists. Given the large volume of antibiotics ordered in the ED, it was difficult for pharmacists to prioritize antibiotic order verification. While stat orders convey high priority, they often lack specificity. When antibiotic orders were selected from the sepsis order set, comments were already included that stated: “STAT. First dose for sepsis protocol” (eAppendix 3 available at doi:10.12788/fp.0194). This standardized communication conveyed a sense of urgency and a collective understanding that patients with suspected sepsis required timely order verification and administration of antibiotics.

Hospital Ward

Mental status and WBC were included on the wards to monitor for possible signs of sepsis, using MEWS + SRS, which was routinely monitored by nursing every 4 to 8 hours. When MEWS + SRS was ≥ 5 points, ward nursing staff called a sepsis alert.^7,16 Early response team (ERT) members received telephone notifications of the alert. ERT staff proceeded with immediate evaluation and treatment at the bedside along with determination for most appropriate LOC. The ERT members included an ICU physician and nurse; respiratory therapist; and nursing supervisor/bed flow coordinator. During bedside evaluation, if the ERT or primary team agreed with a sepsis diagnosis, the ERT or primary team used the sepsis order set to ensure standardized procedures. Stat orders generated through the sepsis order set pathway conveyed a sense of urgency and need for immediate order verification and administration of antibiotics.

In addition to clinical care process improvement, accurate documentation also was emphasized in the EWSS. When a sepsis alert was called, a clinician from the primary team was expected to complete a standardized progress note, which communicated clinical findings, a treatment plan, and captured severity of illness (eAppendix 4 available at doi:10.12788/fp.0194). It included sections for subjective, objective, assessment, and plan. In addition, data objects were created for vital signs and common laboratory findings that retrieved important clinical data from VistA and inserted it into the CPRS note.²¹

Nursing staff on the wards were expected to communicate results with the primary team for clinical decision making when a patient had a MEWS + SRS of 3 to 4. A sepsis alert may have been called at the discretion of clinical team members but was not required if the score was < 5. Additionally, vital signs were expected to be checked by the nursing staff on the wards at least every 4 hours for closer monitoring.

Sepsis Review Meetings

Weekly meetings were scheduled to review sepsis cases to assess diagnosis, treatment, and documentation entered in the patient record. The team conducting sepsis reviews comprised the chief of staff, chief of quality management, director of patient safety, physician utilization management advisor, chief resident in quality and patient safety (CRQS), and inpatient pharmacy supervisor. In addition, ad hoc physicians and nurses from different specialty areas, such as infectious diseases, hospitalist section, ICU, and the ED participated on request for subject matter expertise when needed. At the conclusion of weekly sepsis meetings, sepsis team members provided feedback to the clinical staff for continuous improvement purposes.

Results

Before implementation of an EWSS at NF/SGVHS, a plan was devised to increase awareness and educate staff on sepsis-related mortality in late FY 2017. Awareness and education about sepsis-related mortality was organized at physician, nursing, and pharmacy leadership clinical staff meetings. Posters about early warning signs of sepsis also were displayed on the nursing units for educational purposes and to convey the importance of early recognition/treatment of sepsis. In addition, the CRQS was the quality leader for house staff and led sepsis campaign change efforts for residents/fellows. An immediate improvement in unadjusted mortality at MRVAMC was noted with initial sepsis awareness and education. From FY 2017, quarter 3 to FY 2018, quarter 1, the number of acute LOC inpatient deaths decreased from 48 to 28, a 42% reduction in unadjusted mortality at MRVAMC (Figure 1). Additionally, the acute LOC SMR improved from 1.20 during FY 2017, quarter 3 down to as low as 0.71 during FY 2018, quarter 1 (Figure 2).

The number of MRVAMC inpatient deaths increased from 28 in FY 2018, quarter 1 to 45 in FY 2018, quarter 3. While acute LOC showed improvement in unadjusted mortality after sepsis education/awareness, it was felt continuous improvement could not be sustained with education alone. An EWSS was designed and implemented within the EHR system in FY 2018. Following implementation of EWSS and reeducating staff on early recognition and treatment of sepsis, acute LOC inpatient deaths decreased from 45 in FY 2018, quarter 3 through FY 2019 where unadjusted mortality was as low as 27 during FY 2019, quarter 4. The MRVAMC acute LOC SMR was consistently < 1.0 from FY 2018, quarter 4 through FY 2019, quarter 4.

In addition to the observed decrease in acute LOC inpatient deaths and improved SMR, the number of ERT alerts and sepsis alerts on the inpatient wards were monitored from FY 2017 through FY 2019. ERT alerts listed in Table 3 were nonspecific and initiated by nursing staff on the wards where a patient’s clinical status was identified as worsening while sepsis alerts were specific ERT alerts called by the ward nursing staff due to concerns for sepsis. The inpatient wards included inpatient medicine, surgery, and psychiatry acute care and the intermediate level of care unit while outpatient clinical areas of treatment, intensive care units, stroke alerts, and STEMI alerts were excluded.

Discussion

Implementation of the EWSS was associated with improved unadjusted mortality and adjusted mortality for acute LOC at MRVAMC. Although variation exists with application of EWSS at other medical centers, there was similarity with improved sepsis outcomes reported at other health care systems after EWSS implementation.^7-16

Improved unadjusted mortality and adjusted mortality for acute LOC at MRVAMC was likely due to multiple contributing factors. First, during design and implementation of the EWSS, project work was interdisciplinary with input from physicians, nurses, and pharmacists from multiple specialties (ie, ED, ICU, and the medicine service); quality management and data analysis specialists; and clinical informatics. Second, facility commitment to improving early recognition and treatment of sepsis from leadership level down to front-line staff was evident. Weekly sepsis meetings with the NF/SGVHS chief of staff helped to sustain EWSS efforts and to identify additional improvement opportunities. Third, integrated informatics solutions within the EHR helped identify early sepsis and minimized human error as well as assisted with coordination of sepsis care across services. Fourth, the focus was on both early identification and treatment of sepsis in the ED and hospital wards. Although it cannot be deduced whether there was causation between reduced inpatient mortality and an increased number of nonspecific ERT alerts+ sepsis alerts on the inpatient wards after EWSS implementation, inpatient deaths decreased and SMR improved. Finally, the EWSS emphasized both the importance of evidence-based clinical care of sepsis and standardized documentation to appropriately capture clinical severity of illness.

Limitations

This program has limitations. The EWSS was studied at a single VHA facility. Veteran demographics and local epidemiology may limit conclusion of outcomes to an individual VHA facility located in a specific geographical region. Additional research is necessary to demonstrate reproducibility and determine whether applicable to other VHA facilities and community care settings.

SMR is a risk-adjusted formula developed by the VHA Inpatient Evaluation Center, which included numerous factors such as diagnosis, comorbid conditions, age, marital status, procedures, source of admission, specific laboratory values, medical or surgical diagnosis-related group, ICU stays, immunosuppressive status, and a COVID-19 positive indicator (added after this study). Further research is needed to evaluate sepsis-related outcomes using the EWSS during the COVID-19 pandemic.

EWSS in the literature have demonstrated various approaches to early identification and treatment of sepsis and have used different sepsis screening tools.²² Evidence suggests that the MEWS + SRS sepsis screening tool may result in false-positive screenings.^23-27 Additional research into the specificity of this sepsis screening tool is needed. Ward nursing staff were encouraged to initiate automatic sepsis alerts when MEWS + SRS was ≥ 5; however, this still depended on human factors. Because sepsis alerts are software-specific and others were incompatible with the VHA EHR, it was necessary to design our own EWSS.

Despite improvement with MRVAMC acute LOC unadjusted and adjusted mortality with our EWSS, we did not identify any actual improvement in earlier antibiotic administration times once sepsis was recognized. While accurate documentation regarding degree of sepsis improved, a MRVAMC clinical documentation improvement program was expanded in FY 2018. Therefore, it is difficult to demonstrate causation related to improved sepsis documentation with template changes alone. While sepsis alerts on the inpatient wards were variable since EWSS implementation, nonspecific ERT alerts increased. It is unclear whether some sepsis alerts were called as nonspecific ERT alerts, making it impossible to know the true number of sepsis alerts.

MRVAMC experienced an increase in nurse turnover during FY 2018 and as a teaching hospital had frequent rotating residents and fellows new to processes/protocols. These factors may have contributed to variations in unadjusted mortality. Also the decrease in inpatient mortality and improvement in SMR on acute LOC could have been the result of factors other than the EWSS and the effect of education alone may have been at least as good as that of the EWSS intervention.

Conclusions

Education along with the possible implementation of an EWSS at NF/SGVHS was associated with a decrease in the number of inpatient deaths on MRVAMC’s acute LOC wards from as high as 48 in FY 2017, quarter 3 to as low as 27 in FY 2019, quarter 4 resulting in as large of an improvement as a 44% reduction in unadjusted mortality from FY 2017 to FY 2019. In addition, MRVAMC’s acute LOC SMR improved from > 1.0 to < 1.0, demonstrating fewer inpatient mortalities than predicted from FY 2017 to FY 2019.

This multifaceted interventional strategy may be effectively applied at other VHA health care facilities that use the same EHR system. Next steps may include determining the specificity of MEWS + SRS as a sepsis screening tool; studying outcomes of MRVAMC’s EWSS during the COVID-19 era; and conducting a multicentered study on this EWSS across multiple VHA facilities.

In 1997, Elizabeth McGlynn wrote, “Measuring quality is no simple task.”¹ We are reminded of this seminal Health Affairs article at a very pertinent point—as health care practice progresses, measuring the impact of performance improvement initiatives on clinical care delivery remains integral to monitoring overall effectiveness of quality. Mortality outcomes are a major focus of quality.

Inpatient mortality within the Veterans Health Administration (VHA) was measured as actual number of deaths (unadjusted mortality), and adjusted mortality was calculated using the standardized mortality ratio (SMR). SMR included actual number of deaths during hospitalization or within 1 day of hospital discharge divided by predicted number of deaths using a risk-adjusted formula and was calculated separately for acute level of care (LOC) and the intensive care unit (ICU). Using risk-adjusted SMR, if an observed/expected ratio was > 1.0, there were more inpatient deaths than expected; if < 1.0, fewer inpatient deaths occurred than predicted; and if 1.0, observed number of inpatient deaths was equivalent to expected number of deaths.²

Mortality reduction is a complex area of performance improvement. Health care facilities often focus their efforts on the biggest mortality contributors. According to Dantes and Epstein, sepsis results in about 265,000 deaths annually in the United States.³ Reinhart and colleagues demonstrated that sepsis is a worldwide issue resulting in approximately 30 million cases and 6 million deaths annually.⁴ Furthermore, Kumar and colleagues have noted that when sepsis progresses to septic shock, survival decreases by almost 8% for each hour delay in sepsis identification and treatment.⁵

Improvements in sepsis management have been multifaceted. The Surviving Sepsis Campaign guidelines created sepsis treatment bundles to guide early diagnosis/treatment of sepsis.⁶ In addition to awareness and sepsis care bundles, a plethora of informatics solutions within electronic health record (EHR) systems have demonstrated improved sepsis care.^7-16 Various approaches to early diagnosis and management of sepsis have been collectively referred to as an early warning sepsis system (EWSS).

An EWSS typically contains automated decision support tools that are integrated in the EHR and meant to assist health care professionals with clinical workflow decision-making. Automated decision support tools within the EHR have a variety of functions, such as clinical care reminders and alerts.¹⁷

Sepsis screening tools function as a form of automated decision support and may be incorporated into the EHR to support the EWSS. Although sepsis screening tools vary, they frequently include a combination of data involving vital signs, laboratory values and/or physical examination findings, such as mental status evaluation.The Modified Early Warning Signs (MEWS) + Sepsis Recognition Score (SRS) is one example of a sepsis screening tool.^7,16

At Malcom Randall Veterans Affairs Medical Center (MRVAMC) in Gainesville, Florida, we identified a quality improvement project opportunity to improve sepsis care in the emergency department (ED) and inpatient wards using the VHA EHR system, the Computerized Patient Record System (CPRS), which is supported by the Veterans Information Systems and Technology Architecture (VistA).¹⁸ A VistA/CPRS EWSS was developed using Lean Six Sigma DMAIC (define, measure, analyze, improve, and control) methodology.¹⁹ During the improve stage, informatics solutions were applied and included a combination of EHR interventions, such as template design, an order set, and clinical reminders. Clinical reminders have a wide variety of use, such as reminders for clinical tasks and as automated decision support within clinical workflows using Boolean logic.

To the best of our knowledge, there has been no published application of an EWSS within VistA/CPRS. In this study, we outline the strategic development of an EWSS in VistA/CPRS that assisted clinical staff with identification and treatment of sepsis; improved documentation of sepsis when present; and associated with improvement in unadjusted and adjusted inpatient mortality.

Methods 

According to policy activities that constitute research at MRVAMC, no institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

The North Florida/South Georgia Veterans Health System (NF/SGVHS) includes MRVAMC, a large academic hospital with rotating residents/fellows and multiple specialty care services. MRVAMC comprised 144 beds on the medicine/surgery wards; 48 beds in the psychiatry unit; 18 intermediate LOC beds; and 27 ICU beds. The MRVAMC SMR was identified as an improvement opportunity during fiscal year (FY) 2017 (Table 1). Its adjusted mortality for acute LOC demonstrated an observed/expected ratio of > 1.0 suggesting more inpatient deaths were observed than expected. The number of deaths (unadjusted mortality) on acute LOC at MRVAMC was noted to be rising during the first 3 quarters of FY 2017. A deeper examination of data by Pyramid Analytics (www.pyramidanalytics.com) discovered that sepsis was the primary driver for inpatient mortality on acute LOC at MRVAMC. Our goal was to reduce inpatient sepsis-related mortality via development of an EWSS that leveraged VistA/CPRS to improve early identification and treatment of sepsis in the ED and inpatient wards.

Emergency Department

Given the importance of recognizing sepsis early, the sepsis team focused on improvement opportunities at the initial point of patient contact: ED triage. The goal was to incorporate automated VistA/CPRS decision support to assist clinicians with identifying sepsis in triage using MEWS, which was chosen to optimize immediate hospital-wide buy-in. Clinical staff were already familiar with MEWS, which was in use on the inpatient wards.

Flow through the ED and availability of resources differed from the wards. Hence, modification to MEWS on the wards was necessary to fit clinical workflow in the ED. Temperature, heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), mental status, and white blood cell count (WBC) factored into a MEWS + SRS score on the wards (Table 2). For the ED, MEWS included temperature, HR, RR and SBP, but excluded mental status and WBC. Mental status assessment was excluded due to technical infeasibility (while vital signs could be automatically calculated in real time for a MEWS score, that was not possible for mental status changes). WBC was excluded from the ED as laboratory test results would not be available in triage.

MEWS + SRS scores were calculated in VistA by using clinical reminders. Clinical reminder logic included a series of conditional statements based on various combinations of MEWS + SRS clinical data entered in the EHR. When ED triage vital signs data were entered in CPRS, clinical data were stored and processed according to clinical reminder logic in VistA and displayed to the user in CPRS. While MEWS of ≥ 5 triggered a sepsis alert on the wards, the ≥ 4 threshold was used in the ED given mental status and WBC were excluded from calculations in triage (eAppendix 1 available at doi:10.12788/fp.0194).

Once a sepsis alert was triggered in triage for MEWS ≥ 4, ED nursing staff prioritized bed location and expedited staffing with an ED attending physician for early assessment. The ED attending then performed an assessment to confirm whether sepsis was present and direct early treatment. Although every patient who triggered a sepsis alert in triage did not meet clinical findings of sepsis, patients with MEWS ≥ 4 were frequently ill and required timely intervention.

If an ED attending physician agreed with a sepsis diagnosis, the physician had access to a sepsis workup and treatment order set in CPRS (eAppendix 2 available at doi:10.12788/fp.0194). The sepsis order set incorporated recommendations from the Surviving Sepsis Campaign guidelines and included orders for 2 large-bore peripheral IV lines; aggressive fluid resuscitation (30 mL/kg) for patients with clinical findings of hypoperfusion; broad-spectrum antibiotics; and frequent ordering of laboratory tests and imaging during initial sepsis workup.⁶ Vancomycin and cefepime were selected as routine broad-spectrum antibiotics in the order set when sepsis was suspected based on local antimicrobial stewardship and safety-efficacy profiles. For example, Luther and colleagues demonstrated that cefepime has lower rates of acute kidney injury when combined with vancomycin vs vancomycin + piperacillin-tazobactam.²⁰ If a β-lactam antibiotic could not be used due to a patient’s drug allergy history, aztreonam was available as an alternative option.

The design of the order set also functioned as a communication interface with clinical pharmacists. Given the large volume of antibiotics ordered in the ED, it was difficult for pharmacists to prioritize antibiotic order verification. While stat orders convey high priority, they often lack specificity. When antibiotic orders were selected from the sepsis order set, comments were already included that stated: “STAT. First dose for sepsis protocol” (eAppendix 3 available at doi:10.12788/fp.0194). This standardized communication conveyed a sense of urgency and a collective understanding that patients with suspected sepsis required timely order verification and administration of antibiotics.

Hospital Ward

Mental status and WBC were included on the wards to monitor for possible signs of sepsis, using MEWS + SRS, which was routinely monitored by nursing every 4 to 8 hours. When MEWS + SRS was ≥ 5 points, ward nursing staff called a sepsis alert.^7,16 Early response team (ERT) members received telephone notifications of the alert. ERT staff proceeded with immediate evaluation and treatment at the bedside along with determination for most appropriate LOC. The ERT members included an ICU physician and nurse; respiratory therapist; and nursing supervisor/bed flow coordinator. During bedside evaluation, if the ERT or primary team agreed with a sepsis diagnosis, the ERT or primary team used the sepsis order set to ensure standardized procedures. Stat orders generated through the sepsis order set pathway conveyed a sense of urgency and need for immediate order verification and administration of antibiotics.

In addition to clinical care process improvement, accurate documentation also was emphasized in the EWSS. When a sepsis alert was called, a clinician from the primary team was expected to complete a standardized progress note, which communicated clinical findings, a treatment plan, and captured severity of illness (eAppendix 4 available at doi:10.12788/fp.0194). It included sections for subjective, objective, assessment, and plan. In addition, data objects were created for vital signs and common laboratory findings that retrieved important clinical data from VistA and inserted it into the CPRS note.²¹

Nursing staff on the wards were expected to communicate results with the primary team for clinical decision making when a patient had a MEWS + SRS of 3 to 4. A sepsis alert may have been called at the discretion of clinical team members but was not required if the score was < 5. Additionally, vital signs were expected to be checked by the nursing staff on the wards at least every 4 hours for closer monitoring.

Sepsis Review Meetings

Weekly meetings were scheduled to review sepsis cases to assess diagnosis, treatment, and documentation entered in the patient record. The team conducting sepsis reviews comprised the chief of staff, chief of quality management, director of patient safety, physician utilization management advisor, chief resident in quality and patient safety (CRQS), and inpatient pharmacy supervisor. In addition, ad hoc physicians and nurses from different specialty areas, such as infectious diseases, hospitalist section, ICU, and the ED participated on request for subject matter expertise when needed. At the conclusion of weekly sepsis meetings, sepsis team members provided feedback to the clinical staff for continuous improvement purposes.

Results

Before implementation of an EWSS at NF/SGVHS, a plan was devised to increase awareness and educate staff on sepsis-related mortality in late FY 2017. Awareness and education about sepsis-related mortality was organized at physician, nursing, and pharmacy leadership clinical staff meetings. Posters about early warning signs of sepsis also were displayed on the nursing units for educational purposes and to convey the importance of early recognition/treatment of sepsis. In addition, the CRQS was the quality leader for house staff and led sepsis campaign change efforts for residents/fellows. An immediate improvement in unadjusted mortality at MRVAMC was noted with initial sepsis awareness and education. From FY 2017, quarter 3 to FY 2018, quarter 1, the number of acute LOC inpatient deaths decreased from 48 to 28, a 42% reduction in unadjusted mortality at MRVAMC (Figure 1). Additionally, the acute LOC SMR improved from 1.20 during FY 2017, quarter 3 down to as low as 0.71 during FY 2018, quarter 1 (Figure 2).

The number of MRVAMC inpatient deaths increased from 28 in FY 2018, quarter 1 to 45 in FY 2018, quarter 3. While acute LOC showed improvement in unadjusted mortality after sepsis education/awareness, it was felt continuous improvement could not be sustained with education alone. An EWSS was designed and implemented within the EHR system in FY 2018. Following implementation of EWSS and reeducating staff on early recognition and treatment of sepsis, acute LOC inpatient deaths decreased from 45 in FY 2018, quarter 3 through FY 2019 where unadjusted mortality was as low as 27 during FY 2019, quarter 4. The MRVAMC acute LOC SMR was consistently < 1.0 from FY 2018, quarter 4 through FY 2019, quarter 4.

In addition to the observed decrease in acute LOC inpatient deaths and improved SMR, the number of ERT alerts and sepsis alerts on the inpatient wards were monitored from FY 2017 through FY 2019. ERT alerts listed in Table 3 were nonspecific and initiated by nursing staff on the wards where a patient’s clinical status was identified as worsening while sepsis alerts were specific ERT alerts called by the ward nursing staff due to concerns for sepsis. The inpatient wards included inpatient medicine, surgery, and psychiatry acute care and the intermediate level of care unit while outpatient clinical areas of treatment, intensive care units, stroke alerts, and STEMI alerts were excluded.

Discussion

Implementation of the EWSS was associated with improved unadjusted mortality and adjusted mortality for acute LOC at MRVAMC. Although variation exists with application of EWSS at other medical centers, there was similarity with improved sepsis outcomes reported at other health care systems after EWSS implementation.^7-16

Improved unadjusted mortality and adjusted mortality for acute LOC at MRVAMC was likely due to multiple contributing factors. First, during design and implementation of the EWSS, project work was interdisciplinary with input from physicians, nurses, and pharmacists from multiple specialties (ie, ED, ICU, and the medicine service); quality management and data analysis specialists; and clinical informatics. Second, facility commitment to improving early recognition and treatment of sepsis from leadership level down to front-line staff was evident. Weekly sepsis meetings with the NF/SGVHS chief of staff helped to sustain EWSS efforts and to identify additional improvement opportunities. Third, integrated informatics solutions within the EHR helped identify early sepsis and minimized human error as well as assisted with coordination of sepsis care across services. Fourth, the focus was on both early identification and treatment of sepsis in the ED and hospital wards. Although it cannot be deduced whether there was causation between reduced inpatient mortality and an increased number of nonspecific ERT alerts+ sepsis alerts on the inpatient wards after EWSS implementation, inpatient deaths decreased and SMR improved. Finally, the EWSS emphasized both the importance of evidence-based clinical care of sepsis and standardized documentation to appropriately capture clinical severity of illness.

Limitations

This program has limitations. The EWSS was studied at a single VHA facility. Veteran demographics and local epidemiology may limit conclusion of outcomes to an individual VHA facility located in a specific geographical region. Additional research is necessary to demonstrate reproducibility and determine whether applicable to other VHA facilities and community care settings.

SMR is a risk-adjusted formula developed by the VHA Inpatient Evaluation Center, which included numerous factors such as diagnosis, comorbid conditions, age, marital status, procedures, source of admission, specific laboratory values, medical or surgical diagnosis-related group, ICU stays, immunosuppressive status, and a COVID-19 positive indicator (added after this study). Further research is needed to evaluate sepsis-related outcomes using the EWSS during the COVID-19 pandemic.

EWSS in the literature have demonstrated various approaches to early identification and treatment of sepsis and have used different sepsis screening tools.²² Evidence suggests that the MEWS + SRS sepsis screening tool may result in false-positive screenings.^23-27 Additional research into the specificity of this sepsis screening tool is needed. Ward nursing staff were encouraged to initiate automatic sepsis alerts when MEWS + SRS was ≥ 5; however, this still depended on human factors. Because sepsis alerts are software-specific and others were incompatible with the VHA EHR, it was necessary to design our own EWSS.

Despite improvement with MRVAMC acute LOC unadjusted and adjusted mortality with our EWSS, we did not identify any actual improvement in earlier antibiotic administration times once sepsis was recognized. While accurate documentation regarding degree of sepsis improved, a MRVAMC clinical documentation improvement program was expanded in FY 2018. Therefore, it is difficult to demonstrate causation related to improved sepsis documentation with template changes alone. While sepsis alerts on the inpatient wards were variable since EWSS implementation, nonspecific ERT alerts increased. It is unclear whether some sepsis alerts were called as nonspecific ERT alerts, making it impossible to know the true number of sepsis alerts.

MRVAMC experienced an increase in nurse turnover during FY 2018 and as a teaching hospital had frequent rotating residents and fellows new to processes/protocols. These factors may have contributed to variations in unadjusted mortality. Also the decrease in inpatient mortality and improvement in SMR on acute LOC could have been the result of factors other than the EWSS and the effect of education alone may have been at least as good as that of the EWSS intervention.

Conclusions

Education along with the possible implementation of an EWSS at NF/SGVHS was associated with a decrease in the number of inpatient deaths on MRVAMC’s acute LOC wards from as high as 48 in FY 2017, quarter 3 to as low as 27 in FY 2019, quarter 4 resulting in as large of an improvement as a 44% reduction in unadjusted mortality from FY 2017 to FY 2019. In addition, MRVAMC’s acute LOC SMR improved from > 1.0 to < 1.0, demonstrating fewer inpatient mortalities than predicted from FY 2017 to FY 2019.

This multifaceted interventional strategy may be effectively applied at other VHA health care facilities that use the same EHR system. Next steps may include determining the specificity of MEWS + SRS as a sepsis screening tool; studying outcomes of MRVAMC’s EWSS during the COVID-19 era; and conducting a multicentered study on this EWSS across multiple VHA facilities.

In the United States, health disparities in COVID-19 outcomes (including morbidity and mortality) based on race and ethnicity have been described in the scientific literature and mainstream media.^1-7 According to the US Centers for Disease Control and Prevention (CDC), Hispanic people are 3.2 times more likely to be hospitalized with COVID-19 than non-Hispanic White people.⁸ Further, Hispanic people diagnosed with COVID-19 are 2.3 times more likely to die, adjusted for age, than non-Hispanic White people.⁹ As the epicenter of the COVID-19 pandemic shifted from the Northeast to the South, the CDC reported that, among people who died from COVID-19 in the United States from May to August 2020, the percentage of Hispanic people increased from 16.3% to 26.4%.¹⁰

Published studies on the effect of ethnicity on critical illness or mortality for hospitalized COVID-19 patients are limited and inconsistent. While some studies reported a higher mortality rate for Hispanic patients,^11-15 others showed no difference.^4,16,17 A recent meta-analysis found that intensive care unit (ICU) utilization and mortality were slightly higher among Hispanic COVID-19 inpatients, but this finding did not reach statistical significance.¹⁸ Past studies from different healthcare systems were limited by the small sample size of hospitalized Hispanic patients and the heterogeneity of patients. A comprehensive analysis from a large healthcare system with sufficient sample size is needed to understand the impact of ethnicity on clinical outcomes of hospitalized COVID-19 patients.

Texas Health Resources (THR) is a large integrated healthcare system serving the Dallas-Fort Worth-Arlington (DFW) metropolitan area. According to the 2019 US Census Bureau American Community Survey, Hispanic people comprise 18.4% of the population of this geographic area.¹⁹ Congruent with the CDC’s findings, Hispanic patients account for a disproportionate share (32.2%) of hospitalized COVID-19 patients at THR relative to the area’s demographic composition. Aware of the increased risk, we undertook an analysis of the clinical outcomes and the clinical, social, and demographic characteristics of Hispanic patients hospitalized at THR with COVID-19. Our primary goal was to investigate whether clinical outcomes differ by ethnicity among patients hospitalized with COVID-19 and, if so, whether inpatient care or preadmission factors contribute to this difference.

Study Setting and Overview

We collected data from the single electronic health record (EHR) used by 20 THR hospitals located across the DFW metropolitan area. THR is the largest faith-based, nonprofit health system in North Texas, operating 20 acute care hospitals. Including all access points, such as outpatient facilities and physician group practices, THR serves 7 million residents in 16 counties in North Texas, of whom 16.8% are Hispanic, 73.3% are non-Hispanic, and 9.9% are unclassified, congruent with demographics in the DFW area.

The institutional review boards at THR and UT Southwestern Medical Center approved the study under a waiver of informed consent (as a minimal-risk medical record review). After collection, all data were de-identified prior to statistical analysis.

Cohort, Outcomes, and Covariables

The study cohort included 6097 adult patients with laboratory-confirmed COVID-19 (age ≥18 years) who were admitted as inpatients from March 3 to November 5, 2020. The primary outcomes included ICU utilization and death during hospitalization. We described demographic characteristics using the following variables: age (18–49, 50–64, 65–79, ≥80 years), sex, self-reported ethnicity, and primary spoken language.

We defined a severe baseline condition as an elevated respiratory subscore parsed from the overall MSOFA (Modified Sequential Organ Failure Assessment),²⁰ an elevated Epic Deterioration Index (EDI),²¹ or an elevated C-reactive protein level (CRP) at baseline (any elevated CRP). Baseline referred to the variable mean during the first available 12-hour window of measurement during the COVID-19 hospital admission, including variables obtained in the emergency department (ED). An elevated MSOFA referred to a score of 4, corresponding to an SpO₂/FiO₂ < 150. Elevated EDI referred to a baseline EDI > 45. An elevated CRP referred to a baseline CRP > 20 mg/dL.²²

Variables reflecting access to healthcare included: THR EHR creation year (representing the first time patients accessed the THR health system), insurance payor type, and presence of a primary care provider (PCP). The federal government established the COVID-19 Claims Reimbursement to Health Care Providers and Facilities for Testing, Treatment, and Vaccine Administration for the Uninsured program. The insurance payor for patients covered by this program is designated as COVID-19 HRSA. Presence of a PCP reflects any documented PCP, regardless of affiliation with THR. We selected these access metrics opportunistically, as they were consistently documented in the EHR and readily available for analysis.

We used 12 variables to describe comorbidities or underlying conditions that, according to the CDC, increased patients’ risk of severe illness from COVID-19²³: diagnoses of diabetes, hypertension, obesity, chronic obstructive pulmonary disease (COPD), asthma, smoking, other lung disease, heart failure, kidney disease without end-stage renal disease (ESRD), ESRD, liver disease, and cancer. We identified comorbidities by mining the structured diagnosis codes documented in the EHR prior to and during the COVID-19 admission. Sources for diagnoses included final billed diagnosis codes, working diagnosis codes, problem list, and reason for visit. The definition of diabetes included previously recorded diabetes or baseline hemoglobin A1c > 9%. We also recorded the presence of four major COVID-19 treatments: steroids, remdesivir, tocilizumab, and fresh frozen plasma (FFP) from convalescent patients.^24-26 Each treatment variable was defined by receipt of one or more doses.

Statistical Analysis

To analyze patient outcomes based on ethnicity, we divided the study cohort into a Hispanic group and a non-Hispanic group based on self-reported ethnicity in the EHR. To study the potential impact of primary language among Hispanic patients, we divided them into English-speaking and non-English-speaking patients based on their self-reported primary language. As a result, we analyzed three groups of patients: (1) non-Hispanic, (2) Hispanic and English speaking, and (3) Hispanic and non-English speaking. We tested differences of a given categorical variable across the three groups using the chi-square test for each age subgroup (18–49, 50–64, 65–79, ≥80 years). The Cochran-Mantel-Haenszel test was used for the overall difference adjusted for age. To assess whether an observed disparity in treatment existed across the three groups, we tested the difference in the administration of four major therapeutics for COVID-19, including steroids, remdesivir, tocilizumab, and convalescent plasma. To determine whether any groups had elevated disease severity at hospital admission (baseline), we tested the difference in four disease-severity metrics across the ethnic-language groups: (1) elevated respiratory MSOFA score, (2) elevated EDI, (3) elevated CRP level, and (4) any of the three conditions.

To study the associations with ICU utilization and death, respectively, we performed a multivariable analysis using a generalized linear mixed model with binomial distribution and a logit link function. In each analysis model, the hospital of admission was included as a random-effect variable to account for the potential treatment variations among different hospitals, while other variables were regarded as fixed effects. In the first multivariable analysis (Model 1), all demographic variables, including age, sex, and ethnicity, and different types of comorbidities and underlying conditions, were included as fixed-effect variables in the initial model, and then backward stepwise variable selection was performed to establish the final model (Model 1). We performed the backward stepwise variable selection separately for the outcome of ICU use or mortality. Based on Akaike information criterion (AIC), during each iteration the fixed-effect variable that led to the largest decrease in the AIC value was removed, and the variable selection process was completed when the AIC value stopped decreasing. In Model 2, we added the disease-severity variable at baseline to the selected variable set derived from Model 1 to explore its effect on the associations between ethnicity and clinical outcomes. In Model 3, we added healthcare access–related variables, including first-time healthsystem access, payor type, and PCP availability to Model 2. We performed all statistical analyses using R, version 4.0.2 (R Foundation for Statistical Computing) in RStudio (version 1.3.1093).

Distinct Demographic and Comorbidity Patterns for Three Ethnic-Language Groups

We identified 6097 adult patients (age ≥18 years) who had confirmed COVID-19 disease and were hospitalized between March 3 and November 5, 2020. Demographic characteristics and comorbidity for these patients are summarized in Table 1. Among these patients, 4139 (67.9%) were non-Hispanic and 1958 (32.1%) were Hispanic. Among the Hispanic patients, 1203 (61.4%) identified English as their primary language and 755 (38.6%) identified a non-English primary language. Age distribution was vastly different among the three ethnic-language groups (Table 1). Unlike the relatively balanced distribution across different age groups in the non-Hispanic group, more than half (55.8%) of the English-speaking Hispanic patients were in the youngest age group (18-49 years). A much lower fraction of Hispanic patients was among the oldest (≥80 years) age group (P < .001). Because COVID-19 clinical outcome is strongly associated with age,²⁷ we used age-stratified analysis when comparing group-level differences in patient outcomes.

Sex distribution also was different among the three groups, with the non-English-speaking Hispanic group having more male patients (53.0%). Diabetes and obesity, which are associated with clinical outcomes of COVID-19 patients, were more prevalent in Hispanic patients (Table 1). Non-English-speaking Hispanic patients had the highest diabetes rate (48.7% with documented diabetes; 15.8% with baseline HbA1c > 9%; P < .001). English-speaking Hispanic patients presented with the highest obesity rate (62.8%; P < .001). Appendix Table 1 provides detailed age-group-specific comorbidity distributions among ethnic-language groups.

Patients of Hispanic Ethnicity Experienced a Higher Rate of ICU Utilization and Mortality

Of the 6097 patients overall, 1365 (22.4%) were admitted to the ICU and 543 (8.9%) died in hospital. For non-Hispanic patients (n = 4139), 883 (21.3%) were admitted to the ICU and 373 (9.0%) died in hospital. For English-speaking Hispanic patients (n = 1203), 241 (20.0%) were admitted to the ICU and 91 (7.6%) died in hospital. For non-English-speaking Hispanic patients (n = 755), 241 (31.9%) were admitted to the ICU and 79 (10.5%) died in hospital. Figure 1 summarizes the age-stratified comparison of ICU utilization and mortality across the three ethnic-language patient groups. In all age groups, non-English-speaking Hispanic patients experienced a significantly higher ICU utilization rate compared to non-Hispanic patients (age-adjusted OR, 1.75; 95% CI, 1.47-2.08; P < .001). English-speaking and non-English-speaking Hispanic patients had a significantly higher mortality rate compared to non-Hispanic patients (age-adjusted OR, 1.53; 95% CI, 1.19-1.98; P = .001 for English-speaking Hispanic patients; age-adjusted OR, 1.43; 95% CI,: 1.10-1.86; P = .01 for non-English-speaking Hispanic patients).

To delineate the risk factors associated with ICU utilization and death, we performed multivariable logistic regression with stepwise variable selection. After adjusting for age, sex, and comorbidity (Model 1), the factors ethnicity and primary language were still strongly associated with ICU utilization and mortality (Appendix Table 2). Non-English-speaking Hispanic patients had an OR of 1.74 (95% CI, 1.41-2.15; P < .001) for ICU utilization and an OR of 1.54 (95% CI, 1.12-2.12; P = .008) for mortality compared to non-Hispanic patients. Similarly, English-speaking Hispanic patients had higher ICU utilization (OR, 1.28; 95% CI, 1.05-1.55; P = .01) and a higher mortality rate (OR, 1.60; 95% CI, 1.19-2.14; P = .002).

No Disparity in COVID-19 Therapeutics Observed Across Three Ethnic-Language Groups

Appendix Figure 1 summarizes the comparison of the administration of four major treatments across the three ethnic-language groups. We did not observe any underuse of COVID-19 therapeutics for Hispanic patients. Usage rates for these therapies were significantly higher, after adjusting for age, in Hispanic groups when compared to non-Hispanic patients (OR ranged from 1.21 to 1.96). Steroids were the most common treatment in all patient groups. Tocilizumab was used almost twice as frequently (OR, 1.96; 95% CI, 1.64-2.33; P < .001) in non-English-speaking Hispanic patients compared to non-Hispanic patients.

Patients of Hispanic Ethnicity Had More Severe Disease at Hospital Admission

Figure 2 shows that non-English-speaking Hispanic patients had a higher rate of severe illness at admission based on each of these metrics: high respiratory MSOFA score (OR, 2.43; 95% CI, 1.77-3.33; P < .001), high EDI (OR, 1.85; 95% CI, 1.41-2.41; P < .001), and high CRP level (OR, 2.06; 95% CI, 1.64-2.58; P < .001). English-speaking Hispanic patients also had a greater rate of high CRP level (OR, 1.48; 95% CI, 1.17-1.86; P = .001) compared to non-Hispanic patients. When considering the presentation of any one of these clinical indicators, the English-speaking and non-English-speaking Hispanic patients had a higher rate of severe baseline condition (OR, 1.33; 95% CI, 1.10-1.61; P = .004 for English-speaking patients; OR, 2.27; 95% CI, 1.89-2.72; P < .001 for non-English-speaking patients).

We then studied how the baseline disease condition affects the association between ethnicity and clinical outcomes. We performed a multivariable analysis including baseline disease severity as a covariable (Model 2, Table 2), which showed that baseline disease severity was strongly associated with ICU admission (OR, 4.52; 95% CI, 3.83-5.33; P < .001) and mortality (OR, 3.32; 95% CI, 2.67-4.13; P < .001). The associations between ethnicity and clinical outcomes were reduced after considering the baseline disease condition. The OR dropped to 1.47 (95% CI, 1.18-1.84; P < .001) and 1.34 (95% CI, 0.97-1.87; P = .08) for ICU utilization and mortality, respectively, when comparing non-English-speaking Hispanic patients to non-Hispanic patients. A similar reduction was observed for English-speaking Hispanic patients. Model comparison showed a significant improvement of Model 2 over Model 1 based on ANOVA test (P < .001) as well as AIC.

Hispanic Patients Had Worse Healthcare Access

To explore the etiology for the more severe disease conditions at hospital admission among Hispanic patients, we analyzed variables related to healthcare access. We found that Hispanic patients were likely to have reduced access to healthcare (Table 1; Appendix Figure 2). For a large proportion (16.9%) of the COVID-19 patients in this study, their medical records were first created at THR in 2020, corresponding to the initial time these patients accessed THR for their healthcare. This surge in 2020, compared to previous years with data (2005–2019), corresponds to the number of new patients seen because of COVID-19 (Appendix Figure 2A). Among this new patient population, the proportion of non-English-speaking Hispanic patients in 2020 was 28.3%, compared to 9.1% from 2005 to 2019 (P < .001). The proportion of new English-speaking Hispanic patients in 2020 was 22.1%, compared to an average of 19.2% from 2005 to 2019 (P < .001). In addition, a much smaller proportion of Hispanic patients had a PCP (P < .001) (Table 1; Appendix Figure 2B), with non-English-speaking Hispanic patients having the smallest proportion (58.5%).

Appendix Figure 2C illustrates the comparison of payor types across the three patient groups. A much higher proportion of Hispanic patients used COVID-19 HRSA (P < .001) compared to non-Hispanic patients. Breaking this down further by primary language, 29.1% of non-English-speaking Hispanic patients relied on COVID-19 HRSA due to otherwise uninsured status, compared to 12.7% of English-speaking Hispanic patients and only 5.1% of non-Hispanic patients. Similarly, non-English-speaking Hispanic patients have the highest self-pay rates (2.3%) compared to English-speaking Hispanic patients (1.4%) and non-Hispanic patients (0.7%). In summary, more Hispanic patients, and especially non-English-speaking Hispanic patients, lacked conventional health insurance and experienced limited access to healthcare.

Further evidence showed a trend of correlation between presentation of severe COVID-19 conditions when arriving at the hospital and each of the healthcare access factors analyzed (Appendix Figure 3).

With a large sample size of hospitalized COVID-19 patients at an integrated health system in the DFW metropolitan area, we observed an increased rate of ICU utilization and mortality among Hispanic inpatients. After adjusting for age, we found that non-English-speaking Hispanic patients were 75% more likely to require critical care compared with non-Hispanic patients. English-speaking and non-English-speaking Hispanic patients had an increased mortality rate (age-adjusted) compared to non-Hispanic patients. The association between ethnicity and clinical outcomes remained significant after adjusting for age, sex, and comorbidities. We did not observe any underuse of major COVID-19 therapeutics in Hispanic patients, and excluded in-hospital treatments from the contributors to the outcome differences.

Hispanic patients, especially non-English-speaking Hispanic patients, had a higher rate of severe COVID-19 disease at the time of hospital admission (Figure 2). After including baseline disease severity into the multivariable analysis (Model 2), the overall model improved (P < .001) while the associations between ethnicity and outcomes decreased (Table 2). This suggests disease severity at admission was a main contributor to the observed associations between ethnicity and clinical outcomes. The higher rate of baseline COVID-19 severity in Hispanic patients might also explain their higher rate of receiving major COVID-19 therapeutics (Appendix Figure 1).

This study found that Hispanic patients were less likely to have a PCP and insurance coverage compared with non-Hispanic patients (P < .001). This disparity was more pronounced among non-English-speaking Hispanic patients (Appendix Figure 2). We also observed that a disproportionately larger proportion (50.4%) of patients who visited the healthcare system for the first time in 2020 (the year of the COVID-19 pandemic) was composed of Hispanic patients, compared to merely 28.4% prior to 2020. While there is a possibility that patients had primary care outside THR, the staggering number of Hispanic patients who were new to the health system in 2020, in conjunction with the fact that immigrants tend to be “healthier” compared to their native-born peers (the so-called immigrant paradox),²⁸ led us to conclude that there were few other primary care options for these patients, making THR’s ED the primary care option of choice. The systemic, structural barriers to routine care might be a possible cause for delayed admission and, in turn, elevated baseline COVID-19 severity for Hispanic patients (Appendix Figure 3).

Recent studies have investigated the impact of socioeconomic factors on racial/ethnic disparities in the COVID-19 pandemic.^7,16,17 To our knowledge, no study has directly analyzed the link between healthcare access metrics, COVID-19 severity at admission, and the Hispanic population stratified by primary language. Studies exist on this subject for other diseases, however. For example, healthcare access factors have been associated with sepsis-related mortality.^29,30 In fact, a recent study that explored the potential effect of language barriers on healthcare access demonstrated an association between limited English proficiency and sepsis-related mortality.³¹ Our study found that Hispanic patients whose primary language is not English had the worst clinical outcomes, including more severe baseline COVID-19 conditions, and the least access to healthcare, highlighting the importance of addressing language barriers in COVID-19 care. Further research is needed to confirm the relationship between limited English proficiency and clinical outcomes, as well as potential factors that contribute to such a relationship in different types of diseases.

Our study has a number of limitations. First, it was limited to only one large healthcare system, which means the results may not be generalizable. Because THR is an open system, comorbidity data may be incomplete, and we cannot exclude the possibility that patients accessed care outside THR prior to or during the pandemic. We may overcome this limitation in the future with cross-system health information exchange data. Second, we did not have data for the time of symptom onset, so we were unable to analyze the direct evidence of the possible delayed care. As a result, we were unable to analyze whether treatments were administered in a timely manner or appropriately. Third, our analysis was not adjusted for other socioeconomic factors (eg, income, education) due to lack of data. We used self-identification for ethnicity, but unlike new approaches by the U.S. Census Bureau,³² our survey allowed only one choice to be selected.

Sociodemographic factors among Hispanic inpatients hospitalized for COVID-19 at a large integrated health system—including a primary non-English language, lack of a PCP, and insurance status—were associated with measures of reduced access to care and more severe illness at admission. Structural barriers to care, which may be associated with reduced health literacy and less access to health insurance, can result in delayed treatment and more severe illness at admission and underdiagnosis of medical conditions, contributing to worse outcomes in this population. Our findings suggest that interventions to promote early recognition of signs and symptoms of COVID-19 and to encourage prompt clinical care at the community level may reduce the burden of COVID-19 deaths in racial or ethnic minority communities with language and socioeconomic barriers.

In the United States, health disparities in COVID-19 outcomes (including morbidity and mortality) based on race and ethnicity have been described in the scientific literature and mainstream media.^1-7 According to the US Centers for Disease Control and Prevention (CDC), Hispanic people are 3.2 times more likely to be hospitalized with COVID-19 than non-Hispanic White people.⁸ Further, Hispanic people diagnosed with COVID-19 are 2.3 times more likely to die, adjusted for age, than non-Hispanic White people.⁹ As the epicenter of the COVID-19 pandemic shifted from the Northeast to the South, the CDC reported that, among people who died from COVID-19 in the United States from May to August 2020, the percentage of Hispanic people increased from 16.3% to 26.4%.¹⁰

Published studies on the effect of ethnicity on critical illness or mortality for hospitalized COVID-19 patients are limited and inconsistent. While some studies reported a higher mortality rate for Hispanic patients,^11-15 others showed no difference.^4,16,17 A recent meta-analysis found that intensive care unit (ICU) utilization and mortality were slightly higher among Hispanic COVID-19 inpatients, but this finding did not reach statistical significance.¹⁸ Past studies from different healthcare systems were limited by the small sample size of hospitalized Hispanic patients and the heterogeneity of patients. A comprehensive analysis from a large healthcare system with sufficient sample size is needed to understand the impact of ethnicity on clinical outcomes of hospitalized COVID-19 patients.

Texas Health Resources (THR) is a large integrated healthcare system serving the Dallas-Fort Worth-Arlington (DFW) metropolitan area. According to the 2019 US Census Bureau American Community Survey, Hispanic people comprise 18.4% of the population of this geographic area.¹⁹ Congruent with the CDC’s findings, Hispanic patients account for a disproportionate share (32.2%) of hospitalized COVID-19 patients at THR relative to the area’s demographic composition. Aware of the increased risk, we undertook an analysis of the clinical outcomes and the clinical, social, and demographic characteristics of Hispanic patients hospitalized at THR with COVID-19. Our primary goal was to investigate whether clinical outcomes differ by ethnicity among patients hospitalized with COVID-19 and, if so, whether inpatient care or preadmission factors contribute to this difference.

Study Setting and Overview

We collected data from the single electronic health record (EHR) used by 20 THR hospitals located across the DFW metropolitan area. THR is the largest faith-based, nonprofit health system in North Texas, operating 20 acute care hospitals. Including all access points, such as outpatient facilities and physician group practices, THR serves 7 million residents in 16 counties in North Texas, of whom 16.8% are Hispanic, 73.3% are non-Hispanic, and 9.9% are unclassified, congruent with demographics in the DFW area.

The institutional review boards at THR and UT Southwestern Medical Center approved the study under a waiver of informed consent (as a minimal-risk medical record review). After collection, all data were de-identified prior to statistical analysis.

Cohort, Outcomes, and Covariables

The study cohort included 6097 adult patients with laboratory-confirmed COVID-19 (age ≥18 years) who were admitted as inpatients from March 3 to November 5, 2020. The primary outcomes included ICU utilization and death during hospitalization. We described demographic characteristics using the following variables: age (18–49, 50–64, 65–79, ≥80 years), sex, self-reported ethnicity, and primary spoken language.

We defined a severe baseline condition as an elevated respiratory subscore parsed from the overall MSOFA (Modified Sequential Organ Failure Assessment),²⁰ an elevated Epic Deterioration Index (EDI),²¹ or an elevated C-reactive protein level (CRP) at baseline (any elevated CRP). Baseline referred to the variable mean during the first available 12-hour window of measurement during the COVID-19 hospital admission, including variables obtained in the emergency department (ED). An elevated MSOFA referred to a score of 4, corresponding to an SpO₂/FiO₂ < 150. Elevated EDI referred to a baseline EDI > 45. An elevated CRP referred to a baseline CRP > 20 mg/dL.²²

Variables reflecting access to healthcare included: THR EHR creation year (representing the first time patients accessed the THR health system), insurance payor type, and presence of a primary care provider (PCP). The federal government established the COVID-19 Claims Reimbursement to Health Care Providers and Facilities for Testing, Treatment, and Vaccine Administration for the Uninsured program. The insurance payor for patients covered by this program is designated as COVID-19 HRSA. Presence of a PCP reflects any documented PCP, regardless of affiliation with THR. We selected these access metrics opportunistically, as they were consistently documented in the EHR and readily available for analysis.

We used 12 variables to describe comorbidities or underlying conditions that, according to the CDC, increased patients’ risk of severe illness from COVID-19²³: diagnoses of diabetes, hypertension, obesity, chronic obstructive pulmonary disease (COPD), asthma, smoking, other lung disease, heart failure, kidney disease without end-stage renal disease (ESRD), ESRD, liver disease, and cancer. We identified comorbidities by mining the structured diagnosis codes documented in the EHR prior to and during the COVID-19 admission. Sources for diagnoses included final billed diagnosis codes, working diagnosis codes, problem list, and reason for visit. The definition of diabetes included previously recorded diabetes or baseline hemoglobin A1c > 9%. We also recorded the presence of four major COVID-19 treatments: steroids, remdesivir, tocilizumab, and fresh frozen plasma (FFP) from convalescent patients.^24-26 Each treatment variable was defined by receipt of one or more doses.

Statistical Analysis

To analyze patient outcomes based on ethnicity, we divided the study cohort into a Hispanic group and a non-Hispanic group based on self-reported ethnicity in the EHR. To study the potential impact of primary language among Hispanic patients, we divided them into English-speaking and non-English-speaking patients based on their self-reported primary language. As a result, we analyzed three groups of patients: (1) non-Hispanic, (2) Hispanic and English speaking, and (3) Hispanic and non-English speaking. We tested differences of a given categorical variable across the three groups using the chi-square test for each age subgroup (18–49, 50–64, 65–79, ≥80 years). The Cochran-Mantel-Haenszel test was used for the overall difference adjusted for age. To assess whether an observed disparity in treatment existed across the three groups, we tested the difference in the administration of four major therapeutics for COVID-19, including steroids, remdesivir, tocilizumab, and convalescent plasma. To determine whether any groups had elevated disease severity at hospital admission (baseline), we tested the difference in four disease-severity metrics across the ethnic-language groups: (1) elevated respiratory MSOFA score, (2) elevated EDI, (3) elevated CRP level, and (4) any of the three conditions.

To study the associations with ICU utilization and death, respectively, we performed a multivariable analysis using a generalized linear mixed model with binomial distribution and a logit link function. In each analysis model, the hospital of admission was included as a random-effect variable to account for the potential treatment variations among different hospitals, while other variables were regarded as fixed effects. In the first multivariable analysis (Model 1), all demographic variables, including age, sex, and ethnicity, and different types of comorbidities and underlying conditions, were included as fixed-effect variables in the initial model, and then backward stepwise variable selection was performed to establish the final model (Model 1). We performed the backward stepwise variable selection separately for the outcome of ICU use or mortality. Based on Akaike information criterion (AIC), during each iteration the fixed-effect variable that led to the largest decrease in the AIC value was removed, and the variable selection process was completed when the AIC value stopped decreasing. In Model 2, we added the disease-severity variable at baseline to the selected variable set derived from Model 1 to explore its effect on the associations between ethnicity and clinical outcomes. In Model 3, we added healthcare access–related variables, including first-time healthsystem access, payor type, and PCP availability to Model 2. We performed all statistical analyses using R, version 4.0.2 (R Foundation for Statistical Computing) in RStudio (version 1.3.1093).

Distinct Demographic and Comorbidity Patterns for Three Ethnic-Language Groups

We identified 6097 adult patients (age ≥18 years) who had confirmed COVID-19 disease and were hospitalized between March 3 and November 5, 2020. Demographic characteristics and comorbidity for these patients are summarized in Table 1. Among these patients, 4139 (67.9%) were non-Hispanic and 1958 (32.1%) were Hispanic. Among the Hispanic patients, 1203 (61.4%) identified English as their primary language and 755 (38.6%) identified a non-English primary language. Age distribution was vastly different among the three ethnic-language groups (Table 1). Unlike the relatively balanced distribution across different age groups in the non-Hispanic group, more than half (55.8%) of the English-speaking Hispanic patients were in the youngest age group (18-49 years). A much lower fraction of Hispanic patients was among the oldest (≥80 years) age group (P < .001). Because COVID-19 clinical outcome is strongly associated with age,²⁷ we used age-stratified analysis when comparing group-level differences in patient outcomes.

Sex distribution also was different among the three groups, with the non-English-speaking Hispanic group having more male patients (53.0%). Diabetes and obesity, which are associated with clinical outcomes of COVID-19 patients, were more prevalent in Hispanic patients (Table 1). Non-English-speaking Hispanic patients had the highest diabetes rate (48.7% with documented diabetes; 15.8% with baseline HbA1c > 9%; P < .001). English-speaking Hispanic patients presented with the highest obesity rate (62.8%; P < .001). Appendix Table 1 provides detailed age-group-specific comorbidity distributions among ethnic-language groups.

Patients of Hispanic Ethnicity Experienced a Higher Rate of ICU Utilization and Mortality

Of the 6097 patients overall, 1365 (22.4%) were admitted to the ICU and 543 (8.9%) died in hospital. For non-Hispanic patients (n = 4139), 883 (21.3%) were admitted to the ICU and 373 (9.0%) died in hospital. For English-speaking Hispanic patients (n = 1203), 241 (20.0%) were admitted to the ICU and 91 (7.6%) died in hospital. For non-English-speaking Hispanic patients (n = 755), 241 (31.9%) were admitted to the ICU and 79 (10.5%) died in hospital. Figure 1 summarizes the age-stratified comparison of ICU utilization and mortality across the three ethnic-language patient groups. In all age groups, non-English-speaking Hispanic patients experienced a significantly higher ICU utilization rate compared to non-Hispanic patients (age-adjusted OR, 1.75; 95% CI, 1.47-2.08; P < .001). English-speaking and non-English-speaking Hispanic patients had a significantly higher mortality rate compared to non-Hispanic patients (age-adjusted OR, 1.53; 95% CI, 1.19-1.98; P = .001 for English-speaking Hispanic patients; age-adjusted OR, 1.43; 95% CI,: 1.10-1.86; P = .01 for non-English-speaking Hispanic patients).

To delineate the risk factors associated with ICU utilization and death, we performed multivariable logistic regression with stepwise variable selection. After adjusting for age, sex, and comorbidity (Model 1), the factors ethnicity and primary language were still strongly associated with ICU utilization and mortality (Appendix Table 2). Non-English-speaking Hispanic patients had an OR of 1.74 (95% CI, 1.41-2.15; P < .001) for ICU utilization and an OR of 1.54 (95% CI, 1.12-2.12; P = .008) for mortality compared to non-Hispanic patients. Similarly, English-speaking Hispanic patients had higher ICU utilization (OR, 1.28; 95% CI, 1.05-1.55; P = .01) and a higher mortality rate (OR, 1.60; 95% CI, 1.19-2.14; P = .002).

No Disparity in COVID-19 Therapeutics Observed Across Three Ethnic-Language Groups

Appendix Figure 1 summarizes the comparison of the administration of four major treatments across the three ethnic-language groups. We did not observe any underuse of COVID-19 therapeutics for Hispanic patients. Usage rates for these therapies were significantly higher, after adjusting for age, in Hispanic groups when compared to non-Hispanic patients (OR ranged from 1.21 to 1.96). Steroids were the most common treatment in all patient groups. Tocilizumab was used almost twice as frequently (OR, 1.96; 95% CI, 1.64-2.33; P < .001) in non-English-speaking Hispanic patients compared to non-Hispanic patients.

Patients of Hispanic Ethnicity Had More Severe Disease at Hospital Admission

Figure 2 shows that non-English-speaking Hispanic patients had a higher rate of severe illness at admission based on each of these metrics: high respiratory MSOFA score (OR, 2.43; 95% CI, 1.77-3.33; P < .001), high EDI (OR, 1.85; 95% CI, 1.41-2.41; P < .001), and high CRP level (OR, 2.06; 95% CI, 1.64-2.58; P < .001). English-speaking Hispanic patients also had a greater rate of high CRP level (OR, 1.48; 95% CI, 1.17-1.86; P = .001) compared to non-Hispanic patients. When considering the presentation of any one of these clinical indicators, the English-speaking and non-English-speaking Hispanic patients had a higher rate of severe baseline condition (OR, 1.33; 95% CI, 1.10-1.61; P = .004 for English-speaking patients; OR, 2.27; 95% CI, 1.89-2.72; P < .001 for non-English-speaking patients).

We then studied how the baseline disease condition affects the association between ethnicity and clinical outcomes. We performed a multivariable analysis including baseline disease severity as a covariable (Model 2, Table 2), which showed that baseline disease severity was strongly associated with ICU admission (OR, 4.52; 95% CI, 3.83-5.33; P < .001) and mortality (OR, 3.32; 95% CI, 2.67-4.13; P < .001). The associations between ethnicity and clinical outcomes were reduced after considering the baseline disease condition. The OR dropped to 1.47 (95% CI, 1.18-1.84; P < .001) and 1.34 (95% CI, 0.97-1.87; P = .08) for ICU utilization and mortality, respectively, when comparing non-English-speaking Hispanic patients to non-Hispanic patients. A similar reduction was observed for English-speaking Hispanic patients. Model comparison showed a significant improvement of Model 2 over Model 1 based on ANOVA test (P < .001) as well as AIC.

Hispanic Patients Had Worse Healthcare Access

To explore the etiology for the more severe disease conditions at hospital admission among Hispanic patients, we analyzed variables related to healthcare access. We found that Hispanic patients were likely to have reduced access to healthcare (Table 1; Appendix Figure 2). For a large proportion (16.9%) of the COVID-19 patients in this study, their medical records were first created at THR in 2020, corresponding to the initial time these patients accessed THR for their healthcare. This surge in 2020, compared to previous years with data (2005–2019), corresponds to the number of new patients seen because of COVID-19 (Appendix Figure 2A). Among this new patient population, the proportion of non-English-speaking Hispanic patients in 2020 was 28.3%, compared to 9.1% from 2005 to 2019 (P < .001). The proportion of new English-speaking Hispanic patients in 2020 was 22.1%, compared to an average of 19.2% from 2005 to 2019 (P < .001). In addition, a much smaller proportion of Hispanic patients had a PCP (P < .001) (Table 1; Appendix Figure 2B), with non-English-speaking Hispanic patients having the smallest proportion (58.5%).

Appendix Figure 2C illustrates the comparison of payor types across the three patient groups. A much higher proportion of Hispanic patients used COVID-19 HRSA (P < .001) compared to non-Hispanic patients. Breaking this down further by primary language, 29.1% of non-English-speaking Hispanic patients relied on COVID-19 HRSA due to otherwise uninsured status, compared to 12.7% of English-speaking Hispanic patients and only 5.1% of non-Hispanic patients. Similarly, non-English-speaking Hispanic patients have the highest self-pay rates (2.3%) compared to English-speaking Hispanic patients (1.4%) and non-Hispanic patients (0.7%). In summary, more Hispanic patients, and especially non-English-speaking Hispanic patients, lacked conventional health insurance and experienced limited access to healthcare.

Further evidence showed a trend of correlation between presentation of severe COVID-19 conditions when arriving at the hospital and each of the healthcare access factors analyzed (Appendix Figure 3).

With a large sample size of hospitalized COVID-19 patients at an integrated health system in the DFW metropolitan area, we observed an increased rate of ICU utilization and mortality among Hispanic inpatients. After adjusting for age, we found that non-English-speaking Hispanic patients were 75% more likely to require critical care compared with non-Hispanic patients. English-speaking and non-English-speaking Hispanic patients had an increased mortality rate (age-adjusted) compared to non-Hispanic patients. The association between ethnicity and clinical outcomes remained significant after adjusting for age, sex, and comorbidities. We did not observe any underuse of major COVID-19 therapeutics in Hispanic patients, and excluded in-hospital treatments from the contributors to the outcome differences.

Hispanic patients, especially non-English-speaking Hispanic patients, had a higher rate of severe COVID-19 disease at the time of hospital admission (Figure 2). After including baseline disease severity into the multivariable analysis (Model 2), the overall model improved (P < .001) while the associations between ethnicity and outcomes decreased (Table 2). This suggests disease severity at admission was a main contributor to the observed associations between ethnicity and clinical outcomes. The higher rate of baseline COVID-19 severity in Hispanic patients might also explain their higher rate of receiving major COVID-19 therapeutics (Appendix Figure 1).

This study found that Hispanic patients were less likely to have a PCP and insurance coverage compared with non-Hispanic patients (P < .001). This disparity was more pronounced among non-English-speaking Hispanic patients (Appendix Figure 2). We also observed that a disproportionately larger proportion (50.4%) of patients who visited the healthcare system for the first time in 2020 (the year of the COVID-19 pandemic) was composed of Hispanic patients, compared to merely 28.4% prior to 2020. While there is a possibility that patients had primary care outside THR, the staggering number of Hispanic patients who were new to the health system in 2020, in conjunction with the fact that immigrants tend to be “healthier” compared to their native-born peers (the so-called immigrant paradox),²⁸ led us to conclude that there were few other primary care options for these patients, making THR’s ED the primary care option of choice. The systemic, structural barriers to routine care might be a possible cause for delayed admission and, in turn, elevated baseline COVID-19 severity for Hispanic patients (Appendix Figure 3).

Recent studies have investigated the impact of socioeconomic factors on racial/ethnic disparities in the COVID-19 pandemic.^7,16,17 To our knowledge, no study has directly analyzed the link between healthcare access metrics, COVID-19 severity at admission, and the Hispanic population stratified by primary language. Studies exist on this subject for other diseases, however. For example, healthcare access factors have been associated with sepsis-related mortality.^29,30 In fact, a recent study that explored the potential effect of language barriers on healthcare access demonstrated an association between limited English proficiency and sepsis-related mortality.³¹ Our study found that Hispanic patients whose primary language is not English had the worst clinical outcomes, including more severe baseline COVID-19 conditions, and the least access to healthcare, highlighting the importance of addressing language barriers in COVID-19 care. Further research is needed to confirm the relationship between limited English proficiency and clinical outcomes, as well as potential factors that contribute to such a relationship in different types of diseases.

Our study has a number of limitations. First, it was limited to only one large healthcare system, which means the results may not be generalizable. Because THR is an open system, comorbidity data may be incomplete, and we cannot exclude the possibility that patients accessed care outside THR prior to or during the pandemic. We may overcome this limitation in the future with cross-system health information exchange data. Second, we did not have data for the time of symptom onset, so we were unable to analyze the direct evidence of the possible delayed care. As a result, we were unable to analyze whether treatments were administered in a timely manner or appropriately. Third, our analysis was not adjusted for other socioeconomic factors (eg, income, education) due to lack of data. We used self-identification for ethnicity, but unlike new approaches by the U.S. Census Bureau,³² our survey allowed only one choice to be selected.

Sociodemographic factors among Hispanic inpatients hospitalized for COVID-19 at a large integrated health system—including a primary non-English language, lack of a PCP, and insurance status—were associated with measures of reduced access to care and more severe illness at admission. Structural barriers to care, which may be associated with reduced health literacy and less access to health insurance, can result in delayed treatment and more severe illness at admission and underdiagnosis of medical conditions, contributing to worse outcomes in this population. Our findings suggest that interventions to promote early recognition of signs and symptoms of COVID-19 and to encourage prompt clinical care at the community level may reduce the burden of COVID-19 deaths in racial or ethnic minority communities with language and socioeconomic barriers.

In the United States, health disparities in COVID-19 outcomes (including morbidity and mortality) based on race and ethnicity have been described in the scientific literature and mainstream media.^1-7 According to the US Centers for Disease Control and Prevention (CDC), Hispanic people are 3.2 times more likely to be hospitalized with COVID-19 than non-Hispanic White people.⁸ Further, Hispanic people diagnosed with COVID-19 are 2.3 times more likely to die, adjusted for age, than non-Hispanic White people.⁹ As the epicenter of the COVID-19 pandemic shifted from the Northeast to the South, the CDC reported that, among people who died from COVID-19 in the United States from May to August 2020, the percentage of Hispanic people increased from 16.3% to 26.4%.¹⁰

Published studies on the effect of ethnicity on critical illness or mortality for hospitalized COVID-19 patients are limited and inconsistent. While some studies reported a higher mortality rate for Hispanic patients,^11-15 others showed no difference.^4,16,17 A recent meta-analysis found that intensive care unit (ICU) utilization and mortality were slightly higher among Hispanic COVID-19 inpatients, but this finding did not reach statistical significance.¹⁸ Past studies from different healthcare systems were limited by the small sample size of hospitalized Hispanic patients and the heterogeneity of patients. A comprehensive analysis from a large healthcare system with sufficient sample size is needed to understand the impact of ethnicity on clinical outcomes of hospitalized COVID-19 patients.

Texas Health Resources (THR) is a large integrated healthcare system serving the Dallas-Fort Worth-Arlington (DFW) metropolitan area. According to the 2019 US Census Bureau American Community Survey, Hispanic people comprise 18.4% of the population of this geographic area.¹⁹ Congruent with the CDC’s findings, Hispanic patients account for a disproportionate share (32.2%) of hospitalized COVID-19 patients at THR relative to the area’s demographic composition. Aware of the increased risk, we undertook an analysis of the clinical outcomes and the clinical, social, and demographic characteristics of Hispanic patients hospitalized at THR with COVID-19. Our primary goal was to investigate whether clinical outcomes differ by ethnicity among patients hospitalized with COVID-19 and, if so, whether inpatient care or preadmission factors contribute to this difference.

Study Setting and Overview

We collected data from the single electronic health record (EHR) used by 20 THR hospitals located across the DFW metropolitan area. THR is the largest faith-based, nonprofit health system in North Texas, operating 20 acute care hospitals. Including all access points, such as outpatient facilities and physician group practices, THR serves 7 million residents in 16 counties in North Texas, of whom 16.8% are Hispanic, 73.3% are non-Hispanic, and 9.9% are unclassified, congruent with demographics in the DFW area.

The institutional review boards at THR and UT Southwestern Medical Center approved the study under a waiver of informed consent (as a minimal-risk medical record review). After collection, all data were de-identified prior to statistical analysis.

Cohort, Outcomes, and Covariables

The study cohort included 6097 adult patients with laboratory-confirmed COVID-19 (age ≥18 years) who were admitted as inpatients from March 3 to November 5, 2020. The primary outcomes included ICU utilization and death during hospitalization. We described demographic characteristics using the following variables: age (18–49, 50–64, 65–79, ≥80 years), sex, self-reported ethnicity, and primary spoken language.

We defined a severe baseline condition as an elevated respiratory subscore parsed from the overall MSOFA (Modified Sequential Organ Failure Assessment),²⁰ an elevated Epic Deterioration Index (EDI),²¹ or an elevated C-reactive protein level (CRP) at baseline (any elevated CRP). Baseline referred to the variable mean during the first available 12-hour window of measurement during the COVID-19 hospital admission, including variables obtained in the emergency department (ED). An elevated MSOFA referred to a score of 4, corresponding to an SpO₂/FiO₂ < 150. Elevated EDI referred to a baseline EDI > 45. An elevated CRP referred to a baseline CRP > 20 mg/dL.²²

Variables reflecting access to healthcare included: THR EHR creation year (representing the first time patients accessed the THR health system), insurance payor type, and presence of a primary care provider (PCP). The federal government established the COVID-19 Claims Reimbursement to Health Care Providers and Facilities for Testing, Treatment, and Vaccine Administration for the Uninsured program. The insurance payor for patients covered by this program is designated as COVID-19 HRSA. Presence of a PCP reflects any documented PCP, regardless of affiliation with THR. We selected these access metrics opportunistically, as they were consistently documented in the EHR and readily available for analysis.

We used 12 variables to describe comorbidities or underlying conditions that, according to the CDC, increased patients’ risk of severe illness from COVID-19²³: diagnoses of diabetes, hypertension, obesity, chronic obstructive pulmonary disease (COPD), asthma, smoking, other lung disease, heart failure, kidney disease without end-stage renal disease (ESRD), ESRD, liver disease, and cancer. We identified comorbidities by mining the structured diagnosis codes documented in the EHR prior to and during the COVID-19 admission. Sources for diagnoses included final billed diagnosis codes, working diagnosis codes, problem list, and reason for visit. The definition of diabetes included previously recorded diabetes or baseline hemoglobin A1c > 9%. We also recorded the presence of four major COVID-19 treatments: steroids, remdesivir, tocilizumab, and fresh frozen plasma (FFP) from convalescent patients.^24-26 Each treatment variable was defined by receipt of one or more doses.

Statistical Analysis

To analyze patient outcomes based on ethnicity, we divided the study cohort into a Hispanic group and a non-Hispanic group based on self-reported ethnicity in the EHR. To study the potential impact of primary language among Hispanic patients, we divided them into English-speaking and non-English-speaking patients based on their self-reported primary language. As a result, we analyzed three groups of patients: (1) non-Hispanic, (2) Hispanic and English speaking, and (3) Hispanic and non-English speaking. We tested differences of a given categorical variable across the three groups using the chi-square test for each age subgroup (18–49, 50–64, 65–79, ≥80 years). The Cochran-Mantel-Haenszel test was used for the overall difference adjusted for age. To assess whether an observed disparity in treatment existed across the three groups, we tested the difference in the administration of four major therapeutics for COVID-19, including steroids, remdesivir, tocilizumab, and convalescent plasma. To determine whether any groups had elevated disease severity at hospital admission (baseline), we tested the difference in four disease-severity metrics across the ethnic-language groups: (1) elevated respiratory MSOFA score, (2) elevated EDI, (3) elevated CRP level, and (4) any of the three conditions.

To study the associations with ICU utilization and death, respectively, we performed a multivariable analysis using a generalized linear mixed model with binomial distribution and a logit link function. In each analysis model, the hospital of admission was included as a random-effect variable to account for the potential treatment variations among different hospitals, while other variables were regarded as fixed effects. In the first multivariable analysis (Model 1), all demographic variables, including age, sex, and ethnicity, and different types of comorbidities and underlying conditions, were included as fixed-effect variables in the initial model, and then backward stepwise variable selection was performed to establish the final model (Model 1). We performed the backward stepwise variable selection separately for the outcome of ICU use or mortality. Based on Akaike information criterion (AIC), during each iteration the fixed-effect variable that led to the largest decrease in the AIC value was removed, and the variable selection process was completed when the AIC value stopped decreasing. In Model 2, we added the disease-severity variable at baseline to the selected variable set derived from Model 1 to explore its effect on the associations between ethnicity and clinical outcomes. In Model 3, we added healthcare access–related variables, including first-time healthsystem access, payor type, and PCP availability to Model 2. We performed all statistical analyses using R, version 4.0.2 (R Foundation for Statistical Computing) in RStudio (version 1.3.1093).

Distinct Demographic and Comorbidity Patterns for Three Ethnic-Language Groups

We identified 6097 adult patients (age ≥18 years) who had confirmed COVID-19 disease and were hospitalized between March 3 and November 5, 2020. Demographic characteristics and comorbidity for these patients are summarized in Table 1. Among these patients, 4139 (67.9%) were non-Hispanic and 1958 (32.1%) were Hispanic. Among the Hispanic patients, 1203 (61.4%) identified English as their primary language and 755 (38.6%) identified a non-English primary language. Age distribution was vastly different among the three ethnic-language groups (Table 1). Unlike the relatively balanced distribution across different age groups in the non-Hispanic group, more than half (55.8%) of the English-speaking Hispanic patients were in the youngest age group (18-49 years). A much lower fraction of Hispanic patients was among the oldest (≥80 years) age group (P < .001). Because COVID-19 clinical outcome is strongly associated with age,²⁷ we used age-stratified analysis when comparing group-level differences in patient outcomes.

Sex distribution also was different among the three groups, with the non-English-speaking Hispanic group having more male patients (53.0%). Diabetes and obesity, which are associated with clinical outcomes of COVID-19 patients, were more prevalent in Hispanic patients (Table 1). Non-English-speaking Hispanic patients had the highest diabetes rate (48.7% with documented diabetes; 15.8% with baseline HbA1c > 9%; P < .001). English-speaking Hispanic patients presented with the highest obesity rate (62.8%; P < .001). Appendix Table 1 provides detailed age-group-specific comorbidity distributions among ethnic-language groups.

Patients of Hispanic Ethnicity Experienced a Higher Rate of ICU Utilization and Mortality

Of the 6097 patients overall, 1365 (22.4%) were admitted to the ICU and 543 (8.9%) died in hospital. For non-Hispanic patients (n = 4139), 883 (21.3%) were admitted to the ICU and 373 (9.0%) died in hospital. For English-speaking Hispanic patients (n = 1203), 241 (20.0%) were admitted to the ICU and 91 (7.6%) died in hospital. For non-English-speaking Hispanic patients (n = 755), 241 (31.9%) were admitted to the ICU and 79 (10.5%) died in hospital. Figure 1 summarizes the age-stratified comparison of ICU utilization and mortality across the three ethnic-language patient groups. In all age groups, non-English-speaking Hispanic patients experienced a significantly higher ICU utilization rate compared to non-Hispanic patients (age-adjusted OR, 1.75; 95% CI, 1.47-2.08; P < .001). English-speaking and non-English-speaking Hispanic patients had a significantly higher mortality rate compared to non-Hispanic patients (age-adjusted OR, 1.53; 95% CI, 1.19-1.98; P = .001 for English-speaking Hispanic patients; age-adjusted OR, 1.43; 95% CI,: 1.10-1.86; P = .01 for non-English-speaking Hispanic patients).

To delineate the risk factors associated with ICU utilization and death, we performed multivariable logistic regression with stepwise variable selection. After adjusting for age, sex, and comorbidity (Model 1), the factors ethnicity and primary language were still strongly associated with ICU utilization and mortality (Appendix Table 2). Non-English-speaking Hispanic patients had an OR of 1.74 (95% CI, 1.41-2.15; P < .001) for ICU utilization and an OR of 1.54 (95% CI, 1.12-2.12; P = .008) for mortality compared to non-Hispanic patients. Similarly, English-speaking Hispanic patients had higher ICU utilization (OR, 1.28; 95% CI, 1.05-1.55; P = .01) and a higher mortality rate (OR, 1.60; 95% CI, 1.19-2.14; P = .002).

No Disparity in COVID-19 Therapeutics Observed Across Three Ethnic-Language Groups

Appendix Figure 1 summarizes the comparison of the administration of four major treatments across the three ethnic-language groups. We did not observe any underuse of COVID-19 therapeutics for Hispanic patients. Usage rates for these therapies were significantly higher, after adjusting for age, in Hispanic groups when compared to non-Hispanic patients (OR ranged from 1.21 to 1.96). Steroids were the most common treatment in all patient groups. Tocilizumab was used almost twice as frequently (OR, 1.96; 95% CI, 1.64-2.33; P < .001) in non-English-speaking Hispanic patients compared to non-Hispanic patients.

Patients of Hispanic Ethnicity Had More Severe Disease at Hospital Admission

Figure 2 shows that non-English-speaking Hispanic patients had a higher rate of severe illness at admission based on each of these metrics: high respiratory MSOFA score (OR, 2.43; 95% CI, 1.77-3.33; P < .001), high EDI (OR, 1.85; 95% CI, 1.41-2.41; P < .001), and high CRP level (OR, 2.06; 95% CI, 1.64-2.58; P < .001). English-speaking Hispanic patients also had a greater rate of high CRP level (OR, 1.48; 95% CI, 1.17-1.86; P = .001) compared to non-Hispanic patients. When considering the presentation of any one of these clinical indicators, the English-speaking and non-English-speaking Hispanic patients had a higher rate of severe baseline condition (OR, 1.33; 95% CI, 1.10-1.61; P = .004 for English-speaking patients; OR, 2.27; 95% CI, 1.89-2.72; P < .001 for non-English-speaking patients).

We then studied how the baseline disease condition affects the association between ethnicity and clinical outcomes. We performed a multivariable analysis including baseline disease severity as a covariable (Model 2, Table 2), which showed that baseline disease severity was strongly associated with ICU admission (OR, 4.52; 95% CI, 3.83-5.33; P < .001) and mortality (OR, 3.32; 95% CI, 2.67-4.13; P < .001). The associations between ethnicity and clinical outcomes were reduced after considering the baseline disease condition. The OR dropped to 1.47 (95% CI, 1.18-1.84; P < .001) and 1.34 (95% CI, 0.97-1.87; P = .08) for ICU utilization and mortality, respectively, when comparing non-English-speaking Hispanic patients to non-Hispanic patients. A similar reduction was observed for English-speaking Hispanic patients. Model comparison showed a significant improvement of Model 2 over Model 1 based on ANOVA test (P < .001) as well as AIC.

Hispanic Patients Had Worse Healthcare Access

To explore the etiology for the more severe disease conditions at hospital admission among Hispanic patients, we analyzed variables related to healthcare access. We found that Hispanic patients were likely to have reduced access to healthcare (Table 1; Appendix Figure 2). For a large proportion (16.9%) of the COVID-19 patients in this study, their medical records were first created at THR in 2020, corresponding to the initial time these patients accessed THR for their healthcare. This surge in 2020, compared to previous years with data (2005–2019), corresponds to the number of new patients seen because of COVID-19 (Appendix Figure 2A). Among this new patient population, the proportion of non-English-speaking Hispanic patients in 2020 was 28.3%, compared to 9.1% from 2005 to 2019 (P < .001). The proportion of new English-speaking Hispanic patients in 2020 was 22.1%, compared to an average of 19.2% from 2005 to 2019 (P < .001). In addition, a much smaller proportion of Hispanic patients had a PCP (P < .001) (Table 1; Appendix Figure 2B), with non-English-speaking Hispanic patients having the smallest proportion (58.5%).

Appendix Figure 2C illustrates the comparison of payor types across the three patient groups. A much higher proportion of Hispanic patients used COVID-19 HRSA (P < .001) compared to non-Hispanic patients. Breaking this down further by primary language, 29.1% of non-English-speaking Hispanic patients relied on COVID-19 HRSA due to otherwise uninsured status, compared to 12.7% of English-speaking Hispanic patients and only 5.1% of non-Hispanic patients. Similarly, non-English-speaking Hispanic patients have the highest self-pay rates (2.3%) compared to English-speaking Hispanic patients (1.4%) and non-Hispanic patients (0.7%). In summary, more Hispanic patients, and especially non-English-speaking Hispanic patients, lacked conventional health insurance and experienced limited access to healthcare.

Further evidence showed a trend of correlation between presentation of severe COVID-19 conditions when arriving at the hospital and each of the healthcare access factors analyzed (Appendix Figure 3).

With a large sample size of hospitalized COVID-19 patients at an integrated health system in the DFW metropolitan area, we observed an increased rate of ICU utilization and mortality among Hispanic inpatients. After adjusting for age, we found that non-English-speaking Hispanic patients were 75% more likely to require critical care compared with non-Hispanic patients. English-speaking and non-English-speaking Hispanic patients had an increased mortality rate (age-adjusted) compared to non-Hispanic patients. The association between ethnicity and clinical outcomes remained significant after adjusting for age, sex, and comorbidities. We did not observe any underuse of major COVID-19 therapeutics in Hispanic patients, and excluded in-hospital treatments from the contributors to the outcome differences.

Hispanic patients, especially non-English-speaking Hispanic patients, had a higher rate of severe COVID-19 disease at the time of hospital admission (Figure 2). After including baseline disease severity into the multivariable analysis (Model 2), the overall model improved (P < .001) while the associations between ethnicity and outcomes decreased (Table 2). This suggests disease severity at admission was a main contributor to the observed associations between ethnicity and clinical outcomes. The higher rate of baseline COVID-19 severity in Hispanic patients might also explain their higher rate of receiving major COVID-19 therapeutics (Appendix Figure 1).

This study found that Hispanic patients were less likely to have a PCP and insurance coverage compared with non-Hispanic patients (P < .001). This disparity was more pronounced among non-English-speaking Hispanic patients (Appendix Figure 2). We also observed that a disproportionately larger proportion (50.4%) of patients who visited the healthcare system for the first time in 2020 (the year of the COVID-19 pandemic) was composed of Hispanic patients, compared to merely 28.4% prior to 2020. While there is a possibility that patients had primary care outside THR, the staggering number of Hispanic patients who were new to the health system in 2020, in conjunction with the fact that immigrants tend to be “healthier” compared to their native-born peers (the so-called immigrant paradox),²⁸ led us to conclude that there were few other primary care options for these patients, making THR’s ED the primary care option of choice. The systemic, structural barriers to routine care might be a possible cause for delayed admission and, in turn, elevated baseline COVID-19 severity for Hispanic patients (Appendix Figure 3).

Recent studies have investigated the impact of socioeconomic factors on racial/ethnic disparities in the COVID-19 pandemic.^7,16,17 To our knowledge, no study has directly analyzed the link between healthcare access metrics, COVID-19 severity at admission, and the Hispanic population stratified by primary language. Studies exist on this subject for other diseases, however. For example, healthcare access factors have been associated with sepsis-related mortality.^29,30 In fact, a recent study that explored the potential effect of language barriers on healthcare access demonstrated an association between limited English proficiency and sepsis-related mortality.³¹ Our study found that Hispanic patients whose primary language is not English had the worst clinical outcomes, including more severe baseline COVID-19 conditions, and the least access to healthcare, highlighting the importance of addressing language barriers in COVID-19 care. Further research is needed to confirm the relationship between limited English proficiency and clinical outcomes, as well as potential factors that contribute to such a relationship in different types of diseases.

Our study has a number of limitations. First, it was limited to only one large healthcare system, which means the results may not be generalizable. Because THR is an open system, comorbidity data may be incomplete, and we cannot exclude the possibility that patients accessed care outside THR prior to or during the pandemic. We may overcome this limitation in the future with cross-system health information exchange data. Second, we did not have data for the time of symptom onset, so we were unable to analyze the direct evidence of the possible delayed care. As a result, we were unable to analyze whether treatments were administered in a timely manner or appropriately. Third, our analysis was not adjusted for other socioeconomic factors (eg, income, education) due to lack of data. We used self-identification for ethnicity, but unlike new approaches by the U.S. Census Bureau,³² our survey allowed only one choice to be selected.

Sociodemographic factors among Hispanic inpatients hospitalized for COVID-19 at a large integrated health system—including a primary non-English language, lack of a PCP, and insurance status—were associated with measures of reduced access to care and more severe illness at admission. Structural barriers to care, which may be associated with reduced health literacy and less access to health insurance, can result in delayed treatment and more severe illness at admission and underdiagnosis of medical conditions, contributing to worse outcomes in this population. Our findings suggest that interventions to promote early recognition of signs and symptoms of COVID-19 and to encourage prompt clinical care at the community level may reduce the burden of COVID-19 deaths in racial or ethnic minority communities with language and socioeconomic barriers.

The rate of inpatient admissions among adults aged 65 years and older has decreased by approximately 25% since 2000.^1,2 This long-term trend raises important questions about inpatient-related spending in the traditional Medicare program for hospitals and providers who treat beneficiaries after a hospitalization. As traditional Medicare’s most expensive sector (accounting for 21% of all Medicare spending³), reducing hospitalizations is often championed as an opportunity to moderate Medicare spending growth.

Medicare’s ability to achieve significant savings from declining inpatient use may be tempered by a shift toward more expensive hospitalizations. If marginal hospitalizations among healthier beneficiaries are avoided, then the remaining inpatient users may be sicker and have greater spending per hospitalization and greater need for follow-up services. This study examines trends in Medicare spending related to episodes initiated by an inpatient stay because of its importance to overall Medicare spending and the implications for several Medicare value-based payment initiatives. In care models seeking to contain spending at a population level, such as accountable care organizations and managed care plans, reducing inpatient use and associated services may have the largest impact in curbing overall spending growth per beneficiary. Other models focused on spending at an episode level, including bundled payment initiatives, may face challenges if inpatient episodes become more expensive over time.

As Medicare shifts toward value-based payments, hospitalists and other hospital leaders are often involved in redesigning care delivery models for the hospital or accountable care organization (eg, through readmission reduction initiatives, post–acute care coordination, and bundled-care delivery programs). Not all savings strategies rely on providers to change how services are delivered; Medicare can modify payment rates, such as Affordable Care Act provisions that slowed how quickly Medicare payment rates increased.⁴ For clinicians to navigate the shift toward new payment models, it is important to recognize how each of these elements—declining hospital admissions, spending per inpatient episode, and payment rates—affect spending trends for inpatient services and associated care. Previous articles on overall Medicare inpatient spending have examined inpatient stays alone⁵ or focused mainly on spending per episode^6,7 without quantifying how these elements contributed to overall episode-related Medicare spending per beneficiary. This article addresses this gap by demonstrating how inpatient-related spending trends reflect each component.

This study examined trends in Medicare’s spending on inpatient episodes during the years 2009 to 2017. We described changes in the volume and spending on inpatient-initiated episodes across several dimensions, including beneficiary-level and hospitalization-level factors. We examined whether declines in spending associated with fewer inpatient-initiated episodes have been offset by increased spending per episode and how spending would have differed without changes in Medicare payment rates.

Episode Definition

We constructed an episode measure that captured traditional Medicare spending for 30 days prior to hospital admission, hospitalization duration, and 90 days following hospital discharge (additional details in the Appendix). As with bundled payments that include pre- and postdischarge services, this window allowed us to observe any services that preceded or followed a hospitalization. Using Medicare Part A, B, and D claims data for the years 2008 to 2018, we captured spending across all sectors for episodes with an index hospital admission in the years 2009 to 2017. If a beneficiary died during an episode, we measured healthcare spending until death. We excluded episodes where beneficiaries did not have traditional Medicare Part A and B for the entire episode or lived outside the 50 states or Washington, DC.

Any acute hospitalization triggered a new episode, with one exception: if a beneficiary was discharged and readmitted within 90 days for the same diagnosis-related group (DRG), then the readmission did not trigger a new episode. The spending for that readmission was attributed to the prior hospital stay. In effect, the annual number of episodes is equivalent to the annual number of hospital admissions minus subsequent rehospitalizations for the same DRG. Neither observation stays nor hospitalizations in inpatient rehabilitation, psychiatric, or long-term facilities were considered acute hospital admissions.

We assigned claims from noninpatient sectors to an episode based on whether the claim start date fell within the episode window. All traditional Medicare sectors were measured, including outpatient services, physician claims, post–acute care services, and Medicare Part D prescription drug events.

Our analysis aimed to measure all spending related to inpatient episodes without double-counting spending for overlapping episodes. If episodes overlapped, then spending for overlapping days was weighted to be evenly divided across episodes.

Outcome Measures

The study’s main outcomes summarized episode trends across the entire traditional Medicare population, including beneficiaries without an episode, in annual mean number of episodes per beneficiary and annual mean episode-related spending per beneficiary. The denominator of these measures is person-years, or total number of beneficiary months with Medicare Part A and B coverage divided by 12. The annual mean number of episodes per beneficiary is the total number of episodes initiated in a calendar year divided by person-years. The annual mean episode-related spending per beneficiary is the total amount of spending attributed to episodes divided by person-years. We also measured annual mean spending per episode, or total amount of spending attributed to episodes divided by the total number of episodes.

Medicare annually updates each sector’s payment rates for several factors, including inflation. We constructed an index for each sector to adjust for these annual payment rate changes. We also accounted for sequestration measures in effect since April 2013 that reduced Medicare payments to all sectors by 2%. We report our spending measures twice, with and without adjusting for changes in payment rates. Adjusted numbers reflect payment rates in effect in 2015.

Analysis Approach

We present annual trends on changes in the number of inpatient episodes per beneficiary, mean episode-related spending per beneficiary, and mean spending per episode. To quantify how changes in episode-related spending per beneficiary reflect changes in the number of episodes per beneficiary vs changes in spending per episode, we modified an approach implemented by Rosen and colleagues.⁸We calculated how much episode-related spending per beneficiary would have changed between 2009 and 2017 if spending per episode remained at 2009 levels but the number of episodes per beneficiary declined, as observed between 2009 and 2017 (see example calculation in Appendix). Conversely, we estimated how much episode-related spending per beneficiary would have changed if the number of episodes per beneficiary remained at 2009 levels but spending per episode increased, as observed between 2009 and 2017. In reality, the number of episodes per beneficiary and spending per episode concurrently changed, so the decomposition also includes an interaction term that quantifies how much of the change in spending reflects changes in both factors. We present these estimates for all sectors and separately for inpatient and all other sectors.

To better understand which beneficiaries have declining inpatient use, we performed stratified analyses describing changes in the number of episodes per beneficiary between 2009 and 2017, spending per episode, and total episode-related spending per beneficiary. We report these measures for several subpopulations defined by age, sex, race, dual-eligible status, and whether the beneficiary used long-term nursing home services during the episode’s calendar year. Descriptive statistics also detail how these measures changed between 2009 and 2017 for episodes stratified by characteristics of the index hospital stay: planned vs unplanned, medical vs surgical, and any use of intensive care unit (ICU) or coronary care unit services. We also stratify study measures by whether an episode included any use of post–acute care services (skilled nursing facility, home health, or inpatient rehabilitation facility use). Finally, we aggregate the episodes into major diagnostic categories (MDCs) based on the index hospital stay’s DRG to report study outcomes by condition. Because of a shift in coding hospitalizations for pneumonia as sepsis,^9,10 we exclude these two diseases from their respective MDCs and analyze them jointly as a unique category.

Changes in Number of Inpatient Episodes and Related Spending

From 2009 to 2017, the number of inpatient episodes per 1000 traditional Medicare beneficiaries declined from 326 to 267 (Table 1), or a relative decline of 18.2% (Figure 1). The total volume of inpatient episodes declined by only 13.4%, from 10.2 million to 8.8 million, reflecting that the size of the traditional Medicare population grew during these years. Over the same years, mean payment-rate–adjusted spending per episode increased 11.4% from $20,891 to $23,273.

When considering overall episode-related spending, the large decline in the volume of episodes outweighed increased spending per episode: the mean amount of episode-related Medicare spending per beneficiary decreased 8.9% from $6810 to $6206 (Table 1), or a net change of $604 (Figure 2). This net change reflects decreased spending due to fewer episodes per beneficiary ($1239 reduction in episode-related spending) offset by increased spending per episode (translating to a $776 increase in episode-related spending per beneficiary). These two factors, plus their interaction reflecting the combined influence of these factors ($141), comprise the overall change in episode-related spending per beneficiary over this period.

When these estimates are calculated separately for the inpatient sector and all other sectors, the inpatient sector experienced small increases in spending associated with greater spending per episode ($304) compared with noninpatient sectors ($472). Accordingly, the inpatient sector had a larger net decline in episode-related spending per beneficiary ($420) than noninpatient sectors ($184) after taking into account declining episode volume.

As expected, episode-related spending increased more when measures were not adjusted for annual payment rate increases. Without such adjustment, mean spending per episode increased 25.5%, and episode-related spending per beneficiary was nearly flat (2.6% between 2009 and 2017 [Figure 1]). The decline in unadjusted spending associated with fewer episodes ($1138) was offset by the spending increase associated with higher spending per episode ($1592) (Figure 2).

Analyses Stratified by Beneficiary Characteristics

Every population examined had declines in the number of inpatient episodes, even beneficiaries with more frequent inpatient use (Table 2). Among Medicare beneficiaries aged 85 years and older, the mean number of episodes per 1000 beneficiaries declined by 12.7%, from 524 to 457. Populations with less frequent inpatient use often experienced larger relative declines in number of episodes than populations with more frequent inpatient use. For example, the mean number of episodes per 1000 beneficiaries decreased by 17.7% for beneficiaries without nursing home use (306 to 252), as compared with an 8.1% decline for beneficiaries with nursing home use (from 888 to 816). In contrast, populations with less frequent inpatient use had larger relative increases in spending per episode with adjustment for payment rate changes. For example, spending per episode increased by 13.1% for beneficiaries aged 65 to 74 years ($20,904 to $23,644), but only by 8.6% for beneficiaries 85 years and older ($20,384 to $22,138).

Analyses Stratified by Service Use Characteristics

Some types of inpatient episodes had larger declines in the number of episodes, including episodes with planned admissions for the index hospital stay (28.8% decline from 68 to 48 episodes per 1000 beneficiaries) and episodes without post–acute care use (23.9% decline from 169 to 129 episodes per 1000 beneficiaries) (Appendix Table). In contrast, declines in the number of episodes were similar for index hospital admissions that did or did not involve ICU use (17.8% and 18.3% reduction in mean number of episodes per 1000 beneficiaries, respectively) or that included a surgical procedure or not (17.1% versus 18.6%, respectively). Several types of inpatient episodes had larger increases in spending per episode, such as a 15.1% increase for planned admissions and a 13.2% increase for hospitalizations without ICU use.

According to diagnosis information for an episode’s index hospital stay, inpatient episodes related to conditions affecting the circulatory system had the largest decline in mean number of episodes, decreasing by 31.8% from 78 to 53 episodes per 1000 beneficiaries (Appendix Table). Episodes for other diseases had much smaller declines in volume. Admissions for diagnoses of pneumonia or sepsis had notable increases in the volume of episodes, increasing by 20.7% from 25 to 30 admissions per 1000 beneficiaries.

Medicare spending per beneficiary on inpatient episodes, including services provided pre- and post hospitalization, declined by 8.9% from 2009 to 2017 after adjusting for payment rate changes. This decline reflects two components. First, the number of episodes per 1000 beneficiaries declined by 18.2%. Although the extent of this decrease varied across populations, every group examined had declines in inpatient use. In particular, hospitalizations for conditions affecting the circulatory system, such as heart attacks and cardiac procedures, decreased. Second, as inpatient volume declined, spending per episode increased by 11.4% to an average of $23,273 in 2017. This increase in spending per episode offset how much overall Medicare spending on episode-related care declined.

Medicare is increasingly challenging hospitals to demonstrate the value of inpatient services and associated treatment, which requires hospital leaders to recognize how their facilities’ spending trends relate to these national patterns. Understanding how much national episode-related spending has decreased over time with declining inpatient volume can help an accountable care organization evaluate whether it is feasible to achieve significant savings by reducing hospitalizations. Bundled payment providers focused on managing spending per episode can benefit from identifying which types of hospitalizations have increased spending per episode, especially for certain diagnoses.

These results also highlight the continued importance of a perennial factor in Medicare spending: payment rates. If Medicare payment rates had not increased over our study period, Medicare spending per inpatient episode would have increased by only 11%. Actual Medicare spending per episode increased by 25%, demonstrating that over half of the relative increase in spending per episode reflected increases in Medicare’s payment rates.

Increased spending per episode, even after adjustment for payment rate changes, suggests that services provided during an episode have increased in intensity or shifted toward higher-cost treatments. In order to understand how Medicare episode-related spending changed without introducing assumptions about factors underlying that change, our analysis did not adjust for inpatient acuity. We observed a smaller decline in the volume of hospitalizations with post–acute care use but similar decreases in the volume of hospitalizations with and without ICU use. This finding is consistent with previous evidence suggesting inpatient acuity has increased, with some caveats. The case-mix index for inpatients increased in Medicare claims,¹¹ but some of this increase may reflect expanded opportunities for hospitals to document comorbidities.¹² Geographic areas with larger decreases in inpatient admissions between 2010 and 2013 experienced greater risk-adjusted mortality among inpatients, consistent with a higher level of acuity among inpatients.¹³ The volume of ICU admissions declined, but ICU patients were more likely to have organ failure and to use complex services, such as mechanical ventilation, than patients admitted in earlier years.¹⁴

When interpreting these trends, several points are notable. The underlying health of the Medicare population may contribute to declining inpatient use but is difficult to quantify. The observed decline in cardiac-related hospitalizations is consistent with evidence that the impact of ischemic heart disease, the leading source of disease or injury in the US population, has dramatically declined over recent decades¹⁵ and that the Medicare program has experienced large declines in overall spending and use related to cardiac conditions.^16-18

Other potential factors include a shift toward hospitals treating Medicare beneficiaries as outpatients during an observation stay instead of admitting them as inpatients. Observation stays have increased as traditional Medicare implemented measures to penalize readmissions and limit payments for short inpatient stays.^19-21 Even so, the increase in observation stays would have to be at least three times as large as described in other work to fully substitute for the decrease in inpatient stays: between the years 2007 and 2018, the number of observation stays per 1000 beneficiaries increased by only 26 stays, whereas the number of hospitalizations per 1000 beneficiaries decreased by 83 hospitalizations.²⁰

Outpatient services may also broaden treatment availability in alternative settings or enable beneficiaries to avoid inpatient treatment with appropriate preventative care.^22-27 These considerations are even more relevant as the COVID-19 pandemic spurred reduced admissions and shifted acute services outside of hospitals.^28,29 Some services, such as elective surgeries, have probably shifted from an inpatient to an outpatient setting, which would be consistent with our finding that there are larger relative declines in planned hospitalizations. Although this analysis does not capture spending for outpatient services that are not linked to an inpatient admission, prior work demonstrates that annual growth in total Medicare spending per beneficiary (episode related or not) has recently declined for the inpatient sector but increased for outpatient and physician sectors.³⁰ By offering other outpatient services, hospitals may be able to recoup some declining inpatient revenues. However, outpatient services are reimbursed at a lower rate than inpatient services, suggesting these trends may create financial pressure for hospitals.

There are several limitations to our analysis. First, our analysis is not designed to uncover the reason for the shift away from inpatient services nor to analyze how it has affected beneficiaries’ overall quality of care. Second, in accounting for payment rate changes, we do not consider that facilities may have changed their behavior in response to payment rate changes. If the profitability of treating Medicare patients declined, then facilities may no longer have as much financial incentive to offer services that attract Medicare beneficiaries as inpatients. Third, our analysis excludes the Medicare Advantage population, which more than doubled over this time period and experienced smaller declines in inpatient use over our study years.^31,32 Fourth, our analysis does not include spending on services provided outside of inpatient episodes, so we do not estimate how much declines in episode-related spending contributed to overall Medicare spending. Finally, as with the trends noted for sepsis and pneumonia,⁹ some of the changes in diagnosis categories might reflect changes in coding practices to ensure that conditions with higher DRG payment rates are listed as the primary diagnosis, even if the actual services rendered or conditions treated did not change.

Over an 8-year period, Medicare spending per beneficiary on inpatient episodes, including all services immediately preceding and following hospitalizations, declined by 8.9% after taking into account payment rate increases. This broad shift away from inpatient services among all Medicare beneficiaries suggests policymakers should aim for payment policies that balance financial sustainability for hospitals and associated facilities with more efficient use of inpatient and related services.

The authors thank Sunita Thapa, Lucas Stewart, Christine Lai, and Liliana Podczerwinski for contributions in data analysis and manuscript preparation.

The rate of inpatient admissions among adults aged 65 years and older has decreased by approximately 25% since 2000.^1,2 This long-term trend raises important questions about inpatient-related spending in the traditional Medicare program for hospitals and providers who treat beneficiaries after a hospitalization. As traditional Medicare’s most expensive sector (accounting for 21% of all Medicare spending³), reducing hospitalizations is often championed as an opportunity to moderate Medicare spending growth.

Medicare’s ability to achieve significant savings from declining inpatient use may be tempered by a shift toward more expensive hospitalizations. If marginal hospitalizations among healthier beneficiaries are avoided, then the remaining inpatient users may be sicker and have greater spending per hospitalization and greater need for follow-up services. This study examines trends in Medicare spending related to episodes initiated by an inpatient stay because of its importance to overall Medicare spending and the implications for several Medicare value-based payment initiatives. In care models seeking to contain spending at a population level, such as accountable care organizations and managed care plans, reducing inpatient use and associated services may have the largest impact in curbing overall spending growth per beneficiary. Other models focused on spending at an episode level, including bundled payment initiatives, may face challenges if inpatient episodes become more expensive over time.

As Medicare shifts toward value-based payments, hospitalists and other hospital leaders are often involved in redesigning care delivery models for the hospital or accountable care organization (eg, through readmission reduction initiatives, post–acute care coordination, and bundled-care delivery programs). Not all savings strategies rely on providers to change how services are delivered; Medicare can modify payment rates, such as Affordable Care Act provisions that slowed how quickly Medicare payment rates increased.⁴ For clinicians to navigate the shift toward new payment models, it is important to recognize how each of these elements—declining hospital admissions, spending per inpatient episode, and payment rates—affect spending trends for inpatient services and associated care. Previous articles on overall Medicare inpatient spending have examined inpatient stays alone⁵ or focused mainly on spending per episode^6,7 without quantifying how these elements contributed to overall episode-related Medicare spending per beneficiary. This article addresses this gap by demonstrating how inpatient-related spending trends reflect each component.

This study examined trends in Medicare’s spending on inpatient episodes during the years 2009 to 2017. We described changes in the volume and spending on inpatient-initiated episodes across several dimensions, including beneficiary-level and hospitalization-level factors. We examined whether declines in spending associated with fewer inpatient-initiated episodes have been offset by increased spending per episode and how spending would have differed without changes in Medicare payment rates.

Episode Definition

We constructed an episode measure that captured traditional Medicare spending for 30 days prior to hospital admission, hospitalization duration, and 90 days following hospital discharge (additional details in the Appendix). As with bundled payments that include pre- and postdischarge services, this window allowed us to observe any services that preceded or followed a hospitalization. Using Medicare Part A, B, and D claims data for the years 2008 to 2018, we captured spending across all sectors for episodes with an index hospital admission in the years 2009 to 2017. If a beneficiary died during an episode, we measured healthcare spending until death. We excluded episodes where beneficiaries did not have traditional Medicare Part A and B for the entire episode or lived outside the 50 states or Washington, DC.

Any acute hospitalization triggered a new episode, with one exception: if a beneficiary was discharged and readmitted within 90 days for the same diagnosis-related group (DRG), then the readmission did not trigger a new episode. The spending for that readmission was attributed to the prior hospital stay. In effect, the annual number of episodes is equivalent to the annual number of hospital admissions minus subsequent rehospitalizations for the same DRG. Neither observation stays nor hospitalizations in inpatient rehabilitation, psychiatric, or long-term facilities were considered acute hospital admissions.

We assigned claims from noninpatient sectors to an episode based on whether the claim start date fell within the episode window. All traditional Medicare sectors were measured, including outpatient services, physician claims, post–acute care services, and Medicare Part D prescription drug events.

Our analysis aimed to measure all spending related to inpatient episodes without double-counting spending for overlapping episodes. If episodes overlapped, then spending for overlapping days was weighted to be evenly divided across episodes.

Outcome Measures

The study’s main outcomes summarized episode trends across the entire traditional Medicare population, including beneficiaries without an episode, in annual mean number of episodes per beneficiary and annual mean episode-related spending per beneficiary. The denominator of these measures is person-years, or total number of beneficiary months with Medicare Part A and B coverage divided by 12. The annual mean number of episodes per beneficiary is the total number of episodes initiated in a calendar year divided by person-years. The annual mean episode-related spending per beneficiary is the total amount of spending attributed to episodes divided by person-years. We also measured annual mean spending per episode, or total amount of spending attributed to episodes divided by the total number of episodes.

Medicare annually updates each sector’s payment rates for several factors, including inflation. We constructed an index for each sector to adjust for these annual payment rate changes. We also accounted for sequestration measures in effect since April 2013 that reduced Medicare payments to all sectors by 2%. We report our spending measures twice, with and without adjusting for changes in payment rates. Adjusted numbers reflect payment rates in effect in 2015.

Analysis Approach

We present annual trends on changes in the number of inpatient episodes per beneficiary, mean episode-related spending per beneficiary, and mean spending per episode. To quantify how changes in episode-related spending per beneficiary reflect changes in the number of episodes per beneficiary vs changes in spending per episode, we modified an approach implemented by Rosen and colleagues.⁸We calculated how much episode-related spending per beneficiary would have changed between 2009 and 2017 if spending per episode remained at 2009 levels but the number of episodes per beneficiary declined, as observed between 2009 and 2017 (see example calculation in Appendix). Conversely, we estimated how much episode-related spending per beneficiary would have changed if the number of episodes per beneficiary remained at 2009 levels but spending per episode increased, as observed between 2009 and 2017. In reality, the number of episodes per beneficiary and spending per episode concurrently changed, so the decomposition also includes an interaction term that quantifies how much of the change in spending reflects changes in both factors. We present these estimates for all sectors and separately for inpatient and all other sectors.

To better understand which beneficiaries have declining inpatient use, we performed stratified analyses describing changes in the number of episodes per beneficiary between 2009 and 2017, spending per episode, and total episode-related spending per beneficiary. We report these measures for several subpopulations defined by age, sex, race, dual-eligible status, and whether the beneficiary used long-term nursing home services during the episode’s calendar year. Descriptive statistics also detail how these measures changed between 2009 and 2017 for episodes stratified by characteristics of the index hospital stay: planned vs unplanned, medical vs surgical, and any use of intensive care unit (ICU) or coronary care unit services. We also stratify study measures by whether an episode included any use of post–acute care services (skilled nursing facility, home health, or inpatient rehabilitation facility use). Finally, we aggregate the episodes into major diagnostic categories (MDCs) based on the index hospital stay’s DRG to report study outcomes by condition. Because of a shift in coding hospitalizations for pneumonia as sepsis,^9,10 we exclude these two diseases from their respective MDCs and analyze them jointly as a unique category.

Changes in Number of Inpatient Episodes and Related Spending

From 2009 to 2017, the number of inpatient episodes per 1000 traditional Medicare beneficiaries declined from 326 to 267 (Table 1), or a relative decline of 18.2% (Figure 1). The total volume of inpatient episodes declined by only 13.4%, from 10.2 million to 8.8 million, reflecting that the size of the traditional Medicare population grew during these years. Over the same years, mean payment-rate–adjusted spending per episode increased 11.4% from $20,891 to $23,273.

When considering overall episode-related spending, the large decline in the volume of episodes outweighed increased spending per episode: the mean amount of episode-related Medicare spending per beneficiary decreased 8.9% from $6810 to $6206 (Table 1), or a net change of $604 (Figure 2). This net change reflects decreased spending due to fewer episodes per beneficiary ($1239 reduction in episode-related spending) offset by increased spending per episode (translating to a $776 increase in episode-related spending per beneficiary). These two factors, plus their interaction reflecting the combined influence of these factors ($141), comprise the overall change in episode-related spending per beneficiary over this period.

When these estimates are calculated separately for the inpatient sector and all other sectors, the inpatient sector experienced small increases in spending associated with greater spending per episode ($304) compared with noninpatient sectors ($472). Accordingly, the inpatient sector had a larger net decline in episode-related spending per beneficiary ($420) than noninpatient sectors ($184) after taking into account declining episode volume.

As expected, episode-related spending increased more when measures were not adjusted for annual payment rate increases. Without such adjustment, mean spending per episode increased 25.5%, and episode-related spending per beneficiary was nearly flat (2.6% between 2009 and 2017 [Figure 1]). The decline in unadjusted spending associated with fewer episodes ($1138) was offset by the spending increase associated with higher spending per episode ($1592) (Figure 2).

Analyses Stratified by Beneficiary Characteristics

Every population examined had declines in the number of inpatient episodes, even beneficiaries with more frequent inpatient use (Table 2). Among Medicare beneficiaries aged 85 years and older, the mean number of episodes per 1000 beneficiaries declined by 12.7%, from 524 to 457. Populations with less frequent inpatient use often experienced larger relative declines in number of episodes than populations with more frequent inpatient use. For example, the mean number of episodes per 1000 beneficiaries decreased by 17.7% for beneficiaries without nursing home use (306 to 252), as compared with an 8.1% decline for beneficiaries with nursing home use (from 888 to 816). In contrast, populations with less frequent inpatient use had larger relative increases in spending per episode with adjustment for payment rate changes. For example, spending per episode increased by 13.1% for beneficiaries aged 65 to 74 years ($20,904 to $23,644), but only by 8.6% for beneficiaries 85 years and older ($20,384 to $22,138).

Analyses Stratified by Service Use Characteristics

Some types of inpatient episodes had larger declines in the number of episodes, including episodes with planned admissions for the index hospital stay (28.8% decline from 68 to 48 episodes per 1000 beneficiaries) and episodes without post–acute care use (23.9% decline from 169 to 129 episodes per 1000 beneficiaries) (Appendix Table). In contrast, declines in the number of episodes were similar for index hospital admissions that did or did not involve ICU use (17.8% and 18.3% reduction in mean number of episodes per 1000 beneficiaries, respectively) or that included a surgical procedure or not (17.1% versus 18.6%, respectively). Several types of inpatient episodes had larger increases in spending per episode, such as a 15.1% increase for planned admissions and a 13.2% increase for hospitalizations without ICU use.

According to diagnosis information for an episode’s index hospital stay, inpatient episodes related to conditions affecting the circulatory system had the largest decline in mean number of episodes, decreasing by 31.8% from 78 to 53 episodes per 1000 beneficiaries (Appendix Table). Episodes for other diseases had much smaller declines in volume. Admissions for diagnoses of pneumonia or sepsis had notable increases in the volume of episodes, increasing by 20.7% from 25 to 30 admissions per 1000 beneficiaries.

Medicare spending per beneficiary on inpatient episodes, including services provided pre- and post hospitalization, declined by 8.9% from 2009 to 2017 after adjusting for payment rate changes. This decline reflects two components. First, the number of episodes per 1000 beneficiaries declined by 18.2%. Although the extent of this decrease varied across populations, every group examined had declines in inpatient use. In particular, hospitalizations for conditions affecting the circulatory system, such as heart attacks and cardiac procedures, decreased. Second, as inpatient volume declined, spending per episode increased by 11.4% to an average of $23,273 in 2017. This increase in spending per episode offset how much overall Medicare spending on episode-related care declined.

Medicare is increasingly challenging hospitals to demonstrate the value of inpatient services and associated treatment, which requires hospital leaders to recognize how their facilities’ spending trends relate to these national patterns. Understanding how much national episode-related spending has decreased over time with declining inpatient volume can help an accountable care organization evaluate whether it is feasible to achieve significant savings by reducing hospitalizations. Bundled payment providers focused on managing spending per episode can benefit from identifying which types of hospitalizations have increased spending per episode, especially for certain diagnoses.

These results also highlight the continued importance of a perennial factor in Medicare spending: payment rates. If Medicare payment rates had not increased over our study period, Medicare spending per inpatient episode would have increased by only 11%. Actual Medicare spending per episode increased by 25%, demonstrating that over half of the relative increase in spending per episode reflected increases in Medicare’s payment rates.

Increased spending per episode, even after adjustment for payment rate changes, suggests that services provided during an episode have increased in intensity or shifted toward higher-cost treatments. In order to understand how Medicare episode-related spending changed without introducing assumptions about factors underlying that change, our analysis did not adjust for inpatient acuity. We observed a smaller decline in the volume of hospitalizations with post–acute care use but similar decreases in the volume of hospitalizations with and without ICU use. This finding is consistent with previous evidence suggesting inpatient acuity has increased, with some caveats. The case-mix index for inpatients increased in Medicare claims,¹¹ but some of this increase may reflect expanded opportunities for hospitals to document comorbidities.¹² Geographic areas with larger decreases in inpatient admissions between 2010 and 2013 experienced greater risk-adjusted mortality among inpatients, consistent with a higher level of acuity among inpatients.¹³ The volume of ICU admissions declined, but ICU patients were more likely to have organ failure and to use complex services, such as mechanical ventilation, than patients admitted in earlier years.¹⁴

When interpreting these trends, several points are notable. The underlying health of the Medicare population may contribute to declining inpatient use but is difficult to quantify. The observed decline in cardiac-related hospitalizations is consistent with evidence that the impact of ischemic heart disease, the leading source of disease or injury in the US population, has dramatically declined over recent decades¹⁵ and that the Medicare program has experienced large declines in overall spending and use related to cardiac conditions.^16-18

Other potential factors include a shift toward hospitals treating Medicare beneficiaries as outpatients during an observation stay instead of admitting them as inpatients. Observation stays have increased as traditional Medicare implemented measures to penalize readmissions and limit payments for short inpatient stays.^19-21 Even so, the increase in observation stays would have to be at least three times as large as described in other work to fully substitute for the decrease in inpatient stays: between the years 2007 and 2018, the number of observation stays per 1000 beneficiaries increased by only 26 stays, whereas the number of hospitalizations per 1000 beneficiaries decreased by 83 hospitalizations.²⁰

Outpatient services may also broaden treatment availability in alternative settings or enable beneficiaries to avoid inpatient treatment with appropriate preventative care.^22-27 These considerations are even more relevant as the COVID-19 pandemic spurred reduced admissions and shifted acute services outside of hospitals.^28,29 Some services, such as elective surgeries, have probably shifted from an inpatient to an outpatient setting, which would be consistent with our finding that there are larger relative declines in planned hospitalizations. Although this analysis does not capture spending for outpatient services that are not linked to an inpatient admission, prior work demonstrates that annual growth in total Medicare spending per beneficiary (episode related or not) has recently declined for the inpatient sector but increased for outpatient and physician sectors.³⁰ By offering other outpatient services, hospitals may be able to recoup some declining inpatient revenues. However, outpatient services are reimbursed at a lower rate than inpatient services, suggesting these trends may create financial pressure for hospitals.

There are several limitations to our analysis. First, our analysis is not designed to uncover the reason for the shift away from inpatient services nor to analyze how it has affected beneficiaries’ overall quality of care. Second, in accounting for payment rate changes, we do not consider that facilities may have changed their behavior in response to payment rate changes. If the profitability of treating Medicare patients declined, then facilities may no longer have as much financial incentive to offer services that attract Medicare beneficiaries as inpatients. Third, our analysis excludes the Medicare Advantage population, which more than doubled over this time period and experienced smaller declines in inpatient use over our study years.^31,32 Fourth, our analysis does not include spending on services provided outside of inpatient episodes, so we do not estimate how much declines in episode-related spending contributed to overall Medicare spending. Finally, as with the trends noted for sepsis and pneumonia,⁹ some of the changes in diagnosis categories might reflect changes in coding practices to ensure that conditions with higher DRG payment rates are listed as the primary diagnosis, even if the actual services rendered or conditions treated did not change.

Over an 8-year period, Medicare spending per beneficiary on inpatient episodes, including all services immediately preceding and following hospitalizations, declined by 8.9% after taking into account payment rate increases. This broad shift away from inpatient services among all Medicare beneficiaries suggests policymakers should aim for payment policies that balance financial sustainability for hospitals and associated facilities with more efficient use of inpatient and related services.

The authors thank Sunita Thapa, Lucas Stewart, Christine Lai, and Liliana Podczerwinski for contributions in data analysis and manuscript preparation.

The rate of inpatient admissions among adults aged 65 years and older has decreased by approximately 25% since 2000.^1,2 This long-term trend raises important questions about inpatient-related spending in the traditional Medicare program for hospitals and providers who treat beneficiaries after a hospitalization. As traditional Medicare’s most expensive sector (accounting for 21% of all Medicare spending³), reducing hospitalizations is often championed as an opportunity to moderate Medicare spending growth.

Medicare’s ability to achieve significant savings from declining inpatient use may be tempered by a shift toward more expensive hospitalizations. If marginal hospitalizations among healthier beneficiaries are avoided, then the remaining inpatient users may be sicker and have greater spending per hospitalization and greater need for follow-up services. This study examines trends in Medicare spending related to episodes initiated by an inpatient stay because of its importance to overall Medicare spending and the implications for several Medicare value-based payment initiatives. In care models seeking to contain spending at a population level, such as accountable care organizations and managed care plans, reducing inpatient use and associated services may have the largest impact in curbing overall spending growth per beneficiary. Other models focused on spending at an episode level, including bundled payment initiatives, may face challenges if inpatient episodes become more expensive over time.

As Medicare shifts toward value-based payments, hospitalists and other hospital leaders are often involved in redesigning care delivery models for the hospital or accountable care organization (eg, through readmission reduction initiatives, post–acute care coordination, and bundled-care delivery programs). Not all savings strategies rely on providers to change how services are delivered; Medicare can modify payment rates, such as Affordable Care Act provisions that slowed how quickly Medicare payment rates increased.⁴ For clinicians to navigate the shift toward new payment models, it is important to recognize how each of these elements—declining hospital admissions, spending per inpatient episode, and payment rates—affect spending trends for inpatient services and associated care. Previous articles on overall Medicare inpatient spending have examined inpatient stays alone⁵ or focused mainly on spending per episode^6,7 without quantifying how these elements contributed to overall episode-related Medicare spending per beneficiary. This article addresses this gap by demonstrating how inpatient-related spending trends reflect each component.

This study examined trends in Medicare’s spending on inpatient episodes during the years 2009 to 2017. We described changes in the volume and spending on inpatient-initiated episodes across several dimensions, including beneficiary-level and hospitalization-level factors. We examined whether declines in spending associated with fewer inpatient-initiated episodes have been offset by increased spending per episode and how spending would have differed without changes in Medicare payment rates.

Episode Definition

We constructed an episode measure that captured traditional Medicare spending for 30 days prior to hospital admission, hospitalization duration, and 90 days following hospital discharge (additional details in the Appendix). As with bundled payments that include pre- and postdischarge services, this window allowed us to observe any services that preceded or followed a hospitalization. Using Medicare Part A, B, and D claims data for the years 2008 to 2018, we captured spending across all sectors for episodes with an index hospital admission in the years 2009 to 2017. If a beneficiary died during an episode, we measured healthcare spending until death. We excluded episodes where beneficiaries did not have traditional Medicare Part A and B for the entire episode or lived outside the 50 states or Washington, DC.

Any acute hospitalization triggered a new episode, with one exception: if a beneficiary was discharged and readmitted within 90 days for the same diagnosis-related group (DRG), then the readmission did not trigger a new episode. The spending for that readmission was attributed to the prior hospital stay. In effect, the annual number of episodes is equivalent to the annual number of hospital admissions minus subsequent rehospitalizations for the same DRG. Neither observation stays nor hospitalizations in inpatient rehabilitation, psychiatric, or long-term facilities were considered acute hospital admissions.

We assigned claims from noninpatient sectors to an episode based on whether the claim start date fell within the episode window. All traditional Medicare sectors were measured, including outpatient services, physician claims, post–acute care services, and Medicare Part D prescription drug events.

Our analysis aimed to measure all spending related to inpatient episodes without double-counting spending for overlapping episodes. If episodes overlapped, then spending for overlapping days was weighted to be evenly divided across episodes.

Outcome Measures

The study’s main outcomes summarized episode trends across the entire traditional Medicare population, including beneficiaries without an episode, in annual mean number of episodes per beneficiary and annual mean episode-related spending per beneficiary. The denominator of these measures is person-years, or total number of beneficiary months with Medicare Part A and B coverage divided by 12. The annual mean number of episodes per beneficiary is the total number of episodes initiated in a calendar year divided by person-years. The annual mean episode-related spending per beneficiary is the total amount of spending attributed to episodes divided by person-years. We also measured annual mean spending per episode, or total amount of spending attributed to episodes divided by the total number of episodes.

Medicare annually updates each sector’s payment rates for several factors, including inflation. We constructed an index for each sector to adjust for these annual payment rate changes. We also accounted for sequestration measures in effect since April 2013 that reduced Medicare payments to all sectors by 2%. We report our spending measures twice, with and without adjusting for changes in payment rates. Adjusted numbers reflect payment rates in effect in 2015.

Analysis Approach

We present annual trends on changes in the number of inpatient episodes per beneficiary, mean episode-related spending per beneficiary, and mean spending per episode. To quantify how changes in episode-related spending per beneficiary reflect changes in the number of episodes per beneficiary vs changes in spending per episode, we modified an approach implemented by Rosen and colleagues.⁸We calculated how much episode-related spending per beneficiary would have changed between 2009 and 2017 if spending per episode remained at 2009 levels but the number of episodes per beneficiary declined, as observed between 2009 and 2017 (see example calculation in Appendix). Conversely, we estimated how much episode-related spending per beneficiary would have changed if the number of episodes per beneficiary remained at 2009 levels but spending per episode increased, as observed between 2009 and 2017. In reality, the number of episodes per beneficiary and spending per episode concurrently changed, so the decomposition also includes an interaction term that quantifies how much of the change in spending reflects changes in both factors. We present these estimates for all sectors and separately for inpatient and all other sectors.

To better understand which beneficiaries have declining inpatient use, we performed stratified analyses describing changes in the number of episodes per beneficiary between 2009 and 2017, spending per episode, and total episode-related spending per beneficiary. We report these measures for several subpopulations defined by age, sex, race, dual-eligible status, and whether the beneficiary used long-term nursing home services during the episode’s calendar year. Descriptive statistics also detail how these measures changed between 2009 and 2017 for episodes stratified by characteristics of the index hospital stay: planned vs unplanned, medical vs surgical, and any use of intensive care unit (ICU) or coronary care unit services. We also stratify study measures by whether an episode included any use of post–acute care services (skilled nursing facility, home health, or inpatient rehabilitation facility use). Finally, we aggregate the episodes into major diagnostic categories (MDCs) based on the index hospital stay’s DRG to report study outcomes by condition. Because of a shift in coding hospitalizations for pneumonia as sepsis,^9,10 we exclude these two diseases from their respective MDCs and analyze them jointly as a unique category.

Changes in Number of Inpatient Episodes and Related Spending

From 2009 to 2017, the number of inpatient episodes per 1000 traditional Medicare beneficiaries declined from 326 to 267 (Table 1), or a relative decline of 18.2% (Figure 1). The total volume of inpatient episodes declined by only 13.4%, from 10.2 million to 8.8 million, reflecting that the size of the traditional Medicare population grew during these years. Over the same years, mean payment-rate–adjusted spending per episode increased 11.4% from $20,891 to $23,273.

When considering overall episode-related spending, the large decline in the volume of episodes outweighed increased spending per episode: the mean amount of episode-related Medicare spending per beneficiary decreased 8.9% from $6810 to $6206 (Table 1), or a net change of $604 (Figure 2). This net change reflects decreased spending due to fewer episodes per beneficiary ($1239 reduction in episode-related spending) offset by increased spending per episode (translating to a $776 increase in episode-related spending per beneficiary). These two factors, plus their interaction reflecting the combined influence of these factors ($141), comprise the overall change in episode-related spending per beneficiary over this period.

When these estimates are calculated separately for the inpatient sector and all other sectors, the inpatient sector experienced small increases in spending associated with greater spending per episode ($304) compared with noninpatient sectors ($472). Accordingly, the inpatient sector had a larger net decline in episode-related spending per beneficiary ($420) than noninpatient sectors ($184) after taking into account declining episode volume.

As expected, episode-related spending increased more when measures were not adjusted for annual payment rate increases. Without such adjustment, mean spending per episode increased 25.5%, and episode-related spending per beneficiary was nearly flat (2.6% between 2009 and 2017 [Figure 1]). The decline in unadjusted spending associated with fewer episodes ($1138) was offset by the spending increase associated with higher spending per episode ($1592) (Figure 2).

Analyses Stratified by Beneficiary Characteristics

Every population examined had declines in the number of inpatient episodes, even beneficiaries with more frequent inpatient use (Table 2). Among Medicare beneficiaries aged 85 years and older, the mean number of episodes per 1000 beneficiaries declined by 12.7%, from 524 to 457. Populations with less frequent inpatient use often experienced larger relative declines in number of episodes than populations with more frequent inpatient use. For example, the mean number of episodes per 1000 beneficiaries decreased by 17.7% for beneficiaries without nursing home use (306 to 252), as compared with an 8.1% decline for beneficiaries with nursing home use (from 888 to 816). In contrast, populations with less frequent inpatient use had larger relative increases in spending per episode with adjustment for payment rate changes. For example, spending per episode increased by 13.1% for beneficiaries aged 65 to 74 years ($20,904 to $23,644), but only by 8.6% for beneficiaries 85 years and older ($20,384 to $22,138).

Analyses Stratified by Service Use Characteristics

Some types of inpatient episodes had larger declines in the number of episodes, including episodes with planned admissions for the index hospital stay (28.8% decline from 68 to 48 episodes per 1000 beneficiaries) and episodes without post–acute care use (23.9% decline from 169 to 129 episodes per 1000 beneficiaries) (Appendix Table). In contrast, declines in the number of episodes were similar for index hospital admissions that did or did not involve ICU use (17.8% and 18.3% reduction in mean number of episodes per 1000 beneficiaries, respectively) or that included a surgical procedure or not (17.1% versus 18.6%, respectively). Several types of inpatient episodes had larger increases in spending per episode, such as a 15.1% increase for planned admissions and a 13.2% increase for hospitalizations without ICU use.

According to diagnosis information for an episode’s index hospital stay, inpatient episodes related to conditions affecting the circulatory system had the largest decline in mean number of episodes, decreasing by 31.8% from 78 to 53 episodes per 1000 beneficiaries (Appendix Table). Episodes for other diseases had much smaller declines in volume. Admissions for diagnoses of pneumonia or sepsis had notable increases in the volume of episodes, increasing by 20.7% from 25 to 30 admissions per 1000 beneficiaries.

Medicare spending per beneficiary on inpatient episodes, including services provided pre- and post hospitalization, declined by 8.9% from 2009 to 2017 after adjusting for payment rate changes. This decline reflects two components. First, the number of episodes per 1000 beneficiaries declined by 18.2%. Although the extent of this decrease varied across populations, every group examined had declines in inpatient use. In particular, hospitalizations for conditions affecting the circulatory system, such as heart attacks and cardiac procedures, decreased. Second, as inpatient volume declined, spending per episode increased by 11.4% to an average of $23,273 in 2017. This increase in spending per episode offset how much overall Medicare spending on episode-related care declined.

Medicare is increasingly challenging hospitals to demonstrate the value of inpatient services and associated treatment, which requires hospital leaders to recognize how their facilities’ spending trends relate to these national patterns. Understanding how much national episode-related spending has decreased over time with declining inpatient volume can help an accountable care organization evaluate whether it is feasible to achieve significant savings by reducing hospitalizations. Bundled payment providers focused on managing spending per episode can benefit from identifying which types of hospitalizations have increased spending per episode, especially for certain diagnoses.

These results also highlight the continued importance of a perennial factor in Medicare spending: payment rates. If Medicare payment rates had not increased over our study period, Medicare spending per inpatient episode would have increased by only 11%. Actual Medicare spending per episode increased by 25%, demonstrating that over half of the relative increase in spending per episode reflected increases in Medicare’s payment rates.

Increased spending per episode, even after adjustment for payment rate changes, suggests that services provided during an episode have increased in intensity or shifted toward higher-cost treatments. In order to understand how Medicare episode-related spending changed without introducing assumptions about factors underlying that change, our analysis did not adjust for inpatient acuity. We observed a smaller decline in the volume of hospitalizations with post–acute care use but similar decreases in the volume of hospitalizations with and without ICU use. This finding is consistent with previous evidence suggesting inpatient acuity has increased, with some caveats. The case-mix index for inpatients increased in Medicare claims,¹¹ but some of this increase may reflect expanded opportunities for hospitals to document comorbidities.¹² Geographic areas with larger decreases in inpatient admissions between 2010 and 2013 experienced greater risk-adjusted mortality among inpatients, consistent with a higher level of acuity among inpatients.¹³ The volume of ICU admissions declined, but ICU patients were more likely to have organ failure and to use complex services, such as mechanical ventilation, than patients admitted in earlier years.¹⁴

When interpreting these trends, several points are notable. The underlying health of the Medicare population may contribute to declining inpatient use but is difficult to quantify. The observed decline in cardiac-related hospitalizations is consistent with evidence that the impact of ischemic heart disease, the leading source of disease or injury in the US population, has dramatically declined over recent decades¹⁵ and that the Medicare program has experienced large declines in overall spending and use related to cardiac conditions.^16-18

Other potential factors include a shift toward hospitals treating Medicare beneficiaries as outpatients during an observation stay instead of admitting them as inpatients. Observation stays have increased as traditional Medicare implemented measures to penalize readmissions and limit payments for short inpatient stays.^19-21 Even so, the increase in observation stays would have to be at least three times as large as described in other work to fully substitute for the decrease in inpatient stays: between the years 2007 and 2018, the number of observation stays per 1000 beneficiaries increased by only 26 stays, whereas the number of hospitalizations per 1000 beneficiaries decreased by 83 hospitalizations.²⁰

Outpatient services may also broaden treatment availability in alternative settings or enable beneficiaries to avoid inpatient treatment with appropriate preventative care.^22-27 These considerations are even more relevant as the COVID-19 pandemic spurred reduced admissions and shifted acute services outside of hospitals.^28,29 Some services, such as elective surgeries, have probably shifted from an inpatient to an outpatient setting, which would be consistent with our finding that there are larger relative declines in planned hospitalizations. Although this analysis does not capture spending for outpatient services that are not linked to an inpatient admission, prior work demonstrates that annual growth in total Medicare spending per beneficiary (episode related or not) has recently declined for the inpatient sector but increased for outpatient and physician sectors.³⁰ By offering other outpatient services, hospitals may be able to recoup some declining inpatient revenues. However, outpatient services are reimbursed at a lower rate than inpatient services, suggesting these trends may create financial pressure for hospitals.

There are several limitations to our analysis. First, our analysis is not designed to uncover the reason for the shift away from inpatient services nor to analyze how it has affected beneficiaries’ overall quality of care. Second, in accounting for payment rate changes, we do not consider that facilities may have changed their behavior in response to payment rate changes. If the profitability of treating Medicare patients declined, then facilities may no longer have as much financial incentive to offer services that attract Medicare beneficiaries as inpatients. Third, our analysis excludes the Medicare Advantage population, which more than doubled over this time period and experienced smaller declines in inpatient use over our study years.^31,32 Fourth, our analysis does not include spending on services provided outside of inpatient episodes, so we do not estimate how much declines in episode-related spending contributed to overall Medicare spending. Finally, as with the trends noted for sepsis and pneumonia,⁹ some of the changes in diagnosis categories might reflect changes in coding practices to ensure that conditions with higher DRG payment rates are listed as the primary diagnosis, even if the actual services rendered or conditions treated did not change.

Over an 8-year period, Medicare spending per beneficiary on inpatient episodes, including all services immediately preceding and following hospitalizations, declined by 8.9% after taking into account payment rate increases. This broad shift away from inpatient services among all Medicare beneficiaries suggests policymakers should aim for payment policies that balance financial sustainability for hospitals and associated facilities with more efficient use of inpatient and related services.

The authors thank Sunita Thapa, Lucas Stewart, Christine Lai, and Liliana Podczerwinski for contributions in data analysis and manuscript preparation.

To the Editor:

The tumor necrosis factor α inhibitor adalimumab is the only US Food and Drug Administration–approved treatment of hidradenitis suppurativa (HS). Although 50.6% of patients fulfilled Hidradenitis Suppurativa Clinical Response criteria with adalimumab at 12 weeks, many responders were not satisfied with their disease control, and secondary loss of Hidradenitis Suppurativa Clinical Response fulfillment occurred in 15.9% of patients within approximately 3 years.¹ Without other US Food and Drug Administration–approved HS treatments, some dermatologists have combined adalimumab with methotrexate (MTX) and/or mycophenolate mofetil (MMF) to attempt to increase the duration of satisfactory disease control while on adalimumab. Combining tumor necrosis factor α inhibitors with oral immunosuppressants is a well-established approach in psoriasis, psoriatic arthritis, and inflammatory bowel disease; however, to the best of our knowledge, this approach has not been studied for HS.^2,3

To assess whether there is a role for combining adalimumab with MTX and/or MMF in the treatment of HS, we performed a single-institution retrospective chart review at the University of Connecticut Department of Dermatology to determine whether patients receiving combination therapy stayed on adalimumab longer than those who received adalimumab monotherapy. All patients receiving adalimumab for the treatment of HS with at least 1 follow-up visit 3 or more months after treatment initiation were included. Duration of treatment with adalimumab was defined as the length of time between initiation and termination of adalimumab, regardless of flares, adverse events, or addition of adjuvant therapy that occurred during this time span. Because standardized rating scales measuring the severity of HS at this time are not recorded routinely at our institution, treatment duration with adalimumab was used as a surrogate for measuring therapeutic success. Additionally, treatment duration is a meaningful end point, as patients with HS may require indefinite treatment. Patients were eligible for inclusion if they were receiving adalimumab for the treatment of HS. Patients were excluded if they were lost to follow-up or had received adalimumab for less than 6 months, as data suggest that biologics do not reach peak effect for up to 6 months in HS.⁴

We identified 116 eligible patients with HS, 32 of whom received combination therapy. Five patients received 40 mg of adalimumab every other week, and 111 patients received 40 mg of adalimumab each week. Patients receiving oral immunosuppressants were more likely to be male and as likely to be biologic naïve compared to patients on monotherapy (Table). The average weekly dose of MTX was 14.63 mg, and the average daily dose of MMF was 1000 mg. The average number of days between starting adalimumab and starting an oral immunosuppressant was 114.5 (SD, 217; median, 0) days. Reasons for discontinuation of adalimumab included insufficient response, noncompliance, dislike of injections, adverse events, fear of adverse events, other medical issues unrelated to HS, and insurance coverage issues. Patients who ended treatment with adalimumab owing to insurance coverage issues were still included in our study because insurance coverage remains a major determinant of treatment choice in HS and is relevant to the dynamics of medical decision-making.

Statistical analysis was conducted on all patients inclusive of any reason for discontinuation to avoid bias in the calculation of treatment duration. Cox regression analysis was conducted for all independent variables and was noncontributory. Kaplan-Meier methodology was used to assess the duration of treatment of adalimumab with and without concomitant oral immunosuppressants, and quartile survival times were calculated. Quartile survival time is the time point after adalimumab initiation at which 25% of patients have discontinued adalimumab. We chose quartile survival time instead of average treatment duration to adequately power this study, given our small patient pool.

Although patients receiving adalimumab with oral immunosuppressants had a longer quartile treatment duration (450 days; 95% CI, 185-1800) than the group without oral immunosuppressants (360 days; 95% CI, 200-700), neither MTX nor MMF was shown to significantly prolong duration of therapy with adalimumab (log-rank test: P=.12). Additionally, patients receiving combination therapy were just as likely to discontinue adalimumab as those on monotherapy (χ² test: P=.93). Patients who took both MTX and MMF at different times did show a statistically significant increase in adalimumab quartile treatment duration (1710 days; 95% CI, 1620 [upper limit not calculable]), but this is likely because these patients were kept on adalimumab while trialing adjunctive medications.

The results of our study indicate that MTX and MMF do not prolong duration of adalimumab therapy, which suggests that adalimumab combination therapy with MTX and MMF may not improve HS more than adalimumab alone, and/or partial responders to adalimumab monotherapy are unlikely to be converted to satisfactory responders with the addition of oral immunosuppressants. Limitations of our study include that it was retrospective, used treatment duration as a surrogate for objective efficacy measures, and relied on a single-institution data source. Additionally, owing to our small sample size, we were unable to account for certain potential confounders, including patient weight and insurance status. Future controlled prospective studies using objective end points are needed to further elucidate whether oral immunosuppressants have a role as an adjunct in the treatment of HS.

To the Editor:

The tumor necrosis factor α inhibitor adalimumab is the only US Food and Drug Administration–approved treatment of hidradenitis suppurativa (HS). Although 50.6% of patients fulfilled Hidradenitis Suppurativa Clinical Response criteria with adalimumab at 12 weeks, many responders were not satisfied with their disease control, and secondary loss of Hidradenitis Suppurativa Clinical Response fulfillment occurred in 15.9% of patients within approximately 3 years.¹ Without other US Food and Drug Administration–approved HS treatments, some dermatologists have combined adalimumab with methotrexate (MTX) and/or mycophenolate mofetil (MMF) to attempt to increase the duration of satisfactory disease control while on adalimumab. Combining tumor necrosis factor α inhibitors with oral immunosuppressants is a well-established approach in psoriasis, psoriatic arthritis, and inflammatory bowel disease; however, to the best of our knowledge, this approach has not been studied for HS.^2,3

To assess whether there is a role for combining adalimumab with MTX and/or MMF in the treatment of HS, we performed a single-institution retrospective chart review at the University of Connecticut Department of Dermatology to determine whether patients receiving combination therapy stayed on adalimumab longer than those who received adalimumab monotherapy. All patients receiving adalimumab for the treatment of HS with at least 1 follow-up visit 3 or more months after treatment initiation were included. Duration of treatment with adalimumab was defined as the length of time between initiation and termination of adalimumab, regardless of flares, adverse events, or addition of adjuvant therapy that occurred during this time span. Because standardized rating scales measuring the severity of HS at this time are not recorded routinely at our institution, treatment duration with adalimumab was used as a surrogate for measuring therapeutic success. Additionally, treatment duration is a meaningful end point, as patients with HS may require indefinite treatment. Patients were eligible for inclusion if they were receiving adalimumab for the treatment of HS. Patients were excluded if they were lost to follow-up or had received adalimumab for less than 6 months, as data suggest that biologics do not reach peak effect for up to 6 months in HS.⁴

We identified 116 eligible patients with HS, 32 of whom received combination therapy. Five patients received 40 mg of adalimumab every other week, and 111 patients received 40 mg of adalimumab each week. Patients receiving oral immunosuppressants were more likely to be male and as likely to be biologic naïve compared to patients on monotherapy (Table). The average weekly dose of MTX was 14.63 mg, and the average daily dose of MMF was 1000 mg. The average number of days between starting adalimumab and starting an oral immunosuppressant was 114.5 (SD, 217; median, 0) days. Reasons for discontinuation of adalimumab included insufficient response, noncompliance, dislike of injections, adverse events, fear of adverse events, other medical issues unrelated to HS, and insurance coverage issues. Patients who ended treatment with adalimumab owing to insurance coverage issues were still included in our study because insurance coverage remains a major determinant of treatment choice in HS and is relevant to the dynamics of medical decision-making.

Statistical analysis was conducted on all patients inclusive of any reason for discontinuation to avoid bias in the calculation of treatment duration. Cox regression analysis was conducted for all independent variables and was noncontributory. Kaplan-Meier methodology was used to assess the duration of treatment of adalimumab with and without concomitant oral immunosuppressants, and quartile survival times were calculated. Quartile survival time is the time point after adalimumab initiation at which 25% of patients have discontinued adalimumab. We chose quartile survival time instead of average treatment duration to adequately power this study, given our small patient pool.

Although patients receiving adalimumab with oral immunosuppressants had a longer quartile treatment duration (450 days; 95% CI, 185-1800) than the group without oral immunosuppressants (360 days; 95% CI, 200-700), neither MTX nor MMF was shown to significantly prolong duration of therapy with adalimumab (log-rank test: P=.12). Additionally, patients receiving combination therapy were just as likely to discontinue adalimumab as those on monotherapy (χ² test: P=.93). Patients who took both MTX and MMF at different times did show a statistically significant increase in adalimumab quartile treatment duration (1710 days; 95% CI, 1620 [upper limit not calculable]), but this is likely because these patients were kept on adalimumab while trialing adjunctive medications.

The results of our study indicate that MTX and MMF do not prolong duration of adalimumab therapy, which suggests that adalimumab combination therapy with MTX and MMF may not improve HS more than adalimumab alone, and/or partial responders to adalimumab monotherapy are unlikely to be converted to satisfactory responders with the addition of oral immunosuppressants. Limitations of our study include that it was retrospective, used treatment duration as a surrogate for objective efficacy measures, and relied on a single-institution data source. Additionally, owing to our small sample size, we were unable to account for certain potential confounders, including patient weight and insurance status. Future controlled prospective studies using objective end points are needed to further elucidate whether oral immunosuppressants have a role as an adjunct in the treatment of HS.

To the Editor:

The tumor necrosis factor α inhibitor adalimumab is the only US Food and Drug Administration–approved treatment of hidradenitis suppurativa (HS). Although 50.6% of patients fulfilled Hidradenitis Suppurativa Clinical Response criteria with adalimumab at 12 weeks, many responders were not satisfied with their disease control, and secondary loss of Hidradenitis Suppurativa Clinical Response fulfillment occurred in 15.9% of patients within approximately 3 years.¹ Without other US Food and Drug Administration–approved HS treatments, some dermatologists have combined adalimumab with methotrexate (MTX) and/or mycophenolate mofetil (MMF) to attempt to increase the duration of satisfactory disease control while on adalimumab. Combining tumor necrosis factor α inhibitors with oral immunosuppressants is a well-established approach in psoriasis, psoriatic arthritis, and inflammatory bowel disease; however, to the best of our knowledge, this approach has not been studied for HS.^2,3

To assess whether there is a role for combining adalimumab with MTX and/or MMF in the treatment of HS, we performed a single-institution retrospective chart review at the University of Connecticut Department of Dermatology to determine whether patients receiving combination therapy stayed on adalimumab longer than those who received adalimumab monotherapy. All patients receiving adalimumab for the treatment of HS with at least 1 follow-up visit 3 or more months after treatment initiation were included. Duration of treatment with adalimumab was defined as the length of time between initiation and termination of adalimumab, regardless of flares, adverse events, or addition of adjuvant therapy that occurred during this time span. Because standardized rating scales measuring the severity of HS at this time are not recorded routinely at our institution, treatment duration with adalimumab was used as a surrogate for measuring therapeutic success. Additionally, treatment duration is a meaningful end point, as patients with HS may require indefinite treatment. Patients were eligible for inclusion if they were receiving adalimumab for the treatment of HS. Patients were excluded if they were lost to follow-up or had received adalimumab for less than 6 months, as data suggest that biologics do not reach peak effect for up to 6 months in HS.⁴

We identified 116 eligible patients with HS, 32 of whom received combination therapy. Five patients received 40 mg of adalimumab every other week, and 111 patients received 40 mg of adalimumab each week. Patients receiving oral immunosuppressants were more likely to be male and as likely to be biologic naïve compared to patients on monotherapy (Table). The average weekly dose of MTX was 14.63 mg, and the average daily dose of MMF was 1000 mg. The average number of days between starting adalimumab and starting an oral immunosuppressant was 114.5 (SD, 217; median, 0) days. Reasons for discontinuation of adalimumab included insufficient response, noncompliance, dislike of injections, adverse events, fear of adverse events, other medical issues unrelated to HS, and insurance coverage issues. Patients who ended treatment with adalimumab owing to insurance coverage issues were still included in our study because insurance coverage remains a major determinant of treatment choice in HS and is relevant to the dynamics of medical decision-making.

Statistical analysis was conducted on all patients inclusive of any reason for discontinuation to avoid bias in the calculation of treatment duration. Cox regression analysis was conducted for all independent variables and was noncontributory. Kaplan-Meier methodology was used to assess the duration of treatment of adalimumab with and without concomitant oral immunosuppressants, and quartile survival times were calculated. Quartile survival time is the time point after adalimumab initiation at which 25% of patients have discontinued adalimumab. We chose quartile survival time instead of average treatment duration to adequately power this study, given our small patient pool.

Although patients receiving adalimumab with oral immunosuppressants had a longer quartile treatment duration (450 days; 95% CI, 185-1800) than the group without oral immunosuppressants (360 days; 95% CI, 200-700), neither MTX nor MMF was shown to significantly prolong duration of therapy with adalimumab (log-rank test: P=.12). Additionally, patients receiving combination therapy were just as likely to discontinue adalimumab as those on monotherapy (χ² test: P=.93). Patients who took both MTX and MMF at different times did show a statistically significant increase in adalimumab quartile treatment duration (1710 days; 95% CI, 1620 [upper limit not calculable]), but this is likely because these patients were kept on adalimumab while trialing adjunctive medications.

The results of our study indicate that MTX and MMF do not prolong duration of adalimumab therapy, which suggests that adalimumab combination therapy with MTX and MMF may not improve HS more than adalimumab alone, and/or partial responders to adalimumab monotherapy are unlikely to be converted to satisfactory responders with the addition of oral immunosuppressants. Limitations of our study include that it was retrospective, used treatment duration as a surrogate for objective efficacy measures, and relied on a single-institution data source. Additionally, owing to our small sample size, we were unable to account for certain potential confounders, including patient weight and insurance status. Future controlled prospective studies using objective end points are needed to further elucidate whether oral immunosuppressants have a role as an adjunct in the treatment of HS.

More than half a million individuals are without housing (NWH) on any given night in the United States, as estimated by the US Department of Housing and Urban Development. ¹ Lack of hygiene, increased risk of infection and infestation due to living conditions, and barriers to health care put these individuals at increased risk for disease. ² Skin disease, including fungal infection and acne, are within the top 10 most prevalent diseases worldwide and can cause major psychologic impairment, yet dermatologic concerns and clinical outcomes in NWH patients have not been well characterized. ^2-5 Further, because this vulnerable demographic tends to be underinsured, they frequently present to the emergency department (ED) for management of disease. ^1,6 Survey of common concerns in NWH patients is of utility to consulting dermatologists and nondermatologist providers in the ED, who can familiarize themselves with management of diseases they are more likely to encounter. Few studies examine dermatologic conditions in the ED, and a thorough literature review indicates none have included homelessness as a variable. ^6,7 Additionally, comparison with a matched control group of patients with housing (WH) is limited. ^5,8 We present one of the largest comparisons of cutaneous disease in NWH vs WH patients in a single hospital system to elucidate the types of cutaneous disease that motivate patients to seek care, the location of skin disease, and differences in clinical care.

Methods

A retrospective medical record review of patients seen for an inclusive list of dermatologic diagnoses in the ED or while admitted at University Medical Center New Orleans, Louisiana (UMC), between January 1, 2018, and April 21, 2020, was conducted. This study was qualified as exempt from the institutional review board by Louisiana State University because it proposed zero risk to the patients and remained completely anonymous. Eight hundred forty-two total medical records were reviewed (NWH, 421; WH, 421)(Table 1). Patients with housing were matched based on self-identified race and ethnicity, sex, and age. Disease categories were constructed based on fundamental pathophysiology adapted from Dermatology⁹: infectious, noninfectious inflammatory, neoplasm, trauma and wounds, drug-related eruptions, vascular, pruritic, pigmented, bullous, neuropsychiatric, and other. Other included unspecified eruptions as well as miscellaneous lesions such as calluses. The current chief concern, anatomic location, and configuration were recorded, as well as biopsied lesions and outpatient referrals or inpatient consultations to dermatology or other specialties, including wound care, infectious disease, podiatry, and surgery. χ² analysis was used to analyze significance of cutaneous categories, body location, and referrals. Groups smaller than 5 defaulted to the Fisher exact test.

Results

The total diagnoses (including both chief concerns and secondary diagnoses) are shown in Table 2. Chief concerns were more frequently cutaneous or dermatologic for WH (NWH, 209; WH, 307; P<.001). In both groups, cutaneous infectious etiologies were more likely to be a patient’s presenting chief concern (58% NWH, P=.002; 42% WH, P<.001). Noninfectious inflammatory etiologies and pigmented lesions were more likely to be secondary diagnoses with an unrelated noncutaneous concern; noninfectious inflammatory etiologies were only 16% of the total cutaneous chief concerns (11% NWH, P=.04; 20% WH, P=.03), and no pigmented lesions were chief concerns.

Infection was the most common chief concern, though NWH patients presented with significantly more infectious concerns (NWH, 212; WH, 150; P<.001), particularly infestations (NWH, 33; WH, 8; P<.001) and bacterial etiologies (NWH, 127; WH, 100; P=.04). The majority of bacterial etiologies were either an abscess or cellulitis (NWH, 106; WH, 83), though infected chronic wounds were categorized as bacterial infection when treated definitively as such (eg, in the case of sacral ulcers causing osteomyelitis)(NWH, 21; WH, 17). Of note, infectious etiology was associated with intravenous drug use (IVDU) in both NWH and WH patients. Of 184 NWH who reported IVDU, 127 had an infectious diagnosis (P<.001). Similarly, 43 of 56 total WH patients who reported IVDU had an infectious diagnosis (P<.001). Infestation (within the infectious category) included scabies (NWH, 20; WH, 3) and insect or arthropod bites (NWH, 12; WH, 5). Two NWH patients also presented with swelling of the lower extremities and were subsequently diagnosed with maggot infestations. Fungal and viral etiologies were not significantly increased in either group; however, NWH did have a higher incidence of tinea pedis (NWH, 14; WH, 4; P=.03).

More neoplasms (NWH, 6; WH, 16; P=.03), noninfectious inflammatory eruptions (NWH, 48; WH, 85; P<.001), and cutaneous drug eruptions (NWH, 5; WH, 27; P<.001) were reported in WH patients. There was no significant difference in benign vs malignant neoplastic processes between groups. More noninfectious inflammatory eruptions in WH were specifically driven by a markedly increased incidence of follicular (NWH, 9; WH, 29; P<.001) and urticarial/erythematous (NWH, 3; WH, 13; P=.02) lesions. Follicular etiologies included acne (NWH, 1; WH, 6; P=.12), folliculitis (NWH, 5; WH, 2; P=.45), hidradenitis suppurativa (NWH, 2; WH, 11; P=.02), and pilonidal and sebaceous cysts (NWH, 1; WH, 10; P=.01). Allergic urticaria dominated the urticarial/erythematous category (NWH, 3; WH, 11; P=.06), though there were 2 WH presentations of diffuse erythema and skin peeling.

Another substantial proportion of cutaneous etiologies were due to trauma or chronic wounds. Significantly more traumatic injuries presented in NWH patients vs WH patients (36 vs 31; P=.04). Trauma included human or dog bites (NWH, 5; WH, 4), sunburns (NWH, 3; WH, 0), other burns (NWH, 11; WH, 13), abrasions and lacerations (NWH, 16; WH, 3; P=.004), and foreign bodies (NWH, 1; WH, 1). Wounds consisted of chronic wounds such as those due to diabetes mellitus (foot ulcers) or immobility (sacral ulcers); numbers were similar between groups.

Looking at location, NWH patients had more pathology on the feet (NWH, 62; WH, 39; P=.02), whereas WH patients had more disseminated multiregional concerns (NWH, 55; WH, 75; P=.05). No one body location was notably more likely to warrant a chief concern.

For clinical outcomes, more WH patients received a consultation of any kind (NWH, 171; WH, 217; P<.001), consultation to dermatology (NWH, 49; WH, 87; P<.001), and consultation to surgery (NWH, 64; WH, 110; P<.001)(Table 3 and Figure). More outpatient referrals to dermatology were made for WH patients (NWH, 61; WH, 82; P=.05). Notably, NWH patients presented for 80% fewer hospital follow-up appointments (NWH, 11; WH, 55; P<.001). It is essential to note that these findings were not affected by self-reported race or ethnicity. Results remained significant when broken into cohorts consisting of patients with and without skin of color.

Comment

Cutaneous Concerns in NWH Patients—Although cutaneous disease has been reported to disproportionately affect NWH patients,¹⁰ in our cohort, NWH patients had fewer cutaneous chief concerns than WH patients. However, without comparing with all patients entering the ED at UMC, we cannot make a statement on this claim. We do present a few reasons why NWH patients do not have more cutaneous concerns. First, they may wait to present with cutaneous disease until it becomes more severe (eg, until chronic wounds have progressed to infections). Second, as discussed in depth by Hollestein and Nijsten,³ dermatologic disease may be a major contributor to the overall count of disability-adjusted life years but may play a minor role in individual disability. Therefore, skin disease often is considered less important on an individual basis, despite substantial psychosocial burden, leading to further stigmatization of this vulnerable population and discouraged care-seeking behavior, particularly for noninfectious inflammatory eruptions, which were notably more present in WH individuals. Third, fewer dermatologic lesions were reported on NWH patients, which may explain why all 3 WH pigmented lesions were diagnosed after presentation with a noncutaneous concern (eg, headache, anemia, nausea).

Infectious Cutaneous Diagnoses—The increased presentation of infectious etiologies, especially bacterial, is linked to the increased numbers of IVDUs reported in NWH individuals as well as increased exposure and decreased access to basic hygienic supplies. Intravenous drug use acted as an effect modifier of infectious etiology diagnoses, playing a major role in both NWH and WH cohorts. Although Black and Hispanic individuals as well as individuals with low socioeconomic status have increased proportions of skin cancer, there are inadequate data on the prevalence in NWH individuals.⁴ We found no increase in malignant dermatologic processes in NWH individuals; however, this may be secondary to inadequate screening with a total body skin examination.

Clinical Workup of NWH Patients—Because most NWH individuals present to the ED to receive care, their care compared with WH patients should be considered. In this cohort, WH patients received a less extensive clinical workup. They received almost half as many dermatologic consultations and fewer outpatient referrals to dermatology. Major communication barriers may affect NWH presentation to follow-up, which was drastically lower than WH individuals, as scheduling typically occurs well after discharge from the ED or inpatient unit. We suggest a few alterations to improve dermatologic care for NWH individuals:

• Consider inpatient consultation for serious dermatologic conditions—even if chronic—to improve disease control, considering that many barriers inhibit follow-up in clinic.

• Involve outreach teams, such as the Assertive Community Treatment teams, that assist individuals by delivering medicine for psychiatric disorders, conducting total-body skin examinations, assisting with wound care, providing basic skin barrier creams or medicaments, and carrying information regarding outpatient follow-up.

• Educate ED providers on the most common skin concerns, especially those that fall within the noninfectious inflammatory category, such as hidradenitis suppurativa, which could easily be misdiagnosed as an abscess.

Future Directions—Owing to limitations of a retrospective cohort study, we present several opportunities for further research on this vulnerable population. The severity of disease, especially infectious etiologies, should be graded to determine if NWH patients truly present later in the disease course. The duration and quality of housing for NWH patients could be categorized based on living conditions (eg, on the street vs in a shelter). Although the findings of our NWH cohort presenting to the ED at UMC provide helpful insight into dermatologic disease, these findings may be disparate from those conducted at other locations in the United States. University Medical Center provides care to mostly subsidized insurance plans in a racially diverse community. Improved outcomes for the NWH individuals living in New Orleans start with obtaining a greater understanding of their diseases and where disparities exist that can be bridged with better care.

Acknowledgment—The dataset generated during this study and used for analysis is not publicly available to protect public health information but is available from the corresponding author on reasonable request.

More than half a million individuals are without housing (NWH) on any given night in the United States, as estimated by the US Department of Housing and Urban Development. ¹ Lack of hygiene, increased risk of infection and infestation due to living conditions, and barriers to health care put these individuals at increased risk for disease. ² Skin disease, including fungal infection and acne, are within the top 10 most prevalent diseases worldwide and can cause major psychologic impairment, yet dermatologic concerns and clinical outcomes in NWH patients have not been well characterized. ^2-5 Further, because this vulnerable demographic tends to be underinsured, they frequently present to the emergency department (ED) for management of disease. ^1,6 Survey of common concerns in NWH patients is of utility to consulting dermatologists and nondermatologist providers in the ED, who can familiarize themselves with management of diseases they are more likely to encounter. Few studies examine dermatologic conditions in the ED, and a thorough literature review indicates none have included homelessness as a variable. ^6,7 Additionally, comparison with a matched control group of patients with housing (WH) is limited. ^5,8 We present one of the largest comparisons of cutaneous disease in NWH vs WH patients in a single hospital system to elucidate the types of cutaneous disease that motivate patients to seek care, the location of skin disease, and differences in clinical care.

Methods

A retrospective medical record review of patients seen for an inclusive list of dermatologic diagnoses in the ED or while admitted at University Medical Center New Orleans, Louisiana (UMC), between January 1, 2018, and April 21, 2020, was conducted. This study was qualified as exempt from the institutional review board by Louisiana State University because it proposed zero risk to the patients and remained completely anonymous. Eight hundred forty-two total medical records were reviewed (NWH, 421; WH, 421)(Table 1). Patients with housing were matched based on self-identified race and ethnicity, sex, and age. Disease categories were constructed based on fundamental pathophysiology adapted from Dermatology⁹: infectious, noninfectious inflammatory, neoplasm, trauma and wounds, drug-related eruptions, vascular, pruritic, pigmented, bullous, neuropsychiatric, and other. Other included unspecified eruptions as well as miscellaneous lesions such as calluses. The current chief concern, anatomic location, and configuration were recorded, as well as biopsied lesions and outpatient referrals or inpatient consultations to dermatology or other specialties, including wound care, infectious disease, podiatry, and surgery. χ² analysis was used to analyze significance of cutaneous categories, body location, and referrals. Groups smaller than 5 defaulted to the Fisher exact test.

Results

The total diagnoses (including both chief concerns and secondary diagnoses) are shown in Table 2. Chief concerns were more frequently cutaneous or dermatologic for WH (NWH, 209; WH, 307; P<.001). In both groups, cutaneous infectious etiologies were more likely to be a patient’s presenting chief concern (58% NWH, P=.002; 42% WH, P<.001). Noninfectious inflammatory etiologies and pigmented lesions were more likely to be secondary diagnoses with an unrelated noncutaneous concern; noninfectious inflammatory etiologies were only 16% of the total cutaneous chief concerns (11% NWH, P=.04; 20% WH, P=.03), and no pigmented lesions were chief concerns.

Infection was the most common chief concern, though NWH patients presented with significantly more infectious concerns (NWH, 212; WH, 150; P<.001), particularly infestations (NWH, 33; WH, 8; P<.001) and bacterial etiologies (NWH, 127; WH, 100; P=.04). The majority of bacterial etiologies were either an abscess or cellulitis (NWH, 106; WH, 83), though infected chronic wounds were categorized as bacterial infection when treated definitively as such (eg, in the case of sacral ulcers causing osteomyelitis)(NWH, 21; WH, 17). Of note, infectious etiology was associated with intravenous drug use (IVDU) in both NWH and WH patients. Of 184 NWH who reported IVDU, 127 had an infectious diagnosis (P<.001). Similarly, 43 of 56 total WH patients who reported IVDU had an infectious diagnosis (P<.001). Infestation (within the infectious category) included scabies (NWH, 20; WH, 3) and insect or arthropod bites (NWH, 12; WH, 5). Two NWH patients also presented with swelling of the lower extremities and were subsequently diagnosed with maggot infestations. Fungal and viral etiologies were not significantly increased in either group; however, NWH did have a higher incidence of tinea pedis (NWH, 14; WH, 4; P=.03).

More neoplasms (NWH, 6; WH, 16; P=.03), noninfectious inflammatory eruptions (NWH, 48; WH, 85; P<.001), and cutaneous drug eruptions (NWH, 5; WH, 27; P<.001) were reported in WH patients. There was no significant difference in benign vs malignant neoplastic processes between groups. More noninfectious inflammatory eruptions in WH were specifically driven by a markedly increased incidence of follicular (NWH, 9; WH, 29; P<.001) and urticarial/erythematous (NWH, 3; WH, 13; P=.02) lesions. Follicular etiologies included acne (NWH, 1; WH, 6; P=.12), folliculitis (NWH, 5; WH, 2; P=.45), hidradenitis suppurativa (NWH, 2; WH, 11; P=.02), and pilonidal and sebaceous cysts (NWH, 1; WH, 10; P=.01). Allergic urticaria dominated the urticarial/erythematous category (NWH, 3; WH, 11; P=.06), though there were 2 WH presentations of diffuse erythema and skin peeling.

Another substantial proportion of cutaneous etiologies were due to trauma or chronic wounds. Significantly more traumatic injuries presented in NWH patients vs WH patients (36 vs 31; P=.04). Trauma included human or dog bites (NWH, 5; WH, 4), sunburns (NWH, 3; WH, 0), other burns (NWH, 11; WH, 13), abrasions and lacerations (NWH, 16; WH, 3; P=.004), and foreign bodies (NWH, 1; WH, 1). Wounds consisted of chronic wounds such as those due to diabetes mellitus (foot ulcers) or immobility (sacral ulcers); numbers were similar between groups.

Looking at location, NWH patients had more pathology on the feet (NWH, 62; WH, 39; P=.02), whereas WH patients had more disseminated multiregional concerns (NWH, 55; WH, 75; P=.05). No one body location was notably more likely to warrant a chief concern.

For clinical outcomes, more WH patients received a consultation of any kind (NWH, 171; WH, 217; P<.001), consultation to dermatology (NWH, 49; WH, 87; P<.001), and consultation to surgery (NWH, 64; WH, 110; P<.001)(Table 3 and Figure). More outpatient referrals to dermatology were made for WH patients (NWH, 61; WH, 82; P=.05). Notably, NWH patients presented for 80% fewer hospital follow-up appointments (NWH, 11; WH, 55; P<.001). It is essential to note that these findings were not affected by self-reported race or ethnicity. Results remained significant when broken into cohorts consisting of patients with and without skin of color.

Comment

Cutaneous Concerns in NWH Patients—Although cutaneous disease has been reported to disproportionately affect NWH patients,¹⁰ in our cohort, NWH patients had fewer cutaneous chief concerns than WH patients. However, without comparing with all patients entering the ED at UMC, we cannot make a statement on this claim. We do present a few reasons why NWH patients do not have more cutaneous concerns. First, they may wait to present with cutaneous disease until it becomes more severe (eg, until chronic wounds have progressed to infections). Second, as discussed in depth by Hollestein and Nijsten,³ dermatologic disease may be a major contributor to the overall count of disability-adjusted life years but may play a minor role in individual disability. Therefore, skin disease often is considered less important on an individual basis, despite substantial psychosocial burden, leading to further stigmatization of this vulnerable population and discouraged care-seeking behavior, particularly for noninfectious inflammatory eruptions, which were notably more present in WH individuals. Third, fewer dermatologic lesions were reported on NWH patients, which may explain why all 3 WH pigmented lesions were diagnosed after presentation with a noncutaneous concern (eg, headache, anemia, nausea).

Infectious Cutaneous Diagnoses—The increased presentation of infectious etiologies, especially bacterial, is linked to the increased numbers of IVDUs reported in NWH individuals as well as increased exposure and decreased access to basic hygienic supplies. Intravenous drug use acted as an effect modifier of infectious etiology diagnoses, playing a major role in both NWH and WH cohorts. Although Black and Hispanic individuals as well as individuals with low socioeconomic status have increased proportions of skin cancer, there are inadequate data on the prevalence in NWH individuals.⁴ We found no increase in malignant dermatologic processes in NWH individuals; however, this may be secondary to inadequate screening with a total body skin examination.

Clinical Workup of NWH Patients—Because most NWH individuals present to the ED to receive care, their care compared with WH patients should be considered. In this cohort, WH patients received a less extensive clinical workup. They received almost half as many dermatologic consultations and fewer outpatient referrals to dermatology. Major communication barriers may affect NWH presentation to follow-up, which was drastically lower than WH individuals, as scheduling typically occurs well after discharge from the ED or inpatient unit. We suggest a few alterations to improve dermatologic care for NWH individuals:

• Consider inpatient consultation for serious dermatologic conditions—even if chronic—to improve disease control, considering that many barriers inhibit follow-up in clinic.

• Involve outreach teams, such as the Assertive Community Treatment teams, that assist individuals by delivering medicine for psychiatric disorders, conducting total-body skin examinations, assisting with wound care, providing basic skin barrier creams or medicaments, and carrying information regarding outpatient follow-up.

• Educate ED providers on the most common skin concerns, especially those that fall within the noninfectious inflammatory category, such as hidradenitis suppurativa, which could easily be misdiagnosed as an abscess.

Future Directions—Owing to limitations of a retrospective cohort study, we present several opportunities for further research on this vulnerable population. The severity of disease, especially infectious etiologies, should be graded to determine if NWH patients truly present later in the disease course. The duration and quality of housing for NWH patients could be categorized based on living conditions (eg, on the street vs in a shelter). Although the findings of our NWH cohort presenting to the ED at UMC provide helpful insight into dermatologic disease, these findings may be disparate from those conducted at other locations in the United States. University Medical Center provides care to mostly subsidized insurance plans in a racially diverse community. Improved outcomes for the NWH individuals living in New Orleans start with obtaining a greater understanding of their diseases and where disparities exist that can be bridged with better care.

Acknowledgment—The dataset generated during this study and used for analysis is not publicly available to protect public health information but is available from the corresponding author on reasonable request.

More than half a million individuals are without housing (NWH) on any given night in the United States, as estimated by the US Department of Housing and Urban Development. ¹ Lack of hygiene, increased risk of infection and infestation due to living conditions, and barriers to health care put these individuals at increased risk for disease. ² Skin disease, including fungal infection and acne, are within the top 10 most prevalent diseases worldwide and can cause major psychologic impairment, yet dermatologic concerns and clinical outcomes in NWH patients have not been well characterized. ^2-5 Further, because this vulnerable demographic tends to be underinsured, they frequently present to the emergency department (ED) for management of disease. ^1,6 Survey of common concerns in NWH patients is of utility to consulting dermatologists and nondermatologist providers in the ED, who can familiarize themselves with management of diseases they are more likely to encounter. Few studies examine dermatologic conditions in the ED, and a thorough literature review indicates none have included homelessness as a variable. ^6,7 Additionally, comparison with a matched control group of patients with housing (WH) is limited. ^5,8 We present one of the largest comparisons of cutaneous disease in NWH vs WH patients in a single hospital system to elucidate the types of cutaneous disease that motivate patients to seek care, the location of skin disease, and differences in clinical care.

Methods

A retrospective medical record review of patients seen for an inclusive list of dermatologic diagnoses in the ED or while admitted at University Medical Center New Orleans, Louisiana (UMC), between January 1, 2018, and April 21, 2020, was conducted. This study was qualified as exempt from the institutional review board by Louisiana State University because it proposed zero risk to the patients and remained completely anonymous. Eight hundred forty-two total medical records were reviewed (NWH, 421; WH, 421)(Table 1). Patients with housing were matched based on self-identified race and ethnicity, sex, and age. Disease categories were constructed based on fundamental pathophysiology adapted from Dermatology⁹: infectious, noninfectious inflammatory, neoplasm, trauma and wounds, drug-related eruptions, vascular, pruritic, pigmented, bullous, neuropsychiatric, and other. Other included unspecified eruptions as well as miscellaneous lesions such as calluses. The current chief concern, anatomic location, and configuration were recorded, as well as biopsied lesions and outpatient referrals or inpatient consultations to dermatology or other specialties, including wound care, infectious disease, podiatry, and surgery. χ² analysis was used to analyze significance of cutaneous categories, body location, and referrals. Groups smaller than 5 defaulted to the Fisher exact test.

Results

The total diagnoses (including both chief concerns and secondary diagnoses) are shown in Table 2. Chief concerns were more frequently cutaneous or dermatologic for WH (NWH, 209; WH, 307; P<.001). In both groups, cutaneous infectious etiologies were more likely to be a patient’s presenting chief concern (58% NWH, P=.002; 42% WH, P<.001). Noninfectious inflammatory etiologies and pigmented lesions were more likely to be secondary diagnoses with an unrelated noncutaneous concern; noninfectious inflammatory etiologies were only 16% of the total cutaneous chief concerns (11% NWH, P=.04; 20% WH, P=.03), and no pigmented lesions were chief concerns.

Infection was the most common chief concern, though NWH patients presented with significantly more infectious concerns (NWH, 212; WH, 150; P<.001), particularly infestations (NWH, 33; WH, 8; P<.001) and bacterial etiologies (NWH, 127; WH, 100; P=.04). The majority of bacterial etiologies were either an abscess or cellulitis (NWH, 106; WH, 83), though infected chronic wounds were categorized as bacterial infection when treated definitively as such (eg, in the case of sacral ulcers causing osteomyelitis)(NWH, 21; WH, 17). Of note, infectious etiology was associated with intravenous drug use (IVDU) in both NWH and WH patients. Of 184 NWH who reported IVDU, 127 had an infectious diagnosis (P<.001). Similarly, 43 of 56 total WH patients who reported IVDU had an infectious diagnosis (P<.001). Infestation (within the infectious category) included scabies (NWH, 20; WH, 3) and insect or arthropod bites (NWH, 12; WH, 5). Two NWH patients also presented with swelling of the lower extremities and were subsequently diagnosed with maggot infestations. Fungal and viral etiologies were not significantly increased in either group; however, NWH did have a higher incidence of tinea pedis (NWH, 14; WH, 4; P=.03).

More neoplasms (NWH, 6; WH, 16; P=.03), noninfectious inflammatory eruptions (NWH, 48; WH, 85; P<.001), and cutaneous drug eruptions (NWH, 5; WH, 27; P<.001) were reported in WH patients. There was no significant difference in benign vs malignant neoplastic processes between groups. More noninfectious inflammatory eruptions in WH were specifically driven by a markedly increased incidence of follicular (NWH, 9; WH, 29; P<.001) and urticarial/erythematous (NWH, 3; WH, 13; P=.02) lesions. Follicular etiologies included acne (NWH, 1; WH, 6; P=.12), folliculitis (NWH, 5; WH, 2; P=.45), hidradenitis suppurativa (NWH, 2; WH, 11; P=.02), and pilonidal and sebaceous cysts (NWH, 1; WH, 10; P=.01). Allergic urticaria dominated the urticarial/erythematous category (NWH, 3; WH, 11; P=.06), though there were 2 WH presentations of diffuse erythema and skin peeling.

Another substantial proportion of cutaneous etiologies were due to trauma or chronic wounds. Significantly more traumatic injuries presented in NWH patients vs WH patients (36 vs 31; P=.04). Trauma included human or dog bites (NWH, 5; WH, 4), sunburns (NWH, 3; WH, 0), other burns (NWH, 11; WH, 13), abrasions and lacerations (NWH, 16; WH, 3; P=.004), and foreign bodies (NWH, 1; WH, 1). Wounds consisted of chronic wounds such as those due to diabetes mellitus (foot ulcers) or immobility (sacral ulcers); numbers were similar between groups.

Looking at location, NWH patients had more pathology on the feet (NWH, 62; WH, 39; P=.02), whereas WH patients had more disseminated multiregional concerns (NWH, 55; WH, 75; P=.05). No one body location was notably more likely to warrant a chief concern.

For clinical outcomes, more WH patients received a consultation of any kind (NWH, 171; WH, 217; P<.001), consultation to dermatology (NWH, 49; WH, 87; P<.001), and consultation to surgery (NWH, 64; WH, 110; P<.001)(Table 3 and Figure). More outpatient referrals to dermatology were made for WH patients (NWH, 61; WH, 82; P=.05). Notably, NWH patients presented for 80% fewer hospital follow-up appointments (NWH, 11; WH, 55; P<.001). It is essential to note that these findings were not affected by self-reported race or ethnicity. Results remained significant when broken into cohorts consisting of patients with and without skin of color.

Comment

Cutaneous Concerns in NWH Patients—Although cutaneous disease has been reported to disproportionately affect NWH patients,¹⁰ in our cohort, NWH patients had fewer cutaneous chief concerns than WH patients. However, without comparing with all patients entering the ED at UMC, we cannot make a statement on this claim. We do present a few reasons why NWH patients do not have more cutaneous concerns. First, they may wait to present with cutaneous disease until it becomes more severe (eg, until chronic wounds have progressed to infections). Second, as discussed in depth by Hollestein and Nijsten,³ dermatologic disease may be a major contributor to the overall count of disability-adjusted life years but may play a minor role in individual disability. Therefore, skin disease often is considered less important on an individual basis, despite substantial psychosocial burden, leading to further stigmatization of this vulnerable population and discouraged care-seeking behavior, particularly for noninfectious inflammatory eruptions, which were notably more present in WH individuals. Third, fewer dermatologic lesions were reported on NWH patients, which may explain why all 3 WH pigmented lesions were diagnosed after presentation with a noncutaneous concern (eg, headache, anemia, nausea).

Infectious Cutaneous Diagnoses—The increased presentation of infectious etiologies, especially bacterial, is linked to the increased numbers of IVDUs reported in NWH individuals as well as increased exposure and decreased access to basic hygienic supplies. Intravenous drug use acted as an effect modifier of infectious etiology diagnoses, playing a major role in both NWH and WH cohorts. Although Black and Hispanic individuals as well as individuals with low socioeconomic status have increased proportions of skin cancer, there are inadequate data on the prevalence in NWH individuals.⁴ We found no increase in malignant dermatologic processes in NWH individuals; however, this may be secondary to inadequate screening with a total body skin examination.

Clinical Workup of NWH Patients—Because most NWH individuals present to the ED to receive care, their care compared with WH patients should be considered. In this cohort, WH patients received a less extensive clinical workup. They received almost half as many dermatologic consultations and fewer outpatient referrals to dermatology. Major communication barriers may affect NWH presentation to follow-up, which was drastically lower than WH individuals, as scheduling typically occurs well after discharge from the ED or inpatient unit. We suggest a few alterations to improve dermatologic care for NWH individuals:

• Consider inpatient consultation for serious dermatologic conditions—even if chronic—to improve disease control, considering that many barriers inhibit follow-up in clinic.

• Involve outreach teams, such as the Assertive Community Treatment teams, that assist individuals by delivering medicine for psychiatric disorders, conducting total-body skin examinations, assisting with wound care, providing basic skin barrier creams or medicaments, and carrying information regarding outpatient follow-up.

• Educate ED providers on the most common skin concerns, especially those that fall within the noninfectious inflammatory category, such as hidradenitis suppurativa, which could easily be misdiagnosed as an abscess.

Future Directions—Owing to limitations of a retrospective cohort study, we present several opportunities for further research on this vulnerable population. The severity of disease, especially infectious etiologies, should be graded to determine if NWH patients truly present later in the disease course. The duration and quality of housing for NWH patients could be categorized based on living conditions (eg, on the street vs in a shelter). Although the findings of our NWH cohort presenting to the ED at UMC provide helpful insight into dermatologic disease, these findings may be disparate from those conducted at other locations in the United States. University Medical Center provides care to mostly subsidized insurance plans in a racially diverse community. Improved outcomes for the NWH individuals living in New Orleans start with obtaining a greater understanding of their diseases and where disparities exist that can be bridged with better care.

Acknowledgment—The dataset generated during this study and used for analysis is not publicly available to protect public health information but is available from the corresponding author on reasonable request.

Vancomycin is a widely used IV antibiotic due to its broad-spectrum of activity, bactericidal nature, and low rates of resistance; however, adverse effects (AEs), including nephrotoxicity, are commonly associated with its use.¹ The vancomycin therapeutic monitoring guidelines recognize the incidence of nephrotoxicity and suggest strategies for reducing the risk, including area under the curve/mean inhibitory concentration (AUC/MIC) monitoring rather than trough-only monitoring. Vancomycin-associated acute kidney injury (AKI) has been defined as an increase in serum creatinine (SCr) over a 48-hour period of ≥ 0.3 mg/dL or a percentage increase of ≥ 50%, which is consistent with the Acute Kidney Injury Network (AKIN) guidelines.^2,3 Vancomycin-associated AKI is a common AE, with its incidence reported in previous studies ranging from 10 to 20%.^4,5

The most common crystalloid fluid administered to patients in the United States is 0.9% sodium chloride (NaCl), also known as normal saline (NS), and recent trials have explored its potential to cause AEs.^6-8 Balanced crystalloid solutions, such as Plasma-Lyte and lactated Ringer’s solution (LR), contain buffering agents and lower concentrations of sodium and chloride compared with that of NS. Trials in the intensive care unit (ICU) and emergency department, such as the SMART-MED, SMART-SURG, and SALT-ED have reported a significantly lower rate of AKI when using balanced crystalloids compared with NS due to the concentration of sodium and chloride in NS being supraphysiologic to normal serum concentrations.^6,7 Alternatively, the SPLIT trial evaluated the use of NS compared with Plasma-Lyte for ICU fluid therapy and did not find a statistically significant difference in AKI.⁸ Furthermore, some studies have reported increased risk for hyperchloremia when using NS compared with dextrose 5% in water (D5W) or balanced crystalloids, which can result in metabolic acidosis.^6,7,9,10 These studies have shown how the choice of fluid can have a large effect on the incidence of AEs; bringing into question whether these effects could be additive when combined with the nephrotoxicity associated with vancomycin.^6-9

Vancomycin is physically and chemically stable if diluted in D5W, NS, 5% dextrose in NS, LR, or 5% dextrose in LR.¹ It is not known whether the selection of diluent has an effect on nephrotoxicity or other AEs of vancomycin therapy. Furthermore, clinicians may be unaware or unable to specify which diluent to use. There are currently no practice guidelines that favor one diluent over another for vancomycin; however, trials showing higher rates of AKI and hyperchloremia using NS for fluid resuscitation may indicate an increased potential for vancomycin-associated AKI when using NS as a diluent.^6,7,9 This study was performed to evaluate whether the type of crystalloid used (D5W vs NS) can influence adverse outcomes for patients. While many factors may contribute to these AEs, the potential to reduce the risk of negative adverse outcomes for hospitalized patients is a significant area of exploration.

The primary outcome of this study was the incidence of AKI, defined using AKIN guidelines where the increase in SCr occurred at least 24 hours after starting vancomycin and within 36 hours of receiving the last vancomycin dose.³ AKI was staged using the AKIN guidelines (stage 1: increase in SCr of ≥ 0.3 mg/dL or by 50 to 99%; stage 2: increase in SCr by 100 to 199%; stage 3: increase in SCr by > 200%) based on changes in SCr from baseline during vancomycin therapy or within 36 hours of stopping vancomycin therapy.³ Secondary outcomes included the incidence of hyperglycemia, hyperchloremia, metabolic acidosis, hypernatremia, mortality in hospital, and mortality within 30 days from hospital discharge.

Methods

This single-center, retrospective study of veterans who received IV vancomycin within the North Florida/South Georgia Veterans Health System (NF/SGVHS) in Gainesville, Florida, from July 1, 2015 to June 30, 2020, compared veterans who received vancomycin diluted in NS with those who received vancomycin diluted in D5W to assess for differences in AEs, including AKI, metabolic acidosis (serum bicarbonate level < 23 mmol/L), hyperchloremia (serum chloride levels > 108 mmol/L), hypernatremia (serum sodium > 145 mmol/L), and hyperglycemia (blood glucose > 180 mg/dL). The endpoint values were defined using the reference ranges determined by the local laboratory. At NF/SGVHS, vancomycin is diluted in D5W or NS based primarily on factors such as product availability and cost.

Study Criteria

Veterans were included if they received IV vancomycin between July 1, 2015 and June 30, 2020. The cohorts were grouped into those receiving vancomycin doses diluted in NS and those receiving vancomycin doses diluted in D5W. Veterans were excluded if they received < 80% of vancomycin doses diluted in their respective fluid, if they were on vancomycin for < 48 hours, or if they did not have laboratory results collected both before and after vancomycin therapy to assess a change. There were more patients receiving vancomycin in D5W, so a random sample was selected to have an equal size comparison group with those receiving NS. A sample size calculation was performed with an anticipated AKI incidence of 14%.⁵ To detect a 10% difference in the primary outcome with an α of 0.05 and 75% power, 226 patients (113 in each cohort) were needed for inclusion.

Data were collected using the Data Access Request Tracker tool through the US Department of Veterans Affairs (VA) Informatics and Computing Infrastructure. Data collected included demographics, laboratory data at baseline and during vancomycin therapy, characteristics of antibiotic therapy, and mortality data. Of note, all laboratory values assessed in this study were obtained while the veteran was receiving vancomycin or within 36 hours of receiving the last vancomycin dose to appropriately assess any changes.

Statistical analysis of categorical data were analyzed using a χ² test on the GraphPad online program. This study received institutional review board approval from the University of Florida and was conducted in accordance with protections for human subjects.

Results

A total of 792 veterans received IV vancomycin NF/SGVHS in the defined study period. Of these, 381 veterans were excluded, including having < 80% of doses in a single solution (213 veterans), receiving IV vancomycin for < 48 hours (149 veterans), and not having necessary laboratory data available to assess a change in kidney function (19 veterans). An additional 165 veterans were randomly excluded from the D5W cohort in order to have an equal comparison group to the NS cohort; therefore, a total of 246 veterans were included in the final assessment (123 veterans in each cohort). The median patient age was 73 years (IQR, 68.0, 80.5) in the D5W group and 66 years (IQR, 60.0, 74.0) in the NS group; 83.7% of veterans in the D5W group and 74% veterans in the NS group were white; 94.3% of the D5W group and 100% of the NS group were male (Table 1).

Adverse Effects by Solution

The percentage of AKI in the D5W group was 22.8% compared with 14.6% in the NS group (P = .14), and all cases were classified as stage 1 AKI. Baseline cases of hyperglycemia, hypernatremia, hyperchloremia, or metabolic acidosis were not included in the reported rates of each in order to determine a change during vancomycin therapy (Table 2).

The percentage of patients with hyperglycemia in the D5W group was 32.5% compared with 39.8% in the NS group (P = .29). The percentage of patients with hypernatremia in the D5W group was 15.4% compared with 10.6% in the NS group (P = .34). The percentage of patients with hyperchloremia in the D5W group was 22.8% compared with 17.9% in the NS group (P = .43). The percentage of patients with metabolic acidosis in the D5W group was 48.0% compared with 49.6% in the NS group (P = .90).

Discussion

This retrospective cohort study comparing the AEs of vancomycin diluted in NS and vancomycin diluted with D5W showed no statistically significant differences in the incidence of AKI or any metabolic AEs. Although these results did not show an association between the incidence of AEs and the dilution fluid for vancomycin, other factors may contribute to the overall incidence of AEs. Factors such as cumulative vancomycin dose, duration of therapy, and presence of concomitant nephrotoxins have been known to increase the incidence of AKI and may have a greater impact on this incidence than the fluid used in administering the vancomycin.

These results specifically the incidence of AKI were not consistent with previous trials evaluating the AEs of NS. Based on previous trials, we expected the vancomycin in the NS cohort to have a significantly higher incidence of hypernatremia, hyperchloremia, and AKI. Our results may indicate that the volume of crystalloid received played a greater role on the incidence of AEs. Our study assessed the effect of a diluent for one IV medication that may have been only a few hundred milliliters of fluid per day. The total volume of IV fluid received from vancomycin was not assessed; thus, it is not known how the volume of fluid may have impacted the results.

One consideration with this study is the method used for monitoring vancomycin levels. Most of the patients included in this study were admitted prior to the release of the updated vancomycin guidelines, which advocated for the transition from traditional trough-only monitoring to AUC/MIC. In September 2019, NF/SGVHS ICUs made the transition to this new method of monitoring with a hospital-wide transition following the study end date. The D5W group had a slightly higher percentage of patients admitted to the ICU, thus were more likely to be monitored using AUC/MIC during this period. Literature has shown the AUC/MIC method of monitoring can result in a decreased daily dose, decreased trough levels, and decreased incidence of nephrotoxicity.^11-14 Although the method for monitoring vancomycin has the potential to affect the incidence of AKI, the majority of patients were monitored using the traditional trough-only method with similar trough levels reported in both groups.

Limitations

This study is limited by its retrospective nature, the potential introduction of biases, and the inability to control for confounders that may have influenced the incidence of AEs. Potential confounders present in this study included the use of concomitant nephrotoxic medications, vancomycin dose, and underlying conditions, as these could have impacted the overall incidence of AEs.

The combination of piperacillin/tazobactam plus vancomycin has commonly been associated with an increased risk of nephrotoxicity. Previous studies have identified this nephrotoxic combination to have a significantly increased risk of AKI compared with vancomycin alone or when used in combination with alternative antibiotics such as cefepime or meropenem.^15,16 In our study, there was a higher percentage of patients in the NS group with concomitant piperacillin/tazobactam, so this difference between the groups may have influenced the incidence of AKI. Nephrotoxic medications other than antibiotics were not assessed in this study; however, these also could have impacted our results significantly. While the vancomycin duration of therapy and highest trough levels were similar between groups, the NS group had a larger average daily dose and overall cumulative dose. Studies have identified the risk of nephrotoxicity increases with a vancomycin daily dose of 4 g, troughs > 15 mg/mL, and a duration of therapy > 7 days.^15,16 In our study, the daily doses in both groups were < 4 g, so it is likely the average daily vancomycin dose had little impact on the incidence of AKI.

Another potential confounder identified was assessment of underlying conditions in the patients. Due to the limitations associated with the data extraction method, we could not assess for underlying conditions that may have impacted the results. Notably, the potential nephrotoxicity of NS has mostly been shown in critically ill patients. Therefore, the mixed acutely ill patient sample in this study may have been less likely to develop AKI from NS compared with an exclusively critically ill patient sample.

Selection bias and information bias are common with observational studies. In our study, selection bias may have been present since prospective randomization of patient samples was not possible. Since all data were extracted from the medical health record, information bias may have been present with the potential to impact the results. Due to the single-center nature of this study with a predominantly older, white male veteran patient sample, generalizability to other patient populations may be limited. We would expect the results of this study to be similar among other patient populations of a similar age and demographic; however, the external validity of this study may be weak among other populations. Although this study included enough patients based on sample size estimate, a larger sample size could have allowed for detection of smaller differences between groups and decreased the chance for type II error.

Conclusions

Overall, the results of this study do not suggest that the crystalloid used to dilute IV vancomycin is associated with differences in nephrotoxicity or other relevant AEs. Future studies evaluating the potential for AEs from medication diluent are warranted and would benefit from a prospective, randomized design. Further studies are both necessary and crucial for enhancing the quality of care to minimize the rates of AEs of commonly used medications.

Acknowledgment
This material is the result of work supported with resources and the use of facilities at the North Florida/South Georgia Veterans Health System in Gainesville, Florida.

Vancomycin is a widely used IV antibiotic due to its broad-spectrum of activity, bactericidal nature, and low rates of resistance; however, adverse effects (AEs), including nephrotoxicity, are commonly associated with its use.¹ The vancomycin therapeutic monitoring guidelines recognize the incidence of nephrotoxicity and suggest strategies for reducing the risk, including area under the curve/mean inhibitory concentration (AUC/MIC) monitoring rather than trough-only monitoring. Vancomycin-associated acute kidney injury (AKI) has been defined as an increase in serum creatinine (SCr) over a 48-hour period of ≥ 0.3 mg/dL or a percentage increase of ≥ 50%, which is consistent with the Acute Kidney Injury Network (AKIN) guidelines.^2,3 Vancomycin-associated AKI is a common AE, with its incidence reported in previous studies ranging from 10 to 20%.^4,5

The most common crystalloid fluid administered to patients in the United States is 0.9% sodium chloride (NaCl), also known as normal saline (NS), and recent trials have explored its potential to cause AEs.^6-8 Balanced crystalloid solutions, such as Plasma-Lyte and lactated Ringer’s solution (LR), contain buffering agents and lower concentrations of sodium and chloride compared with that of NS. Trials in the intensive care unit (ICU) and emergency department, such as the SMART-MED, SMART-SURG, and SALT-ED have reported a significantly lower rate of AKI when using balanced crystalloids compared with NS due to the concentration of sodium and chloride in NS being supraphysiologic to normal serum concentrations.^6,7 Alternatively, the SPLIT trial evaluated the use of NS compared with Plasma-Lyte for ICU fluid therapy and did not find a statistically significant difference in AKI.⁸ Furthermore, some studies have reported increased risk for hyperchloremia when using NS compared with dextrose 5% in water (D5W) or balanced crystalloids, which can result in metabolic acidosis.^6,7,9,10 These studies have shown how the choice of fluid can have a large effect on the incidence of AEs; bringing into question whether these effects could be additive when combined with the nephrotoxicity associated with vancomycin.^6-9

Vancomycin is physically and chemically stable if diluted in D5W, NS, 5% dextrose in NS, LR, or 5% dextrose in LR.¹ It is not known whether the selection of diluent has an effect on nephrotoxicity or other AEs of vancomycin therapy. Furthermore, clinicians may be unaware or unable to specify which diluent to use. There are currently no practice guidelines that favor one diluent over another for vancomycin; however, trials showing higher rates of AKI and hyperchloremia using NS for fluid resuscitation may indicate an increased potential for vancomycin-associated AKI when using NS as a diluent.^6,7,9 This study was performed to evaluate whether the type of crystalloid used (D5W vs NS) can influence adverse outcomes for patients. While many factors may contribute to these AEs, the potential to reduce the risk of negative adverse outcomes for hospitalized patients is a significant area of exploration.

The primary outcome of this study was the incidence of AKI, defined using AKIN guidelines where the increase in SCr occurred at least 24 hours after starting vancomycin and within 36 hours of receiving the last vancomycin dose.³ AKI was staged using the AKIN guidelines (stage 1: increase in SCr of ≥ 0.3 mg/dL or by 50 to 99%; stage 2: increase in SCr by 100 to 199%; stage 3: increase in SCr by > 200%) based on changes in SCr from baseline during vancomycin therapy or within 36 hours of stopping vancomycin therapy.³ Secondary outcomes included the incidence of hyperglycemia, hyperchloremia, metabolic acidosis, hypernatremia, mortality in hospital, and mortality within 30 days from hospital discharge.

Methods

This single-center, retrospective study of veterans who received IV vancomycin within the North Florida/South Georgia Veterans Health System (NF/SGVHS) in Gainesville, Florida, from July 1, 2015 to June 30, 2020, compared veterans who received vancomycin diluted in NS with those who received vancomycin diluted in D5W to assess for differences in AEs, including AKI, metabolic acidosis (serum bicarbonate level < 23 mmol/L), hyperchloremia (serum chloride levels > 108 mmol/L), hypernatremia (serum sodium > 145 mmol/L), and hyperglycemia (blood glucose > 180 mg/dL). The endpoint values were defined using the reference ranges determined by the local laboratory. At NF/SGVHS, vancomycin is diluted in D5W or NS based primarily on factors such as product availability and cost.

Study Criteria

Veterans were included if they received IV vancomycin between July 1, 2015 and June 30, 2020. The cohorts were grouped into those receiving vancomycin doses diluted in NS and those receiving vancomycin doses diluted in D5W. Veterans were excluded if they received < 80% of vancomycin doses diluted in their respective fluid, if they were on vancomycin for < 48 hours, or if they did not have laboratory results collected both before and after vancomycin therapy to assess a change. There were more patients receiving vancomycin in D5W, so a random sample was selected to have an equal size comparison group with those receiving NS. A sample size calculation was performed with an anticipated AKI incidence of 14%.⁵ To detect a 10% difference in the primary outcome with an α of 0.05 and 75% power, 226 patients (113 in each cohort) were needed for inclusion.

Data were collected using the Data Access Request Tracker tool through the US Department of Veterans Affairs (VA) Informatics and Computing Infrastructure. Data collected included demographics, laboratory data at baseline and during vancomycin therapy, characteristics of antibiotic therapy, and mortality data. Of note, all laboratory values assessed in this study were obtained while the veteran was receiving vancomycin or within 36 hours of receiving the last vancomycin dose to appropriately assess any changes.

Statistical analysis of categorical data were analyzed using a χ² test on the GraphPad online program. This study received institutional review board approval from the University of Florida and was conducted in accordance with protections for human subjects.

Results

A total of 792 veterans received IV vancomycin NF/SGVHS in the defined study period. Of these, 381 veterans were excluded, including having < 80% of doses in a single solution (213 veterans), receiving IV vancomycin for < 48 hours (149 veterans), and not having necessary laboratory data available to assess a change in kidney function (19 veterans). An additional 165 veterans were randomly excluded from the D5W cohort in order to have an equal comparison group to the NS cohort; therefore, a total of 246 veterans were included in the final assessment (123 veterans in each cohort). The median patient age was 73 years (IQR, 68.0, 80.5) in the D5W group and 66 years (IQR, 60.0, 74.0) in the NS group; 83.7% of veterans in the D5W group and 74% veterans in the NS group were white; 94.3% of the D5W group and 100% of the NS group were male (Table 1).

Adverse Effects by Solution

The percentage of AKI in the D5W group was 22.8% compared with 14.6% in the NS group (P = .14), and all cases were classified as stage 1 AKI. Baseline cases of hyperglycemia, hypernatremia, hyperchloremia, or metabolic acidosis were not included in the reported rates of each in order to determine a change during vancomycin therapy (Table 2).

The percentage of patients with hyperglycemia in the D5W group was 32.5% compared with 39.8% in the NS group (P = .29). The percentage of patients with hypernatremia in the D5W group was 15.4% compared with 10.6% in the NS group (P = .34). The percentage of patients with hyperchloremia in the D5W group was 22.8% compared with 17.9% in the NS group (P = .43). The percentage of patients with metabolic acidosis in the D5W group was 48.0% compared with 49.6% in the NS group (P = .90).

Discussion

This retrospective cohort study comparing the AEs of vancomycin diluted in NS and vancomycin diluted with D5W showed no statistically significant differences in the incidence of AKI or any metabolic AEs. Although these results did not show an association between the incidence of AEs and the dilution fluid for vancomycin, other factors may contribute to the overall incidence of AEs. Factors such as cumulative vancomycin dose, duration of therapy, and presence of concomitant nephrotoxins have been known to increase the incidence of AKI and may have a greater impact on this incidence than the fluid used in administering the vancomycin.

These results specifically the incidence of AKI were not consistent with previous trials evaluating the AEs of NS. Based on previous trials, we expected the vancomycin in the NS cohort to have a significantly higher incidence of hypernatremia, hyperchloremia, and AKI. Our results may indicate that the volume of crystalloid received played a greater role on the incidence of AEs. Our study assessed the effect of a diluent for one IV medication that may have been only a few hundred milliliters of fluid per day. The total volume of IV fluid received from vancomycin was not assessed; thus, it is not known how the volume of fluid may have impacted the results.

One consideration with this study is the method used for monitoring vancomycin levels. Most of the patients included in this study were admitted prior to the release of the updated vancomycin guidelines, which advocated for the transition from traditional trough-only monitoring to AUC/MIC. In September 2019, NF/SGVHS ICUs made the transition to this new method of monitoring with a hospital-wide transition following the study end date. The D5W group had a slightly higher percentage of patients admitted to the ICU, thus were more likely to be monitored using AUC/MIC during this period. Literature has shown the AUC/MIC method of monitoring can result in a decreased daily dose, decreased trough levels, and decreased incidence of nephrotoxicity.^11-14 Although the method for monitoring vancomycin has the potential to affect the incidence of AKI, the majority of patients were monitored using the traditional trough-only method with similar trough levels reported in both groups.

Limitations

This study is limited by its retrospective nature, the potential introduction of biases, and the inability to control for confounders that may have influenced the incidence of AEs. Potential confounders present in this study included the use of concomitant nephrotoxic medications, vancomycin dose, and underlying conditions, as these could have impacted the overall incidence of AEs.

The combination of piperacillin/tazobactam plus vancomycin has commonly been associated with an increased risk of nephrotoxicity. Previous studies have identified this nephrotoxic combination to have a significantly increased risk of AKI compared with vancomycin alone or when used in combination with alternative antibiotics such as cefepime or meropenem.^15,16 In our study, there was a higher percentage of patients in the NS group with concomitant piperacillin/tazobactam, so this difference between the groups may have influenced the incidence of AKI. Nephrotoxic medications other than antibiotics were not assessed in this study; however, these also could have impacted our results significantly. While the vancomycin duration of therapy and highest trough levels were similar between groups, the NS group had a larger average daily dose and overall cumulative dose. Studies have identified the risk of nephrotoxicity increases with a vancomycin daily dose of 4 g, troughs > 15 mg/mL, and a duration of therapy > 7 days.^15,16 In our study, the daily doses in both groups were < 4 g, so it is likely the average daily vancomycin dose had little impact on the incidence of AKI.

Another potential confounder identified was assessment of underlying conditions in the patients. Due to the limitations associated with the data extraction method, we could not assess for underlying conditions that may have impacted the results. Notably, the potential nephrotoxicity of NS has mostly been shown in critically ill patients. Therefore, the mixed acutely ill patient sample in this study may have been less likely to develop AKI from NS compared with an exclusively critically ill patient sample.

Selection bias and information bias are common with observational studies. In our study, selection bias may have been present since prospective randomization of patient samples was not possible. Since all data were extracted from the medical health record, information bias may have been present with the potential to impact the results. Due to the single-center nature of this study with a predominantly older, white male veteran patient sample, generalizability to other patient populations may be limited. We would expect the results of this study to be similar among other patient populations of a similar age and demographic; however, the external validity of this study may be weak among other populations. Although this study included enough patients based on sample size estimate, a larger sample size could have allowed for detection of smaller differences between groups and decreased the chance for type II error.

Conclusions

Overall, the results of this study do not suggest that the crystalloid used to dilute IV vancomycin is associated with differences in nephrotoxicity or other relevant AEs. Future studies evaluating the potential for AEs from medication diluent are warranted and would benefit from a prospective, randomized design. Further studies are both necessary and crucial for enhancing the quality of care to minimize the rates of AEs of commonly used medications.

Acknowledgment
This material is the result of work supported with resources and the use of facilities at the North Florida/South Georgia Veterans Health System in Gainesville, Florida.

Vancomycin is a widely used IV antibiotic due to its broad-spectrum of activity, bactericidal nature, and low rates of resistance; however, adverse effects (AEs), including nephrotoxicity, are commonly associated with its use.¹ The vancomycin therapeutic monitoring guidelines recognize the incidence of nephrotoxicity and suggest strategies for reducing the risk, including area under the curve/mean inhibitory concentration (AUC/MIC) monitoring rather than trough-only monitoring. Vancomycin-associated acute kidney injury (AKI) has been defined as an increase in serum creatinine (SCr) over a 48-hour period of ≥ 0.3 mg/dL or a percentage increase of ≥ 50%, which is consistent with the Acute Kidney Injury Network (AKIN) guidelines.^2,3 Vancomycin-associated AKI is a common AE, with its incidence reported in previous studies ranging from 10 to 20%.^4,5

The most common crystalloid fluid administered to patients in the United States is 0.9% sodium chloride (NaCl), also known as normal saline (NS), and recent trials have explored its potential to cause AEs.^6-8 Balanced crystalloid solutions, such as Plasma-Lyte and lactated Ringer’s solution (LR), contain buffering agents and lower concentrations of sodium and chloride compared with that of NS. Trials in the intensive care unit (ICU) and emergency department, such as the SMART-MED, SMART-SURG, and SALT-ED have reported a significantly lower rate of AKI when using balanced crystalloids compared with NS due to the concentration of sodium and chloride in NS being supraphysiologic to normal serum concentrations.^6,7 Alternatively, the SPLIT trial evaluated the use of NS compared with Plasma-Lyte for ICU fluid therapy and did not find a statistically significant difference in AKI.⁸ Furthermore, some studies have reported increased risk for hyperchloremia when using NS compared with dextrose 5% in water (D5W) or balanced crystalloids, which can result in metabolic acidosis.^6,7,9,10 These studies have shown how the choice of fluid can have a large effect on the incidence of AEs; bringing into question whether these effects could be additive when combined with the nephrotoxicity associated with vancomycin.^6-9

Vancomycin is physically and chemically stable if diluted in D5W, NS, 5% dextrose in NS, LR, or 5% dextrose in LR.¹ It is not known whether the selection of diluent has an effect on nephrotoxicity or other AEs of vancomycin therapy. Furthermore, clinicians may be unaware or unable to specify which diluent to use. There are currently no practice guidelines that favor one diluent over another for vancomycin; however, trials showing higher rates of AKI and hyperchloremia using NS for fluid resuscitation may indicate an increased potential for vancomycin-associated AKI when using NS as a diluent.^6,7,9 This study was performed to evaluate whether the type of crystalloid used (D5W vs NS) can influence adverse outcomes for patients. While many factors may contribute to these AEs, the potential to reduce the risk of negative adverse outcomes for hospitalized patients is a significant area of exploration.

The primary outcome of this study was the incidence of AKI, defined using AKIN guidelines where the increase in SCr occurred at least 24 hours after starting vancomycin and within 36 hours of receiving the last vancomycin dose.³ AKI was staged using the AKIN guidelines (stage 1: increase in SCr of ≥ 0.3 mg/dL or by 50 to 99%; stage 2: increase in SCr by 100 to 199%; stage 3: increase in SCr by > 200%) based on changes in SCr from baseline during vancomycin therapy or within 36 hours of stopping vancomycin therapy.³ Secondary outcomes included the incidence of hyperglycemia, hyperchloremia, metabolic acidosis, hypernatremia, mortality in hospital, and mortality within 30 days from hospital discharge.

Methods

This single-center, retrospective study of veterans who received IV vancomycin within the North Florida/South Georgia Veterans Health System (NF/SGVHS) in Gainesville, Florida, from July 1, 2015 to June 30, 2020, compared veterans who received vancomycin diluted in NS with those who received vancomycin diluted in D5W to assess for differences in AEs, including AKI, metabolic acidosis (serum bicarbonate level < 23 mmol/L), hyperchloremia (serum chloride levels > 108 mmol/L), hypernatremia (serum sodium > 145 mmol/L), and hyperglycemia (blood glucose > 180 mg/dL). The endpoint values were defined using the reference ranges determined by the local laboratory. At NF/SGVHS, vancomycin is diluted in D5W or NS based primarily on factors such as product availability and cost.

Study Criteria

Veterans were included if they received IV vancomycin between July 1, 2015 and June 30, 2020. The cohorts were grouped into those receiving vancomycin doses diluted in NS and those receiving vancomycin doses diluted in D5W. Veterans were excluded if they received < 80% of vancomycin doses diluted in their respective fluid, if they were on vancomycin for < 48 hours, or if they did not have laboratory results collected both before and after vancomycin therapy to assess a change. There were more patients receiving vancomycin in D5W, so a random sample was selected to have an equal size comparison group with those receiving NS. A sample size calculation was performed with an anticipated AKI incidence of 14%.⁵ To detect a 10% difference in the primary outcome with an α of 0.05 and 75% power, 226 patients (113 in each cohort) were needed for inclusion.

Data were collected using the Data Access Request Tracker tool through the US Department of Veterans Affairs (VA) Informatics and Computing Infrastructure. Data collected included demographics, laboratory data at baseline and during vancomycin therapy, characteristics of antibiotic therapy, and mortality data. Of note, all laboratory values assessed in this study were obtained while the veteran was receiving vancomycin or within 36 hours of receiving the last vancomycin dose to appropriately assess any changes.

Statistical analysis of categorical data were analyzed using a χ² test on the GraphPad online program. This study received institutional review board approval from the University of Florida and was conducted in accordance with protections for human subjects.

Results

A total of 792 veterans received IV vancomycin NF/SGVHS in the defined study period. Of these, 381 veterans were excluded, including having < 80% of doses in a single solution (213 veterans), receiving IV vancomycin for < 48 hours (149 veterans), and not having necessary laboratory data available to assess a change in kidney function (19 veterans). An additional 165 veterans were randomly excluded from the D5W cohort in order to have an equal comparison group to the NS cohort; therefore, a total of 246 veterans were included in the final assessment (123 veterans in each cohort). The median patient age was 73 years (IQR, 68.0, 80.5) in the D5W group and 66 years (IQR, 60.0, 74.0) in the NS group; 83.7% of veterans in the D5W group and 74% veterans in the NS group were white; 94.3% of the D5W group and 100% of the NS group were male (Table 1).

Adverse Effects by Solution

The percentage of AKI in the D5W group was 22.8% compared with 14.6% in the NS group (P = .14), and all cases were classified as stage 1 AKI. Baseline cases of hyperglycemia, hypernatremia, hyperchloremia, or metabolic acidosis were not included in the reported rates of each in order to determine a change during vancomycin therapy (Table 2).

The percentage of patients with hyperglycemia in the D5W group was 32.5% compared with 39.8% in the NS group (P = .29). The percentage of patients with hypernatremia in the D5W group was 15.4% compared with 10.6% in the NS group (P = .34). The percentage of patients with hyperchloremia in the D5W group was 22.8% compared with 17.9% in the NS group (P = .43). The percentage of patients with metabolic acidosis in the D5W group was 48.0% compared with 49.6% in the NS group (P = .90).

Discussion

This retrospective cohort study comparing the AEs of vancomycin diluted in NS and vancomycin diluted with D5W showed no statistically significant differences in the incidence of AKI or any metabolic AEs. Although these results did not show an association between the incidence of AEs and the dilution fluid for vancomycin, other factors may contribute to the overall incidence of AEs. Factors such as cumulative vancomycin dose, duration of therapy, and presence of concomitant nephrotoxins have been known to increase the incidence of AKI and may have a greater impact on this incidence than the fluid used in administering the vancomycin.

These results specifically the incidence of AKI were not consistent with previous trials evaluating the AEs of NS. Based on previous trials, we expected the vancomycin in the NS cohort to have a significantly higher incidence of hypernatremia, hyperchloremia, and AKI. Our results may indicate that the volume of crystalloid received played a greater role on the incidence of AEs. Our study assessed the effect of a diluent for one IV medication that may have been only a few hundred milliliters of fluid per day. The total volume of IV fluid received from vancomycin was not assessed; thus, it is not known how the volume of fluid may have impacted the results.

One consideration with this study is the method used for monitoring vancomycin levels. Most of the patients included in this study were admitted prior to the release of the updated vancomycin guidelines, which advocated for the transition from traditional trough-only monitoring to AUC/MIC. In September 2019, NF/SGVHS ICUs made the transition to this new method of monitoring with a hospital-wide transition following the study end date. The D5W group had a slightly higher percentage of patients admitted to the ICU, thus were more likely to be monitored using AUC/MIC during this period. Literature has shown the AUC/MIC method of monitoring can result in a decreased daily dose, decreased trough levels, and decreased incidence of nephrotoxicity.^11-14 Although the method for monitoring vancomycin has the potential to affect the incidence of AKI, the majority of patients were monitored using the traditional trough-only method with similar trough levels reported in both groups.

Limitations

This study is limited by its retrospective nature, the potential introduction of biases, and the inability to control for confounders that may have influenced the incidence of AEs. Potential confounders present in this study included the use of concomitant nephrotoxic medications, vancomycin dose, and underlying conditions, as these could have impacted the overall incidence of AEs.

The combination of piperacillin/tazobactam plus vancomycin has commonly been associated with an increased risk of nephrotoxicity. Previous studies have identified this nephrotoxic combination to have a significantly increased risk of AKI compared with vancomycin alone or when used in combination with alternative antibiotics such as cefepime or meropenem.^15,16 In our study, there was a higher percentage of patients in the NS group with concomitant piperacillin/tazobactam, so this difference between the groups may have influenced the incidence of AKI. Nephrotoxic medications other than antibiotics were not assessed in this study; however, these also could have impacted our results significantly. While the vancomycin duration of therapy and highest trough levels were similar between groups, the NS group had a larger average daily dose and overall cumulative dose. Studies have identified the risk of nephrotoxicity increases with a vancomycin daily dose of 4 g, troughs > 15 mg/mL, and a duration of therapy > 7 days.^15,16 In our study, the daily doses in both groups were < 4 g, so it is likely the average daily vancomycin dose had little impact on the incidence of AKI.

Another potential confounder identified was assessment of underlying conditions in the patients. Due to the limitations associated with the data extraction method, we could not assess for underlying conditions that may have impacted the results. Notably, the potential nephrotoxicity of NS has mostly been shown in critically ill patients. Therefore, the mixed acutely ill patient sample in this study may have been less likely to develop AKI from NS compared with an exclusively critically ill patient sample.

Selection bias and information bias are common with observational studies. In our study, selection bias may have been present since prospective randomization of patient samples was not possible. Since all data were extracted from the medical health record, information bias may have been present with the potential to impact the results. Due to the single-center nature of this study with a predominantly older, white male veteran patient sample, generalizability to other patient populations may be limited. We would expect the results of this study to be similar among other patient populations of a similar age and demographic; however, the external validity of this study may be weak among other populations. Although this study included enough patients based on sample size estimate, a larger sample size could have allowed for detection of smaller differences between groups and decreased the chance for type II error.

Conclusions

Overall, the results of this study do not suggest that the crystalloid used to dilute IV vancomycin is associated with differences in nephrotoxicity or other relevant AEs. Future studies evaluating the potential for AEs from medication diluent are warranted and would benefit from a prospective, randomized design. Further studies are both necessary and crucial for enhancing the quality of care to minimize the rates of AEs of commonly used medications.

Acknowledgment
This material is the result of work supported with resources and the use of facilities at the North Florida/South Georgia Veterans Health System in Gainesville, Florida.