Testosterone treatment is clinically indicated when a patient presents with symptoms and signs and biochemical evidence of testosterone deficiency, ie, male hypogonadism. Laboratory confirmation of hypogonadism requires repeatedly low serum testosterone concentrations; between 8 am and 10 am on ≥ 2 separate occasions, and evaluation should include measurement of gonadotropin, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) concentrations. If the diagnosis of hypogonadism is established, it is important to determine whether the etiology is due to a structural or congenital disorder of the hypothalamic-pituitary-testicular (HPT) axis (organic hypogonadism) or a comorbid condition that results in suppressed function of an intact HPT axis and that is potentially reversible or treatable (functional hypogonadism).^1,2 Prior to initiation of treatment, clinicians should discuss potential benefits and risks of testosterone and monitoring during treatment, using a shared decision-making process with the patient.¹

Recent studies have reported an increase in testosterone prescriptions and raised concerns regarding health care provider (HCP) prescribing practices despite current clinical practice guidelines from major societies, such as the Endocrine Society. In the US from 2001 to 2011, testosterone use among men aged ≥ 40 years increased more than 3-fold in all age groups.³ Subsequently in the years from 2013 to 2016, prescription rates declined perhaps due to the cardiovascular and stroke concerns.⁴

In the US Department of Veterans Affairs (VA), new testosterone prescriptions across VA medical centers increased from 20,437 in fiscal year (FY) 2009 to 36,394 in FY 2012. Yet only 3.1% of men who received testosterone therapy had 2 or more low morning total or free testosterone concentrations measured; LH and/or FSH levels assessed; and presence of contraindications to therapy documented. Remarkably, 16.5% of these veterans did not have a testosterone level tested prior to being prescribed testosterone. Among veterans who were prescribed testosterone, 1.4% had a diagnosis of prostate cancer, 7.6% had a diagnosis of obstructive sleep apnea (OSA), and 3.5% had elevated hematocrit at baseline.⁵ These findings raised concerns of whether the diagnosis and etiology of hypogonadism were appropriately established and risks were considered before testosterone treatment was initiated.^5,6

To further understand VA prescribing practices of testosterone therapy, a 2018 VA Office of the Inspector General (OIG) report evaluated the initiation and follow-up of testosterone replacement therapy. The OIG randomly sampled and reviewed 1,091 male patients who filled at least 1 outpatient testosterone prescription from VA in FY 2014 and who did not have a prior testosterone prescription in FY 2013. Patients were followed through September 30, 2015. Within 1 year prior to initiating testosterone, only 1.5% had clinical signs and symptoms of testosterone deficiency documented prior to testosterone testing (76% within 18 months of starting testosterone); only 9.1% of veterans had the recommended measurements of 2 low morning testosterone levels; and only 12% had LH and FSH levels measured. Within 3 to 6 months after starting testosterone therapy, only 24% of veterans were assessed for symptom improvement, and 29% to 33% were evaluated for adverse effects, hematocrit levels and adherence to the therapy. The OIG report concluded that VA HCPs were not adhering to guidelines (referencing the Endocrine Society guidelines) when evaluating and treating veterans with testosterone deficiency.⁷

Considering the OIG recommendations and need to improve current practices among providers, VA Puget Sound Health Care System (VAPSHCS) in Washington established a multidisciplinary workgroup consisting of an endocrinologist, geriatrician, primary care provider (PCP), pharmacists, VA information technology (IT) specialist, and health products support (HPS) clinical team in the spring of 2019 to assess and improve testosterone prescribing practices.

Methods

A testosterone order template was developed, approved by VAPSHCS Pharmacy and Therapeutics Committee, and implemented on July 1, 2019, at VAPSHCS, a 1a medical facility caring for more than 112,000 veterans. Given its potential risks and the propensity for varied prescribing practices, testosterone was designated as a restricted drug requiring a prior authorization drug request (PADR) and required completion of the testosterone order template in the Computerized Patient Record System (CPRS).

Testosterone Order Template

The testosterone order template had 2 components. Completion of the template for new testosterone orders was required to initiate treatment unless the patient had known organic hypogonadism or was a transgender male. The template ensured documentation of defined symptoms and signs of testosterone deficiency; low serum testosterone levels on at least 2 occasions and LH and FSH concentrations; no contraindications to testosterone treatment; discussion of risks and benefits of therapy; and baseline hematocrit (Figure 1). Relevant educational content (eg, risks and benefits of testosterone) was incorporated in the template. The second template was required for the first renewal of testosterone to document adherence to or reason for discontinuation of testosterone; improvement of symptoms and signs; and confirm monitoring hematocrit and testosterone levels during treatment.

Prior to implementation, the PADR template was introduced to HCPs at 2 chief-of-medicine rounds on the diagnosis and evaluation of hypogonadism by a pharmacist and endocrinologist. These educational sessions used case examples and discussions to teach the appropriate use of testosterone therapy in men with hypogonadism. The target audience was PCPs, residents, and other specialists who might prescribe testosterone.

Retrospective Chart Review

To assess the impact of the new testosterone order template on adherence to OIG recommendations, a retrospective chart review was completed comparing the appropriateness of initiating testosterone replacement therapy pretemplate period (July 1 to December 31, 2018) vs posttemplate period (July 1 to December 31, 2019). Inclusion and exclusion criteria were modeled after the 2018 OIG report to allow for comparison with the OIG study population. Eligible veterans in each time period included males who received a new testosterone prescription without having been prescribed testosterone in the previous 12 months. Exclusion criteria included community care network prescriptions (CCNRx); current testosterone prescription from a different VA site; clinic administration of testosterone in the previous 12 months; an organic hypogonadism (ie, Klinefelter syndrome) or gender dysphoria diagnosis; and whether the testosterone prescription was never dispensed (PADR was denied or veteran never had the prescription filled). Veterans who met the inclusion criteria in CPRS were identified by an algorithm developed by the VAPSHCS pharmacoeconomist.

Determining the appropriateness of testosterone prescribing, such as symptoms and laboratory measurements to confirm the diagnosis of hypogonadism, was based on the OIG report and Endocrine Society guidelines. A chart review of the 12 months before testosterone prescribing was completed for each veteran, assessing for documentation of symptoms of testosterone deficiency and laboratory measurements of serum testosterone, LH, and FSH. Also, documentation of a discussion of risks and benefits of testosterone therapy in the 3 months before prescribing was assessed, which matched the timeframe in the VA OIG report.

Interim Analysis

After initial template implementation, the multidisciplinary workgroup reconvened for a preplanned interim analysis in November 2019. The evaluation at this meeting revealed multiple order pathways in CPRS that were not linked to the PADR testosterone order template. Testosterone could be ordered in the generic order dialog, medications by drug class, and medications by alphabet, and endocrinology specialty menus without prompting to complete the testosterone order template or redirection to the restricted drug menu (Figure 2). These alternative testosterone ordering pathways were removed in early December 2019 and additional data collection was conducted for 3 months after discontinuation of alternative order pathways, the posttemplate/no alternative ordering pathways period, from December 7, 2019 to February 29, 2020.

Exclusion of Previous Testosterone Prescriptions Predating Chart Review Period, Subgroup Analysis

In the OIG report and the initial retrospective chart review, only veterans without a testosterone prescription in the previous 12 months were evaluated. To assess whether a previous testosterone prescription influenced completion of the PADR and order template, a further subgroup analysis was conducted that excluded veterans who had a previous testosterone prescription at any time before the chart review periods. Therefore, “new testosterone prescription” refers to a veteran who never had a history of being on testosterone vs “former testosterone prescription,” meaning a patient could have had a previous testosterone prescription > 1 year prior to a new VA testosterone prescription.

Results

One hundred seventy-five veterans with a new testosterone prescription were identified in the pretemplate period; of these 80 (46%) met eligibility criteria; only 20 eligible veterans (25%) had a completed PADR (Figure 3). Ninety-one veterans with a new testosterone prescription were identified in the posttemplate period of which 41 (46%) veterans were eligible; 18 eligible veterans (44%) had a completed PADR, but only 7 (17%) had a completed testosterone order template.

After excluding veterans who had alternative ordering pathways for testosterone, 46 veterans were identified in the posttemplate/no alternative ordering pathways period of which 19 (41%) veterans were eligible. Compared with the posttemplate period, a higher proportion of eligible veterans, 68% (13) had a completed PADR, and 58% (11) had a testosterone order template during the posttemplate/no alternative ordering pathways period.

Discussion

The 2018 OIG report found that VA practitioners demonstrated poor adherence to evidence-based clinical practice guidelines for testosterone treatment in men with hypogonadism. Based on OIG recommendations, we developed a PADR testosterone ordering template to help HCPs improve practice by better adherence to guidelines for the diagnosis and treatment of hypogonadism in veterans. Before implementation of the PADR template, the percentage of veterans at VAPSHCS who had biochemical confirmation of hypogonadism was higher than that in the OIG report. Activation of the PADR testosterone ordering template (with or without removal of options for alternative ordering pathways of testosterone) resulted only in an improvement of laboratory confirmation and evaluation of etiology of hypogonadism. This is when we reasoned that clinicians may have access to prior records and laboratory testing beyond just the past year, and this information may have influenced their use of the PADR template. Subsequently, with exclusion of veterans who were previously prescribed testosterone, implementation of the PADR testosterone order template improved documentation of symptoms of testosterone deficiency, discussion of risks and benefits of testosterone therapy, and biochemical diagnosis and evaluation of hypogonadism relative to the period before implementation.

The lack of effects of implementing the testosterone order template on documentation of symptoms of testosterone deficiency and discussion of risks and benefits of testosterone therapy may be due to local expertise resulting in the relatively high adherence to these guideline recommendations at VAPSHCS before activation of the template vs that in the OIG report. The template improved documentation of the diagnosis and evaluation of hypogonadism for genuinely new testosterone prescriptions in veterans without a history of testosterone prescriptions; while those with a previous prescription had limited improvement. It is possible that in veterans who had testosterone prescribed previously, HCPs may have assumed or had bias that the diagnosis and evaluation of hypogonadism originally made was adequate. This finding underscores the need to develop strategies for reviewing PADR requests where there is historical testosterone use. Perhaps a clinical team member, such as a clinical pharmacist, with the background and training in guidelines for the evaluation of hypogonadism could review PADR requests in veterans with previous testosterone use.

Removal of alternative ordering pathways for testosterone increased the completion rate of PADR requests and the testosterone ordering template, although the latter was not completed in one-third of veterans. Possible reasons for HCPs’ suboptimal completion of the testosterone template despite the PADR initiation include clinicians’ lack of willingness to read the PADR completely and familiarize themselves with the clinical guidelines due to workload demands of PCPs. In addition there maybe pressure from patients to receive testosterone for age-related symptoms due to heavy marketing. In addition, there may have been pharmacists who reviewed the PADR and approved the incomplete testosterone template. At VAPSHCS there were up to 40 pharmacists during different periods reviewing the testosterone PADRs. Likely, not everyone was completely familiar with this implementation process, and a possible future consideration would be further education to staff pharmacists who are verifying these prescriptions. There were several advantages to using this new testosterone order template when HCPs attempted to order a prescription. First, they were prompted to complete the PADR. Subsequently, a pharmacist reviewed the template and approved or rejected the prescription if the template was incomplete. The completed template served as documentation in the electronic health record for the prescribing HCP. The template was constructed to populate the required laboratory tests for ease of use and documentation. In addition, educational information regarding the symptoms and signs of testosterone deficiency, laboratory tests needed to confirm and evaluate hypogonadism, contraindications to testosterone treatment, and risks and benefits of therapy were incorporated into the template to assist HCPs in understanding the requirements for a complete diagnosis and evaluation. Finally, on completion of the template, HCPs were able to order testosterone via link to various testosterone formulations.

Before its implementation, the PADR testosterone order template was introduced to PCPs and internal medicine residents at 2 case-based conferences aimed at the diagnosis and treatment of male hypogonadism. These conferences were well received and helped launch the testosterone PADR template at VAPSHCS. Similar outreach to HCPs who prescribe testosterone is highly recommended in other VA facilities before implementation of the testosterone ordering template. It is possible that more targeted education to other HCPs would have resulted in greater use of the testosterone ordering template and adherence to clinical practice guidelines.

The VAPSHCS multidisciplinary workgroup was essential for the development, implementation, evaluation, and revision of the PADR and testosterone ordering template. The workgroup met routinely to follow up on the ease of installation in CPRS and discuss technical corrections that were needed. This was an essential for quality improvement, as loopholes in CPRS were identified where the HCP could order testosterone without being prompted to use the new PADR testosterone order template (alternative ordering pathways). The workgroup swiftly informed the IT specialist and HPS team to remove alternative ordering pathways of testosterone. Continuous quality improvement evaluations are highly recommended during implementation of the template in other facilities to accommodate specific local modifications that might be needed.

Conclusions

The creation and implementation of a PADR testosterone order template may be a beneficial approach to improve the diagnosis of hypogonadism and facilitate appropriate use of testosterone therapy in veterans in accordance with established clinical practice guidelines, particularly in veterans without any prior testosterone use. Key future strategies to improve testosterone prescribing should focus on identifying clinical team members, such as a local clinical pharmacist, to review and steward PADR requests to ensure that testosterone is indicated, and treatment is appropriately monitored.

Testosterone treatment is clinically indicated when a patient presents with symptoms and signs and biochemical evidence of testosterone deficiency, ie, male hypogonadism. Laboratory confirmation of hypogonadism requires repeatedly low serum testosterone concentrations; between 8 am and 10 am on ≥ 2 separate occasions, and evaluation should include measurement of gonadotropin, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) concentrations. If the diagnosis of hypogonadism is established, it is important to determine whether the etiology is due to a structural or congenital disorder of the hypothalamic-pituitary-testicular (HPT) axis (organic hypogonadism) or a comorbid condition that results in suppressed function of an intact HPT axis and that is potentially reversible or treatable (functional hypogonadism).^1,2 Prior to initiation of treatment, clinicians should discuss potential benefits and risks of testosterone and monitoring during treatment, using a shared decision-making process with the patient.¹

Recent studies have reported an increase in testosterone prescriptions and raised concerns regarding health care provider (HCP) prescribing practices despite current clinical practice guidelines from major societies, such as the Endocrine Society. In the US from 2001 to 2011, testosterone use among men aged ≥ 40 years increased more than 3-fold in all age groups.³ Subsequently in the years from 2013 to 2016, prescription rates declined perhaps due to the cardiovascular and stroke concerns.⁴

In the US Department of Veterans Affairs (VA), new testosterone prescriptions across VA medical centers increased from 20,437 in fiscal year (FY) 2009 to 36,394 in FY 2012. Yet only 3.1% of men who received testosterone therapy had 2 or more low morning total or free testosterone concentrations measured; LH and/or FSH levels assessed; and presence of contraindications to therapy documented. Remarkably, 16.5% of these veterans did not have a testosterone level tested prior to being prescribed testosterone. Among veterans who were prescribed testosterone, 1.4% had a diagnosis of prostate cancer, 7.6% had a diagnosis of obstructive sleep apnea (OSA), and 3.5% had elevated hematocrit at baseline.⁵ These findings raised concerns of whether the diagnosis and etiology of hypogonadism were appropriately established and risks were considered before testosterone treatment was initiated.^5,6

To further understand VA prescribing practices of testosterone therapy, a 2018 VA Office of the Inspector General (OIG) report evaluated the initiation and follow-up of testosterone replacement therapy. The OIG randomly sampled and reviewed 1,091 male patients who filled at least 1 outpatient testosterone prescription from VA in FY 2014 and who did not have a prior testosterone prescription in FY 2013. Patients were followed through September 30, 2015. Within 1 year prior to initiating testosterone, only 1.5% had clinical signs and symptoms of testosterone deficiency documented prior to testosterone testing (76% within 18 months of starting testosterone); only 9.1% of veterans had the recommended measurements of 2 low morning testosterone levels; and only 12% had LH and FSH levels measured. Within 3 to 6 months after starting testosterone therapy, only 24% of veterans were assessed for symptom improvement, and 29% to 33% were evaluated for adverse effects, hematocrit levels and adherence to the therapy. The OIG report concluded that VA HCPs were not adhering to guidelines (referencing the Endocrine Society guidelines) when evaluating and treating veterans with testosterone deficiency.⁷

Considering the OIG recommendations and need to improve current practices among providers, VA Puget Sound Health Care System (VAPSHCS) in Washington established a multidisciplinary workgroup consisting of an endocrinologist, geriatrician, primary care provider (PCP), pharmacists, VA information technology (IT) specialist, and health products support (HPS) clinical team in the spring of 2019 to assess and improve testosterone prescribing practices.

Methods

A testosterone order template was developed, approved by VAPSHCS Pharmacy and Therapeutics Committee, and implemented on July 1, 2019, at VAPSHCS, a 1a medical facility caring for more than 112,000 veterans. Given its potential risks and the propensity for varied prescribing practices, testosterone was designated as a restricted drug requiring a prior authorization drug request (PADR) and required completion of the testosterone order template in the Computerized Patient Record System (CPRS).

Testosterone Order Template

The testosterone order template had 2 components. Completion of the template for new testosterone orders was required to initiate treatment unless the patient had known organic hypogonadism or was a transgender male. The template ensured documentation of defined symptoms and signs of testosterone deficiency; low serum testosterone levels on at least 2 occasions and LH and FSH concentrations; no contraindications to testosterone treatment; discussion of risks and benefits of therapy; and baseline hematocrit (Figure 1). Relevant educational content (eg, risks and benefits of testosterone) was incorporated in the template. The second template was required for the first renewal of testosterone to document adherence to or reason for discontinuation of testosterone; improvement of symptoms and signs; and confirm monitoring hematocrit and testosterone levels during treatment.

Prior to implementation, the PADR template was introduced to HCPs at 2 chief-of-medicine rounds on the diagnosis and evaluation of hypogonadism by a pharmacist and endocrinologist. These educational sessions used case examples and discussions to teach the appropriate use of testosterone therapy in men with hypogonadism. The target audience was PCPs, residents, and other specialists who might prescribe testosterone.

Retrospective Chart Review

To assess the impact of the new testosterone order template on adherence to OIG recommendations, a retrospective chart review was completed comparing the appropriateness of initiating testosterone replacement therapy pretemplate period (July 1 to December 31, 2018) vs posttemplate period (July 1 to December 31, 2019). Inclusion and exclusion criteria were modeled after the 2018 OIG report to allow for comparison with the OIG study population. Eligible veterans in each time period included males who received a new testosterone prescription without having been prescribed testosterone in the previous 12 months. Exclusion criteria included community care network prescriptions (CCNRx); current testosterone prescription from a different VA site; clinic administration of testosterone in the previous 12 months; an organic hypogonadism (ie, Klinefelter syndrome) or gender dysphoria diagnosis; and whether the testosterone prescription was never dispensed (PADR was denied or veteran never had the prescription filled). Veterans who met the inclusion criteria in CPRS were identified by an algorithm developed by the VAPSHCS pharmacoeconomist.

Determining the appropriateness of testosterone prescribing, such as symptoms and laboratory measurements to confirm the diagnosis of hypogonadism, was based on the OIG report and Endocrine Society guidelines. A chart review of the 12 months before testosterone prescribing was completed for each veteran, assessing for documentation of symptoms of testosterone deficiency and laboratory measurements of serum testosterone, LH, and FSH. Also, documentation of a discussion of risks and benefits of testosterone therapy in the 3 months before prescribing was assessed, which matched the timeframe in the VA OIG report.

Interim Analysis

After initial template implementation, the multidisciplinary workgroup reconvened for a preplanned interim analysis in November 2019. The evaluation at this meeting revealed multiple order pathways in CPRS that were not linked to the PADR testosterone order template. Testosterone could be ordered in the generic order dialog, medications by drug class, and medications by alphabet, and endocrinology specialty menus without prompting to complete the testosterone order template or redirection to the restricted drug menu (Figure 2). These alternative testosterone ordering pathways were removed in early December 2019 and additional data collection was conducted for 3 months after discontinuation of alternative order pathways, the posttemplate/no alternative ordering pathways period, from December 7, 2019 to February 29, 2020.

Exclusion of Previous Testosterone Prescriptions Predating Chart Review Period, Subgroup Analysis

In the OIG report and the initial retrospective chart review, only veterans without a testosterone prescription in the previous 12 months were evaluated. To assess whether a previous testosterone prescription influenced completion of the PADR and order template, a further subgroup analysis was conducted that excluded veterans who had a previous testosterone prescription at any time before the chart review periods. Therefore, “new testosterone prescription” refers to a veteran who never had a history of being on testosterone vs “former testosterone prescription,” meaning a patient could have had a previous testosterone prescription > 1 year prior to a new VA testosterone prescription.

Results

One hundred seventy-five veterans with a new testosterone prescription were identified in the pretemplate period; of these 80 (46%) met eligibility criteria; only 20 eligible veterans (25%) had a completed PADR (Figure 3). Ninety-one veterans with a new testosterone prescription were identified in the posttemplate period of which 41 (46%) veterans were eligible; 18 eligible veterans (44%) had a completed PADR, but only 7 (17%) had a completed testosterone order template.

After excluding veterans who had alternative ordering pathways for testosterone, 46 veterans were identified in the posttemplate/no alternative ordering pathways period of which 19 (41%) veterans were eligible. Compared with the posttemplate period, a higher proportion of eligible veterans, 68% (13) had a completed PADR, and 58% (11) had a testosterone order template during the posttemplate/no alternative ordering pathways period.

Discussion

The 2018 OIG report found that VA practitioners demonstrated poor adherence to evidence-based clinical practice guidelines for testosterone treatment in men with hypogonadism. Based on OIG recommendations, we developed a PADR testosterone ordering template to help HCPs improve practice by better adherence to guidelines for the diagnosis and treatment of hypogonadism in veterans. Before implementation of the PADR template, the percentage of veterans at VAPSHCS who had biochemical confirmation of hypogonadism was higher than that in the OIG report. Activation of the PADR testosterone ordering template (with or without removal of options for alternative ordering pathways of testosterone) resulted only in an improvement of laboratory confirmation and evaluation of etiology of hypogonadism. This is when we reasoned that clinicians may have access to prior records and laboratory testing beyond just the past year, and this information may have influenced their use of the PADR template. Subsequently, with exclusion of veterans who were previously prescribed testosterone, implementation of the PADR testosterone order template improved documentation of symptoms of testosterone deficiency, discussion of risks and benefits of testosterone therapy, and biochemical diagnosis and evaluation of hypogonadism relative to the period before implementation.

The lack of effects of implementing the testosterone order template on documentation of symptoms of testosterone deficiency and discussion of risks and benefits of testosterone therapy may be due to local expertise resulting in the relatively high adherence to these guideline recommendations at VAPSHCS before activation of the template vs that in the OIG report. The template improved documentation of the diagnosis and evaluation of hypogonadism for genuinely new testosterone prescriptions in veterans without a history of testosterone prescriptions; while those with a previous prescription had limited improvement. It is possible that in veterans who had testosterone prescribed previously, HCPs may have assumed or had bias that the diagnosis and evaluation of hypogonadism originally made was adequate. This finding underscores the need to develop strategies for reviewing PADR requests where there is historical testosterone use. Perhaps a clinical team member, such as a clinical pharmacist, with the background and training in guidelines for the evaluation of hypogonadism could review PADR requests in veterans with previous testosterone use.

Removal of alternative ordering pathways for testosterone increased the completion rate of PADR requests and the testosterone ordering template, although the latter was not completed in one-third of veterans. Possible reasons for HCPs’ suboptimal completion of the testosterone template despite the PADR initiation include clinicians’ lack of willingness to read the PADR completely and familiarize themselves with the clinical guidelines due to workload demands of PCPs. In addition there maybe pressure from patients to receive testosterone for age-related symptoms due to heavy marketing. In addition, there may have been pharmacists who reviewed the PADR and approved the incomplete testosterone template. At VAPSHCS there were up to 40 pharmacists during different periods reviewing the testosterone PADRs. Likely, not everyone was completely familiar with this implementation process, and a possible future consideration would be further education to staff pharmacists who are verifying these prescriptions. There were several advantages to using this new testosterone order template when HCPs attempted to order a prescription. First, they were prompted to complete the PADR. Subsequently, a pharmacist reviewed the template and approved or rejected the prescription if the template was incomplete. The completed template served as documentation in the electronic health record for the prescribing HCP. The template was constructed to populate the required laboratory tests for ease of use and documentation. In addition, educational information regarding the symptoms and signs of testosterone deficiency, laboratory tests needed to confirm and evaluate hypogonadism, contraindications to testosterone treatment, and risks and benefits of therapy were incorporated into the template to assist HCPs in understanding the requirements for a complete diagnosis and evaluation. Finally, on completion of the template, HCPs were able to order testosterone via link to various testosterone formulations.

Before its implementation, the PADR testosterone order template was introduced to PCPs and internal medicine residents at 2 case-based conferences aimed at the diagnosis and treatment of male hypogonadism. These conferences were well received and helped launch the testosterone PADR template at VAPSHCS. Similar outreach to HCPs who prescribe testosterone is highly recommended in other VA facilities before implementation of the testosterone ordering template. It is possible that more targeted education to other HCPs would have resulted in greater use of the testosterone ordering template and adherence to clinical practice guidelines.

The VAPSHCS multidisciplinary workgroup was essential for the development, implementation, evaluation, and revision of the PADR and testosterone ordering template. The workgroup met routinely to follow up on the ease of installation in CPRS and discuss technical corrections that were needed. This was an essential for quality improvement, as loopholes in CPRS were identified where the HCP could order testosterone without being prompted to use the new PADR testosterone order template (alternative ordering pathways). The workgroup swiftly informed the IT specialist and HPS team to remove alternative ordering pathways of testosterone. Continuous quality improvement evaluations are highly recommended during implementation of the template in other facilities to accommodate specific local modifications that might be needed.

Conclusions

The creation and implementation of a PADR testosterone order template may be a beneficial approach to improve the diagnosis of hypogonadism and facilitate appropriate use of testosterone therapy in veterans in accordance with established clinical practice guidelines, particularly in veterans without any prior testosterone use. Key future strategies to improve testosterone prescribing should focus on identifying clinical team members, such as a local clinical pharmacist, to review and steward PADR requests to ensure that testosterone is indicated, and treatment is appropriately monitored.

Testosterone treatment is clinically indicated when a patient presents with symptoms and signs and biochemical evidence of testosterone deficiency, ie, male hypogonadism. Laboratory confirmation of hypogonadism requires repeatedly low serum testosterone concentrations; between 8 am and 10 am on ≥ 2 separate occasions, and evaluation should include measurement of gonadotropin, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) concentrations. If the diagnosis of hypogonadism is established, it is important to determine whether the etiology is due to a structural or congenital disorder of the hypothalamic-pituitary-testicular (HPT) axis (organic hypogonadism) or a comorbid condition that results in suppressed function of an intact HPT axis and that is potentially reversible or treatable (functional hypogonadism).^1,2 Prior to initiation of treatment, clinicians should discuss potential benefits and risks of testosterone and monitoring during treatment, using a shared decision-making process with the patient.¹

Recent studies have reported an increase in testosterone prescriptions and raised concerns regarding health care provider (HCP) prescribing practices despite current clinical practice guidelines from major societies, such as the Endocrine Society. In the US from 2001 to 2011, testosterone use among men aged ≥ 40 years increased more than 3-fold in all age groups.³ Subsequently in the years from 2013 to 2016, prescription rates declined perhaps due to the cardiovascular and stroke concerns.⁴

In the US Department of Veterans Affairs (VA), new testosterone prescriptions across VA medical centers increased from 20,437 in fiscal year (FY) 2009 to 36,394 in FY 2012. Yet only 3.1% of men who received testosterone therapy had 2 or more low morning total or free testosterone concentrations measured; LH and/or FSH levels assessed; and presence of contraindications to therapy documented. Remarkably, 16.5% of these veterans did not have a testosterone level tested prior to being prescribed testosterone. Among veterans who were prescribed testosterone, 1.4% had a diagnosis of prostate cancer, 7.6% had a diagnosis of obstructive sleep apnea (OSA), and 3.5% had elevated hematocrit at baseline.⁵ These findings raised concerns of whether the diagnosis and etiology of hypogonadism were appropriately established and risks were considered before testosterone treatment was initiated.^5,6

To further understand VA prescribing practices of testosterone therapy, a 2018 VA Office of the Inspector General (OIG) report evaluated the initiation and follow-up of testosterone replacement therapy. The OIG randomly sampled and reviewed 1,091 male patients who filled at least 1 outpatient testosterone prescription from VA in FY 2014 and who did not have a prior testosterone prescription in FY 2013. Patients were followed through September 30, 2015. Within 1 year prior to initiating testosterone, only 1.5% had clinical signs and symptoms of testosterone deficiency documented prior to testosterone testing (76% within 18 months of starting testosterone); only 9.1% of veterans had the recommended measurements of 2 low morning testosterone levels; and only 12% had LH and FSH levels measured. Within 3 to 6 months after starting testosterone therapy, only 24% of veterans were assessed for symptom improvement, and 29% to 33% were evaluated for adverse effects, hematocrit levels and adherence to the therapy. The OIG report concluded that VA HCPs were not adhering to guidelines (referencing the Endocrine Society guidelines) when evaluating and treating veterans with testosterone deficiency.⁷

Considering the OIG recommendations and need to improve current practices among providers, VA Puget Sound Health Care System (VAPSHCS) in Washington established a multidisciplinary workgroup consisting of an endocrinologist, geriatrician, primary care provider (PCP), pharmacists, VA information technology (IT) specialist, and health products support (HPS) clinical team in the spring of 2019 to assess and improve testosterone prescribing practices.

Methods

A testosterone order template was developed, approved by VAPSHCS Pharmacy and Therapeutics Committee, and implemented on July 1, 2019, at VAPSHCS, a 1a medical facility caring for more than 112,000 veterans. Given its potential risks and the propensity for varied prescribing practices, testosterone was designated as a restricted drug requiring a prior authorization drug request (PADR) and required completion of the testosterone order template in the Computerized Patient Record System (CPRS).

Testosterone Order Template

The testosterone order template had 2 components. Completion of the template for new testosterone orders was required to initiate treatment unless the patient had known organic hypogonadism or was a transgender male. The template ensured documentation of defined symptoms and signs of testosterone deficiency; low serum testosterone levels on at least 2 occasions and LH and FSH concentrations; no contraindications to testosterone treatment; discussion of risks and benefits of therapy; and baseline hematocrit (Figure 1). Relevant educational content (eg, risks and benefits of testosterone) was incorporated in the template. The second template was required for the first renewal of testosterone to document adherence to or reason for discontinuation of testosterone; improvement of symptoms and signs; and confirm monitoring hematocrit and testosterone levels during treatment.

Prior to implementation, the PADR template was introduced to HCPs at 2 chief-of-medicine rounds on the diagnosis and evaluation of hypogonadism by a pharmacist and endocrinologist. These educational sessions used case examples and discussions to teach the appropriate use of testosterone therapy in men with hypogonadism. The target audience was PCPs, residents, and other specialists who might prescribe testosterone.

Retrospective Chart Review

To assess the impact of the new testosterone order template on adherence to OIG recommendations, a retrospective chart review was completed comparing the appropriateness of initiating testosterone replacement therapy pretemplate period (July 1 to December 31, 2018) vs posttemplate period (July 1 to December 31, 2019). Inclusion and exclusion criteria were modeled after the 2018 OIG report to allow for comparison with the OIG study population. Eligible veterans in each time period included males who received a new testosterone prescription without having been prescribed testosterone in the previous 12 months. Exclusion criteria included community care network prescriptions (CCNRx); current testosterone prescription from a different VA site; clinic administration of testosterone in the previous 12 months; an organic hypogonadism (ie, Klinefelter syndrome) or gender dysphoria diagnosis; and whether the testosterone prescription was never dispensed (PADR was denied or veteran never had the prescription filled). Veterans who met the inclusion criteria in CPRS were identified by an algorithm developed by the VAPSHCS pharmacoeconomist.

Determining the appropriateness of testosterone prescribing, such as symptoms and laboratory measurements to confirm the diagnosis of hypogonadism, was based on the OIG report and Endocrine Society guidelines. A chart review of the 12 months before testosterone prescribing was completed for each veteran, assessing for documentation of symptoms of testosterone deficiency and laboratory measurements of serum testosterone, LH, and FSH. Also, documentation of a discussion of risks and benefits of testosterone therapy in the 3 months before prescribing was assessed, which matched the timeframe in the VA OIG report.

Interim Analysis

After initial template implementation, the multidisciplinary workgroup reconvened for a preplanned interim analysis in November 2019. The evaluation at this meeting revealed multiple order pathways in CPRS that were not linked to the PADR testosterone order template. Testosterone could be ordered in the generic order dialog, medications by drug class, and medications by alphabet, and endocrinology specialty menus without prompting to complete the testosterone order template or redirection to the restricted drug menu (Figure 2). These alternative testosterone ordering pathways were removed in early December 2019 and additional data collection was conducted for 3 months after discontinuation of alternative order pathways, the posttemplate/no alternative ordering pathways period, from December 7, 2019 to February 29, 2020.

Exclusion of Previous Testosterone Prescriptions Predating Chart Review Period, Subgroup Analysis

In the OIG report and the initial retrospective chart review, only veterans without a testosterone prescription in the previous 12 months were evaluated. To assess whether a previous testosterone prescription influenced completion of the PADR and order template, a further subgroup analysis was conducted that excluded veterans who had a previous testosterone prescription at any time before the chart review periods. Therefore, “new testosterone prescription” refers to a veteran who never had a history of being on testosterone vs “former testosterone prescription,” meaning a patient could have had a previous testosterone prescription > 1 year prior to a new VA testosterone prescription.

Results

One hundred seventy-five veterans with a new testosterone prescription were identified in the pretemplate period; of these 80 (46%) met eligibility criteria; only 20 eligible veterans (25%) had a completed PADR (Figure 3). Ninety-one veterans with a new testosterone prescription were identified in the posttemplate period of which 41 (46%) veterans were eligible; 18 eligible veterans (44%) had a completed PADR, but only 7 (17%) had a completed testosterone order template.

After excluding veterans who had alternative ordering pathways for testosterone, 46 veterans were identified in the posttemplate/no alternative ordering pathways period of which 19 (41%) veterans were eligible. Compared with the posttemplate period, a higher proportion of eligible veterans, 68% (13) had a completed PADR, and 58% (11) had a testosterone order template during the posttemplate/no alternative ordering pathways period.

Discussion

The 2018 OIG report found that VA practitioners demonstrated poor adherence to evidence-based clinical practice guidelines for testosterone treatment in men with hypogonadism. Based on OIG recommendations, we developed a PADR testosterone ordering template to help HCPs improve practice by better adherence to guidelines for the diagnosis and treatment of hypogonadism in veterans. Before implementation of the PADR template, the percentage of veterans at VAPSHCS who had biochemical confirmation of hypogonadism was higher than that in the OIG report. Activation of the PADR testosterone ordering template (with or without removal of options for alternative ordering pathways of testosterone) resulted only in an improvement of laboratory confirmation and evaluation of etiology of hypogonadism. This is when we reasoned that clinicians may have access to prior records and laboratory testing beyond just the past year, and this information may have influenced their use of the PADR template. Subsequently, with exclusion of veterans who were previously prescribed testosterone, implementation of the PADR testosterone order template improved documentation of symptoms of testosterone deficiency, discussion of risks and benefits of testosterone therapy, and biochemical diagnosis and evaluation of hypogonadism relative to the period before implementation.

The lack of effects of implementing the testosterone order template on documentation of symptoms of testosterone deficiency and discussion of risks and benefits of testosterone therapy may be due to local expertise resulting in the relatively high adherence to these guideline recommendations at VAPSHCS before activation of the template vs that in the OIG report. The template improved documentation of the diagnosis and evaluation of hypogonadism for genuinely new testosterone prescriptions in veterans without a history of testosterone prescriptions; while those with a previous prescription had limited improvement. It is possible that in veterans who had testosterone prescribed previously, HCPs may have assumed or had bias that the diagnosis and evaluation of hypogonadism originally made was adequate. This finding underscores the need to develop strategies for reviewing PADR requests where there is historical testosterone use. Perhaps a clinical team member, such as a clinical pharmacist, with the background and training in guidelines for the evaluation of hypogonadism could review PADR requests in veterans with previous testosterone use.

Removal of alternative ordering pathways for testosterone increased the completion rate of PADR requests and the testosterone ordering template, although the latter was not completed in one-third of veterans. Possible reasons for HCPs’ suboptimal completion of the testosterone template despite the PADR initiation include clinicians’ lack of willingness to read the PADR completely and familiarize themselves with the clinical guidelines due to workload demands of PCPs. In addition there maybe pressure from patients to receive testosterone for age-related symptoms due to heavy marketing. In addition, there may have been pharmacists who reviewed the PADR and approved the incomplete testosterone template. At VAPSHCS there were up to 40 pharmacists during different periods reviewing the testosterone PADRs. Likely, not everyone was completely familiar with this implementation process, and a possible future consideration would be further education to staff pharmacists who are verifying these prescriptions. There were several advantages to using this new testosterone order template when HCPs attempted to order a prescription. First, they were prompted to complete the PADR. Subsequently, a pharmacist reviewed the template and approved or rejected the prescription if the template was incomplete. The completed template served as documentation in the electronic health record for the prescribing HCP. The template was constructed to populate the required laboratory tests for ease of use and documentation. In addition, educational information regarding the symptoms and signs of testosterone deficiency, laboratory tests needed to confirm and evaluate hypogonadism, contraindications to testosterone treatment, and risks and benefits of therapy were incorporated into the template to assist HCPs in understanding the requirements for a complete diagnosis and evaluation. Finally, on completion of the template, HCPs were able to order testosterone via link to various testosterone formulations.

Before its implementation, the PADR testosterone order template was introduced to PCPs and internal medicine residents at 2 case-based conferences aimed at the diagnosis and treatment of male hypogonadism. These conferences were well received and helped launch the testosterone PADR template at VAPSHCS. Similar outreach to HCPs who prescribe testosterone is highly recommended in other VA facilities before implementation of the testosterone ordering template. It is possible that more targeted education to other HCPs would have resulted in greater use of the testosterone ordering template and adherence to clinical practice guidelines.

The VAPSHCS multidisciplinary workgroup was essential for the development, implementation, evaluation, and revision of the PADR and testosterone ordering template. The workgroup met routinely to follow up on the ease of installation in CPRS and discuss technical corrections that were needed. This was an essential for quality improvement, as loopholes in CPRS were identified where the HCP could order testosterone without being prompted to use the new PADR testosterone order template (alternative ordering pathways). The workgroup swiftly informed the IT specialist and HPS team to remove alternative ordering pathways of testosterone. Continuous quality improvement evaluations are highly recommended during implementation of the template in other facilities to accommodate specific local modifications that might be needed.

Conclusions

The creation and implementation of a PADR testosterone order template may be a beneficial approach to improve the diagnosis of hypogonadism and facilitate appropriate use of testosterone therapy in veterans in accordance with established clinical practice guidelines, particularly in veterans without any prior testosterone use. Key future strategies to improve testosterone prescribing should focus on identifying clinical team members, such as a local clinical pharmacist, to review and steward PADR requests to ensure that testosterone is indicated, and treatment is appropriately monitored.

Modified endoscopy documentation software can automatically generate endoscopic retrograde cholangiopancreatography (ERCP) quality metrics, based on a trial at two referral centers.

Providers were prompted during procedures, and inputting any missed data took providers less than 30 additional seconds per patient. The approach led to highly accurate quality reports, lead author Gregory A. Coté, MD, MS, of the Medical University of South Carolina, Charleston, and colleagues wrote in Techniques and Innovations in Gastrointestinal Endoscopy.

The investigators suggested that these findings may lead to the kind of quality reports already used for colonoscopy, which are easier to produce. Such reports are important, they wrote, as the U.S. health care system shifts to value-based reimbursement models, which in turn puts greater scrutiny on the quality of endoscopic procedures. However, doing so with ERCP isn’t entirely straightforward.

“Measuring adherence to ERCP quality indicators is especially challenging given: variance in indications, intraprocedural maneuvers, potential outcomes of a complex procedure, and variability in physician report documentation,” Dr. Coté and colleagues wrote. “In order to operationalize robust tracking of clinically relevant adherence to ERCP quality indicators in clinical practice – that is, to provide real-time feedback to providers, health systems, payors, and patients – an automated system of measurement must be developed.”

The quality indicators used in the study were largely drawn from an American Society for Gastrointestinal Endoscopy/American College of Gastroenterology task force document, with exclusion of those that were subjective or required systematic follow-up. The investigators modified existing endoscopy documentation software at two referral centers to include mandatory, structured data fields, principally with inclusion of quality improvements deemed high priority by the society consensus document, study authors, or both. For instance, providers were obligated to select a specific indication instead of various, synonymous terms (for example, “biliary stricture” vs. “common bile duct stricture”). Examples of quality indicators included successful cannulation of the desired duct, successful retrieval of stone less than 10 mm, or successful placement of a bile duct stent when indicated. Endoscopists were also required to note the presence of postoperative foregut anatomy or presence of existing sphincterotomy, variables which serve to stratefy the quality indicator outcome for degree of difficulty and allow appropriate comparisons of data. In addition, the study authors included inquiries about use of rectal indomethacin, use of prophylactic pancreatic duct stent, and documentation of need for repeat ERCP, follow-up x-ray, or both.

After 9 months, the system recorded 1,376 ERCP procedures conducted by eight providers, with a median annualized volume of 237 procedures (range, 37-336). Almost one-third (29%) of the patients had not had prior sphincterotomy.

Automated reporting of ERCP was compared with manual record review, which confirmed high (98%-100%) accuracy. This high level of accuracy “obviates the need for manual adjudication of medical records,” the investigators wrote.

They used data from one provider to create a template report card, and while exact comparisons across providers and institutions were not published, an example report card that was published with the study showed how such comparisons could be generated in the real world.

“The tool presented in this study allows for an objective assessment of ERCP performance which can provide explicit feedback to providers and allow transparent assessment of quality outcomes; it has the potential to improve the quality of ERCP akin to what has been demonstrated using colonoscopy report cards,” the investigators wrote. “Importantly, this can be achieved with minimal alteration to providers’ routine procedure documentation.”

Dr. Coté and colleagues also noted that the software modifications “can be implemented in other endoscopy units using the same or similar software.”

Taking the project to the next level would require widespread collaboration, according to the investigators.

“A key next step is to operationalize the transfer of data across multiple institutions, allowing for the creation of interim, standard-quality indicator reports that could be disseminated to providers, health systems, and payors,” they wrote. “If applied to a national cohort, this tool could accurately assess the current landscape of ERCP quality and provide tremendous opportunities for systematic improvement.”

One author disclosed a relationship with Provation Medical, but the remaining authors declared no relevant conflicts.

Modified endoscopy documentation software can automatically generate endoscopic retrograde cholangiopancreatography (ERCP) quality metrics, based on a trial at two referral centers.

Providers were prompted during procedures, and inputting any missed data took providers less than 30 additional seconds per patient. The approach led to highly accurate quality reports, lead author Gregory A. Coté, MD, MS, of the Medical University of South Carolina, Charleston, and colleagues wrote in Techniques and Innovations in Gastrointestinal Endoscopy.

The investigators suggested that these findings may lead to the kind of quality reports already used for colonoscopy, which are easier to produce. Such reports are important, they wrote, as the U.S. health care system shifts to value-based reimbursement models, which in turn puts greater scrutiny on the quality of endoscopic procedures. However, doing so with ERCP isn’t entirely straightforward.

“Measuring adherence to ERCP quality indicators is especially challenging given: variance in indications, intraprocedural maneuvers, potential outcomes of a complex procedure, and variability in physician report documentation,” Dr. Coté and colleagues wrote. “In order to operationalize robust tracking of clinically relevant adherence to ERCP quality indicators in clinical practice – that is, to provide real-time feedback to providers, health systems, payors, and patients – an automated system of measurement must be developed.”

The quality indicators used in the study were largely drawn from an American Society for Gastrointestinal Endoscopy/American College of Gastroenterology task force document, with exclusion of those that were subjective or required systematic follow-up. The investigators modified existing endoscopy documentation software at two referral centers to include mandatory, structured data fields, principally with inclusion of quality improvements deemed high priority by the society consensus document, study authors, or both. For instance, providers were obligated to select a specific indication instead of various, synonymous terms (for example, “biliary stricture” vs. “common bile duct stricture”). Examples of quality indicators included successful cannulation of the desired duct, successful retrieval of stone less than 10 mm, or successful placement of a bile duct stent when indicated. Endoscopists were also required to note the presence of postoperative foregut anatomy or presence of existing sphincterotomy, variables which serve to stratefy the quality indicator outcome for degree of difficulty and allow appropriate comparisons of data. In addition, the study authors included inquiries about use of rectal indomethacin, use of prophylactic pancreatic duct stent, and documentation of need for repeat ERCP, follow-up x-ray, or both.

After 9 months, the system recorded 1,376 ERCP procedures conducted by eight providers, with a median annualized volume of 237 procedures (range, 37-336). Almost one-third (29%) of the patients had not had prior sphincterotomy.

Automated reporting of ERCP was compared with manual record review, which confirmed high (98%-100%) accuracy. This high level of accuracy “obviates the need for manual adjudication of medical records,” the investigators wrote.

They used data from one provider to create a template report card, and while exact comparisons across providers and institutions were not published, an example report card that was published with the study showed how such comparisons could be generated in the real world.

“The tool presented in this study allows for an objective assessment of ERCP performance which can provide explicit feedback to providers and allow transparent assessment of quality outcomes; it has the potential to improve the quality of ERCP akin to what has been demonstrated using colonoscopy report cards,” the investigators wrote. “Importantly, this can be achieved with minimal alteration to providers’ routine procedure documentation.”

Dr. Coté and colleagues also noted that the software modifications “can be implemented in other endoscopy units using the same or similar software.”

Taking the project to the next level would require widespread collaboration, according to the investigators.

“A key next step is to operationalize the transfer of data across multiple institutions, allowing for the creation of interim, standard-quality indicator reports that could be disseminated to providers, health systems, and payors,” they wrote. “If applied to a national cohort, this tool could accurately assess the current landscape of ERCP quality and provide tremendous opportunities for systematic improvement.”

One author disclosed a relationship with Provation Medical, but the remaining authors declared no relevant conflicts.

Modified endoscopy documentation software can automatically generate endoscopic retrograde cholangiopancreatography (ERCP) quality metrics, based on a trial at two referral centers.

Providers were prompted during procedures, and inputting any missed data took providers less than 30 additional seconds per patient. The approach led to highly accurate quality reports, lead author Gregory A. Coté, MD, MS, of the Medical University of South Carolina, Charleston, and colleagues wrote in Techniques and Innovations in Gastrointestinal Endoscopy.

The investigators suggested that these findings may lead to the kind of quality reports already used for colonoscopy, which are easier to produce. Such reports are important, they wrote, as the U.S. health care system shifts to value-based reimbursement models, which in turn puts greater scrutiny on the quality of endoscopic procedures. However, doing so with ERCP isn’t entirely straightforward.

“Measuring adherence to ERCP quality indicators is especially challenging given: variance in indications, intraprocedural maneuvers, potential outcomes of a complex procedure, and variability in physician report documentation,” Dr. Coté and colleagues wrote. “In order to operationalize robust tracking of clinically relevant adherence to ERCP quality indicators in clinical practice – that is, to provide real-time feedback to providers, health systems, payors, and patients – an automated system of measurement must be developed.”

The quality indicators used in the study were largely drawn from an American Society for Gastrointestinal Endoscopy/American College of Gastroenterology task force document, with exclusion of those that were subjective or required systematic follow-up. The investigators modified existing endoscopy documentation software at two referral centers to include mandatory, structured data fields, principally with inclusion of quality improvements deemed high priority by the society consensus document, study authors, or both. For instance, providers were obligated to select a specific indication instead of various, synonymous terms (for example, “biliary stricture” vs. “common bile duct stricture”). Examples of quality indicators included successful cannulation of the desired duct, successful retrieval of stone less than 10 mm, or successful placement of a bile duct stent when indicated. Endoscopists were also required to note the presence of postoperative foregut anatomy or presence of existing sphincterotomy, variables which serve to stratefy the quality indicator outcome for degree of difficulty and allow appropriate comparisons of data. In addition, the study authors included inquiries about use of rectal indomethacin, use of prophylactic pancreatic duct stent, and documentation of need for repeat ERCP, follow-up x-ray, or both.

After 9 months, the system recorded 1,376 ERCP procedures conducted by eight providers, with a median annualized volume of 237 procedures (range, 37-336). Almost one-third (29%) of the patients had not had prior sphincterotomy.

Automated reporting of ERCP was compared with manual record review, which confirmed high (98%-100%) accuracy. This high level of accuracy “obviates the need for manual adjudication of medical records,” the investigators wrote.

They used data from one provider to create a template report card, and while exact comparisons across providers and institutions were not published, an example report card that was published with the study showed how such comparisons could be generated in the real world.

“The tool presented in this study allows for an objective assessment of ERCP performance which can provide explicit feedback to providers and allow transparent assessment of quality outcomes; it has the potential to improve the quality of ERCP akin to what has been demonstrated using colonoscopy report cards,” the investigators wrote. “Importantly, this can be achieved with minimal alteration to providers’ routine procedure documentation.”

Dr. Coté and colleagues also noted that the software modifications “can be implemented in other endoscopy units using the same or similar software.”

Taking the project to the next level would require widespread collaboration, according to the investigators.

“A key next step is to operationalize the transfer of data across multiple institutions, allowing for the creation of interim, standard-quality indicator reports that could be disseminated to providers, health systems, and payors,” they wrote. “If applied to a national cohort, this tool could accurately assess the current landscape of ERCP quality and provide tremendous opportunities for systematic improvement.”

One author disclosed a relationship with Provation Medical, but the remaining authors declared no relevant conflicts.

More than 70% of Americans are overweight or obese and 1 in 10 has type 2 diabetes mellitus (T2DM). In the last 20 years, the prevalence of obesity and DM has each increased drastically according to the Centers for Disease Control and Prevention.^1,2 Thus, an increase in severe insulin-resistant DM is predicted. Severe insulin resistance occurs when insulin doses exceed 200 units per day or 2 units/kg per day.^3-5 Treating this condition demands large volumes of U-100 insulin and a high frequency of injections (usually 4-7 per day), which can lead to reduced patient adherence.^8-10 Likewise, large injected volumes are more painful and can lead to altered absorption.^3,9-11

U-500 insulin (500 units/mL) is 5 times more concentrated than U-100 insulin and has advantages in the management of severe insulin-resistant DM.^11-13 Its pharmacokinetic profile is unique, for the clinical effect can last for up to 24 hours.^4-6 U-500 can replace basal-bolus and other complex insulin regimens, offering convenient, effective glycemic control with 2 or 3 injections per day.^11,14-20 U-500 can also improve the quality of life and adherence compared with formulations that require more frequent injections.^7,14,21 Historically, only exceptional or “special” cases were treated with U-500, but demand for concentrated insulins has increased in the last decade as clinicians adjust their care for this growing patient population.¹⁷

The purpose of this study was to determine whether a population of subjects with severe insulin-resistant T2DM would benefit from the use of U-500 vs U-100 insulin regimens. The hypothesis was that this population would obtain equal or better glycemic control while achieving improved adherence. Other studies have demonstrated that U-500 yields improvements in glycemic control but also potentially increases hypoglycemic episodes.^15-18,22-24 To our knowledge, this study is the first to evaluate the clinical outcomes of subjects with severe insulin-resistant T2DM who changed from U-100 to U-500 vs subjects who remained on high-dose U-100 insulin.

Methods

This was a single-site, retrospective chart review of subjects with T2DM who attended the endocrinology specialty clinic at the James A. Haley Veterans’ Hospital (JAHVA) in Tampa, Florida, between July 2002 and June 2011. The study included a group of subjects using U-500 insulin and a comparison group using U-100 insulin. The study was approved by the JAHVA Research & Development Committee and by the University of South Florida Institutional Review Board.

Inclusion criteria included diagnosis of T2DM, body mass index (BMI) of more than 30, use of U-500 insulin, or > 200 units daily of U-100 insulin. Exclusion criteria included hypoglycemia unawareness, type 1 DM, and use of an insulin pump. A total of 142 subjects met the inclusion criteria (68 in the U-500 group and 74 in the U-100 group).

All study subjects had at least 1 DM education session. U-500 subjects used insulin vials and 1-mL volumetric hypodermal syringes. All U-500 prescriptions were issued electronically in units and volume (U-500 insulin was available exclusively in vials during the time frame from which data were collected). Subjects in the U-100 group used insulin vials or pen devices. Laboratory studies were processed in house by the institution using high-pressure liquid chromatography to determine hemoglobin A_1C (Hb A_1C) levels. All study subjects required at least 2 Hb A_1C measurements over the observed 12 months for inclusion.

Transition to U-500 Insulin

U-500 transition was considered routinely and presented as an option for patients requiring > 200 units of insulin daily. The transition criteria included adherence to medications, follow-up appointments, and glucose monitoring recommendations, and ability to learn and apply insulin self-adjustment instructions. All subjects were given an additional U-500 insulin education session before transition. The endocrinologist calculated all starting doses by reducing the total daily dose by 20%.

Data Collection

Data were collected using the automatic data mining tools within the JAHVA Computerized Patient Record System and confirmed individually by clinical staff. Demographic data included age, race, and sex. Other parameters were weight; BMI; Hb A_1C; estimated glomerular filtration rate (eGFR); duration of DM; use of metformin and other oral agents; total daily insulin dose; number of daily injections; prior history of atherosclerotic cardiovascular disease (ASCVD), including coronary artery disease (CAD), cerebrovascular accident (CVA), or peripheral vascular disease (PVD); occurrence of severe hypoglycemia (symptomatic hypoglycemia requiring treatment assistance from another individual) and of new cardiovascular events, classified as CAD, CVA, or PVD.

For the U-500 group, data were collected and analyzed for the 3 months before (baseline) and the 12 months after the initiation of concentrated insulin. For the U-100 group, data were collected and analyzed for the comparable 3 months before (baseline) and the 12 months after the first clinic visit in which the subject started using more than 200 units per day of U-100. Frequency of follow-up visits was individualized according to clinical needs.

Clinical Endpoints

Primary outcomes included changes in Hb A_1C from baseline to the following 12 months, and the occurrence of severe hypoglycemia. Secondary outcomes included the occurrence of new ASCVD events during the study, and changes in weight, BMI, and number of injections.

Statistical Analysis

A descriptive analysis was applied to the categorical variables using absolute and relative frequencies. For continuous variables, mean and SD, or median and interquartile range, according to the distribution were calculated. Differences in baseline characteristics between groups were determined using chi-square and t test.

The primary and secondary outcomes were assessed through univariate and multivariate general linear models. Multivariate models were used to compare differences in the variation of Hb A_1C over time. Data were incomplete for the Hb A_1C in 27 subjects, 6% of the dataset (Each subject had more than one variable or observation). Therefore, a multiple imputation was used to account for the incompleteness on Hb A_1C (value substitutions by the mean and by the prior Hb A_1C and models were balanced against the unaltered data). A P value of ≤ .05 was used to determine statistical significance. The statistical analyses were performed using IBM SPSS Statistics 21.

Results

Most patients were male (94%) of white race (86%), with a mean age of 57 years and comparable duration of DM (Table 1). Demographics were balanced between the groups, except for weight and BMI, both higher in the U-500 group (P < .001). Use of oral antidiabetic agents was not significantly different between groups, nor were comorbid conditions, with nearly 50% of subjects in each group affected by CKD and ASCVD, of which CAD was the most common (approximately 40% of both groups). Only about one-third of subjects used metformin and/or other oral agents, likely due to the high prevalence of CKD (contraindicating metformin) and high insulin requirements (due to correlation with β cell failure). A subgroup analysis of subjects on metformin did not demonstrate significant differences in risk of severe hypoglycemia or in Hb A_1C levels (data not shown).

Both groups had similar initial Hb A_1C baselines (> 9%) and both improved glycemic control during the study period. However, the Hb A_1C reduction was greater in the U-500 group (P= .034), 0.84% vs 0.56% for U-100 and the between-groups difference was 0.4%. (Figure 1, Tables 2 and 3).

The univariate general linear model shows a statistically significant difference in the levels of Hb A_1C within each treatment group, regardless of the imputation strategy. However, the differences were not significant when comparing postintervention Hb A_1C means between groups with unaltered data (P = .23), because the U-500 group Hb A_1C improvement gap narrowed at the end of study. In the multivariate analysis, irrespective of imputation method, the differences in Hb A_1C between group treated with U-100 and U-500 were statistically significant (Table 3).

Overall, more subjects in the U-500 group than in the U-100 group achieved Hb A_1C levels < 8.5% (56% vs 46%, respectively, P = .003) and the proportion of subjects achieving Hb A_1C levels < 7.5% doubled that of the U-100 group (26% vs 12%; Figure 2). Five subjects in the U-500 group experienced severe hypoglycemia, compared with 1 in the U-100 group (P = .08). The total daily insulin dose was significantly higher in the U-500 group (296 units daily) than in the U-100 group (209 units daily) (P < .001) (Table 2). Baseline weight and BMI differences were also significant for the U-500 and U-100 groups (P < .001). Weight gain of approximately 2 kg occurred in both groups, a change that was not statistically significant (P = .79)

Discussion

The purpose of the study was to compare subjects with obesity and T2DM using U-500 concentrated insulin with similarly matched subjects using U-100 insulin. Available studies using U-500 insulin, including prospective trials, have reported the experience after transitioning patients who “failed” U-100 regimens.^{13-16,18,21-24} This failure is a relative and transient condition that, in theory, could be improved with medical intervention and lifestyle changes. Such changes cannot be easily quantified in a clinical trial or retrospective study without a control group. This study was an attempt to fill this knowledge gap.

The U-500 intervention resulted in a 0.8% overall reduction in Hb A_1C and a significant 0.4% reduction compared to subjects using U-100. While both groups had improvement in Hb A_1C , U-500 was associated with superior reductions in Hb A_1C . This finding confirms prior assertions that U-500, compared with U-100, is associated with larger Hb A_1C improvement.^14-16

The preintervention and postintervention Hb A_1C means were > 8% in both groups. This finding suggests that lowering Hb A_1C is challenging, similar to published results demonstrating that Hb A_1C levels < 7% are achieved by fewer than one-third of U-500 users.^16-18 The explanation for this finding remains elusive, due to the methodologic limitations of a retrospective analysis. A possible explanation is the high prevalence of CKD and ASCVD among the study population, conditions which, according to guidelines justify less aggressive glycemic control efforts.²⁵ Multiple prior studies using retrospective data^8,13-16 and 2 prospective trials^18,22 demonstrated similar Hb A_1C reductions after failure of U-100 regimens.

In this study, U-500 resulted in a nominal increase in the risk of severe hypoglycemic episodes. A detailed review of the events found that most of these patients had preestablished CKD and ASCVD, and half of the subjects with sever hypoglycemic episodes had new vascular events during the study (Appendix). These findings suggest a possible correlation between CKD and ASCVD complications and the risk of severe hypoglycemic events. Pharmacokinetic profiles for U-500 have not been studied in subjects with CKD, but the clinical effect of CKD is likely prolonged by the expected reduction in insulin clearance. Similarly, the frailty associated with preexisting ASCVD, or the related polypharmacy, could be factors increasing the risk of hypoglycemia and deserve further study.

Most of the U-500 subjects used it twice daily in this study, which could have contributed to the higher hypoglycemia rate. In a prospective randomized trial Hood and colleagues reported a rate of symptomatic hypoglycemia exceeding 90% in the 2 study groups, and 8 subjects (of 325 total) had severe hypoglycemia during the 6-month observation. The group assigned to 2 daily injections had a significantly higher rate of hypoglycemic events compared with a group that had 3 injections per day.¹⁸ Additional studies are required to ascertain whether U-500, compared with specific U-100 regimens (basal-bolus vs premixed; human vs insulin analogs), results in a higher risk of severe hypoglycemia.

This study also investigated the incidence of new cardiovascular events, and no difference was found between the 2 groups. A longer observation would be required to better assess whether U-500 therapy can reduce the incidence of microvascular and macrovascular complications. The similar incidence of complications is further evidence of the similarity between the 2 studied groups. It was also reassuring to find that weight gains were small and nearly identical in both insulin groups.

Strengths and Limitations

This study has several limitations. Data about hospitalizations for congestive heart failure, amputations, progression of diabetic retinopathy, neuropathy, and nephropathy were not collected for this analysis. As both groups of subjects were relatively small, statistical power to assess outcomes is a concern. Retrospective chart reviews may also be affected by incomplete data collections and multiple biases. No data were available for other hypoglycemic episodes, especially to calculate the rate of the more common forms of hypoglycemia. The period of data analyzed spanned only about 15 months. A longer, longitudinal assessment of the differences between these 2 groups may yield more differences, and clearer results and conclusions. Moreover, the data set had aged before publication of this report; however, the authors think that the analysis and information remain highly clinically relevant. The uncommon use of U-500, and prominence as a “special case” insulin may also lead to a detection bias for severe hypoglycemia in the U-500 group. In contrast, lapses in documentation of hypoglycemia in subjects using U-100 could have occurred. Finally, the differences in total daily dose and body weight among groups were significant and may reflect on important physiologic differences between the 2 groups that may affect the reproducibility of our results.

Nevertheless, this study had notable strengths. Comparing U-500 insulin users with similar subjects using U-100 over a period of time provides head-to-head data with potentially important clinical utility. Also, we collected and analyzed a sizable number of clinically important variables, including cardiovascular risk factors, the occurrence of new cardiovascular events, and prevalence of renal disease. The use of linear regression and multivariate analysis using multiple models also strengthened the results. Previous studies compared the outcomes in subjects using U-500 insulin with only their historical selves.^{8,13-16,18,19,22-25} Therefore, these studies analyzed the data for preconversion and postconversion of U-500 only and consistently favored U-500. This design in a retrospective study cannot eliminate the selection and/or intervention biases, as the subjects of study had inevitably “failed” prior therapies. Similarly, there is no prospective clinical trial data comparing patients on U-500 with patients on high doses of U-100 insulin. Finally, the patients in our study had high rates of comorbidities, which may have increased the applicability of our results to those of “real-life” patients in the community. To our knowledge, no other study has attempted a similar study design approach either prospectively or retrospectively.

Conclusions

In this population of elderly veterans with severely insulin-resistant T2DM, with a high incidence of CKD and ASCVD, U-500 insulin was associated with significantly greater reductions in Hb A_1C than U-100 insulin-based regimens, while requiring fewer injections. No difference was noted in the incidence of new ASCVD events. More studies are needed to assess whether U-500 may increase the risk of severe hypoglycemic episodes.

Acknowledgments

The authors recognize the invaluable help provided by the editorial staff of University of South Florida IMpact, the Intramural Review to Support Research and Scientific Publication, and especially to Richard F. Lockey, MD, who has mentored us in this beautiful journey of scientific writing and for his editorial assistance. A portion of this study preliminary data was presented as an abstract at ENDO 2013, The Endocrine Society Annual meeting in San Francisco, CA, June 15-18, 2013.

Appendix. Severe Hypoglycemic Events

Subject 1: U-500 user, 61-year-old African American male. Hypoglycemia occurred during fasting and was associated with a seizure-like event 9 months after transition to concentrated insulin. He was taken by ambulance to a local hospital. No additional data were obtained. Hb A_1C was 8.2% in the month before the episode (lowest of the studied period) and increased to 9.1% in the last segment of the study.

Subject 2: U-500 user, 57-year-old white male. The severe hypoglycemic episode occurred approximately 8 months after transition. His Hb A_1C was 5.6% around the time of the event, the lowest of the studied period, and increased to 6.8% over the next 4 months. No other data were available.

Subject 3: U-500 user, 67-year-old white male. The event occurred at home in the morning while fasting, 3 months after transition. He was assisted by his family. Hb A_1C was 7.1% 10 weeks after the event and was 7% at the end of the studied period. He had a history of CKD and PVD.

Subject 4: U-500 user, 68-year-old white male. He presented with altered consciousness, hypoglycemia, and elevated troponin levels, which was later confirmed as a non-ST elevation myocardial infarction (NSTEMI), 7 months after transition. Hb A_1C during the events was 7.1% and was followed by a 9.3% level 9 weeks later. He had history of CKD and PVD.

Subject 5: U-500 user, 67-year-old white man. Hypoglycemia occurred 6 months after transition to U-500. Hb A_1C was 8.4% 2 months prior, and was followed by a 7% during the admission for severe hypoglycemia. 3 months later, his HbA1c rose to 8.2%. He had an extensive history of CAD and had a NSTEMI during the study period.

Subject 6: U-100 user, 65-year-old white man. He was found unconscious in the morning while fasting, 6 months after his first clinic visit. He had CKD and advanced ASCVD with prior CAD, PVD, and CVA. He had also had a recent CVA that had affected his movement and cognition.

More than 70% of Americans are overweight or obese and 1 in 10 has type 2 diabetes mellitus (T2DM). In the last 20 years, the prevalence of obesity and DM has each increased drastically according to the Centers for Disease Control and Prevention.^1,2 Thus, an increase in severe insulin-resistant DM is predicted. Severe insulin resistance occurs when insulin doses exceed 200 units per day or 2 units/kg per day.^3-5 Treating this condition demands large volumes of U-100 insulin and a high frequency of injections (usually 4-7 per day), which can lead to reduced patient adherence.^8-10 Likewise, large injected volumes are more painful and can lead to altered absorption.^3,9-11

U-500 insulin (500 units/mL) is 5 times more concentrated than U-100 insulin and has advantages in the management of severe insulin-resistant DM.^11-13 Its pharmacokinetic profile is unique, for the clinical effect can last for up to 24 hours.^4-6 U-500 can replace basal-bolus and other complex insulin regimens, offering convenient, effective glycemic control with 2 or 3 injections per day.^11,14-20 U-500 can also improve the quality of life and adherence compared with formulations that require more frequent injections.^7,14,21 Historically, only exceptional or “special” cases were treated with U-500, but demand for concentrated insulins has increased in the last decade as clinicians adjust their care for this growing patient population.¹⁷

The purpose of this study was to determine whether a population of subjects with severe insulin-resistant T2DM would benefit from the use of U-500 vs U-100 insulin regimens. The hypothesis was that this population would obtain equal or better glycemic control while achieving improved adherence. Other studies have demonstrated that U-500 yields improvements in glycemic control but also potentially increases hypoglycemic episodes.^15-18,22-24 To our knowledge, this study is the first to evaluate the clinical outcomes of subjects with severe insulin-resistant T2DM who changed from U-100 to U-500 vs subjects who remained on high-dose U-100 insulin.

Methods

This was a single-site, retrospective chart review of subjects with T2DM who attended the endocrinology specialty clinic at the James A. Haley Veterans’ Hospital (JAHVA) in Tampa, Florida, between July 2002 and June 2011. The study included a group of subjects using U-500 insulin and a comparison group using U-100 insulin. The study was approved by the JAHVA Research & Development Committee and by the University of South Florida Institutional Review Board.

Inclusion criteria included diagnosis of T2DM, body mass index (BMI) of more than 30, use of U-500 insulin, or > 200 units daily of U-100 insulin. Exclusion criteria included hypoglycemia unawareness, type 1 DM, and use of an insulin pump. A total of 142 subjects met the inclusion criteria (68 in the U-500 group and 74 in the U-100 group).

All study subjects had at least 1 DM education session. U-500 subjects used insulin vials and 1-mL volumetric hypodermal syringes. All U-500 prescriptions were issued electronically in units and volume (U-500 insulin was available exclusively in vials during the time frame from which data were collected). Subjects in the U-100 group used insulin vials or pen devices. Laboratory studies were processed in house by the institution using high-pressure liquid chromatography to determine hemoglobin A_1C (Hb A_1C) levels. All study subjects required at least 2 Hb A_1C measurements over the observed 12 months for inclusion.

Transition to U-500 Insulin

U-500 transition was considered routinely and presented as an option for patients requiring > 200 units of insulin daily. The transition criteria included adherence to medications, follow-up appointments, and glucose monitoring recommendations, and ability to learn and apply insulin self-adjustment instructions. All subjects were given an additional U-500 insulin education session before transition. The endocrinologist calculated all starting doses by reducing the total daily dose by 20%.

Data Collection

Data were collected using the automatic data mining tools within the JAHVA Computerized Patient Record System and confirmed individually by clinical staff. Demographic data included age, race, and sex. Other parameters were weight; BMI; Hb A_1C; estimated glomerular filtration rate (eGFR); duration of DM; use of metformin and other oral agents; total daily insulin dose; number of daily injections; prior history of atherosclerotic cardiovascular disease (ASCVD), including coronary artery disease (CAD), cerebrovascular accident (CVA), or peripheral vascular disease (PVD); occurrence of severe hypoglycemia (symptomatic hypoglycemia requiring treatment assistance from another individual) and of new cardiovascular events, classified as CAD, CVA, or PVD.

For the U-500 group, data were collected and analyzed for the 3 months before (baseline) and the 12 months after the initiation of concentrated insulin. For the U-100 group, data were collected and analyzed for the comparable 3 months before (baseline) and the 12 months after the first clinic visit in which the subject started using more than 200 units per day of U-100. Frequency of follow-up visits was individualized according to clinical needs.

Clinical Endpoints

Primary outcomes included changes in Hb A_1C from baseline to the following 12 months, and the occurrence of severe hypoglycemia. Secondary outcomes included the occurrence of new ASCVD events during the study, and changes in weight, BMI, and number of injections.

Statistical Analysis

A descriptive analysis was applied to the categorical variables using absolute and relative frequencies. For continuous variables, mean and SD, or median and interquartile range, according to the distribution were calculated. Differences in baseline characteristics between groups were determined using chi-square and t test.

The primary and secondary outcomes were assessed through univariate and multivariate general linear models. Multivariate models were used to compare differences in the variation of Hb A_1C over time. Data were incomplete for the Hb A_1C in 27 subjects, 6% of the dataset (Each subject had more than one variable or observation). Therefore, a multiple imputation was used to account for the incompleteness on Hb A_1C (value substitutions by the mean and by the prior Hb A_1C and models were balanced against the unaltered data). A P value of ≤ .05 was used to determine statistical significance. The statistical analyses were performed using IBM SPSS Statistics 21.

Results

Most patients were male (94%) of white race (86%), with a mean age of 57 years and comparable duration of DM (Table 1). Demographics were balanced between the groups, except for weight and BMI, both higher in the U-500 group (P < .001). Use of oral antidiabetic agents was not significantly different between groups, nor were comorbid conditions, with nearly 50% of subjects in each group affected by CKD and ASCVD, of which CAD was the most common (approximately 40% of both groups). Only about one-third of subjects used metformin and/or other oral agents, likely due to the high prevalence of CKD (contraindicating metformin) and high insulin requirements (due to correlation with β cell failure). A subgroup analysis of subjects on metformin did not demonstrate significant differences in risk of severe hypoglycemia or in Hb A_1C levels (data not shown).

Both groups had similar initial Hb A_1C baselines (> 9%) and both improved glycemic control during the study period. However, the Hb A_1C reduction was greater in the U-500 group (P= .034), 0.84% vs 0.56% for U-100 and the between-groups difference was 0.4%. (Figure 1, Tables 2 and 3).

The univariate general linear model shows a statistically significant difference in the levels of Hb A_1C within each treatment group, regardless of the imputation strategy. However, the differences were not significant when comparing postintervention Hb A_1C means between groups with unaltered data (P = .23), because the U-500 group Hb A_1C improvement gap narrowed at the end of study. In the multivariate analysis, irrespective of imputation method, the differences in Hb A_1C between group treated with U-100 and U-500 were statistically significant (Table 3).

Overall, more subjects in the U-500 group than in the U-100 group achieved Hb A_1C levels < 8.5% (56% vs 46%, respectively, P = .003) and the proportion of subjects achieving Hb A_1C levels < 7.5% doubled that of the U-100 group (26% vs 12%; Figure 2). Five subjects in the U-500 group experienced severe hypoglycemia, compared with 1 in the U-100 group (P = .08). The total daily insulin dose was significantly higher in the U-500 group (296 units daily) than in the U-100 group (209 units daily) (P < .001) (Table 2). Baseline weight and BMI differences were also significant for the U-500 and U-100 groups (P < .001). Weight gain of approximately 2 kg occurred in both groups, a change that was not statistically significant (P = .79)

Discussion

The purpose of the study was to compare subjects with obesity and T2DM using U-500 concentrated insulin with similarly matched subjects using U-100 insulin. Available studies using U-500 insulin, including prospective trials, have reported the experience after transitioning patients who “failed” U-100 regimens.^{13-16,18,21-24} This failure is a relative and transient condition that, in theory, could be improved with medical intervention and lifestyle changes. Such changes cannot be easily quantified in a clinical trial or retrospective study without a control group. This study was an attempt to fill this knowledge gap.

The U-500 intervention resulted in a 0.8% overall reduction in Hb A_1C and a significant 0.4% reduction compared to subjects using U-100. While both groups had improvement in Hb A_1C , U-500 was associated with superior reductions in Hb A_1C . This finding confirms prior assertions that U-500, compared with U-100, is associated with larger Hb A_1C improvement.^14-16

The preintervention and postintervention Hb A_1C means were > 8% in both groups. This finding suggests that lowering Hb A_1C is challenging, similar to published results demonstrating that Hb A_1C levels < 7% are achieved by fewer than one-third of U-500 users.^16-18 The explanation for this finding remains elusive, due to the methodologic limitations of a retrospective analysis. A possible explanation is the high prevalence of CKD and ASCVD among the study population, conditions which, according to guidelines justify less aggressive glycemic control efforts.²⁵ Multiple prior studies using retrospective data^8,13-16 and 2 prospective trials^18,22 demonstrated similar Hb A_1C reductions after failure of U-100 regimens.

In this study, U-500 resulted in a nominal increase in the risk of severe hypoglycemic episodes. A detailed review of the events found that most of these patients had preestablished CKD and ASCVD, and half of the subjects with sever hypoglycemic episodes had new vascular events during the study (Appendix). These findings suggest a possible correlation between CKD and ASCVD complications and the risk of severe hypoglycemic events. Pharmacokinetic profiles for U-500 have not been studied in subjects with CKD, but the clinical effect of CKD is likely prolonged by the expected reduction in insulin clearance. Similarly, the frailty associated with preexisting ASCVD, or the related polypharmacy, could be factors increasing the risk of hypoglycemia and deserve further study.

Most of the U-500 subjects used it twice daily in this study, which could have contributed to the higher hypoglycemia rate. In a prospective randomized trial Hood and colleagues reported a rate of symptomatic hypoglycemia exceeding 90% in the 2 study groups, and 8 subjects (of 325 total) had severe hypoglycemia during the 6-month observation. The group assigned to 2 daily injections had a significantly higher rate of hypoglycemic events compared with a group that had 3 injections per day.¹⁸ Additional studies are required to ascertain whether U-500, compared with specific U-100 regimens (basal-bolus vs premixed; human vs insulin analogs), results in a higher risk of severe hypoglycemia.

This study also investigated the incidence of new cardiovascular events, and no difference was found between the 2 groups. A longer observation would be required to better assess whether U-500 therapy can reduce the incidence of microvascular and macrovascular complications. The similar incidence of complications is further evidence of the similarity between the 2 studied groups. It was also reassuring to find that weight gains were small and nearly identical in both insulin groups.

Strengths and Limitations

This study has several limitations. Data about hospitalizations for congestive heart failure, amputations, progression of diabetic retinopathy, neuropathy, and nephropathy were not collected for this analysis. As both groups of subjects were relatively small, statistical power to assess outcomes is a concern. Retrospective chart reviews may also be affected by incomplete data collections and multiple biases. No data were available for other hypoglycemic episodes, especially to calculate the rate of the more common forms of hypoglycemia. The period of data analyzed spanned only about 15 months. A longer, longitudinal assessment of the differences between these 2 groups may yield more differences, and clearer results and conclusions. Moreover, the data set had aged before publication of this report; however, the authors think that the analysis and information remain highly clinically relevant. The uncommon use of U-500, and prominence as a “special case” insulin may also lead to a detection bias for severe hypoglycemia in the U-500 group. In contrast, lapses in documentation of hypoglycemia in subjects using U-100 could have occurred. Finally, the differences in total daily dose and body weight among groups were significant and may reflect on important physiologic differences between the 2 groups that may affect the reproducibility of our results.

Nevertheless, this study had notable strengths. Comparing U-500 insulin users with similar subjects using U-100 over a period of time provides head-to-head data with potentially important clinical utility. Also, we collected and analyzed a sizable number of clinically important variables, including cardiovascular risk factors, the occurrence of new cardiovascular events, and prevalence of renal disease. The use of linear regression and multivariate analysis using multiple models also strengthened the results. Previous studies compared the outcomes in subjects using U-500 insulin with only their historical selves.^{8,13-16,18,19,22-25} Therefore, these studies analyzed the data for preconversion and postconversion of U-500 only and consistently favored U-500. This design in a retrospective study cannot eliminate the selection and/or intervention biases, as the subjects of study had inevitably “failed” prior therapies. Similarly, there is no prospective clinical trial data comparing patients on U-500 with patients on high doses of U-100 insulin. Finally, the patients in our study had high rates of comorbidities, which may have increased the applicability of our results to those of “real-life” patients in the community. To our knowledge, no other study has attempted a similar study design approach either prospectively or retrospectively.

Conclusions

In this population of elderly veterans with severely insulin-resistant T2DM, with a high incidence of CKD and ASCVD, U-500 insulin was associated with significantly greater reductions in Hb A_1C than U-100 insulin-based regimens, while requiring fewer injections. No difference was noted in the incidence of new ASCVD events. More studies are needed to assess whether U-500 may increase the risk of severe hypoglycemic episodes.

Acknowledgments

The authors recognize the invaluable help provided by the editorial staff of University of South Florida IMpact, the Intramural Review to Support Research and Scientific Publication, and especially to Richard F. Lockey, MD, who has mentored us in this beautiful journey of scientific writing and for his editorial assistance. A portion of this study preliminary data was presented as an abstract at ENDO 2013, The Endocrine Society Annual meeting in San Francisco, CA, June 15-18, 2013.

Appendix. Severe Hypoglycemic Events

Subject 1: U-500 user, 61-year-old African American male. Hypoglycemia occurred during fasting and was associated with a seizure-like event 9 months after transition to concentrated insulin. He was taken by ambulance to a local hospital. No additional data were obtained. Hb A_1C was 8.2% in the month before the episode (lowest of the studied period) and increased to 9.1% in the last segment of the study.

Subject 2: U-500 user, 57-year-old white male. The severe hypoglycemic episode occurred approximately 8 months after transition. His Hb A_1C was 5.6% around the time of the event, the lowest of the studied period, and increased to 6.8% over the next 4 months. No other data were available.

Subject 3: U-500 user, 67-year-old white male. The event occurred at home in the morning while fasting, 3 months after transition. He was assisted by his family. Hb A_1C was 7.1% 10 weeks after the event and was 7% at the end of the studied period. He had a history of CKD and PVD.

Subject 4: U-500 user, 68-year-old white male. He presented with altered consciousness, hypoglycemia, and elevated troponin levels, which was later confirmed as a non-ST elevation myocardial infarction (NSTEMI), 7 months after transition. Hb A_1C during the events was 7.1% and was followed by a 9.3% level 9 weeks later. He had history of CKD and PVD.

Subject 5: U-500 user, 67-year-old white man. Hypoglycemia occurred 6 months after transition to U-500. Hb A_1C was 8.4% 2 months prior, and was followed by a 7% during the admission for severe hypoglycemia. 3 months later, his HbA1c rose to 8.2%. He had an extensive history of CAD and had a NSTEMI during the study period.

Subject 6: U-100 user, 65-year-old white man. He was found unconscious in the morning while fasting, 6 months after his first clinic visit. He had CKD and advanced ASCVD with prior CAD, PVD, and CVA. He had also had a recent CVA that had affected his movement and cognition.

More than 70% of Americans are overweight or obese and 1 in 10 has type 2 diabetes mellitus (T2DM). In the last 20 years, the prevalence of obesity and DM has each increased drastically according to the Centers for Disease Control and Prevention.^1,2 Thus, an increase in severe insulin-resistant DM is predicted. Severe insulin resistance occurs when insulin doses exceed 200 units per day or 2 units/kg per day.^3-5 Treating this condition demands large volumes of U-100 insulin and a high frequency of injections (usually 4-7 per day), which can lead to reduced patient adherence.^8-10 Likewise, large injected volumes are more painful and can lead to altered absorption.^3,9-11

U-500 insulin (500 units/mL) is 5 times more concentrated than U-100 insulin and has advantages in the management of severe insulin-resistant DM.^11-13 Its pharmacokinetic profile is unique, for the clinical effect can last for up to 24 hours.^4-6 U-500 can replace basal-bolus and other complex insulin regimens, offering convenient, effective glycemic control with 2 or 3 injections per day.^11,14-20 U-500 can also improve the quality of life and adherence compared with formulations that require more frequent injections.^7,14,21 Historically, only exceptional or “special” cases were treated with U-500, but demand for concentrated insulins has increased in the last decade as clinicians adjust their care for this growing patient population.¹⁷

The purpose of this study was to determine whether a population of subjects with severe insulin-resistant T2DM would benefit from the use of U-500 vs U-100 insulin regimens. The hypothesis was that this population would obtain equal or better glycemic control while achieving improved adherence. Other studies have demonstrated that U-500 yields improvements in glycemic control but also potentially increases hypoglycemic episodes.^15-18,22-24 To our knowledge, this study is the first to evaluate the clinical outcomes of subjects with severe insulin-resistant T2DM who changed from U-100 to U-500 vs subjects who remained on high-dose U-100 insulin.

Methods

This was a single-site, retrospective chart review of subjects with T2DM who attended the endocrinology specialty clinic at the James A. Haley Veterans’ Hospital (JAHVA) in Tampa, Florida, between July 2002 and June 2011. The study included a group of subjects using U-500 insulin and a comparison group using U-100 insulin. The study was approved by the JAHVA Research & Development Committee and by the University of South Florida Institutional Review Board.

Inclusion criteria included diagnosis of T2DM, body mass index (BMI) of more than 30, use of U-500 insulin, or > 200 units daily of U-100 insulin. Exclusion criteria included hypoglycemia unawareness, type 1 DM, and use of an insulin pump. A total of 142 subjects met the inclusion criteria (68 in the U-500 group and 74 in the U-100 group).

All study subjects had at least 1 DM education session. U-500 subjects used insulin vials and 1-mL volumetric hypodermal syringes. All U-500 prescriptions were issued electronically in units and volume (U-500 insulin was available exclusively in vials during the time frame from which data were collected). Subjects in the U-100 group used insulin vials or pen devices. Laboratory studies were processed in house by the institution using high-pressure liquid chromatography to determine hemoglobin A_1C (Hb A_1C) levels. All study subjects required at least 2 Hb A_1C measurements over the observed 12 months for inclusion.

Transition to U-500 Insulin

U-500 transition was considered routinely and presented as an option for patients requiring > 200 units of insulin daily. The transition criteria included adherence to medications, follow-up appointments, and glucose monitoring recommendations, and ability to learn and apply insulin self-adjustment instructions. All subjects were given an additional U-500 insulin education session before transition. The endocrinologist calculated all starting doses by reducing the total daily dose by 20%.

Data Collection

Data were collected using the automatic data mining tools within the JAHVA Computerized Patient Record System and confirmed individually by clinical staff. Demographic data included age, race, and sex. Other parameters were weight; BMI; Hb A_1C; estimated glomerular filtration rate (eGFR); duration of DM; use of metformin and other oral agents; total daily insulin dose; number of daily injections; prior history of atherosclerotic cardiovascular disease (ASCVD), including coronary artery disease (CAD), cerebrovascular accident (CVA), or peripheral vascular disease (PVD); occurrence of severe hypoglycemia (symptomatic hypoglycemia requiring treatment assistance from another individual) and of new cardiovascular events, classified as CAD, CVA, or PVD.

For the U-500 group, data were collected and analyzed for the 3 months before (baseline) and the 12 months after the initiation of concentrated insulin. For the U-100 group, data were collected and analyzed for the comparable 3 months before (baseline) and the 12 months after the first clinic visit in which the subject started using more than 200 units per day of U-100. Frequency of follow-up visits was individualized according to clinical needs.

Clinical Endpoints

Primary outcomes included changes in Hb A_1C from baseline to the following 12 months, and the occurrence of severe hypoglycemia. Secondary outcomes included the occurrence of new ASCVD events during the study, and changes in weight, BMI, and number of injections.

Statistical Analysis

A descriptive analysis was applied to the categorical variables using absolute and relative frequencies. For continuous variables, mean and SD, or median and interquartile range, according to the distribution were calculated. Differences in baseline characteristics between groups were determined using chi-square and t test.

The primary and secondary outcomes were assessed through univariate and multivariate general linear models. Multivariate models were used to compare differences in the variation of Hb A_1C over time. Data were incomplete for the Hb A_1C in 27 subjects, 6% of the dataset (Each subject had more than one variable or observation). Therefore, a multiple imputation was used to account for the incompleteness on Hb A_1C (value substitutions by the mean and by the prior Hb A_1C and models were balanced against the unaltered data). A P value of ≤ .05 was used to determine statistical significance. The statistical analyses were performed using IBM SPSS Statistics 21.

Results

Most patients were male (94%) of white race (86%), with a mean age of 57 years and comparable duration of DM (Table 1). Demographics were balanced between the groups, except for weight and BMI, both higher in the U-500 group (P < .001). Use of oral antidiabetic agents was not significantly different between groups, nor were comorbid conditions, with nearly 50% of subjects in each group affected by CKD and ASCVD, of which CAD was the most common (approximately 40% of both groups). Only about one-third of subjects used metformin and/or other oral agents, likely due to the high prevalence of CKD (contraindicating metformin) and high insulin requirements (due to correlation with β cell failure). A subgroup analysis of subjects on metformin did not demonstrate significant differences in risk of severe hypoglycemia or in Hb A_1C levels (data not shown).

Both groups had similar initial Hb A_1C baselines (> 9%) and both improved glycemic control during the study period. However, the Hb A_1C reduction was greater in the U-500 group (P= .034), 0.84% vs 0.56% for U-100 and the between-groups difference was 0.4%. (Figure 1, Tables 2 and 3).

The univariate general linear model shows a statistically significant difference in the levels of Hb A_1C within each treatment group, regardless of the imputation strategy. However, the differences were not significant when comparing postintervention Hb A_1C means between groups with unaltered data (P = .23), because the U-500 group Hb A_1C improvement gap narrowed at the end of study. In the multivariate analysis, irrespective of imputation method, the differences in Hb A_1C between group treated with U-100 and U-500 were statistically significant (Table 3).

Overall, more subjects in the U-500 group than in the U-100 group achieved Hb A_1C levels < 8.5% (56% vs 46%, respectively, P = .003) and the proportion of subjects achieving Hb A_1C levels < 7.5% doubled that of the U-100 group (26% vs 12%; Figure 2). Five subjects in the U-500 group experienced severe hypoglycemia, compared with 1 in the U-100 group (P = .08). The total daily insulin dose was significantly higher in the U-500 group (296 units daily) than in the U-100 group (209 units daily) (P < .001) (Table 2). Baseline weight and BMI differences were also significant for the U-500 and U-100 groups (P < .001). Weight gain of approximately 2 kg occurred in both groups, a change that was not statistically significant (P = .79)

Discussion

The purpose of the study was to compare subjects with obesity and T2DM using U-500 concentrated insulin with similarly matched subjects using U-100 insulin. Available studies using U-500 insulin, including prospective trials, have reported the experience after transitioning patients who “failed” U-100 regimens.^{13-16,18,21-24} This failure is a relative and transient condition that, in theory, could be improved with medical intervention and lifestyle changes. Such changes cannot be easily quantified in a clinical trial or retrospective study without a control group. This study was an attempt to fill this knowledge gap.

The U-500 intervention resulted in a 0.8% overall reduction in Hb A_1C and a significant 0.4% reduction compared to subjects using U-100. While both groups had improvement in Hb A_1C , U-500 was associated with superior reductions in Hb A_1C . This finding confirms prior assertions that U-500, compared with U-100, is associated with larger Hb A_1C improvement.^14-16

The preintervention and postintervention Hb A_1C means were > 8% in both groups. This finding suggests that lowering Hb A_1C is challenging, similar to published results demonstrating that Hb A_1C levels < 7% are achieved by fewer than one-third of U-500 users.^16-18 The explanation for this finding remains elusive, due to the methodologic limitations of a retrospective analysis. A possible explanation is the high prevalence of CKD and ASCVD among the study population, conditions which, according to guidelines justify less aggressive glycemic control efforts.²⁵ Multiple prior studies using retrospective data^8,13-16 and 2 prospective trials^18,22 demonstrated similar Hb A_1C reductions after failure of U-100 regimens.

In this study, U-500 resulted in a nominal increase in the risk of severe hypoglycemic episodes. A detailed review of the events found that most of these patients had preestablished CKD and ASCVD, and half of the subjects with sever hypoglycemic episodes had new vascular events during the study (Appendix). These findings suggest a possible correlation between CKD and ASCVD complications and the risk of severe hypoglycemic events. Pharmacokinetic profiles for U-500 have not been studied in subjects with CKD, but the clinical effect of CKD is likely prolonged by the expected reduction in insulin clearance. Similarly, the frailty associated with preexisting ASCVD, or the related polypharmacy, could be factors increasing the risk of hypoglycemia and deserve further study.

Most of the U-500 subjects used it twice daily in this study, which could have contributed to the higher hypoglycemia rate. In a prospective randomized trial Hood and colleagues reported a rate of symptomatic hypoglycemia exceeding 90% in the 2 study groups, and 8 subjects (of 325 total) had severe hypoglycemia during the 6-month observation. The group assigned to 2 daily injections had a significantly higher rate of hypoglycemic events compared with a group that had 3 injections per day.¹⁸ Additional studies are required to ascertain whether U-500, compared with specific U-100 regimens (basal-bolus vs premixed; human vs insulin analogs), results in a higher risk of severe hypoglycemia.

This study also investigated the incidence of new cardiovascular events, and no difference was found between the 2 groups. A longer observation would be required to better assess whether U-500 therapy can reduce the incidence of microvascular and macrovascular complications. The similar incidence of complications is further evidence of the similarity between the 2 studied groups. It was also reassuring to find that weight gains were small and nearly identical in both insulin groups.

Strengths and Limitations

This study has several limitations. Data about hospitalizations for congestive heart failure, amputations, progression of diabetic retinopathy, neuropathy, and nephropathy were not collected for this analysis. As both groups of subjects were relatively small, statistical power to assess outcomes is a concern. Retrospective chart reviews may also be affected by incomplete data collections and multiple biases. No data were available for other hypoglycemic episodes, especially to calculate the rate of the more common forms of hypoglycemia. The period of data analyzed spanned only about 15 months. A longer, longitudinal assessment of the differences between these 2 groups may yield more differences, and clearer results and conclusions. Moreover, the data set had aged before publication of this report; however, the authors think that the analysis and information remain highly clinically relevant. The uncommon use of U-500, and prominence as a “special case” insulin may also lead to a detection bias for severe hypoglycemia in the U-500 group. In contrast, lapses in documentation of hypoglycemia in subjects using U-100 could have occurred. Finally, the differences in total daily dose and body weight among groups were significant and may reflect on important physiologic differences between the 2 groups that may affect the reproducibility of our results.

Nevertheless, this study had notable strengths. Comparing U-500 insulin users with similar subjects using U-100 over a period of time provides head-to-head data with potentially important clinical utility. Also, we collected and analyzed a sizable number of clinically important variables, including cardiovascular risk factors, the occurrence of new cardiovascular events, and prevalence of renal disease. The use of linear regression and multivariate analysis using multiple models also strengthened the results. Previous studies compared the outcomes in subjects using U-500 insulin with only their historical selves.^{8,13-16,18,19,22-25} Therefore, these studies analyzed the data for preconversion and postconversion of U-500 only and consistently favored U-500. This design in a retrospective study cannot eliminate the selection and/or intervention biases, as the subjects of study had inevitably “failed” prior therapies. Similarly, there is no prospective clinical trial data comparing patients on U-500 with patients on high doses of U-100 insulin. Finally, the patients in our study had high rates of comorbidities, which may have increased the applicability of our results to those of “real-life” patients in the community. To our knowledge, no other study has attempted a similar study design approach either prospectively or retrospectively.

Conclusions

In this population of elderly veterans with severely insulin-resistant T2DM, with a high incidence of CKD and ASCVD, U-500 insulin was associated with significantly greater reductions in Hb A_1C than U-100 insulin-based regimens, while requiring fewer injections. No difference was noted in the incidence of new ASCVD events. More studies are needed to assess whether U-500 may increase the risk of severe hypoglycemic episodes.

Acknowledgments

The authors recognize the invaluable help provided by the editorial staff of University of South Florida IMpact, the Intramural Review to Support Research and Scientific Publication, and especially to Richard F. Lockey, MD, who has mentored us in this beautiful journey of scientific writing and for his editorial assistance. A portion of this study preliminary data was presented as an abstract at ENDO 2013, The Endocrine Society Annual meeting in San Francisco, CA, June 15-18, 2013.

Appendix. Severe Hypoglycemic Events

Subject 1: U-500 user, 61-year-old African American male. Hypoglycemia occurred during fasting and was associated with a seizure-like event 9 months after transition to concentrated insulin. He was taken by ambulance to a local hospital. No additional data were obtained. Hb A_1C was 8.2% in the month before the episode (lowest of the studied period) and increased to 9.1% in the last segment of the study.

Subject 2: U-500 user, 57-year-old white male. The severe hypoglycemic episode occurred approximately 8 months after transition. His Hb A_1C was 5.6% around the time of the event, the lowest of the studied period, and increased to 6.8% over the next 4 months. No other data were available.

Subject 3: U-500 user, 67-year-old white male. The event occurred at home in the morning while fasting, 3 months after transition. He was assisted by his family. Hb A_1C was 7.1% 10 weeks after the event and was 7% at the end of the studied period. He had a history of CKD and PVD.

Subject 4: U-500 user, 68-year-old white male. He presented with altered consciousness, hypoglycemia, and elevated troponin levels, which was later confirmed as a non-ST elevation myocardial infarction (NSTEMI), 7 months after transition. Hb A_1C during the events was 7.1% and was followed by a 9.3% level 9 weeks later. He had history of CKD and PVD.

Subject 5: U-500 user, 67-year-old white man. Hypoglycemia occurred 6 months after transition to U-500. Hb A_1C was 8.4% 2 months prior, and was followed by a 7% during the admission for severe hypoglycemia. 3 months later, his HbA1c rose to 8.2%. He had an extensive history of CAD and had a NSTEMI during the study period.

Subject 6: U-100 user, 65-year-old white man. He was found unconscious in the morning while fasting, 6 months after his first clinic visit. He had CKD and advanced ASCVD with prior CAD, PVD, and CVA. He had also had a recent CVA that had affected his movement and cognition.

New models are needed for delivering long-term care (LTC) that are home-based, cost-effective, and appropriate for older adults with a range of care needs.^1,2 In fiscal year (FY) 2015, the US Department of Veterans Affairs (VA) spent $7.4 billion on LTC, accounting for 13% of total VA health care spending. Overall, 71% of LTC spending in FY 2015 was allocated to institutional care.³ Beyond cost, 95% of older adults prefer to remain in community rather than institutional LTC settings, such as nursing homes.⁴ The COVID-19 pandemic created additional concerns related to the spread of infectious disease, with > 37% of COVID-19 deaths in the United States occurring in nursing homes irrespective of facility quality.^5,6

One community-based LTC alternative developed within the VA is the Medical Foster Home (MFH) program. The MFH program is an adult foster care program in which veterans who are unable to live independently receive round-the-clock care in the home of a community-based caregiver.⁷ MFH caregivers usually have previous experience caring for family, working in a nursing home, or working as a caregiver in another capacity. These caregivers are responsible for providing 24-hour supervision and support to residents in their MFH and can care for up to 3 adults. In the MFH program, VA home-based primary care (HBPC) teams composed of physicians, registered nurses, physical and occupational therapists, social workers, pharmacists, dieticians, and psychologists, provide primary care for MFH veterans and oversee care in the caregiver’s home.

The goal of the VA HBPC program is to improve veterans’ access to medical care and shift LTC services from institutional to noninstitutional settings by providing in-home care for those who are too sick or disabled to go to a clinic for care. On average, veterans pay the MFH caregiver $2,500 out-of-pocket per month for their care.⁸ In 2016, there were 992 veterans residing in MFHs across the country.⁹ Since MFH program implementation expanded nationwide in 2008, more than 4,000 veterans have resided in MFHs in 45 states and territories.¹⁰

The VA is required to pay for nursing home care for veterans who have a qualifying VA service-connected disability or who meet a specific threshold of disability.¹¹ Currently, the VA is not authorized to pay for MFH care for veterans who meet the eligibility criteria for VA-paid nursing home care. Over the past decade, the VA has introduced and expanded several initiatives and programs to help veterans who require LTC remain in their homes and communities. These include but are not limited to the Veteran Directed Care program, the Choose Home Initiative, and the Caregiver Support Program.^12-14 Additionally, attempts have been made to pass legislation to authorize the VA to pay for MFH for veterans’ care whose military benefits include coverage for nursing home care.¹⁵ This legislation and VA initiatives are clear signs that the VA is committed to supporting programs such as the MFH program. Given this commitment, demand for the MFH program will likely increase.

Therefore, VA practitioners need to better identify which veterans are currently in the MFH program. While veterans are expected to need nursing home level care to qualify for MFH enrollment, little has been published about the physical and mental health care needs of veterans currently receiving MFH care. One previous study compared the demographics, diagnostic characteristics, and care utilization of MFH veterans with that of veterans receiving LTC in VA community living centers (CLCs), and found that veterans in MFHs had similar levels of frailty and comorbidity and had a higher mean age when compared with veterans in CLCs.¹⁶

Our study assessed a sample of veterans living in MFHs and describes these veterans’ clinical and functional characteristics. We used the Minimum Data Set 3.0 (MDS) to complete the assessments to allow comparisons with other populations residing in long-term care.^17,18 While MDS assessments are required for Medicare/Medicaid-certified nursing home residents and for residents in VA CLCs, this study was the first attempt to perform in-home MDS data assessments in MFHs. This collection of descriptive clinical data is an important first step in providing VA practitioners with information about the characteristics of veterans currently cared for in MFHs and policymakers with data to think critically about which veterans are willing to pay for the MFH program.

Methods

This study was part of a larger research project assessing the impact of the MFH program on veterans’ outcomes and health care spending as well as factors influencing program growth.^{7,9,10,16,19-23} We report on the characteristics of veterans staying in MFHs, using data from the MDS, including a clinical assessment of patients’ cognitive, function, and health care–related needs, collected from participants recruited for this study.

Five research nurses were trained to administer the MDS assessment to veterans in MFHs. Data were collected between April 2014 and December 2015 from veterans at MFH sites associated with 4 urban VA medical centers in 4 different Veterans Integrated Service Networks (58 total homes). While the VA medical centers (VAMCs)were urban, many of the MFHs were in rural areas, given that MFHs can be up to 50 miles from the associated VAMC. We selected MFH sites for this study based on MFH program veteran census. Specifically, we identified MFH sites with high veteran enrollment to ensure we would have a sufficiently large sample for participant recruitment.

Veterans who had resided in an MFH for at least 90 days were eligible to participate. Of the 155 veterans mailed a letter of invitation to participate, 92 (59%) completed the in-home MDS assessment. Reasons for not participating included: 13 veterans died prior to data collection, 18 veterans declined to participate, 18 family members or legal guardians of cognitively impaired veterans did not want the veteran to participate, and 14 veterans left the MFH program or were hospitalized at the time of data collection.

Family members and legal guardians who declined participation on behalf of a veteran reported that they felt the veteran was too frail to participate or that participating would be an added burden on the veteran. Based on the census of veterans residing in all MFHs nationally in November 2015 (N = 972), 9.5% of MFH veterans were included in this study.⁷This study was approved by the VA Central Institutional Review Board (CIRB #12–31), in addition to the local VA research and development review boards where MFH MDS assessments were collected.

Assessment Instrument and Variables

The MDS 3.0 assesses numerous aspects of clinical and functional status. Several resident-level characteristics from the MDS 3.0 were included in this study. The Cognitive Function Scale (CFS) was used to categorize cognitive function. The CFS is a categorical variable that is created from MDS 3.0 data. The CFS integrates self- and staff-reported data to classify individuals as cognitively intact, mildly impaired, moderately impaired, or severely impaired based on respondents’ Brief Interview for Mental Status (BIMS) assessment or staff-reported cognitive function collected as part of the MDS 3.0.²⁴ We explored depression by calculating a mean summary severity score for all respondents from the Patient Health Questionnaire-9 item interview (PHQ-9).²⁵ PHQ-9 summary scores range from 0 to 27, with mean scores of ≤ 4 indicating no or minimal depression, and higher scores corresponding to more severe depression as scores increase. For respondents who were unable to complete the PHQ-9, we calculated mean PHQ Observational Version (PHQ-9-OV) scores.

We included 2 variables to characterize behaviors: wandering frequency and presence and frequency of aggressive behaviors. We summarized aggressive behaviors using the Aggressive and Reactive Behavior Scale, which characterizes whether a resident has none, mild, moderate, or severe behavioral symptoms based on the presence and frequency of physical and verbal behaviors and resistance to care.^26,27 We included items that described pain, number of falls since admission or prior assessment, degree of urinary and bowel continence (always continent vs not always continent) and mobility device use to describe respondents’ health conditions and functional status. To characterize pain, we used veteran’s self-reported frequency and intensity of pain experienced in the prior 5 days and classified the experienced pain as none, mild, moderate, or severe. Finally, demographic characteristics included age and gender.

To determine functional status, we included measures of needing help to perform activities of daily living (ADLs). The MDS allows us to understand functional status ranging from ADLs lost early in the trajectory of functional decline (ie, bathing, hygiene) to those lost in the middle (ie, walking, dressing, toileting, transferring) to those lost late in the trajectory of functional decline (ie, bed mobility and eating).^28,29 To assess MFH veterans’ independence in mobility, we considered the veteran’s ability to walk without supervision or assistance in the hallway outside of their room, ability to move between their room and hallway, and ability to move throughout the house. Mobility includes use of an assistive device such as a cane, walker, or wheelchair if the veteran can use it without assistance. We summarized dependency in ADLs, using a combined score of dependence in bed mobility, transfer, locomotion on unit, dressing, eating, toilet use, and personal hygiene that ranges from 0 (independent) to 28 (completely dependent).³⁰ Additionally, we created 3-category variables to indicate the degree of dependence in performing ADLs (independent, supervision or assistance, and completely dependent).

Finally, we included diagnoses identified as active to explore differences in neurologic, mood, psychiatric, and chronic disease morbidity. In the MDS 3.0 assessment, an active diagnosis is defined as a diagnosis documented by a licensed independent practitioner in the prior 60 days that has affected the resident or their care in the prior 7 days.

Analysis

We conducted statistical analyses using Stata MP version 15.1 (StataCorp). We summarized demographic characteristics, cognitive function scores, depression scores, pain status, behavioral symptoms, incidence of falls, degree of continence, functional status, and comorbidities, using means and standard deviations for continuous variables and frequencies and proportions for categorical variables.

Results

Of the 92 MFH veterans in our sample, 85% were male and 83% were aged ≥ 65 years (Table 1). Veterans had an average length of stay of 927 days at the time of MDS assessment. More than half (55%) of MFH veterans had cognitive impairment (ranging from mild to severe). The mean (SD) depression score was 3.3 (3.9), indicating minimal depression. For veterans who could not complete the depression questionnaire, the mean (SD) staff-assessed depression score was 5.9 (5.5), suggesting mild depression. Overall, 22% of the sample had aggressive behaviors but only 7 were noted to be severe. Few residents had caregiver-reported wandering. Self-reported pain intensity indicated that 45% of the sample had mild, moderate, or severe pain. While more than half the cohort had complete bowel continence (53%), only 36% had complete urinary continence. Use of mobility devices was common, with 56% of residents using a wheelchair, 42% using a walker, and 14% using a cane. One-fourth of veterans had fallen at least once since admission to the MFH.

Of the 11 ADLs assessed, the percentage of MFH veterans requiring assistance with early and mid-loss ADLs ranged from 63% for transferring to 84% for bathing (Table 2). Even for the late-loss ADL of eating, 57% of the MFH cohort required assistance. Overall, MFH veterans had an average ADL dependency score of 11.

Discussion

In this study, we describe the characteristics of veterans receiving LTC in a sample of MFHs. This is the first study to assess veteran health and function across a group of MFHs. To help provide context for the description of MFH residents, we compared demographic characteristics, cognitive impairment, depression, pain, behaviors, functional status, and morbidity of veterans in the MFH program to long-stay residents in community nursing homes (eAppendix 1-3 available at doi:10.12788/fp.0102). A comparison with this reference population suggests that these MFH and nursing home cohorts are similar in terms of age, wandering behavior, incidence of falls, and prevalence of neurologic, psychiatric, and chronic diseases. Compared with nursing home residents, veterans in the MFH cohort had slightly higher mood symptom scores, were more likely to display aggressive behavior, and were more likely to report experiencing moderate and severe pain.

Additionally, MFH veterans displayed a lower level of cognitive impairment, fewer functional impairments, measured by the ADL dependency score, and were less likely to be bowel or bladder incontinent. Despite an overall lower ADL dependency score, a similar proportion of MFH veterans and nursing home residents were totally dependent in performing 7 of 11 ADLs and a higher proportion of MFH veterans were completely dependent for toileting (22% long-stay nursing home vs 31% MFH). The only ADLs for which there was a higher proportion of long-stay nursing home residents who were totally dependent compared with MFH residents were walking in room (54% long-stay nursing home vs 38% MFH), walking in the corridor (57% long-stay nursing home vs 33% MFH), and locomotion off the unit (36% long-stay nursing home vs 22% MFH).

While the rates of total ADL dependence among veterans in MFHs suggest that MFHs are providing care to a subset of veterans with high levels of functional impairment and care needs, MFHs are also providing care to veterans who are more independent in performing ADLs and who resemble low-care nursing home residents. A low-care nursing home resident is broadly defined as an one who does not need assistance performing late-loss ADLs (bed mobility, transferring, toileting, and eating) and who does not have the Resource Utilization Group classification of special rehab or clinically complex.^31,32 Due to their overall higher functional capacity, low-care residents, even those with chronic medical care needs, may be more appropriately cared for in less intensive care settings than in nursing homes. About 5% to 30% of long-stay nursing home residents can be classified as low care.^31,33-37 Additionally, a majority of newly admitted nursing home patients report a preference for or support community discharge rather than long-stay nursing home care, suggesting that many nursing home residents have the potential and desire to transition to a community-based setting.³³

Based on the prevalence of veterans in our sample who are similar to low-care nursing home residents and the national focus on shifting LTC to community-based settings, MFHs may be an ideal setting for both low-care nursing home residents and those seeking community-based alternatives to traditional, institutionalized LTC. Additionally, given that we observed greater behavioral and pain needs and similar rates of comorbidities in MFH veterans relative to long-stay nursing home residents, our results indicate that MFHs also have the capacity to care for veterans with higher care needs who desire community-based LTC.

Previous research identified barriers to program MFH growth that may contribute to referral of veterans with fewer ADL dependencies compared with long-stay nursing home residents. A key barrier to MFH referral is that nursing home referral requires selection of a home, whereas MFH referral involves matching veterans with appropriate caregivers, which requires time to align the veteran’s needs with the right caregiver in the right home.⁷ Given the rigors of finding a match, VA staff who refer veterans may preferentially refer veterans with greater ADL impairments to nursing homes, assuming that higher levels of care needs will complicate the matching process and reserve MFH referral for only the highest functioning candidates.¹⁹ However, the ADL data presented here indicate that many MFH residents with significant levels of ADL dependence are living in MFHs. Meeting the care needs of those who have higher ADL dependencies is possible because MFH coordinators and HBPC providers deliver individual, ongoing education to MFH caregivers about caring for MFH veterans and provide available resources needed to safely care for MFH veterans across the spectrum of ADL dependency.⁷

Veterans with higher levels of functional dependence may also be referred to nursing homes rather than to MFHs because of payment issues. Independent of the VA, veterans or their families negotiate a contract with their caregiver to pay out-of-pocket for MFH caregiving as well as room and board. Particularly for veterans who have military benefits to cover nursing home care costs, the out-of-pocket payment for veterans with high degrees of functional dependence increase as needs increase. These out-of-pocket payments may serve as a barrier to MFH enrollment. The proposed Long-Term Care Veterans Choice Act, which would allow the VA to pay for MFH care for eligible veterans may address this barrier.¹⁵

Another possible explanation for the higher rates of functional independence in the MFH cohort is that veterans with functional impairment are not being referred to MFHs. A previous study of the MFH program found that health care providers were often unaware of the program and as a result did not refer eligible veterans to this alternative LTC option.⁷ The changes proposed by the Long-Term Care Veterans Choice Act may result in an increase in demand in MFH care and thus increase awareness of the program among VA physicians.¹⁵

Limitations

There are several potential limitations in this study. First, there are limits to the generalizability of the MFH sample given that the sample of veterans was not randomly selected and that weights were not applied to account for nonresponse bias. Second, charting requirements in MFHs are less intensive compared with nursing home tracking. While the training for research nurses on how to conduct MDS assessments in MFHs was designed to simulate the process in nursing homes, MDS data were likely impacted by differences in charting practices. In addition, MFH caregivers may report certain items, such as aggressive behaviors, more often because they observe MFH veterans round-the-clock compared with NH caregivers who work in shifts and have a lower caregiver to resident ratio. The current data suggest differences in prevalence of behavioral symptoms.

Future studies should examine whether this reflects differences in the populations served or differences in how MFH caregivers track and manage behavioral symptoms. Third, this study was conducted at only MFH sites associated with 4 VAMCs, thus our findings may not be generalizable to veterans in other areas. Finally, there may be differences in the veterans who agreed to participate in the study compared with those who declined to participate. For example, it is possible that the eligible MFH veterans who declined to participate in this study were more functionally impaired than those who did participate. More than one-third (39%) of the family members of cognitively impaired MFH veterans who did not participate cited concerns about the veteran’s frailty as a primary reason for declining to participate. Consequently, the high level of functional status among veterans included in this study compared to nursing home residents may be in part a result of selection bias from more ADL-impaired veterans declining to participate in the study.

Conclusions

Although the MFH program has provided LTC nationally to veterans for nearly 2 decades, this study is the first to administer in-home MDS assessments to veterans in MFHs, allowing for a detailed description of cognitive, functional, and behavioral characteristics of MFH residents. In this study, we found that veterans currently receiving care in MFHs have a wide range of care needs. Our findings indicate that MFHs are caring for some veterans with high functional impairment as well as those who are completely independent in performing ADLs.

Moreover, these results are a preliminary attempt to assist VA health care providers in determining which veterans can be cared for in an MFH such that they can make informed referrals to this alternative LTC setting. To improve the generalizability of these findings, future studies should collect MDS 3.0 assessments longitudinally from a representative sample of veterans in MFHs. Further research is needed to explore how VA providers make the decision to refer a veteran to an MFH compared to a nursing home. Additionally, the percentage of veterans in this study who reported experiencing pain may indicate the need to identify innovative, integrated pain management programs for home settings.

New models are needed for delivering long-term care (LTC) that are home-based, cost-effective, and appropriate for older adults with a range of care needs.^1,2 In fiscal year (FY) 2015, the US Department of Veterans Affairs (VA) spent $7.4 billion on LTC, accounting for 13% of total VA health care spending. Overall, 71% of LTC spending in FY 2015 was allocated to institutional care.³ Beyond cost, 95% of older adults prefer to remain in community rather than institutional LTC settings, such as nursing homes.⁴ The COVID-19 pandemic created additional concerns related to the spread of infectious disease, with > 37% of COVID-19 deaths in the United States occurring in nursing homes irrespective of facility quality.^5,6

One community-based LTC alternative developed within the VA is the Medical Foster Home (MFH) program. The MFH program is an adult foster care program in which veterans who are unable to live independently receive round-the-clock care in the home of a community-based caregiver.⁷ MFH caregivers usually have previous experience caring for family, working in a nursing home, or working as a caregiver in another capacity. These caregivers are responsible for providing 24-hour supervision and support to residents in their MFH and can care for up to 3 adults. In the MFH program, VA home-based primary care (HBPC) teams composed of physicians, registered nurses, physical and occupational therapists, social workers, pharmacists, dieticians, and psychologists, provide primary care for MFH veterans and oversee care in the caregiver’s home.

The goal of the VA HBPC program is to improve veterans’ access to medical care and shift LTC services from institutional to noninstitutional settings by providing in-home care for those who are too sick or disabled to go to a clinic for care. On average, veterans pay the MFH caregiver $2,500 out-of-pocket per month for their care.⁸ In 2016, there were 992 veterans residing in MFHs across the country.⁹ Since MFH program implementation expanded nationwide in 2008, more than 4,000 veterans have resided in MFHs in 45 states and territories.¹⁰

The VA is required to pay for nursing home care for veterans who have a qualifying VA service-connected disability or who meet a specific threshold of disability.¹¹ Currently, the VA is not authorized to pay for MFH care for veterans who meet the eligibility criteria for VA-paid nursing home care. Over the past decade, the VA has introduced and expanded several initiatives and programs to help veterans who require LTC remain in their homes and communities. These include but are not limited to the Veteran Directed Care program, the Choose Home Initiative, and the Caregiver Support Program.^12-14 Additionally, attempts have been made to pass legislation to authorize the VA to pay for MFH for veterans’ care whose military benefits include coverage for nursing home care.¹⁵ This legislation and VA initiatives are clear signs that the VA is committed to supporting programs such as the MFH program. Given this commitment, demand for the MFH program will likely increase.

Therefore, VA practitioners need to better identify which veterans are currently in the MFH program. While veterans are expected to need nursing home level care to qualify for MFH enrollment, little has been published about the physical and mental health care needs of veterans currently receiving MFH care. One previous study compared the demographics, diagnostic characteristics, and care utilization of MFH veterans with that of veterans receiving LTC in VA community living centers (CLCs), and found that veterans in MFHs had similar levels of frailty and comorbidity and had a higher mean age when compared with veterans in CLCs.¹⁶

Our study assessed a sample of veterans living in MFHs and describes these veterans’ clinical and functional characteristics. We used the Minimum Data Set 3.0 (MDS) to complete the assessments to allow comparisons with other populations residing in long-term care.^17,18 While MDS assessments are required for Medicare/Medicaid-certified nursing home residents and for residents in VA CLCs, this study was the first attempt to perform in-home MDS data assessments in MFHs. This collection of descriptive clinical data is an important first step in providing VA practitioners with information about the characteristics of veterans currently cared for in MFHs and policymakers with data to think critically about which veterans are willing to pay for the MFH program.

Methods

This study was part of a larger research project assessing the impact of the MFH program on veterans’ outcomes and health care spending as well as factors influencing program growth.^{7,9,10,16,19-23} We report on the characteristics of veterans staying in MFHs, using data from the MDS, including a clinical assessment of patients’ cognitive, function, and health care–related needs, collected from participants recruited for this study.

Five research nurses were trained to administer the MDS assessment to veterans in MFHs. Data were collected between April 2014 and December 2015 from veterans at MFH sites associated with 4 urban VA medical centers in 4 different Veterans Integrated Service Networks (58 total homes). While the VA medical centers (VAMCs)were urban, many of the MFHs were in rural areas, given that MFHs can be up to 50 miles from the associated VAMC. We selected MFH sites for this study based on MFH program veteran census. Specifically, we identified MFH sites with high veteran enrollment to ensure we would have a sufficiently large sample for participant recruitment.

Veterans who had resided in an MFH for at least 90 days were eligible to participate. Of the 155 veterans mailed a letter of invitation to participate, 92 (59%) completed the in-home MDS assessment. Reasons for not participating included: 13 veterans died prior to data collection, 18 veterans declined to participate, 18 family members or legal guardians of cognitively impaired veterans did not want the veteran to participate, and 14 veterans left the MFH program or were hospitalized at the time of data collection.

Family members and legal guardians who declined participation on behalf of a veteran reported that they felt the veteran was too frail to participate or that participating would be an added burden on the veteran. Based on the census of veterans residing in all MFHs nationally in November 2015 (N = 972), 9.5% of MFH veterans were included in this study.⁷This study was approved by the VA Central Institutional Review Board (CIRB #12–31), in addition to the local VA research and development review boards where MFH MDS assessments were collected.

Assessment Instrument and Variables

The MDS 3.0 assesses numerous aspects of clinical and functional status. Several resident-level characteristics from the MDS 3.0 were included in this study. The Cognitive Function Scale (CFS) was used to categorize cognitive function. The CFS is a categorical variable that is created from MDS 3.0 data. The CFS integrates self- and staff-reported data to classify individuals as cognitively intact, mildly impaired, moderately impaired, or severely impaired based on respondents’ Brief Interview for Mental Status (BIMS) assessment or staff-reported cognitive function collected as part of the MDS 3.0.²⁴ We explored depression by calculating a mean summary severity score for all respondents from the Patient Health Questionnaire-9 item interview (PHQ-9).²⁵ PHQ-9 summary scores range from 0 to 27, with mean scores of ≤ 4 indicating no or minimal depression, and higher scores corresponding to more severe depression as scores increase. For respondents who were unable to complete the PHQ-9, we calculated mean PHQ Observational Version (PHQ-9-OV) scores.

We included 2 variables to characterize behaviors: wandering frequency and presence and frequency of aggressive behaviors. We summarized aggressive behaviors using the Aggressive and Reactive Behavior Scale, which characterizes whether a resident has none, mild, moderate, or severe behavioral symptoms based on the presence and frequency of physical and verbal behaviors and resistance to care.^26,27 We included items that described pain, number of falls since admission or prior assessment, degree of urinary and bowel continence (always continent vs not always continent) and mobility device use to describe respondents’ health conditions and functional status. To characterize pain, we used veteran’s self-reported frequency and intensity of pain experienced in the prior 5 days and classified the experienced pain as none, mild, moderate, or severe. Finally, demographic characteristics included age and gender.

To determine functional status, we included measures of needing help to perform activities of daily living (ADLs). The MDS allows us to understand functional status ranging from ADLs lost early in the trajectory of functional decline (ie, bathing, hygiene) to those lost in the middle (ie, walking, dressing, toileting, transferring) to those lost late in the trajectory of functional decline (ie, bed mobility and eating).^28,29 To assess MFH veterans’ independence in mobility, we considered the veteran’s ability to walk without supervision or assistance in the hallway outside of their room, ability to move between their room and hallway, and ability to move throughout the house. Mobility includes use of an assistive device such as a cane, walker, or wheelchair if the veteran can use it without assistance. We summarized dependency in ADLs, using a combined score of dependence in bed mobility, transfer, locomotion on unit, dressing, eating, toilet use, and personal hygiene that ranges from 0 (independent) to 28 (completely dependent).³⁰ Additionally, we created 3-category variables to indicate the degree of dependence in performing ADLs (independent, supervision or assistance, and completely dependent).

Finally, we included diagnoses identified as active to explore differences in neurologic, mood, psychiatric, and chronic disease morbidity. In the MDS 3.0 assessment, an active diagnosis is defined as a diagnosis documented by a licensed independent practitioner in the prior 60 days that has affected the resident or their care in the prior 7 days.

Analysis

We conducted statistical analyses using Stata MP version 15.1 (StataCorp). We summarized demographic characteristics, cognitive function scores, depression scores, pain status, behavioral symptoms, incidence of falls, degree of continence, functional status, and comorbidities, using means and standard deviations for continuous variables and frequencies and proportions for categorical variables.

Results

Of the 92 MFH veterans in our sample, 85% were male and 83% were aged ≥ 65 years (Table 1). Veterans had an average length of stay of 927 days at the time of MDS assessment. More than half (55%) of MFH veterans had cognitive impairment (ranging from mild to severe). The mean (SD) depression score was 3.3 (3.9), indicating minimal depression. For veterans who could not complete the depression questionnaire, the mean (SD) staff-assessed depression score was 5.9 (5.5), suggesting mild depression. Overall, 22% of the sample had aggressive behaviors but only 7 were noted to be severe. Few residents had caregiver-reported wandering. Self-reported pain intensity indicated that 45% of the sample had mild, moderate, or severe pain. While more than half the cohort had complete bowel continence (53%), only 36% had complete urinary continence. Use of mobility devices was common, with 56% of residents using a wheelchair, 42% using a walker, and 14% using a cane. One-fourth of veterans had fallen at least once since admission to the MFH.

Of the 11 ADLs assessed, the percentage of MFH veterans requiring assistance with early and mid-loss ADLs ranged from 63% for transferring to 84% for bathing (Table 2). Even for the late-loss ADL of eating, 57% of the MFH cohort required assistance. Overall, MFH veterans had an average ADL dependency score of 11.

Discussion

In this study, we describe the characteristics of veterans receiving LTC in a sample of MFHs. This is the first study to assess veteran health and function across a group of MFHs. To help provide context for the description of MFH residents, we compared demographic characteristics, cognitive impairment, depression, pain, behaviors, functional status, and morbidity of veterans in the MFH program to long-stay residents in community nursing homes (eAppendix 1-3 available at doi:10.12788/fp.0102). A comparison with this reference population suggests that these MFH and nursing home cohorts are similar in terms of age, wandering behavior, incidence of falls, and prevalence of neurologic, psychiatric, and chronic diseases. Compared with nursing home residents, veterans in the MFH cohort had slightly higher mood symptom scores, were more likely to display aggressive behavior, and were more likely to report experiencing moderate and severe pain.

Additionally, MFH veterans displayed a lower level of cognitive impairment, fewer functional impairments, measured by the ADL dependency score, and were less likely to be bowel or bladder incontinent. Despite an overall lower ADL dependency score, a similar proportion of MFH veterans and nursing home residents were totally dependent in performing 7 of 11 ADLs and a higher proportion of MFH veterans were completely dependent for toileting (22% long-stay nursing home vs 31% MFH). The only ADLs for which there was a higher proportion of long-stay nursing home residents who were totally dependent compared with MFH residents were walking in room (54% long-stay nursing home vs 38% MFH), walking in the corridor (57% long-stay nursing home vs 33% MFH), and locomotion off the unit (36% long-stay nursing home vs 22% MFH).

While the rates of total ADL dependence among veterans in MFHs suggest that MFHs are providing care to a subset of veterans with high levels of functional impairment and care needs, MFHs are also providing care to veterans who are more independent in performing ADLs and who resemble low-care nursing home residents. A low-care nursing home resident is broadly defined as an one who does not need assistance performing late-loss ADLs (bed mobility, transferring, toileting, and eating) and who does not have the Resource Utilization Group classification of special rehab or clinically complex.^31,32 Due to their overall higher functional capacity, low-care residents, even those with chronic medical care needs, may be more appropriately cared for in less intensive care settings than in nursing homes. About 5% to 30% of long-stay nursing home residents can be classified as low care.^31,33-37 Additionally, a majority of newly admitted nursing home patients report a preference for or support community discharge rather than long-stay nursing home care, suggesting that many nursing home residents have the potential and desire to transition to a community-based setting.³³

Based on the prevalence of veterans in our sample who are similar to low-care nursing home residents and the national focus on shifting LTC to community-based settings, MFHs may be an ideal setting for both low-care nursing home residents and those seeking community-based alternatives to traditional, institutionalized LTC. Additionally, given that we observed greater behavioral and pain needs and similar rates of comorbidities in MFH veterans relative to long-stay nursing home residents, our results indicate that MFHs also have the capacity to care for veterans with higher care needs who desire community-based LTC.

Previous research identified barriers to program MFH growth that may contribute to referral of veterans with fewer ADL dependencies compared with long-stay nursing home residents. A key barrier to MFH referral is that nursing home referral requires selection of a home, whereas MFH referral involves matching veterans with appropriate caregivers, which requires time to align the veteran’s needs with the right caregiver in the right home.⁷ Given the rigors of finding a match, VA staff who refer veterans may preferentially refer veterans with greater ADL impairments to nursing homes, assuming that higher levels of care needs will complicate the matching process and reserve MFH referral for only the highest functioning candidates.¹⁹ However, the ADL data presented here indicate that many MFH residents with significant levels of ADL dependence are living in MFHs. Meeting the care needs of those who have higher ADL dependencies is possible because MFH coordinators and HBPC providers deliver individual, ongoing education to MFH caregivers about caring for MFH veterans and provide available resources needed to safely care for MFH veterans across the spectrum of ADL dependency.⁷

Veterans with higher levels of functional dependence may also be referred to nursing homes rather than to MFHs because of payment issues. Independent of the VA, veterans or their families negotiate a contract with their caregiver to pay out-of-pocket for MFH caregiving as well as room and board. Particularly for veterans who have military benefits to cover nursing home care costs, the out-of-pocket payment for veterans with high degrees of functional dependence increase as needs increase. These out-of-pocket payments may serve as a barrier to MFH enrollment. The proposed Long-Term Care Veterans Choice Act, which would allow the VA to pay for MFH care for eligible veterans may address this barrier.¹⁵

Another possible explanation for the higher rates of functional independence in the MFH cohort is that veterans with functional impairment are not being referred to MFHs. A previous study of the MFH program found that health care providers were often unaware of the program and as a result did not refer eligible veterans to this alternative LTC option.⁷ The changes proposed by the Long-Term Care Veterans Choice Act may result in an increase in demand in MFH care and thus increase awareness of the program among VA physicians.¹⁵

Limitations

There are several potential limitations in this study. First, there are limits to the generalizability of the MFH sample given that the sample of veterans was not randomly selected and that weights were not applied to account for nonresponse bias. Second, charting requirements in MFHs are less intensive compared with nursing home tracking. While the training for research nurses on how to conduct MDS assessments in MFHs was designed to simulate the process in nursing homes, MDS data were likely impacted by differences in charting practices. In addition, MFH caregivers may report certain items, such as aggressive behaviors, more often because they observe MFH veterans round-the-clock compared with NH caregivers who work in shifts and have a lower caregiver to resident ratio. The current data suggest differences in prevalence of behavioral symptoms.

Future studies should examine whether this reflects differences in the populations served or differences in how MFH caregivers track and manage behavioral symptoms. Third, this study was conducted at only MFH sites associated with 4 VAMCs, thus our findings may not be generalizable to veterans in other areas. Finally, there may be differences in the veterans who agreed to participate in the study compared with those who declined to participate. For example, it is possible that the eligible MFH veterans who declined to participate in this study were more functionally impaired than those who did participate. More than one-third (39%) of the family members of cognitively impaired MFH veterans who did not participate cited concerns about the veteran’s frailty as a primary reason for declining to participate. Consequently, the high level of functional status among veterans included in this study compared to nursing home residents may be in part a result of selection bias from more ADL-impaired veterans declining to participate in the study.

Conclusions

Although the MFH program has provided LTC nationally to veterans for nearly 2 decades, this study is the first to administer in-home MDS assessments to veterans in MFHs, allowing for a detailed description of cognitive, functional, and behavioral characteristics of MFH residents. In this study, we found that veterans currently receiving care in MFHs have a wide range of care needs. Our findings indicate that MFHs are caring for some veterans with high functional impairment as well as those who are completely independent in performing ADLs.

Moreover, these results are a preliminary attempt to assist VA health care providers in determining which veterans can be cared for in an MFH such that they can make informed referrals to this alternative LTC setting. To improve the generalizability of these findings, future studies should collect MDS 3.0 assessments longitudinally from a representative sample of veterans in MFHs. Further research is needed to explore how VA providers make the decision to refer a veteran to an MFH compared to a nursing home. Additionally, the percentage of veterans in this study who reported experiencing pain may indicate the need to identify innovative, integrated pain management programs for home settings.

New models are needed for delivering long-term care (LTC) that are home-based, cost-effective, and appropriate for older adults with a range of care needs.^1,2 In fiscal year (FY) 2015, the US Department of Veterans Affairs (VA) spent $7.4 billion on LTC, accounting for 13% of total VA health care spending. Overall, 71% of LTC spending in FY 2015 was allocated to institutional care.³ Beyond cost, 95% of older adults prefer to remain in community rather than institutional LTC settings, such as nursing homes.⁴ The COVID-19 pandemic created additional concerns related to the spread of infectious disease, with > 37% of COVID-19 deaths in the United States occurring in nursing homes irrespective of facility quality.^5,6

One community-based LTC alternative developed within the VA is the Medical Foster Home (MFH) program. The MFH program is an adult foster care program in which veterans who are unable to live independently receive round-the-clock care in the home of a community-based caregiver.⁷ MFH caregivers usually have previous experience caring for family, working in a nursing home, or working as a caregiver in another capacity. These caregivers are responsible for providing 24-hour supervision and support to residents in their MFH and can care for up to 3 adults. In the MFH program, VA home-based primary care (HBPC) teams composed of physicians, registered nurses, physical and occupational therapists, social workers, pharmacists, dieticians, and psychologists, provide primary care for MFH veterans and oversee care in the caregiver’s home.

The goal of the VA HBPC program is to improve veterans’ access to medical care and shift LTC services from institutional to noninstitutional settings by providing in-home care for those who are too sick or disabled to go to a clinic for care. On average, veterans pay the MFH caregiver $2,500 out-of-pocket per month for their care.⁸ In 2016, there were 992 veterans residing in MFHs across the country.⁹ Since MFH program implementation expanded nationwide in 2008, more than 4,000 veterans have resided in MFHs in 45 states and territories.¹⁰

The VA is required to pay for nursing home care for veterans who have a qualifying VA service-connected disability or who meet a specific threshold of disability.¹¹ Currently, the VA is not authorized to pay for MFH care for veterans who meet the eligibility criteria for VA-paid nursing home care. Over the past decade, the VA has introduced and expanded several initiatives and programs to help veterans who require LTC remain in their homes and communities. These include but are not limited to the Veteran Directed Care program, the Choose Home Initiative, and the Caregiver Support Program.^12-14 Additionally, attempts have been made to pass legislation to authorize the VA to pay for MFH for veterans’ care whose military benefits include coverage for nursing home care.¹⁵ This legislation and VA initiatives are clear signs that the VA is committed to supporting programs such as the MFH program. Given this commitment, demand for the MFH program will likely increase.

Therefore, VA practitioners need to better identify which veterans are currently in the MFH program. While veterans are expected to need nursing home level care to qualify for MFH enrollment, little has been published about the physical and mental health care needs of veterans currently receiving MFH care. One previous study compared the demographics, diagnostic characteristics, and care utilization of MFH veterans with that of veterans receiving LTC in VA community living centers (CLCs), and found that veterans in MFHs had similar levels of frailty and comorbidity and had a higher mean age when compared with veterans in CLCs.¹⁶

Our study assessed a sample of veterans living in MFHs and describes these veterans’ clinical and functional characteristics. We used the Minimum Data Set 3.0 (MDS) to complete the assessments to allow comparisons with other populations residing in long-term care.^17,18 While MDS assessments are required for Medicare/Medicaid-certified nursing home residents and for residents in VA CLCs, this study was the first attempt to perform in-home MDS data assessments in MFHs. This collection of descriptive clinical data is an important first step in providing VA practitioners with information about the characteristics of veterans currently cared for in MFHs and policymakers with data to think critically about which veterans are willing to pay for the MFH program.

Methods

This study was part of a larger research project assessing the impact of the MFH program on veterans’ outcomes and health care spending as well as factors influencing program growth.^{7,9,10,16,19-23} We report on the characteristics of veterans staying in MFHs, using data from the MDS, including a clinical assessment of patients’ cognitive, function, and health care–related needs, collected from participants recruited for this study.

Five research nurses were trained to administer the MDS assessment to veterans in MFHs. Data were collected between April 2014 and December 2015 from veterans at MFH sites associated with 4 urban VA medical centers in 4 different Veterans Integrated Service Networks (58 total homes). While the VA medical centers (VAMCs)were urban, many of the MFHs were in rural areas, given that MFHs can be up to 50 miles from the associated VAMC. We selected MFH sites for this study based on MFH program veteran census. Specifically, we identified MFH sites with high veteran enrollment to ensure we would have a sufficiently large sample for participant recruitment.

Veterans who had resided in an MFH for at least 90 days were eligible to participate. Of the 155 veterans mailed a letter of invitation to participate, 92 (59%) completed the in-home MDS assessment. Reasons for not participating included: 13 veterans died prior to data collection, 18 veterans declined to participate, 18 family members or legal guardians of cognitively impaired veterans did not want the veteran to participate, and 14 veterans left the MFH program or were hospitalized at the time of data collection.

Family members and legal guardians who declined participation on behalf of a veteran reported that they felt the veteran was too frail to participate or that participating would be an added burden on the veteran. Based on the census of veterans residing in all MFHs nationally in November 2015 (N = 972), 9.5% of MFH veterans were included in this study.⁷This study was approved by the VA Central Institutional Review Board (CIRB #12–31), in addition to the local VA research and development review boards where MFH MDS assessments were collected.

Assessment Instrument and Variables

The MDS 3.0 assesses numerous aspects of clinical and functional status. Several resident-level characteristics from the MDS 3.0 were included in this study. The Cognitive Function Scale (CFS) was used to categorize cognitive function. The CFS is a categorical variable that is created from MDS 3.0 data. The CFS integrates self- and staff-reported data to classify individuals as cognitively intact, mildly impaired, moderately impaired, or severely impaired based on respondents’ Brief Interview for Mental Status (BIMS) assessment or staff-reported cognitive function collected as part of the MDS 3.0.²⁴ We explored depression by calculating a mean summary severity score for all respondents from the Patient Health Questionnaire-9 item interview (PHQ-9).²⁵ PHQ-9 summary scores range from 0 to 27, with mean scores of ≤ 4 indicating no or minimal depression, and higher scores corresponding to more severe depression as scores increase. For respondents who were unable to complete the PHQ-9, we calculated mean PHQ Observational Version (PHQ-9-OV) scores.

We included 2 variables to characterize behaviors: wandering frequency and presence and frequency of aggressive behaviors. We summarized aggressive behaviors using the Aggressive and Reactive Behavior Scale, which characterizes whether a resident has none, mild, moderate, or severe behavioral symptoms based on the presence and frequency of physical and verbal behaviors and resistance to care.^26,27 We included items that described pain, number of falls since admission or prior assessment, degree of urinary and bowel continence (always continent vs not always continent) and mobility device use to describe respondents’ health conditions and functional status. To characterize pain, we used veteran’s self-reported frequency and intensity of pain experienced in the prior 5 days and classified the experienced pain as none, mild, moderate, or severe. Finally, demographic characteristics included age and gender.

To determine functional status, we included measures of needing help to perform activities of daily living (ADLs). The MDS allows us to understand functional status ranging from ADLs lost early in the trajectory of functional decline (ie, bathing, hygiene) to those lost in the middle (ie, walking, dressing, toileting, transferring) to those lost late in the trajectory of functional decline (ie, bed mobility and eating).^28,29 To assess MFH veterans’ independence in mobility, we considered the veteran’s ability to walk without supervision or assistance in the hallway outside of their room, ability to move between their room and hallway, and ability to move throughout the house. Mobility includes use of an assistive device such as a cane, walker, or wheelchair if the veteran can use it without assistance. We summarized dependency in ADLs, using a combined score of dependence in bed mobility, transfer, locomotion on unit, dressing, eating, toilet use, and personal hygiene that ranges from 0 (independent) to 28 (completely dependent).³⁰ Additionally, we created 3-category variables to indicate the degree of dependence in performing ADLs (independent, supervision or assistance, and completely dependent).

Finally, we included diagnoses identified as active to explore differences in neurologic, mood, psychiatric, and chronic disease morbidity. In the MDS 3.0 assessment, an active diagnosis is defined as a diagnosis documented by a licensed independent practitioner in the prior 60 days that has affected the resident or their care in the prior 7 days.

Analysis

We conducted statistical analyses using Stata MP version 15.1 (StataCorp). We summarized demographic characteristics, cognitive function scores, depression scores, pain status, behavioral symptoms, incidence of falls, degree of continence, functional status, and comorbidities, using means and standard deviations for continuous variables and frequencies and proportions for categorical variables.

Results

Of the 92 MFH veterans in our sample, 85% were male and 83% were aged ≥ 65 years (Table 1). Veterans had an average length of stay of 927 days at the time of MDS assessment. More than half (55%) of MFH veterans had cognitive impairment (ranging from mild to severe). The mean (SD) depression score was 3.3 (3.9), indicating minimal depression. For veterans who could not complete the depression questionnaire, the mean (SD) staff-assessed depression score was 5.9 (5.5), suggesting mild depression. Overall, 22% of the sample had aggressive behaviors but only 7 were noted to be severe. Few residents had caregiver-reported wandering. Self-reported pain intensity indicated that 45% of the sample had mild, moderate, or severe pain. While more than half the cohort had complete bowel continence (53%), only 36% had complete urinary continence. Use of mobility devices was common, with 56% of residents using a wheelchair, 42% using a walker, and 14% using a cane. One-fourth of veterans had fallen at least once since admission to the MFH.

Of the 11 ADLs assessed, the percentage of MFH veterans requiring assistance with early and mid-loss ADLs ranged from 63% for transferring to 84% for bathing (Table 2). Even for the late-loss ADL of eating, 57% of the MFH cohort required assistance. Overall, MFH veterans had an average ADL dependency score of 11.

Discussion

In this study, we describe the characteristics of veterans receiving LTC in a sample of MFHs. This is the first study to assess veteran health and function across a group of MFHs. To help provide context for the description of MFH residents, we compared demographic characteristics, cognitive impairment, depression, pain, behaviors, functional status, and morbidity of veterans in the MFH program to long-stay residents in community nursing homes (eAppendix 1-3 available at doi:10.12788/fp.0102). A comparison with this reference population suggests that these MFH and nursing home cohorts are similar in terms of age, wandering behavior, incidence of falls, and prevalence of neurologic, psychiatric, and chronic diseases. Compared with nursing home residents, veterans in the MFH cohort had slightly higher mood symptom scores, were more likely to display aggressive behavior, and were more likely to report experiencing moderate and severe pain.

Additionally, MFH veterans displayed a lower level of cognitive impairment, fewer functional impairments, measured by the ADL dependency score, and were less likely to be bowel or bladder incontinent. Despite an overall lower ADL dependency score, a similar proportion of MFH veterans and nursing home residents were totally dependent in performing 7 of 11 ADLs and a higher proportion of MFH veterans were completely dependent for toileting (22% long-stay nursing home vs 31% MFH). The only ADLs for which there was a higher proportion of long-stay nursing home residents who were totally dependent compared with MFH residents were walking in room (54% long-stay nursing home vs 38% MFH), walking in the corridor (57% long-stay nursing home vs 33% MFH), and locomotion off the unit (36% long-stay nursing home vs 22% MFH).

While the rates of total ADL dependence among veterans in MFHs suggest that MFHs are providing care to a subset of veterans with high levels of functional impairment and care needs, MFHs are also providing care to veterans who are more independent in performing ADLs and who resemble low-care nursing home residents. A low-care nursing home resident is broadly defined as an one who does not need assistance performing late-loss ADLs (bed mobility, transferring, toileting, and eating) and who does not have the Resource Utilization Group classification of special rehab or clinically complex.^31,32 Due to their overall higher functional capacity, low-care residents, even those with chronic medical care needs, may be more appropriately cared for in less intensive care settings than in nursing homes. About 5% to 30% of long-stay nursing home residents can be classified as low care.^31,33-37 Additionally, a majority of newly admitted nursing home patients report a preference for or support community discharge rather than long-stay nursing home care, suggesting that many nursing home residents have the potential and desire to transition to a community-based setting.³³

Based on the prevalence of veterans in our sample who are similar to low-care nursing home residents and the national focus on shifting LTC to community-based settings, MFHs may be an ideal setting for both low-care nursing home residents and those seeking community-based alternatives to traditional, institutionalized LTC. Additionally, given that we observed greater behavioral and pain needs and similar rates of comorbidities in MFH veterans relative to long-stay nursing home residents, our results indicate that MFHs also have the capacity to care for veterans with higher care needs who desire community-based LTC.

Previous research identified barriers to program MFH growth that may contribute to referral of veterans with fewer ADL dependencies compared with long-stay nursing home residents. A key barrier to MFH referral is that nursing home referral requires selection of a home, whereas MFH referral involves matching veterans with appropriate caregivers, which requires time to align the veteran’s needs with the right caregiver in the right home.⁷ Given the rigors of finding a match, VA staff who refer veterans may preferentially refer veterans with greater ADL impairments to nursing homes, assuming that higher levels of care needs will complicate the matching process and reserve MFH referral for only the highest functioning candidates.¹⁹ However, the ADL data presented here indicate that many MFH residents with significant levels of ADL dependence are living in MFHs. Meeting the care needs of those who have higher ADL dependencies is possible because MFH coordinators and HBPC providers deliver individual, ongoing education to MFH caregivers about caring for MFH veterans and provide available resources needed to safely care for MFH veterans across the spectrum of ADL dependency.⁷

Veterans with higher levels of functional dependence may also be referred to nursing homes rather than to MFHs because of payment issues. Independent of the VA, veterans or their families negotiate a contract with their caregiver to pay out-of-pocket for MFH caregiving as well as room and board. Particularly for veterans who have military benefits to cover nursing home care costs, the out-of-pocket payment for veterans with high degrees of functional dependence increase as needs increase. These out-of-pocket payments may serve as a barrier to MFH enrollment. The proposed Long-Term Care Veterans Choice Act, which would allow the VA to pay for MFH care for eligible veterans may address this barrier.¹⁵

Another possible explanation for the higher rates of functional independence in the MFH cohort is that veterans with functional impairment are not being referred to MFHs. A previous study of the MFH program found that health care providers were often unaware of the program and as a result did not refer eligible veterans to this alternative LTC option.⁷ The changes proposed by the Long-Term Care Veterans Choice Act may result in an increase in demand in MFH care and thus increase awareness of the program among VA physicians.¹⁵

Limitations

There are several potential limitations in this study. First, there are limits to the generalizability of the MFH sample given that the sample of veterans was not randomly selected and that weights were not applied to account for nonresponse bias. Second, charting requirements in MFHs are less intensive compared with nursing home tracking. While the training for research nurses on how to conduct MDS assessments in MFHs was designed to simulate the process in nursing homes, MDS data were likely impacted by differences in charting practices. In addition, MFH caregivers may report certain items, such as aggressive behaviors, more often because they observe MFH veterans round-the-clock compared with NH caregivers who work in shifts and have a lower caregiver to resident ratio. The current data suggest differences in prevalence of behavioral symptoms.

Future studies should examine whether this reflects differences in the populations served or differences in how MFH caregivers track and manage behavioral symptoms. Third, this study was conducted at only MFH sites associated with 4 VAMCs, thus our findings may not be generalizable to veterans in other areas. Finally, there may be differences in the veterans who agreed to participate in the study compared with those who declined to participate. For example, it is possible that the eligible MFH veterans who declined to participate in this study were more functionally impaired than those who did participate. More than one-third (39%) of the family members of cognitively impaired MFH veterans who did not participate cited concerns about the veteran’s frailty as a primary reason for declining to participate. Consequently, the high level of functional status among veterans included in this study compared to nursing home residents may be in part a result of selection bias from more ADL-impaired veterans declining to participate in the study.

Conclusions

Although the MFH program has provided LTC nationally to veterans for nearly 2 decades, this study is the first to administer in-home MDS assessments to veterans in MFHs, allowing for a detailed description of cognitive, functional, and behavioral characteristics of MFH residents. In this study, we found that veterans currently receiving care in MFHs have a wide range of care needs. Our findings indicate that MFHs are caring for some veterans with high functional impairment as well as those who are completely independent in performing ADLs.

Moreover, these results are a preliminary attempt to assist VA health care providers in determining which veterans can be cared for in an MFH such that they can make informed referrals to this alternative LTC setting. To improve the generalizability of these findings, future studies should collect MDS 3.0 assessments longitudinally from a representative sample of veterans in MFHs. Further research is needed to explore how VA providers make the decision to refer a veteran to an MFH compared to a nursing home. Additionally, the percentage of veterans in this study who reported experiencing pain may indicate the need to identify innovative, integrated pain management programs for home settings.

The US Department of Veterans Affairs (VA) is the largest health care organization in the US, staffing more than 1,200 facilities and servicing about 9 million veterans.¹ Identifying VA practices that promote effective health care delivery has the potential to impact thousands of patients every day. The Surgical service at the Minneapolis VA Medical Center (MVAMC) in Minnesota often questioned colleagues whether many of the ordered tests, including blood cultures for patients with suspected infections, were clinically necessary. Despite recommendations for utilizing culture-driven results in choosing appropriate antimicrobials, it was debated whether these additional tests were simply drawn and ignored resulting only in increased costs and venipuncture discomfort for the patient. Thus, the purpose of this quality improvement study was to determine whether positive blood culture results actually influence clinical management at MVAMC.

Background

Accepted best practice when responding to positive blood culture results entails empiric treatment with broad-spectrum antibiotics that subsequently narrows in breadth of coverage once the pathogen has been identified.^2-4 This strategy has been labeled deescalation. Despite the acceptance of these standards, surveys of clinician attitudes towards antibiotics showed that 90% of physicians and residents stated they wanted more education on antimicrobials and 80% desired better schooling on antibiotic choices.^5,6 Additionally, in an online survey 18% of 402 inpatient and emergency department providers, including residents, fellows, intensive care unit (ICU) and emergency department attending physicians, hospitalists, physician assistants, and nurse practitioners, described a lack of confidence when deescalating antibiotic therapy and 45% reported that they had received training on antimicrobial prescribing that was not fully adequate.⁷

These surveys hint at a potential gap in provider education or confidence, which may serve as a barrier to ideal care, further confounding other individualized considerations taken into account when deescalating care. These considerations include patient renal toxicity profiles, the potential for missed pathogens not identified in culture results, unknown sources of infection, and the mindset of many providers to remain on broad therapy if the patient’s condition is improving.^8-10 A specific barrier to deescalation within the VA is the variance in antimicrobial stewardship practices between facilities. In a recent widespread survey of VA practices, Chou and colleagues identified that only 29 of 130 (22.3%) responding facilities had a formal policy to establish an antimicrobial stewardship program.¹¹

Overcoming these barriers to deescalation through effective stewardship practices can help to promote improved clinical outcomes. Most studies have demonstrated that outcomes of deescalation strategies have equivalent or improved mortalityand equivalent or even decreased length of ICU stay.^12-26 Although a 2014 study by Leone and colleagues reported longer overall ICU stay in deescalation treatment groups with equivalent mortality outcomes, newer data do not support these findings.^16,20,22

Furthermore, antibiotics can be expensive. Deescalation, particularly in response to positive blood culture results, has been associated with reduced antibiotic cost due to both a decrease in overall antibiotic usage and the utilization of less expensive choices.^22,24,26,27 The findings of these individual studies were corroborated in 2013 by a meta-analysis, including 89 additional studies.²⁸ Besides the direct costs of the drugs, the development of regional antibiotic resistance has been labeled as one of the most pressing concerns in public health, and major initiatives have been undertaken to stem its spread.^29,30 The majority of clinicians believe that deescalation of antibiotics would reduce antibiotic resistance. Thus, deescalation is widely cited as one of the primary goals in the management of resistance development.^{5,24,26,28,31,32}

Due to the proposed benefits and challenges of implementation, MVAMC instituted a program where the electronic health records (EHR) for all patients with positive blood culture results were reviewed by the on-call infectious disease attending physician to advise the primary care team on antibiotic administration. The MVAMC system for notification of positive blood culture results has 2 components. The first is phone notification to the on-call resident when the positive result of the pathogen identification is noted by the microbiology laboratory staff. Notably, this protocol of phone notification is only performed when identifying the pathogen and not for the subsequent sensitivity profile. The second component occurs each morning when the on-call infectious disease attending physician reviews all positive blood culture results and the current therapy. If the infectious disease attending physician feels some alterations in management are warranted, the physician calls the primary service. Additionally, the primary service may always request a formal consult with the infectious disease team. This quality improvement study was initiated to examine the success of this deescalation/stewardship program to determine whether positive blood culture results influenced clinical management.

Methods

From July 1, 2015 to June 30, 2016, 212 positive blood cultures at the MVAMC were analyzed. Four cases that did not have an antibiotic spectrum score were excluded, leaving 208 cases reviewed. Duplicate blood cultures were excluded from analysis. The microbiology laboratory used the BD Bactec automated blood culture system using the Plus aerobic and Lytic anaerobic media (Becton, Dickinson and Company).

Antibiotic alterations in response to culture results were classified as either deescalation or escalation, using a spectrum score developed by Madaras-Kelly and colleagues.³³ These investigators performed a 3-round modified Delphi survey of infectious disease staff of physicians and pharmacists. The resulting consensus spectrum score for each respective antibiotic reflected the relative susceptibilities of various pathogens to antibiotics and the intrinsic resistance of the pathogens. It is a nonlinear scale from 0 to 60 with a score of 0 indicating no antibacterial activity and a score of 60 indicating complete coverage of all critically identified pathogens. For example, a narrow-spectrum antibiotic such as metronidazole received a spectrum score of 4.0 and a broad-spectrum antibiotic such as piperacillin/tazobactam received a 42.3 score.

Data Recording and Analysis

Charts for review were flagged based on positive blood culture results from the microbiology laboratory. EHRs were manually reviewed to determine when antibiotics were started/stopped and when a member of the primary care team, usually a resident, was notified of culture results as documented by the microbiology laboratory personnel. Any alteration in antibiotics that fit the criteria of deescalation or escalation that occurred within 24 hours of notification of either critical laboratory value was recorded. The identity of infectious pathogens and the primary site of infection were not recorded as these data were not within the scope of the purpose of this study. We did not control for possible contaminants within positive blood cultures.

There were 3 time frames considered when determining culture driven alterations to the antibiotic regimen. The first 2 were changes within the 24 hours after notification of either (1) pathogen identification or (2) pathogen sensitivity. These were defined as culture-driven alterations in response to those particular laboratory findings. The third—whole case time frame—spanned from pathogen identification to 24 hours after sensitivity information was recorded. In cases where ≥ 1 antibiotic alteration was noted within a respective time frame, a classification of deescalation or escalation was still assigned. This was done by summing each change in spectrum score that occurred from antibiotic regimen alterations within the time frame, and classifying the net effect on the spectrum of coverage as either deescalation or escalation. Data were recorded in spreadsheet. RStudio 3.5.3 was used for statistical analysis.

Results

Of 208 cases assigned a spectrum score, 47 (22.6%) had the breadth of antibiotic coverage deescalated by the primary care team within 24 hours of pathogen identification with a mean (SD) physician response time of 8.0 (7.3) hours. Fourteen cases (6.7%) had the breadth of antibiotic coverage escalated from pathogen identification with a mean (SD) response time of 8.0 (7.4) hours. When taken together, within 24 hours of pathogen identification from positive blood cultures 61 cases (29.3%) had altered antibiotics, leaving 70.7% of cases unaltered (Tables 1 and 2). In this nonquantitative spectrum score method, deescalations typically involved larger changes in spectrum score than escalations.

Physician notification of pathogen sensitivities resulted in deescalation in 69 cases (33.2%) within 24 hours, with a mean (SD) response time of 10.4 (7) hours. The mean time to deescalation in response to pathogen identification was significantly shorter than the mean time to deescalation in response to sensitivities (P = .049). Broadening of coverage based on sensitivity information was reported for 17 cases (8.2%) within 24 hours, with a mean (SD) response time of 7.6 (6) hours (Table 3). In response to pathogen sensitivity results from positive blood cultures, 58.6% of cases had no antibiotic alterations. Deescalations involved notably larger changes in spectrum score than escalations.

More than half (58.6%) of cases resulted in an antibiotic alteration from empiric treatment when considering the time frame from empiric antibiotics to 24 hours after receiving sensitivity information. These were deemed the whole-case, culture-driven results. In addition to antibiotic alterations that occurred within 24 hours of either pathogen identification or sensitivity information, the whole-case category also considered antibiotic alterations that occurred more than 24 hours after pathogen identification was known and before sensitivity information was available, although this was rare. Some of these patients may have had their antibiotics altered twice, first after pathogen identification and later once sensitivities became available with the net effect recorded as the whole-case administration. Of those that had their antibiotics modified in response to laboratory results, by a ratio of 6.4:1, the change was a deescalation rather than an escalation.

Discussion

The strategy of the infectious disease team at MVAMC is one of deescalation. One challenge of quantifying deescalation was to make a reliable and agreed-upon definition of just what deescalation entails. In 2003, the pharmaceutical company Merck was granted a trademark for the phrase “De-Escalation Therapy” under the international class code 41 for educational and entertainment services. This seemed to correspond to marketing efforts for the antibiotic imipenem/cilastatin. Although the company trademarked the term, it was never defined. The usage of the phrase evolved from a reduction of the dosage of a specific antibiotic to a reduction in the number of antibiotics prescribed to that of monotherapy. The phrase continues to evolve and has now become associated with a change from combination therapy or broad-spectrum antibiotics to monotherapy, switching to an antibiotic that covers fewer pathogens, or even shortening the duration of antibiotic therapy.³⁴ The trademark expired at about the same time the imipenem/cilastatin patent expired. Notably, this drug had initially been marketed for use in empiric antibiotic therapy.³⁵

Barriers

The goal of the stewardship program was not to see a narrowing of the antibiotic spectrum in all patients. Some diseases such as diverticulitis or diabetic foot infections are usually associated with multiple pathogens where relatively broad-spectrum antibiotics seem to be preferred.^36,37 Heenen and colleagues reported that infectious disease specialists recommended deescalation in < 50% of cases they examined.³⁸

Comparing different institutions’ deescalation rates can be confusing due to varying definitions, differing patient populations, and health care provider behavior. Thus, the published rates of deescalation range widely from 10 to 70%.^2,39,40 In addition to the varied definitions of deescalation, it is challenging to directly compare the rate of deescalation between studies due to institutional variation in empirical broad-spectrum antibiotic usage. A hospital that uses broad-spectrum antibiotics at a higher rate than another has the potential to deescalate more often than one that has low rates of empirical broad-spectrum antibiotic use. Some studies use a conservative definition of deescalation such as narrowing the spectrum of coverage, while others use a more general definition, including both the narrowing of spectrum and/or the discontinuation of antibiotics from empirical therapy.^41-45 The more specific and validated definition of deescalation used in this study may allow for standardized comparisons. Another unique feature of this study is that all positive blood cultures were followed, not only those of a particular disease.

Clinical Response

This quality improvement study was initiated to determine whether positive blood culture results actually affect clinical management at MVAMC. The answer seems to be yes, with blood culture results altering antibiotic administration in about 60% of cases with the predominant change being deescalation. This overall rate of deescalation is toward the higher end of previously documented rates and coincides with the upper bound of the clinically advised deescalation rate described by Heenen and colleagues.³⁸

As noted, the spectrum score is not quantitative. Still, one may be able to contend that the values may provide some insight into the magnitude of the changes in antibiotic selection. Deescalations were on average much larger changes in spectrum than escalations. The larger magnitude of deescalations reflects that when already starting with a very broad spectrum of coverage, it is much easier to get narrower than even broader. Stated another way, when starting therapy using piperacillin/tazobactam at a spectrum score of 42.3 on a 60-point scale, there is much more room for deescalation to 0 than escalation to 60. Additionally, escalations were more likely with much smaller of a spectrum change due to accurate empirical judgment of the suspected pathogens with new findings only necessitating a minor expansion of the spectrum of coverage.

Future Study

Similar studies designed using the spectrum score methodology would allow for more meaningful interinstitutional comparison of antibiotic administration through the use of a unified definition of deescalation and escalation. Comparison of deescalation and escalation rates between hospital systems with similar patient populations with and without prompt infectious disease review and phone notification of blood culture results could further verify the value of such a protocol. It could also help determine which empiric antibiotics may be most effective in individual patient morbidity and mortality outcomes, length of stay, costs, and the development of antibiotic resistance. Chou and colleagues found that only 49 of 130 responding VA facilities had antimicrobial stewardship teams in place with even fewer (29) having a formal policy to establish an antimicrobial stewardship program.¹¹ This significant variation in the practices of VA facilities across the nation underscores the benefit to be gained from implementation of value-added protocols such as daily infectious disease case monitoring and microbiology laboratory phone notification of positive blood culture results as it occurs at MVAMC.

They also noted that systems of patient-level antibiotic review, and the presence of at least one full-time infectious disease physician were both associated with a statistically significant decrease in the use of antimicrobials, corroborating the results of this analysis.¹¹ Adapting the current system of infectious disease specialist review of positive blood culture results to use remote monitoring through the EHR could help to defer some of the cost of needing an in-house specialist while retaining the benefit of the oversite.

Another option for study would be a before and after design to determine whether the program of infectious disease specialist review led to increased use of deescalation strategies similar to studies investigating the efficacy of antimicrobial subcommittee implementation.^{13,20,23,24,26}

Conclusions

This analysis of empiric antibiotic use at the MVAMC indicates promising rates of deescalation. The results indicate that the medical service may be right and that positive blood culture results appear to affect clinical decision making in an appropriate and timely fashion. The VA is the largest health care organization in the US. Thus, identifying and propagating effective stewardship practices on a widespread basis can have a significant effect on the public health of the nation.

These data suggest that the program implemented at the MVAMC of phone notification to the primary care team along with daily infectious disease staff monitoring of blood culture information should be widely adopted at sister institutions using either in-house or remote specialist review.

The US Department of Veterans Affairs (VA) is the largest health care organization in the US, staffing more than 1,200 facilities and servicing about 9 million veterans.¹ Identifying VA practices that promote effective health care delivery has the potential to impact thousands of patients every day. The Surgical service at the Minneapolis VA Medical Center (MVAMC) in Minnesota often questioned colleagues whether many of the ordered tests, including blood cultures for patients with suspected infections, were clinically necessary. Despite recommendations for utilizing culture-driven results in choosing appropriate antimicrobials, it was debated whether these additional tests were simply drawn and ignored resulting only in increased costs and venipuncture discomfort for the patient. Thus, the purpose of this quality improvement study was to determine whether positive blood culture results actually influence clinical management at MVAMC.

Background

Accepted best practice when responding to positive blood culture results entails empiric treatment with broad-spectrum antibiotics that subsequently narrows in breadth of coverage once the pathogen has been identified.^2-4 This strategy has been labeled deescalation. Despite the acceptance of these standards, surveys of clinician attitudes towards antibiotics showed that 90% of physicians and residents stated they wanted more education on antimicrobials and 80% desired better schooling on antibiotic choices.^5,6 Additionally, in an online survey 18% of 402 inpatient and emergency department providers, including residents, fellows, intensive care unit (ICU) and emergency department attending physicians, hospitalists, physician assistants, and nurse practitioners, described a lack of confidence when deescalating antibiotic therapy and 45% reported that they had received training on antimicrobial prescribing that was not fully adequate.⁷

These surveys hint at a potential gap in provider education or confidence, which may serve as a barrier to ideal care, further confounding other individualized considerations taken into account when deescalating care. These considerations include patient renal toxicity profiles, the potential for missed pathogens not identified in culture results, unknown sources of infection, and the mindset of many providers to remain on broad therapy if the patient’s condition is improving.^8-10 A specific barrier to deescalation within the VA is the variance in antimicrobial stewardship practices between facilities. In a recent widespread survey of VA practices, Chou and colleagues identified that only 29 of 130 (22.3%) responding facilities had a formal policy to establish an antimicrobial stewardship program.¹¹

Overcoming these barriers to deescalation through effective stewardship practices can help to promote improved clinical outcomes. Most studies have demonstrated that outcomes of deescalation strategies have equivalent or improved mortalityand equivalent or even decreased length of ICU stay.^12-26 Although a 2014 study by Leone and colleagues reported longer overall ICU stay in deescalation treatment groups with equivalent mortality outcomes, newer data do not support these findings.^16,20,22

Furthermore, antibiotics can be expensive. Deescalation, particularly in response to positive blood culture results, has been associated with reduced antibiotic cost due to both a decrease in overall antibiotic usage and the utilization of less expensive choices.^22,24,26,27 The findings of these individual studies were corroborated in 2013 by a meta-analysis, including 89 additional studies.²⁸ Besides the direct costs of the drugs, the development of regional antibiotic resistance has been labeled as one of the most pressing concerns in public health, and major initiatives have been undertaken to stem its spread.^29,30 The majority of clinicians believe that deescalation of antibiotics would reduce antibiotic resistance. Thus, deescalation is widely cited as one of the primary goals in the management of resistance development.^{5,24,26,28,31,32}

Due to the proposed benefits and challenges of implementation, MVAMC instituted a program where the electronic health records (EHR) for all patients with positive blood culture results were reviewed by the on-call infectious disease attending physician to advise the primary care team on antibiotic administration. The MVAMC system for notification of positive blood culture results has 2 components. The first is phone notification to the on-call resident when the positive result of the pathogen identification is noted by the microbiology laboratory staff. Notably, this protocol of phone notification is only performed when identifying the pathogen and not for the subsequent sensitivity profile. The second component occurs each morning when the on-call infectious disease attending physician reviews all positive blood culture results and the current therapy. If the infectious disease attending physician feels some alterations in management are warranted, the physician calls the primary service. Additionally, the primary service may always request a formal consult with the infectious disease team. This quality improvement study was initiated to examine the success of this deescalation/stewardship program to determine whether positive blood culture results influenced clinical management.

Methods

From July 1, 2015 to June 30, 2016, 212 positive blood cultures at the MVAMC were analyzed. Four cases that did not have an antibiotic spectrum score were excluded, leaving 208 cases reviewed. Duplicate blood cultures were excluded from analysis. The microbiology laboratory used the BD Bactec automated blood culture system using the Plus aerobic and Lytic anaerobic media (Becton, Dickinson and Company).

Antibiotic alterations in response to culture results were classified as either deescalation or escalation, using a spectrum score developed by Madaras-Kelly and colleagues.³³ These investigators performed a 3-round modified Delphi survey of infectious disease staff of physicians and pharmacists. The resulting consensus spectrum score for each respective antibiotic reflected the relative susceptibilities of various pathogens to antibiotics and the intrinsic resistance of the pathogens. It is a nonlinear scale from 0 to 60 with a score of 0 indicating no antibacterial activity and a score of 60 indicating complete coverage of all critically identified pathogens. For example, a narrow-spectrum antibiotic such as metronidazole received a spectrum score of 4.0 and a broad-spectrum antibiotic such as piperacillin/tazobactam received a 42.3 score.

Data Recording and Analysis

Charts for review were flagged based on positive blood culture results from the microbiology laboratory. EHRs were manually reviewed to determine when antibiotics were started/stopped and when a member of the primary care team, usually a resident, was notified of culture results as documented by the microbiology laboratory personnel. Any alteration in antibiotics that fit the criteria of deescalation or escalation that occurred within 24 hours of notification of either critical laboratory value was recorded. The identity of infectious pathogens and the primary site of infection were not recorded as these data were not within the scope of the purpose of this study. We did not control for possible contaminants within positive blood cultures.

There were 3 time frames considered when determining culture driven alterations to the antibiotic regimen. The first 2 were changes within the 24 hours after notification of either (1) pathogen identification or (2) pathogen sensitivity. These were defined as culture-driven alterations in response to those particular laboratory findings. The third—whole case time frame—spanned from pathogen identification to 24 hours after sensitivity information was recorded. In cases where ≥ 1 antibiotic alteration was noted within a respective time frame, a classification of deescalation or escalation was still assigned. This was done by summing each change in spectrum score that occurred from antibiotic regimen alterations within the time frame, and classifying the net effect on the spectrum of coverage as either deescalation or escalation. Data were recorded in spreadsheet. RStudio 3.5.3 was used for statistical analysis.

Results

Of 208 cases assigned a spectrum score, 47 (22.6%) had the breadth of antibiotic coverage deescalated by the primary care team within 24 hours of pathogen identification with a mean (SD) physician response time of 8.0 (7.3) hours. Fourteen cases (6.7%) had the breadth of antibiotic coverage escalated from pathogen identification with a mean (SD) response time of 8.0 (7.4) hours. When taken together, within 24 hours of pathogen identification from positive blood cultures 61 cases (29.3%) had altered antibiotics, leaving 70.7% of cases unaltered (Tables 1 and 2). In this nonquantitative spectrum score method, deescalations typically involved larger changes in spectrum score than escalations.

Physician notification of pathogen sensitivities resulted in deescalation in 69 cases (33.2%) within 24 hours, with a mean (SD) response time of 10.4 (7) hours. The mean time to deescalation in response to pathogen identification was significantly shorter than the mean time to deescalation in response to sensitivities (P = .049). Broadening of coverage based on sensitivity information was reported for 17 cases (8.2%) within 24 hours, with a mean (SD) response time of 7.6 (6) hours (Table 3). In response to pathogen sensitivity results from positive blood cultures, 58.6% of cases had no antibiotic alterations. Deescalations involved notably larger changes in spectrum score than escalations.

More than half (58.6%) of cases resulted in an antibiotic alteration from empiric treatment when considering the time frame from empiric antibiotics to 24 hours after receiving sensitivity information. These were deemed the whole-case, culture-driven results. In addition to antibiotic alterations that occurred within 24 hours of either pathogen identification or sensitivity information, the whole-case category also considered antibiotic alterations that occurred more than 24 hours after pathogen identification was known and before sensitivity information was available, although this was rare. Some of these patients may have had their antibiotics altered twice, first after pathogen identification and later once sensitivities became available with the net effect recorded as the whole-case administration. Of those that had their antibiotics modified in response to laboratory results, by a ratio of 6.4:1, the change was a deescalation rather than an escalation.

Discussion

The strategy of the infectious disease team at MVAMC is one of deescalation. One challenge of quantifying deescalation was to make a reliable and agreed-upon definition of just what deescalation entails. In 2003, the pharmaceutical company Merck was granted a trademark for the phrase “De-Escalation Therapy” under the international class code 41 for educational and entertainment services. This seemed to correspond to marketing efforts for the antibiotic imipenem/cilastatin. Although the company trademarked the term, it was never defined. The usage of the phrase evolved from a reduction of the dosage of a specific antibiotic to a reduction in the number of antibiotics prescribed to that of monotherapy. The phrase continues to evolve and has now become associated with a change from combination therapy or broad-spectrum antibiotics to monotherapy, switching to an antibiotic that covers fewer pathogens, or even shortening the duration of antibiotic therapy.³⁴ The trademark expired at about the same time the imipenem/cilastatin patent expired. Notably, this drug had initially been marketed for use in empiric antibiotic therapy.³⁵

Barriers

The goal of the stewardship program was not to see a narrowing of the antibiotic spectrum in all patients. Some diseases such as diverticulitis or diabetic foot infections are usually associated with multiple pathogens where relatively broad-spectrum antibiotics seem to be preferred.^36,37 Heenen and colleagues reported that infectious disease specialists recommended deescalation in < 50% of cases they examined.³⁸

Comparing different institutions’ deescalation rates can be confusing due to varying definitions, differing patient populations, and health care provider behavior. Thus, the published rates of deescalation range widely from 10 to 70%.^2,39,40 In addition to the varied definitions of deescalation, it is challenging to directly compare the rate of deescalation between studies due to institutional variation in empirical broad-spectrum antibiotic usage. A hospital that uses broad-spectrum antibiotics at a higher rate than another has the potential to deescalate more often than one that has low rates of empirical broad-spectrum antibiotic use. Some studies use a conservative definition of deescalation such as narrowing the spectrum of coverage, while others use a more general definition, including both the narrowing of spectrum and/or the discontinuation of antibiotics from empirical therapy.^41-45 The more specific and validated definition of deescalation used in this study may allow for standardized comparisons. Another unique feature of this study is that all positive blood cultures were followed, not only those of a particular disease.

Clinical Response

This quality improvement study was initiated to determine whether positive blood culture results actually affect clinical management at MVAMC. The answer seems to be yes, with blood culture results altering antibiotic administration in about 60% of cases with the predominant change being deescalation. This overall rate of deescalation is toward the higher end of previously documented rates and coincides with the upper bound of the clinically advised deescalation rate described by Heenen and colleagues.³⁸

As noted, the spectrum score is not quantitative. Still, one may be able to contend that the values may provide some insight into the magnitude of the changes in antibiotic selection. Deescalations were on average much larger changes in spectrum than escalations. The larger magnitude of deescalations reflects that when already starting with a very broad spectrum of coverage, it is much easier to get narrower than even broader. Stated another way, when starting therapy using piperacillin/tazobactam at a spectrum score of 42.3 on a 60-point scale, there is much more room for deescalation to 0 than escalation to 60. Additionally, escalations were more likely with much smaller of a spectrum change due to accurate empirical judgment of the suspected pathogens with new findings only necessitating a minor expansion of the spectrum of coverage.

Future Study

Similar studies designed using the spectrum score methodology would allow for more meaningful interinstitutional comparison of antibiotic administration through the use of a unified definition of deescalation and escalation. Comparison of deescalation and escalation rates between hospital systems with similar patient populations with and without prompt infectious disease review and phone notification of blood culture results could further verify the value of such a protocol. It could also help determine which empiric antibiotics may be most effective in individual patient morbidity and mortality outcomes, length of stay, costs, and the development of antibiotic resistance. Chou and colleagues found that only 49 of 130 responding VA facilities had antimicrobial stewardship teams in place with even fewer (29) having a formal policy to establish an antimicrobial stewardship program.¹¹ This significant variation in the practices of VA facilities across the nation underscores the benefit to be gained from implementation of value-added protocols such as daily infectious disease case monitoring and microbiology laboratory phone notification of positive blood culture results as it occurs at MVAMC.

They also noted that systems of patient-level antibiotic review, and the presence of at least one full-time infectious disease physician were both associated with a statistically significant decrease in the use of antimicrobials, corroborating the results of this analysis.¹¹ Adapting the current system of infectious disease specialist review of positive blood culture results to use remote monitoring through the EHR could help to defer some of the cost of needing an in-house specialist while retaining the benefit of the oversite.

Another option for study would be a before and after design to determine whether the program of infectious disease specialist review led to increased use of deescalation strategies similar to studies investigating the efficacy of antimicrobial subcommittee implementation.^{13,20,23,24,26}

Conclusions

This analysis of empiric antibiotic use at the MVAMC indicates promising rates of deescalation. The results indicate that the medical service may be right and that positive blood culture results appear to affect clinical decision making in an appropriate and timely fashion. The VA is the largest health care organization in the US. Thus, identifying and propagating effective stewardship practices on a widespread basis can have a significant effect on the public health of the nation.

These data suggest that the program implemented at the MVAMC of phone notification to the primary care team along with daily infectious disease staff monitoring of blood culture information should be widely adopted at sister institutions using either in-house or remote specialist review.

The US Department of Veterans Affairs (VA) is the largest health care organization in the US, staffing more than 1,200 facilities and servicing about 9 million veterans.¹ Identifying VA practices that promote effective health care delivery has the potential to impact thousands of patients every day. The Surgical service at the Minneapolis VA Medical Center (MVAMC) in Minnesota often questioned colleagues whether many of the ordered tests, including blood cultures for patients with suspected infections, were clinically necessary. Despite recommendations for utilizing culture-driven results in choosing appropriate antimicrobials, it was debated whether these additional tests were simply drawn and ignored resulting only in increased costs and venipuncture discomfort for the patient. Thus, the purpose of this quality improvement study was to determine whether positive blood culture results actually influence clinical management at MVAMC.

Background

Accepted best practice when responding to positive blood culture results entails empiric treatment with broad-spectrum antibiotics that subsequently narrows in breadth of coverage once the pathogen has been identified.^2-4 This strategy has been labeled deescalation. Despite the acceptance of these standards, surveys of clinician attitudes towards antibiotics showed that 90% of physicians and residents stated they wanted more education on antimicrobials and 80% desired better schooling on antibiotic choices.^5,6 Additionally, in an online survey 18% of 402 inpatient and emergency department providers, including residents, fellows, intensive care unit (ICU) and emergency department attending physicians, hospitalists, physician assistants, and nurse practitioners, described a lack of confidence when deescalating antibiotic therapy and 45% reported that they had received training on antimicrobial prescribing that was not fully adequate.⁷

These surveys hint at a potential gap in provider education or confidence, which may serve as a barrier to ideal care, further confounding other individualized considerations taken into account when deescalating care. These considerations include patient renal toxicity profiles, the potential for missed pathogens not identified in culture results, unknown sources of infection, and the mindset of many providers to remain on broad therapy if the patient’s condition is improving.^8-10 A specific barrier to deescalation within the VA is the variance in antimicrobial stewardship practices between facilities. In a recent widespread survey of VA practices, Chou and colleagues identified that only 29 of 130 (22.3%) responding facilities had a formal policy to establish an antimicrobial stewardship program.¹¹

Overcoming these barriers to deescalation through effective stewardship practices can help to promote improved clinical outcomes. Most studies have demonstrated that outcomes of deescalation strategies have equivalent or improved mortalityand equivalent or even decreased length of ICU stay.^12-26 Although a 2014 study by Leone and colleagues reported longer overall ICU stay in deescalation treatment groups with equivalent mortality outcomes, newer data do not support these findings.^16,20,22

Furthermore, antibiotics can be expensive. Deescalation, particularly in response to positive blood culture results, has been associated with reduced antibiotic cost due to both a decrease in overall antibiotic usage and the utilization of less expensive choices.^22,24,26,27 The findings of these individual studies were corroborated in 2013 by a meta-analysis, including 89 additional studies.²⁸ Besides the direct costs of the drugs, the development of regional antibiotic resistance has been labeled as one of the most pressing concerns in public health, and major initiatives have been undertaken to stem its spread.^29,30 The majority of clinicians believe that deescalation of antibiotics would reduce antibiotic resistance. Thus, deescalation is widely cited as one of the primary goals in the management of resistance development.^{5,24,26,28,31,32}

Due to the proposed benefits and challenges of implementation, MVAMC instituted a program where the electronic health records (EHR) for all patients with positive blood culture results were reviewed by the on-call infectious disease attending physician to advise the primary care team on antibiotic administration. The MVAMC system for notification of positive blood culture results has 2 components. The first is phone notification to the on-call resident when the positive result of the pathogen identification is noted by the microbiology laboratory staff. Notably, this protocol of phone notification is only performed when identifying the pathogen and not for the subsequent sensitivity profile. The second component occurs each morning when the on-call infectious disease attending physician reviews all positive blood culture results and the current therapy. If the infectious disease attending physician feels some alterations in management are warranted, the physician calls the primary service. Additionally, the primary service may always request a formal consult with the infectious disease team. This quality improvement study was initiated to examine the success of this deescalation/stewardship program to determine whether positive blood culture results influenced clinical management.

Methods

From July 1, 2015 to June 30, 2016, 212 positive blood cultures at the MVAMC were analyzed. Four cases that did not have an antibiotic spectrum score were excluded, leaving 208 cases reviewed. Duplicate blood cultures were excluded from analysis. The microbiology laboratory used the BD Bactec automated blood culture system using the Plus aerobic and Lytic anaerobic media (Becton, Dickinson and Company).

Antibiotic alterations in response to culture results were classified as either deescalation or escalation, using a spectrum score developed by Madaras-Kelly and colleagues.³³ These investigators performed a 3-round modified Delphi survey of infectious disease staff of physicians and pharmacists. The resulting consensus spectrum score for each respective antibiotic reflected the relative susceptibilities of various pathogens to antibiotics and the intrinsic resistance of the pathogens. It is a nonlinear scale from 0 to 60 with a score of 0 indicating no antibacterial activity and a score of 60 indicating complete coverage of all critically identified pathogens. For example, a narrow-spectrum antibiotic such as metronidazole received a spectrum score of 4.0 and a broad-spectrum antibiotic such as piperacillin/tazobactam received a 42.3 score.

Data Recording and Analysis

Charts for review were flagged based on positive blood culture results from the microbiology laboratory. EHRs were manually reviewed to determine when antibiotics were started/stopped and when a member of the primary care team, usually a resident, was notified of culture results as documented by the microbiology laboratory personnel. Any alteration in antibiotics that fit the criteria of deescalation or escalation that occurred within 24 hours of notification of either critical laboratory value was recorded. The identity of infectious pathogens and the primary site of infection were not recorded as these data were not within the scope of the purpose of this study. We did not control for possible contaminants within positive blood cultures.

There were 3 time frames considered when determining culture driven alterations to the antibiotic regimen. The first 2 were changes within the 24 hours after notification of either (1) pathogen identification or (2) pathogen sensitivity. These were defined as culture-driven alterations in response to those particular laboratory findings. The third—whole case time frame—spanned from pathogen identification to 24 hours after sensitivity information was recorded. In cases where ≥ 1 antibiotic alteration was noted within a respective time frame, a classification of deescalation or escalation was still assigned. This was done by summing each change in spectrum score that occurred from antibiotic regimen alterations within the time frame, and classifying the net effect on the spectrum of coverage as either deescalation or escalation. Data were recorded in spreadsheet. RStudio 3.5.3 was used for statistical analysis.

Results

Of 208 cases assigned a spectrum score, 47 (22.6%) had the breadth of antibiotic coverage deescalated by the primary care team within 24 hours of pathogen identification with a mean (SD) physician response time of 8.0 (7.3) hours. Fourteen cases (6.7%) had the breadth of antibiotic coverage escalated from pathogen identification with a mean (SD) response time of 8.0 (7.4) hours. When taken together, within 24 hours of pathogen identification from positive blood cultures 61 cases (29.3%) had altered antibiotics, leaving 70.7% of cases unaltered (Tables 1 and 2). In this nonquantitative spectrum score method, deescalations typically involved larger changes in spectrum score than escalations.

Physician notification of pathogen sensitivities resulted in deescalation in 69 cases (33.2%) within 24 hours, with a mean (SD) response time of 10.4 (7) hours. The mean time to deescalation in response to pathogen identification was significantly shorter than the mean time to deescalation in response to sensitivities (P = .049). Broadening of coverage based on sensitivity information was reported for 17 cases (8.2%) within 24 hours, with a mean (SD) response time of 7.6 (6) hours (Table 3). In response to pathogen sensitivity results from positive blood cultures, 58.6% of cases had no antibiotic alterations. Deescalations involved notably larger changes in spectrum score than escalations.

More than half (58.6%) of cases resulted in an antibiotic alteration from empiric treatment when considering the time frame from empiric antibiotics to 24 hours after receiving sensitivity information. These were deemed the whole-case, culture-driven results. In addition to antibiotic alterations that occurred within 24 hours of either pathogen identification or sensitivity information, the whole-case category also considered antibiotic alterations that occurred more than 24 hours after pathogen identification was known and before sensitivity information was available, although this was rare. Some of these patients may have had their antibiotics altered twice, first after pathogen identification and later once sensitivities became available with the net effect recorded as the whole-case administration. Of those that had their antibiotics modified in response to laboratory results, by a ratio of 6.4:1, the change was a deescalation rather than an escalation.

Discussion

The strategy of the infectious disease team at MVAMC is one of deescalation. One challenge of quantifying deescalation was to make a reliable and agreed-upon definition of just what deescalation entails. In 2003, the pharmaceutical company Merck was granted a trademark for the phrase “De-Escalation Therapy” under the international class code 41 for educational and entertainment services. This seemed to correspond to marketing efforts for the antibiotic imipenem/cilastatin. Although the company trademarked the term, it was never defined. The usage of the phrase evolved from a reduction of the dosage of a specific antibiotic to a reduction in the number of antibiotics prescribed to that of monotherapy. The phrase continues to evolve and has now become associated with a change from combination therapy or broad-spectrum antibiotics to monotherapy, switching to an antibiotic that covers fewer pathogens, or even shortening the duration of antibiotic therapy.³⁴ The trademark expired at about the same time the imipenem/cilastatin patent expired. Notably, this drug had initially been marketed for use in empiric antibiotic therapy.³⁵

Barriers

The goal of the stewardship program was not to see a narrowing of the antibiotic spectrum in all patients. Some diseases such as diverticulitis or diabetic foot infections are usually associated with multiple pathogens where relatively broad-spectrum antibiotics seem to be preferred.^36,37 Heenen and colleagues reported that infectious disease specialists recommended deescalation in < 50% of cases they examined.³⁸

Comparing different institutions’ deescalation rates can be confusing due to varying definitions, differing patient populations, and health care provider behavior. Thus, the published rates of deescalation range widely from 10 to 70%.^2,39,40 In addition to the varied definitions of deescalation, it is challenging to directly compare the rate of deescalation between studies due to institutional variation in empirical broad-spectrum antibiotic usage. A hospital that uses broad-spectrum antibiotics at a higher rate than another has the potential to deescalate more often than one that has low rates of empirical broad-spectrum antibiotic use. Some studies use a conservative definition of deescalation such as narrowing the spectrum of coverage, while others use a more general definition, including both the narrowing of spectrum and/or the discontinuation of antibiotics from empirical therapy.^41-45 The more specific and validated definition of deescalation used in this study may allow for standardized comparisons. Another unique feature of this study is that all positive blood cultures were followed, not only those of a particular disease.

Clinical Response

This quality improvement study was initiated to determine whether positive blood culture results actually affect clinical management at MVAMC. The answer seems to be yes, with blood culture results altering antibiotic administration in about 60% of cases with the predominant change being deescalation. This overall rate of deescalation is toward the higher end of previously documented rates and coincides with the upper bound of the clinically advised deescalation rate described by Heenen and colleagues.³⁸

As noted, the spectrum score is not quantitative. Still, one may be able to contend that the values may provide some insight into the magnitude of the changes in antibiotic selection. Deescalations were on average much larger changes in spectrum than escalations. The larger magnitude of deescalations reflects that when already starting with a very broad spectrum of coverage, it is much easier to get narrower than even broader. Stated another way, when starting therapy using piperacillin/tazobactam at a spectrum score of 42.3 on a 60-point scale, there is much more room for deescalation to 0 than escalation to 60. Additionally, escalations were more likely with much smaller of a spectrum change due to accurate empirical judgment of the suspected pathogens with new findings only necessitating a minor expansion of the spectrum of coverage.

Future Study

Similar studies designed using the spectrum score methodology would allow for more meaningful interinstitutional comparison of antibiotic administration through the use of a unified definition of deescalation and escalation. Comparison of deescalation and escalation rates between hospital systems with similar patient populations with and without prompt infectious disease review and phone notification of blood culture results could further verify the value of such a protocol. It could also help determine which empiric antibiotics may be most effective in individual patient morbidity and mortality outcomes, length of stay, costs, and the development of antibiotic resistance. Chou and colleagues found that only 49 of 130 responding VA facilities had antimicrobial stewardship teams in place with even fewer (29) having a formal policy to establish an antimicrobial stewardship program.¹¹ This significant variation in the practices of VA facilities across the nation underscores the benefit to be gained from implementation of value-added protocols such as daily infectious disease case monitoring and microbiology laboratory phone notification of positive blood culture results as it occurs at MVAMC.

They also noted that systems of patient-level antibiotic review, and the presence of at least one full-time infectious disease physician were both associated with a statistically significant decrease in the use of antimicrobials, corroborating the results of this analysis.¹¹ Adapting the current system of infectious disease specialist review of positive blood culture results to use remote monitoring through the EHR could help to defer some of the cost of needing an in-house specialist while retaining the benefit of the oversite.

Another option for study would be a before and after design to determine whether the program of infectious disease specialist review led to increased use of deescalation strategies similar to studies investigating the efficacy of antimicrobial subcommittee implementation.^{13,20,23,24,26}

Conclusions

This analysis of empiric antibiotic use at the MVAMC indicates promising rates of deescalation. The results indicate that the medical service may be right and that positive blood culture results appear to affect clinical decision making in an appropriate and timely fashion. The VA is the largest health care organization in the US. Thus, identifying and propagating effective stewardship practices on a widespread basis can have a significant effect on the public health of the nation.

These data suggest that the program implemented at the MVAMC of phone notification to the primary care team along with daily infectious disease staff monitoring of blood culture information should be widely adopted at sister institutions using either in-house or remote specialist review.

Levels of insulinlike factor 3 (INSL3) drop noticeably in men who have abused anabolic androgenic steroids (AAS), even well after stoppage. The results suggest that the effects of AAS use on testosterone-producing Leydig cells may be long-lasting, as some clinicians have suspected. Although there is some variation of INSL3 levels among AAS users, the metric is more accurate than testosterone levels and could be a key element of future diagnostic tests.

Those are the conclusions of a new study, led by Jon Jarløv Rasmussen, MD, PhD, of the department of endocrinology at Rigshospitalet in Copenhagen*, published March 9, 2021, in the Journal of Clinical Endocrinology & Metabolism.  

Results mirror clinical experience  

The drop in levels, both among current and past users, is in keeping with clinical experience of endocrinologists, according to Channa Jayasena, MD, PhD, a reproductive endocrinologist at Imperial College London. He suspects lasting damage in former and current users who come to him when they discover their sperm count is low. "How long that damage lasts is another matter," Dr. Jayasena, who was not involved in the study, said in an interview.   

In search of a reliable measure  

Low testosterone levels have been shown to be associated with AAS use in some studies, but not in others. That inconsistency led the researchers in search of a more reliable measure. "Serum testosterone is not a stable marker but can fluctuate considerably within minutes to hours, whereas serum insulinlike factor 3 [levels] do not," said Dr. Rasmussen.  
INSL3 appears to be involved in bone metabolism regulation as well as spermatogenesis.  

Dr. Rasmussen agreed that INSL3 levels could be clinically useful for tracking Leydig cell function, especially in combination with other hormone markers like serum testosterone and gonadotropins. The group is now considering a clinical trial for treatment of hypogonadal men following illicit use of anabolic steroids, which will include INSL3 serum levels as a planned endpoint.  

The researchers conducted a cross-sectional study of men aged 18-50 years who had participated in recreational strength training. Cohort 1 included 37 AAS users, 33 former users, and 30 never users. Cohort 2 included 9 current users, 9 former users, and 14 never users. They assigned participant AAS use status based on self-reporting, along with measurement of biomedical parameters including gonadotropins, sexual hormone-binding globulin (SHBG), and hematocrit.  
Compared with never users' median value of 0.59 mcg/L, INSL3 serum levels were lower among current AAS (median, 0.04 mcg/L; P < .001) and former AAS (0.39 mcg/L; P = .005) users. A linear multivariate regression that adjusted for luteinizing hormone, SHBG, age, body-fat percentage, smoking status, use of other illicit drugs found lower levels among former users, compared with never users (mean difference, -0.16 mcg/L; P = .011). 

An analysis of elapsed duration since AAS cessation found longer duration of AAS use was associated with reduced INSL3 levels (mean difference, -0.08; P = .022). 

Although serum inhibin B levels reached the levels of never users after about 21 months, and luteinizing hormone levels recovered in about 12 months, neither serum testosterone nor INSL3 levels recovered in former users. 

The study authors received funding from Anti Doping Denmark. Dr. Jayasena has no relevant financial disclosures. 

*Dr. Rasmussen's affiliation has been corrected.

Levels of insulinlike factor 3 (INSL3) drop noticeably in men who have abused anabolic androgenic steroids (AAS), even well after stoppage. The results suggest that the effects of AAS use on testosterone-producing Leydig cells may be long-lasting, as some clinicians have suspected. Although there is some variation of INSL3 levels among AAS users, the metric is more accurate than testosterone levels and could be a key element of future diagnostic tests.

Those are the conclusions of a new study, led by Jon Jarløv Rasmussen, MD, PhD, of the department of endocrinology at Rigshospitalet in Copenhagen*, published March 9, 2021, in the Journal of Clinical Endocrinology & Metabolism.  

Results mirror clinical experience  

The drop in levels, both among current and past users, is in keeping with clinical experience of endocrinologists, according to Channa Jayasena, MD, PhD, a reproductive endocrinologist at Imperial College London. He suspects lasting damage in former and current users who come to him when they discover their sperm count is low. "How long that damage lasts is another matter," Dr. Jayasena, who was not involved in the study, said in an interview.   

In search of a reliable measure  

Low testosterone levels have been shown to be associated with AAS use in some studies, but not in others. That inconsistency led the researchers in search of a more reliable measure. "Serum testosterone is not a stable marker but can fluctuate considerably within minutes to hours, whereas serum insulinlike factor 3 [levels] do not," said Dr. Rasmussen.  
INSL3 appears to be involved in bone metabolism regulation as well as spermatogenesis.  

Dr. Rasmussen agreed that INSL3 levels could be clinically useful for tracking Leydig cell function, especially in combination with other hormone markers like serum testosterone and gonadotropins. The group is now considering a clinical trial for treatment of hypogonadal men following illicit use of anabolic steroids, which will include INSL3 serum levels as a planned endpoint.  

The researchers conducted a cross-sectional study of men aged 18-50 years who had participated in recreational strength training. Cohort 1 included 37 AAS users, 33 former users, and 30 never users. Cohort 2 included 9 current users, 9 former users, and 14 never users. They assigned participant AAS use status based on self-reporting, along with measurement of biomedical parameters including gonadotropins, sexual hormone-binding globulin (SHBG), and hematocrit.  
Compared with never users' median value of 0.59 mcg/L, INSL3 serum levels were lower among current AAS (median, 0.04 mcg/L; P < .001) and former AAS (0.39 mcg/L; P = .005) users. A linear multivariate regression that adjusted for luteinizing hormone, SHBG, age, body-fat percentage, smoking status, use of other illicit drugs found lower levels among former users, compared with never users (mean difference, -0.16 mcg/L; P = .011). 

An analysis of elapsed duration since AAS cessation found longer duration of AAS use was associated with reduced INSL3 levels (mean difference, -0.08; P = .022). 

Although serum inhibin B levels reached the levels of never users after about 21 months, and luteinizing hormone levels recovered in about 12 months, neither serum testosterone nor INSL3 levels recovered in former users. 

The study authors received funding from Anti Doping Denmark. Dr. Jayasena has no relevant financial disclosures. 

*Dr. Rasmussen's affiliation has been corrected.

Levels of insulinlike factor 3 (INSL3) drop noticeably in men who have abused anabolic androgenic steroids (AAS), even well after stoppage. The results suggest that the effects of AAS use on testosterone-producing Leydig cells may be long-lasting, as some clinicians have suspected. Although there is some variation of INSL3 levels among AAS users, the metric is more accurate than testosterone levels and could be a key element of future diagnostic tests.

Those are the conclusions of a new study, led by Jon Jarløv Rasmussen, MD, PhD, of the department of endocrinology at Rigshospitalet in Copenhagen*, published March 9, 2021, in the Journal of Clinical Endocrinology & Metabolism.  

Results mirror clinical experience  

The drop in levels, both among current and past users, is in keeping with clinical experience of endocrinologists, according to Channa Jayasena, MD, PhD, a reproductive endocrinologist at Imperial College London. He suspects lasting damage in former and current users who come to him when they discover their sperm count is low. "How long that damage lasts is another matter," Dr. Jayasena, who was not involved in the study, said in an interview.   

In search of a reliable measure  

Low testosterone levels have been shown to be associated with AAS use in some studies, but not in others. That inconsistency led the researchers in search of a more reliable measure. "Serum testosterone is not a stable marker but can fluctuate considerably within minutes to hours, whereas serum insulinlike factor 3 [levels] do not," said Dr. Rasmussen.  
INSL3 appears to be involved in bone metabolism regulation as well as spermatogenesis.  

Dr. Rasmussen agreed that INSL3 levels could be clinically useful for tracking Leydig cell function, especially in combination with other hormone markers like serum testosterone and gonadotropins. The group is now considering a clinical trial for treatment of hypogonadal men following illicit use of anabolic steroids, which will include INSL3 serum levels as a planned endpoint.  

The researchers conducted a cross-sectional study of men aged 18-50 years who had participated in recreational strength training. Cohort 1 included 37 AAS users, 33 former users, and 30 never users. Cohort 2 included 9 current users, 9 former users, and 14 never users. They assigned participant AAS use status based on self-reporting, along with measurement of biomedical parameters including gonadotropins, sexual hormone-binding globulin (SHBG), and hematocrit.  
Compared with never users' median value of 0.59 mcg/L, INSL3 serum levels were lower among current AAS (median, 0.04 mcg/L; P < .001) and former AAS (0.39 mcg/L; P = .005) users. A linear multivariate regression that adjusted for luteinizing hormone, SHBG, age, body-fat percentage, smoking status, use of other illicit drugs found lower levels among former users, compared with never users (mean difference, -0.16 mcg/L; P = .011). 

An analysis of elapsed duration since AAS cessation found longer duration of AAS use was associated with reduced INSL3 levels (mean difference, -0.08; P = .022). 

Although serum inhibin B levels reached the levels of never users after about 21 months, and luteinizing hormone levels recovered in about 12 months, neither serum testosterone nor INSL3 levels recovered in former users. 

The study authors received funding from Anti Doping Denmark. Dr. Jayasena has no relevant financial disclosures. 

*Dr. Rasmussen's affiliation has been corrected.

The Food and Drug Administration has approved a new, once-daily oral stimulant medication for treatment of ADHD in people aged 6 years and older.

Azstarys (KemPharm) combines extended-release serdexmethylphenidate (SDX), KemPharm’s prodrug of dexmethylphenidate (d-MPH), coformulated with immediate-release d-MPH. 

Following absorption in the gastrointestinal tract, SDX is converted to d-MPH, which is gradually released throughout the day, providing symptom control both rapidly with the d-MPH and for an extended duration with SDX.

The dual action of Azstarys addresses an unmet need for a medication that has early onset of action and long duration of therapy, with steady ADHD symptom control in one capsule, Corium, the company that will lead U.S. commercialization of the drug, stated in a news release.

“The data documenting the efficacy and safety of this new dual-action medicine, the first ever to use the novel prodrug serdexmethylphenidate together with dexmethylphenidate, is welcome news for clinicians and families to consider when choosing an appropriate ADHD therapy for children,” Ann Childress, MD, president of the Center for Psychiatry and Behavioral Medicine in Las Vegas, who led the phase 3 trial of the drug, said in the release.

The study included 150 children aged 6-12 years with ADHD. Compared with placebo, treatment with Azstarys led to significant improvement in ADHD symptoms, as measured by the primary endpoint, the change from baseline in Swanson, Kotkin, Agler, M-Flynn, and Pelham Rating Scale–Combined scores averaged over 13 hours.

Adverse events seen more often with Azstarys than placebo were headache (5.4% vs. 1.3%), upper abdominal pain (4.1% vs. 1.3%), insomnia (2.7% vs. 1.3%) and pharyngitis (2.7% vs. 0%). No serious adverse events were reported.

The FDA has recommended a schedule II controlled substance classification for Azstarys and the Drug Enforcement Administration will decide on scheduling within 90 days.

Pending the DEA’s action, the launch of Azstarys is anticipated this summer. Azstarys will be available in three once-daily dosage strengths of SDX/d-MPH: 26.1/5.2 mg, 39.2/7.8 mg, and 52.3/10.4 mg.

A version of this article first appeared on Medscape.com.

The Food and Drug Administration has approved a new, once-daily oral stimulant medication for treatment of ADHD in people aged 6 years and older.

Azstarys (KemPharm) combines extended-release serdexmethylphenidate (SDX), KemPharm’s prodrug of dexmethylphenidate (d-MPH), coformulated with immediate-release d-MPH. 

Following absorption in the gastrointestinal tract, SDX is converted to d-MPH, which is gradually released throughout the day, providing symptom control both rapidly with the d-MPH and for an extended duration with SDX.

The dual action of Azstarys addresses an unmet need for a medication that has early onset of action and long duration of therapy, with steady ADHD symptom control in one capsule, Corium, the company that will lead U.S. commercialization of the drug, stated in a news release.

“The data documenting the efficacy and safety of this new dual-action medicine, the first ever to use the novel prodrug serdexmethylphenidate together with dexmethylphenidate, is welcome news for clinicians and families to consider when choosing an appropriate ADHD therapy for children,” Ann Childress, MD, president of the Center for Psychiatry and Behavioral Medicine in Las Vegas, who led the phase 3 trial of the drug, said in the release.

The study included 150 children aged 6-12 years with ADHD. Compared with placebo, treatment with Azstarys led to significant improvement in ADHD symptoms, as measured by the primary endpoint, the change from baseline in Swanson, Kotkin, Agler, M-Flynn, and Pelham Rating Scale–Combined scores averaged over 13 hours.

Adverse events seen more often with Azstarys than placebo were headache (5.4% vs. 1.3%), upper abdominal pain (4.1% vs. 1.3%), insomnia (2.7% vs. 1.3%) and pharyngitis (2.7% vs. 0%). No serious adverse events were reported.

The FDA has recommended a schedule II controlled substance classification for Azstarys and the Drug Enforcement Administration will decide on scheduling within 90 days.

Pending the DEA’s action, the launch of Azstarys is anticipated this summer. Azstarys will be available in three once-daily dosage strengths of SDX/d-MPH: 26.1/5.2 mg, 39.2/7.8 mg, and 52.3/10.4 mg.

A version of this article first appeared on Medscape.com.

The Food and Drug Administration has approved a new, once-daily oral stimulant medication for treatment of ADHD in people aged 6 years and older.

Azstarys (KemPharm) combines extended-release serdexmethylphenidate (SDX), KemPharm’s prodrug of dexmethylphenidate (d-MPH), coformulated with immediate-release d-MPH. 

Following absorption in the gastrointestinal tract, SDX is converted to d-MPH, which is gradually released throughout the day, providing symptom control both rapidly with the d-MPH and for an extended duration with SDX.

The dual action of Azstarys addresses an unmet need for a medication that has early onset of action and long duration of therapy, with steady ADHD symptom control in one capsule, Corium, the company that will lead U.S. commercialization of the drug, stated in a news release.

“The data documenting the efficacy and safety of this new dual-action medicine, the first ever to use the novel prodrug serdexmethylphenidate together with dexmethylphenidate, is welcome news for clinicians and families to consider when choosing an appropriate ADHD therapy for children,” Ann Childress, MD, president of the Center for Psychiatry and Behavioral Medicine in Las Vegas, who led the phase 3 trial of the drug, said in the release.

The study included 150 children aged 6-12 years with ADHD. Compared with placebo, treatment with Azstarys led to significant improvement in ADHD symptoms, as measured by the primary endpoint, the change from baseline in Swanson, Kotkin, Agler, M-Flynn, and Pelham Rating Scale–Combined scores averaged over 13 hours.

Adverse events seen more often with Azstarys than placebo were headache (5.4% vs. 1.3%), upper abdominal pain (4.1% vs. 1.3%), insomnia (2.7% vs. 1.3%) and pharyngitis (2.7% vs. 0%). No serious adverse events were reported.

The FDA has recommended a schedule II controlled substance classification for Azstarys and the Drug Enforcement Administration will decide on scheduling within 90 days.

Pending the DEA’s action, the launch of Azstarys is anticipated this summer. Azstarys will be available in three once-daily dosage strengths of SDX/d-MPH: 26.1/5.2 mg, 39.2/7.8 mg, and 52.3/10.4 mg.

A version of this article first appeared on Medscape.com.

A 2.4-mg weekly injection of the glucagon-like peptide-1 (GLP-1) receptor agonist semaglutide led to a clinically meaningful 5% loss in weight for roughly two-thirds of patients with both overweight/obesity and type 2 diabetes, researchers report.

These findings from the Semaglutide Treatment Effect in People With Obesity 2 (STEP 2) trial, one of four phase 3 trials of this drug, which is currently under regulatory review for weight loss, were published March 2 in The Lancet.

More than 1,000 patients (mean initial weight, 100 kg [220 pounds]) were randomly assigned to receive a lifestyle intervention plus a weekly injection of semaglutide 2.4 mg or semaglutide 1.0 mg or placebo. At 68 weeks, they had lost a mean of 9.6%, 7.0%, and 3.4%, respectively, of their starting weight.

In addition, 69% of patients who had received semaglutide 2.4 mg experienced a clinically meaningful 5% loss of weight, compared with 57% of patients who had received the lower dose and 29% of patients who had received placebo.

The higher dose of semaglutide was associated with a greater improvement in cardiometabolic risk factors. The safety profile was similar to that seen with other drugs in this class.
 

“By far the best results with any weight loss medicine in diabetes”

Importantly, “more than a quarter of participants lost over 15% of their body weight,” senior author Ildiko Lingvay, MD, stressed. This “is by far the best result we had with any weight loss medicine in patients with diabetes,” Dr. Lingvay, of the University of Texas, Dallas, said in a statement from the university.

Sara Freeman/MDedge News

“The drug works by suppressing appetite centers in the brain to reduce caloric intake,” she explained. “The medication continually tells the body that you just ate, you’re full.”

Similarly, lead author Melanie J. Davies, MD, said that the STEP 2 results “are exciting and represent a new era in weight management in people with type 2 diabetes.

Sara Freeman/MDedge News

One of four trials under review

More than 90% of people with type 2 diabetes are overweight or have obesity, and more than 20% of people with obesity have diabetes, wrote Dr. Davies and colleagues.

Semaglutide (Ozempic), administered subcutaneously at a dose of 0.5 mg to 1 mg weekly, is approved by the Food and Drug Administration for the treatment of type 2 diabetes. Dosing studies indicated that it is associated with weight loss.

As previously reported, four trials of the use of semaglutide for weight loss (STEP 1, 2, 3, and 4) have been completed. The combined data were submitted to the FDA on Dec. 4, 2020 (a decision is expected within 6 months) and to the European Medicines Agency on Dec. 18, 2020.

The STEP 1 and STEP 3 trials of semaglutide 2.4 mg vs. placebo were recently published. The STEP 1 trial involved 1,961 adults with obesity or overweight; the STEP 3 trial, 611 adults with obesity or overweight. In each of the trials, some patients also underwent an intensive lifestyle intervention, and some did not. In both trials, patients with type 2 diabetes were excluded.

Topline results from STEP 2 were reported in June 2020.
 

STEP 2 enrolled patients with type 2 diabetes

STEP 2 involved 1,210 adults in 149 outpatient clinics in 12 countries in Europe, North America, South America, the Middle East, South Africa, and Asia. All participants had type 2 diabetes.

For all patients, the body mass index was ≥27 kg/m², and the A1c concentration was 7%-10%. The mean BMI was 35.7 kg/m², and the mean A1c was 8.1%.

The mean age of the patients was 55 years, and 51% were women; 62% were White, 26% were Asian, 13% were Hispanic, 8% were Black, and 4% were of other ethnicity.

Participants were managed with diet and exercise alone or underwent treatment with a stable dose of up to three oral glucose-lowering agents (metformin, sulfonylureas, SGLT2 inhibitors, or thiazolidinediones) for at least 90 days. They were then randomly assigned in 1:1:1 ratio to receive semaglutide 2.4 mg, semaglutide 1.0 mg, or placebo.

The starting dose of semaglutide was 0.25 mg/wk; the dose was escalated every 4 weeks to reach the target dose.

All patients received monthly counseling from a dietitian about calories (the goal was a 500-calorie/day deficit) and activity (the goal was 150 minutes of walking or stair climbing per week).

The mean A1c dropped by 1.6% and 1.5% in the semaglutide groups and by 0.4% in the placebo group.

Adverse events were more frequent among the patients who received semaglutide (88% and 82%) than in the placebo group (77%).

Gastrointestinal events that were mainly mild to moderate in severity were reported by 64% of patients in the 2.4-mg semaglutide group, 58% in the 1.0-mg semaglutide group, and 34% in the placebo group.

Semaglutide (Rybelsus) is approved in the United States as a once-daily oral agent for use in type 2 diabetes in doses of 7 mg and 14 mg to improve glycemic control along with diet and exercise. It is the first GLP-1 agonist available in tablet form.

The study was supported by Novo Nordisk. The authors’ relevant financial relationships are listed in the original article.

A version of this article first appeared on Medscape.com.

A 2.4-mg weekly injection of the glucagon-like peptide-1 (GLP-1) receptor agonist semaglutide led to a clinically meaningful 5% loss in weight for roughly two-thirds of patients with both overweight/obesity and type 2 diabetes, researchers report.

These findings from the Semaglutide Treatment Effect in People With Obesity 2 (STEP 2) trial, one of four phase 3 trials of this drug, which is currently under regulatory review for weight loss, were published March 2 in The Lancet.

More than 1,000 patients (mean initial weight, 100 kg [220 pounds]) were randomly assigned to receive a lifestyle intervention plus a weekly injection of semaglutide 2.4 mg or semaglutide 1.0 mg or placebo. At 68 weeks, they had lost a mean of 9.6%, 7.0%, and 3.4%, respectively, of their starting weight.

In addition, 69% of patients who had received semaglutide 2.4 mg experienced a clinically meaningful 5% loss of weight, compared with 57% of patients who had received the lower dose and 29% of patients who had received placebo.

The higher dose of semaglutide was associated with a greater improvement in cardiometabolic risk factors. The safety profile was similar to that seen with other drugs in this class.
 

“By far the best results with any weight loss medicine in diabetes”

Importantly, “more than a quarter of participants lost over 15% of their body weight,” senior author Ildiko Lingvay, MD, stressed. This “is by far the best result we had with any weight loss medicine in patients with diabetes,” Dr. Lingvay, of the University of Texas, Dallas, said in a statement from the university.

Sara Freeman/MDedge News

“The drug works by suppressing appetite centers in the brain to reduce caloric intake,” she explained. “The medication continually tells the body that you just ate, you’re full.”

Similarly, lead author Melanie J. Davies, MD, said that the STEP 2 results “are exciting and represent a new era in weight management in people with type 2 diabetes.

Sara Freeman/MDedge News

One of four trials under review

More than 90% of people with type 2 diabetes are overweight or have obesity, and more than 20% of people with obesity have diabetes, wrote Dr. Davies and colleagues.

Semaglutide (Ozempic), administered subcutaneously at a dose of 0.5 mg to 1 mg weekly, is approved by the Food and Drug Administration for the treatment of type 2 diabetes. Dosing studies indicated that it is associated with weight loss.

As previously reported, four trials of the use of semaglutide for weight loss (STEP 1, 2, 3, and 4) have been completed. The combined data were submitted to the FDA on Dec. 4, 2020 (a decision is expected within 6 months) and to the European Medicines Agency on Dec. 18, 2020.

The STEP 1 and STEP 3 trials of semaglutide 2.4 mg vs. placebo were recently published. The STEP 1 trial involved 1,961 adults with obesity or overweight; the STEP 3 trial, 611 adults with obesity or overweight. In each of the trials, some patients also underwent an intensive lifestyle intervention, and some did not. In both trials, patients with type 2 diabetes were excluded.

Topline results from STEP 2 were reported in June 2020.
 

STEP 2 enrolled patients with type 2 diabetes

STEP 2 involved 1,210 adults in 149 outpatient clinics in 12 countries in Europe, North America, South America, the Middle East, South Africa, and Asia. All participants had type 2 diabetes.

For all patients, the body mass index was ≥27 kg/m², and the A1c concentration was 7%-10%. The mean BMI was 35.7 kg/m², and the mean A1c was 8.1%.

The mean age of the patients was 55 years, and 51% were women; 62% were White, 26% were Asian, 13% were Hispanic, 8% were Black, and 4% were of other ethnicity.

Participants were managed with diet and exercise alone or underwent treatment with a stable dose of up to three oral glucose-lowering agents (metformin, sulfonylureas, SGLT2 inhibitors, or thiazolidinediones) for at least 90 days. They were then randomly assigned in 1:1:1 ratio to receive semaglutide 2.4 mg, semaglutide 1.0 mg, or placebo.

The starting dose of semaglutide was 0.25 mg/wk; the dose was escalated every 4 weeks to reach the target dose.

All patients received monthly counseling from a dietitian about calories (the goal was a 500-calorie/day deficit) and activity (the goal was 150 minutes of walking or stair climbing per week).

The mean A1c dropped by 1.6% and 1.5% in the semaglutide groups and by 0.4% in the placebo group.

Adverse events were more frequent among the patients who received semaglutide (88% and 82%) than in the placebo group (77%).

Gastrointestinal events that were mainly mild to moderate in severity were reported by 64% of patients in the 2.4-mg semaglutide group, 58% in the 1.0-mg semaglutide group, and 34% in the placebo group.

Semaglutide (Rybelsus) is approved in the United States as a once-daily oral agent for use in type 2 diabetes in doses of 7 mg and 14 mg to improve glycemic control along with diet and exercise. It is the first GLP-1 agonist available in tablet form.

The study was supported by Novo Nordisk. The authors’ relevant financial relationships are listed in the original article.

A version of this article first appeared on Medscape.com.

A 2.4-mg weekly injection of the glucagon-like peptide-1 (GLP-1) receptor agonist semaglutide led to a clinically meaningful 5% loss in weight for roughly two-thirds of patients with both overweight/obesity and type 2 diabetes, researchers report.

These findings from the Semaglutide Treatment Effect in People With Obesity 2 (STEP 2) trial, one of four phase 3 trials of this drug, which is currently under regulatory review for weight loss, were published March 2 in The Lancet.

More than 1,000 patients (mean initial weight, 100 kg [220 pounds]) were randomly assigned to receive a lifestyle intervention plus a weekly injection of semaglutide 2.4 mg or semaglutide 1.0 mg or placebo. At 68 weeks, they had lost a mean of 9.6%, 7.0%, and 3.4%, respectively, of their starting weight.

In addition, 69% of patients who had received semaglutide 2.4 mg experienced a clinically meaningful 5% loss of weight, compared with 57% of patients who had received the lower dose and 29% of patients who had received placebo.

The higher dose of semaglutide was associated with a greater improvement in cardiometabolic risk factors. The safety profile was similar to that seen with other drugs in this class.
 

“By far the best results with any weight loss medicine in diabetes”

Importantly, “more than a quarter of participants lost over 15% of their body weight,” senior author Ildiko Lingvay, MD, stressed. This “is by far the best result we had with any weight loss medicine in patients with diabetes,” Dr. Lingvay, of the University of Texas, Dallas, said in a statement from the university.

Sara Freeman/MDedge News

“The drug works by suppressing appetite centers in the brain to reduce caloric intake,” she explained. “The medication continually tells the body that you just ate, you’re full.”

Similarly, lead author Melanie J. Davies, MD, said that the STEP 2 results “are exciting and represent a new era in weight management in people with type 2 diabetes.

Sara Freeman/MDedge News

One of four trials under review

More than 90% of people with type 2 diabetes are overweight or have obesity, and more than 20% of people with obesity have diabetes, wrote Dr. Davies and colleagues.

Semaglutide (Ozempic), administered subcutaneously at a dose of 0.5 mg to 1 mg weekly, is approved by the Food and Drug Administration for the treatment of type 2 diabetes. Dosing studies indicated that it is associated with weight loss.

As previously reported, four trials of the use of semaglutide for weight loss (STEP 1, 2, 3, and 4) have been completed. The combined data were submitted to the FDA on Dec. 4, 2020 (a decision is expected within 6 months) and to the European Medicines Agency on Dec. 18, 2020.

The STEP 1 and STEP 3 trials of semaglutide 2.4 mg vs. placebo were recently published. The STEP 1 trial involved 1,961 adults with obesity or overweight; the STEP 3 trial, 611 adults with obesity or overweight. In each of the trials, some patients also underwent an intensive lifestyle intervention, and some did not. In both trials, patients with type 2 diabetes were excluded.

Topline results from STEP 2 were reported in June 2020.
 

STEP 2 enrolled patients with type 2 diabetes

STEP 2 involved 1,210 adults in 149 outpatient clinics in 12 countries in Europe, North America, South America, the Middle East, South Africa, and Asia. All participants had type 2 diabetes.

For all patients, the body mass index was ≥27 kg/m², and the A1c concentration was 7%-10%. The mean BMI was 35.7 kg/m², and the mean A1c was 8.1%.

The mean age of the patients was 55 years, and 51% were women; 62% were White, 26% were Asian, 13% were Hispanic, 8% were Black, and 4% were of other ethnicity.

Participants were managed with diet and exercise alone or underwent treatment with a stable dose of up to three oral glucose-lowering agents (metformin, sulfonylureas, SGLT2 inhibitors, or thiazolidinediones) for at least 90 days. They were then randomly assigned in 1:1:1 ratio to receive semaglutide 2.4 mg, semaglutide 1.0 mg, or placebo.

The starting dose of semaglutide was 0.25 mg/wk; the dose was escalated every 4 weeks to reach the target dose.

All patients received monthly counseling from a dietitian about calories (the goal was a 500-calorie/day deficit) and activity (the goal was 150 minutes of walking or stair climbing per week).

The mean A1c dropped by 1.6% and 1.5% in the semaglutide groups and by 0.4% in the placebo group.

Adverse events were more frequent among the patients who received semaglutide (88% and 82%) than in the placebo group (77%).

Gastrointestinal events that were mainly mild to moderate in severity were reported by 64% of patients in the 2.4-mg semaglutide group, 58% in the 1.0-mg semaglutide group, and 34% in the placebo group.

Semaglutide (Rybelsus) is approved in the United States as a once-daily oral agent for use in type 2 diabetes in doses of 7 mg and 14 mg to improve glycemic control along with diet and exercise. It is the first GLP-1 agonist available in tablet form.

The study was supported by Novo Nordisk. The authors’ relevant financial relationships are listed in the original article.

A version of this article first appeared on Medscape.com.

A third COVID-19 vaccine is now in circulation and states are starting to drop mask mandates, but the latest decline in weekly child cases barely registers as a decline, according to new data from the American Academy of Pediatrics and the Children’s Hospital Association.

The number of new COVID-19 cases in children fell from 64,264 (Feb. 19-25) to 63,562 (Feb. 26 to March 4). That’s only 702 cases – a drop of just 1.1% – the smallest by far since weekly cases peaked in mid-January, the AAP and CHA said in their weekly COVID-19 report. Since that peak, the last 7 weeks of declines have looked like this: 21.7%, 15.3%, 16.2%, 15.7%, 28.7%, 9.0%, and 1.1%.

Meanwhile, children’s share of the COVID-19 burden increased to its highest point ever: 18.0% of all new cases occurred in children during the week ending March 4, climbing from 15.7% the week before and eclipsing the previous high of 16.9%. Cumulatively, the 3.23 million cases in children represent 13.2% of all COVID-19 cases reported in 49 states (excluding New York), the District of Columbia, New York City, Puerto Rico, and Guam.

At the state level, the new leader in cumulative share of cases is Vermont at 19.4%, which just edged past Wyoming’s 19.3% as of the week ending March 4. The other states above 18% are Alaska (19.2%) and South Carolina (18.2%). The lowest rates can be found in Florida (8.1%), New Jersey (10.2%), Iowa (10.4%), and Utah (10.5%), the AAP and CHA said.

The overall rate of COVID-19 cases nationwide was 4,294 cases per 100,000 children as of March 4, up from 4,209 per 100,000 the week before. That measure had doubled between Dec. 3 (1,941 per 100,000) and Feb. 4 (3,899) but has only risen about 10% in the last month, the AAP/CHA data show.

Perhaps the most surprising news of the week involves the number of COVID-19 deaths in children, which went from 256 the previous week to 253 after Ohio made a downward revision of its mortality data. So far, children represent just 0.06% of all coronavirus-related deaths, a figure that has held steady since last summer in the 43 states (along with New York City and Guam) that are reporting mortality data by age, the AAP and CHA said.

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A third COVID-19 vaccine is now in circulation and states are starting to drop mask mandates, but the latest decline in weekly child cases barely registers as a decline, according to new data from the American Academy of Pediatrics and the Children’s Hospital Association.

The number of new COVID-19 cases in children fell from 64,264 (Feb. 19-25) to 63,562 (Feb. 26 to March 4). That’s only 702 cases – a drop of just 1.1% – the smallest by far since weekly cases peaked in mid-January, the AAP and CHA said in their weekly COVID-19 report. Since that peak, the last 7 weeks of declines have looked like this: 21.7%, 15.3%, 16.2%, 15.7%, 28.7%, 9.0%, and 1.1%.

Meanwhile, children’s share of the COVID-19 burden increased to its highest point ever: 18.0% of all new cases occurred in children during the week ending March 4, climbing from 15.7% the week before and eclipsing the previous high of 16.9%. Cumulatively, the 3.23 million cases in children represent 13.2% of all COVID-19 cases reported in 49 states (excluding New York), the District of Columbia, New York City, Puerto Rico, and Guam.

At the state level, the new leader in cumulative share of cases is Vermont at 19.4%, which just edged past Wyoming’s 19.3% as of the week ending March 4. The other states above 18% are Alaska (19.2%) and South Carolina (18.2%). The lowest rates can be found in Florida (8.1%), New Jersey (10.2%), Iowa (10.4%), and Utah (10.5%), the AAP and CHA said.

The overall rate of COVID-19 cases nationwide was 4,294 cases per 100,000 children as of March 4, up from 4,209 per 100,000 the week before. That measure had doubled between Dec. 3 (1,941 per 100,000) and Feb. 4 (3,899) but has only risen about 10% in the last month, the AAP/CHA data show.

Perhaps the most surprising news of the week involves the number of COVID-19 deaths in children, which went from 256 the previous week to 253 after Ohio made a downward revision of its mortality data. So far, children represent just 0.06% of all coronavirus-related deaths, a figure that has held steady since last summer in the 43 states (along with New York City and Guam) that are reporting mortality data by age, the AAP and CHA said.

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A third COVID-19 vaccine is now in circulation and states are starting to drop mask mandates, but the latest decline in weekly child cases barely registers as a decline, according to new data from the American Academy of Pediatrics and the Children’s Hospital Association.

The number of new COVID-19 cases in children fell from 64,264 (Feb. 19-25) to 63,562 (Feb. 26 to March 4). That’s only 702 cases – a drop of just 1.1% – the smallest by far since weekly cases peaked in mid-January, the AAP and CHA said in their weekly COVID-19 report. Since that peak, the last 7 weeks of declines have looked like this: 21.7%, 15.3%, 16.2%, 15.7%, 28.7%, 9.0%, and 1.1%.

Meanwhile, children’s share of the COVID-19 burden increased to its highest point ever: 18.0% of all new cases occurred in children during the week ending March 4, climbing from 15.7% the week before and eclipsing the previous high of 16.9%. Cumulatively, the 3.23 million cases in children represent 13.2% of all COVID-19 cases reported in 49 states (excluding New York), the District of Columbia, New York City, Puerto Rico, and Guam.

At the state level, the new leader in cumulative share of cases is Vermont at 19.4%, which just edged past Wyoming’s 19.3% as of the week ending March 4. The other states above 18% are Alaska (19.2%) and South Carolina (18.2%). The lowest rates can be found in Florida (8.1%), New Jersey (10.2%), Iowa (10.4%), and Utah (10.5%), the AAP and CHA said.

The overall rate of COVID-19 cases nationwide was 4,294 cases per 100,000 children as of March 4, up from 4,209 per 100,000 the week before. That measure had doubled between Dec. 3 (1,941 per 100,000) and Feb. 4 (3,899) but has only risen about 10% in the last month, the AAP/CHA data show.

Perhaps the most surprising news of the week involves the number of COVID-19 deaths in children, which went from 256 the previous week to 253 after Ohio made a downward revision of its mortality data. So far, children represent just 0.06% of all coronavirus-related deaths, a figure that has held steady since last summer in the 43 states (along with New York City and Guam) that are reporting mortality data by age, the AAP and CHA said.

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Hundreds of thousands of deaths worldwide from COVID-19 could have been avoided if obesity rates were lower, a new report says.

An analysis by the World Obesity Federation found that of the 2.5 million COVID-19 deaths reported by the end of February 2021, almost 90% (2.2 million) were in countries where more than half the population is classified as overweight.

The report, released to coincide with World Obesity Day, calls for obesity to be recognized as a disease in its own right around the world, and for people with obesity to be included in priority lists for COVID-19 testing and vaccination.

“Overweight is a highly significant predictor of developing complications from COVID-19, including the need for hospitalization, for intensive care and for mechanical ventilation,” the WOF notes in the report.

It adds that in countries where less than half the adult population is classified as overweight (body mass index > 25 mg/kg²), for example, Vietnam, the likelihood of death from COVID-19 is a small fraction – around one-tenth – of the level seen in countries where more than half the population is classified as overweight.

And while it acknowledges that figures for COVID-19 deaths are affected by the age structure of national populations and a country’s relative wealth and reporting capacity, “our findings appear to be independent of these contributory factors. Furthermore, other studies have found that overweight remains a highly significant predictor of the need for COVID-19 health care after accounting for these other influences.”

As an example, based on the U.K. experience, where an estimated 36% of COVID-19 hospitalizations have been attributed to lack of physical activity and excess body weight, it can be suggested that up to a third of the costs – between $6 trillion and $7 trillion over the longer period – might be attributable to these predisposing risks.

The report said the prevalence of obesity in the United Kingdom is expected to rise from 27.8% in 2016 to more than 35% by 2025.

Rachel Batterham, lead adviser on obesity at the Royal College of Physicians, commented: “The link between high levels of obesity and deaths from COVID-19 in the U.K. is indisputable, as is the urgent need to address the factors that lead so many people to be living with obesity.

“With 30% of COVID-19 hospitalizations in the U.K. directly attributed to overweight and obesity, and three-quarters of all critically ill patients having overweight or obesity, the human and financial costs are high.”
 

Window of opportunity to prioritize obesity as a disease

WOF says that evolving evidence on the close association between COVID-19 and underlying obesity “provides a new urgency … for political and collective action.”

“Obesity is a disease that does not receive prioritization commensurate with its prevalence and impact, which is rising fastest in emerging economies. It is a gateway to many other noncommunicable diseases and mental-health illness and is now a major factor in COVID-19 complications and mortality.”

The WOF also shows that COVID-19 is not a special case, noting that several other respiratory viruses lead to more severe consequences in people living with excess bodyweight, giving good reasons to expect the next pandemic to have similar effects. “For these reasons we need to recognize overweight as a major risk factor for infectious diseases including respiratory viruses.”

“To prevent pandemic health crises in future requires action now: we call on all readers to support the World Obesity Federation’s call for stronger, more resilient economies that prioritize investment in people’s health.”

There is, it stresses, “a window of opportunity to advocate for, fund and implement these actions in all countries to ensure better, more resilient and sustainable health for all, “now and in our postCOVID-19 future.”

It proposes a ROOTS approach:

• Recognize that obesity is a disease in its own right.
• Obesity monitoring and surveillance must be enhanced.
• Obesity prevention strategies must be developed.
• Treatment of obesity.
• Systems-based approaches should be applied.

A version of this article first appeared on Medscape.com.

Hundreds of thousands of deaths worldwide from COVID-19 could have been avoided if obesity rates were lower, a new report says.

An analysis by the World Obesity Federation found that of the 2.5 million COVID-19 deaths reported by the end of February 2021, almost 90% (2.2 million) were in countries where more than half the population is classified as overweight.

The report, released to coincide with World Obesity Day, calls for obesity to be recognized as a disease in its own right around the world, and for people with obesity to be included in priority lists for COVID-19 testing and vaccination.

“Overweight is a highly significant predictor of developing complications from COVID-19, including the need for hospitalization, for intensive care and for mechanical ventilation,” the WOF notes in the report.

It adds that in countries where less than half the adult population is classified as overweight (body mass index > 25 mg/kg²), for example, Vietnam, the likelihood of death from COVID-19 is a small fraction – around one-tenth – of the level seen in countries where more than half the population is classified as overweight.

And while it acknowledges that figures for COVID-19 deaths are affected by the age structure of national populations and a country’s relative wealth and reporting capacity, “our findings appear to be independent of these contributory factors. Furthermore, other studies have found that overweight remains a highly significant predictor of the need for COVID-19 health care after accounting for these other influences.”

As an example, based on the U.K. experience, where an estimated 36% of COVID-19 hospitalizations have been attributed to lack of physical activity and excess body weight, it can be suggested that up to a third of the costs – between $6 trillion and $7 trillion over the longer period – might be attributable to these predisposing risks.

The report said the prevalence of obesity in the United Kingdom is expected to rise from 27.8% in 2016 to more than 35% by 2025.

Rachel Batterham, lead adviser on obesity at the Royal College of Physicians, commented: “The link between high levels of obesity and deaths from COVID-19 in the U.K. is indisputable, as is the urgent need to address the factors that lead so many people to be living with obesity.

“With 30% of COVID-19 hospitalizations in the U.K. directly attributed to overweight and obesity, and three-quarters of all critically ill patients having overweight or obesity, the human and financial costs are high.”
 

Window of opportunity to prioritize obesity as a disease

WOF says that evolving evidence on the close association between COVID-19 and underlying obesity “provides a new urgency … for political and collective action.”

“Obesity is a disease that does not receive prioritization commensurate with its prevalence and impact, which is rising fastest in emerging economies. It is a gateway to many other noncommunicable diseases and mental-health illness and is now a major factor in COVID-19 complications and mortality.”

The WOF also shows that COVID-19 is not a special case, noting that several other respiratory viruses lead to more severe consequences in people living with excess bodyweight, giving good reasons to expect the next pandemic to have similar effects. “For these reasons we need to recognize overweight as a major risk factor for infectious diseases including respiratory viruses.”

“To prevent pandemic health crises in future requires action now: we call on all readers to support the World Obesity Federation’s call for stronger, more resilient economies that prioritize investment in people’s health.”

There is, it stresses, “a window of opportunity to advocate for, fund and implement these actions in all countries to ensure better, more resilient and sustainable health for all, “now and in our postCOVID-19 future.”

It proposes a ROOTS approach:

• Recognize that obesity is a disease in its own right.
• Obesity monitoring and surveillance must be enhanced.
• Obesity prevention strategies must be developed.
• Treatment of obesity.
• Systems-based approaches should be applied.

A version of this article first appeared on Medscape.com.

Hundreds of thousands of deaths worldwide from COVID-19 could have been avoided if obesity rates were lower, a new report says.

An analysis by the World Obesity Federation found that of the 2.5 million COVID-19 deaths reported by the end of February 2021, almost 90% (2.2 million) were in countries where more than half the population is classified as overweight.

The report, released to coincide with World Obesity Day, calls for obesity to be recognized as a disease in its own right around the world, and for people with obesity to be included in priority lists for COVID-19 testing and vaccination.

“Overweight is a highly significant predictor of developing complications from COVID-19, including the need for hospitalization, for intensive care and for mechanical ventilation,” the WOF notes in the report.

It adds that in countries where less than half the adult population is classified as overweight (body mass index > 25 mg/kg²), for example, Vietnam, the likelihood of death from COVID-19 is a small fraction – around one-tenth – of the level seen in countries where more than half the population is classified as overweight.

And while it acknowledges that figures for COVID-19 deaths are affected by the age structure of national populations and a country’s relative wealth and reporting capacity, “our findings appear to be independent of these contributory factors. Furthermore, other studies have found that overweight remains a highly significant predictor of the need for COVID-19 health care after accounting for these other influences.”

As an example, based on the U.K. experience, where an estimated 36% of COVID-19 hospitalizations have been attributed to lack of physical activity and excess body weight, it can be suggested that up to a third of the costs – between $6 trillion and $7 trillion over the longer period – might be attributable to these predisposing risks.

The report said the prevalence of obesity in the United Kingdom is expected to rise from 27.8% in 2016 to more than 35% by 2025.

Rachel Batterham, lead adviser on obesity at the Royal College of Physicians, commented: “The link between high levels of obesity and deaths from COVID-19 in the U.K. is indisputable, as is the urgent need to address the factors that lead so many people to be living with obesity.

“With 30% of COVID-19 hospitalizations in the U.K. directly attributed to overweight and obesity, and three-quarters of all critically ill patients having overweight or obesity, the human and financial costs are high.”
 

Window of opportunity to prioritize obesity as a disease

WOF says that evolving evidence on the close association between COVID-19 and underlying obesity “provides a new urgency … for political and collective action.”

“Obesity is a disease that does not receive prioritization commensurate with its prevalence and impact, which is rising fastest in emerging economies. It is a gateway to many other noncommunicable diseases and mental-health illness and is now a major factor in COVID-19 complications and mortality.”

The WOF also shows that COVID-19 is not a special case, noting that several other respiratory viruses lead to more severe consequences in people living with excess bodyweight, giving good reasons to expect the next pandemic to have similar effects. “For these reasons we need to recognize overweight as a major risk factor for infectious diseases including respiratory viruses.”

“To prevent pandemic health crises in future requires action now: we call on all readers to support the World Obesity Federation’s call for stronger, more resilient economies that prioritize investment in people’s health.”

There is, it stresses, “a window of opportunity to advocate for, fund and implement these actions in all countries to ensure better, more resilient and sustainable health for all, “now and in our postCOVID-19 future.”

It proposes a ROOTS approach:

• Recognize that obesity is a disease in its own right.
• Obesity monitoring and surveillance must be enhanced.
• Obesity prevention strategies must be developed.
• Treatment of obesity.
• Systems-based approaches should be applied.

A version of this article first appeared on Medscape.com.