Original Research

Vancomycin was isolated in the 1950s, but due to impurities causing adverse events and semisynthetic penicillin production, its use was greatly reduced.^1,2 However, this medication gained in popularity 30 years later as a first-line treatment for methicillin-resistant Staphylococcus aureus infections.

In 2009 the Infectious Diseases Society of America (IDSA), American Society of Health System Pharmacists, and Society of Infectious Diseases Pharmacists developed a consensus review of the therapeutic monitoring and dosing of vancomycin in adult patients.³ Trough serum concentration levels are recommended as the most accurate and convenient method to monitor vancomycin. Per IDSA guidelines, an optimal trough is intended to be high enough to clear infections (> 10 mg/L) and prevent the development of vancomycin intermediate and resistant bacteria. Troughs should be obtained just before the next dose in steady-state conditions (starting just before the fourth dose) in patients with normal renal function.

Since the development of these guidelines, vancomycin trough levels are often drawn early.^4-7 This may lead to an overestimation of the true trough concentration. A study by Morrison and colleagues in Boston, Massachusetts, found that 41.3% of vancomycin troughs were drawn early, and this resulted in statistically significant increases in the vancomycin concentrations, the rate of vancomycin regimen adjustments (decrease, discontinuation, or holding of dose), and the repeat vancomycin level orders compared with correctly timed troughs.⁵ It was noted by the study authors that lowering the daily dose of vancomycin based on early trough levels could lead to an underdosing of vancomycin and an increase in intermediate or resistant bacteria.

Related: IDWEEK: Antibiotic ‘time-out’ cut vancomycin use 

The prevalence and implications of early trough samples have been measured at only 1 facility, and it is unknown whether these data can be reproduced elsewhere.⁵ Thus, this study sought to determine the prevalence and corresponding clinical actions of early trough levels at the Captain James A. Lovell Federal Health Care Center (JALFHCC). This is a unique facility that in 2010 combined a VA hospital with a DoD hospital. This facility cares for 67,000 military and retiree beneficiaries each year from southwestern Wisconsin and northwestern Illinois.The primary objective of this study was to measure the rate of early troughs drawn and their resultant effect on vancomycin regimens compared with correctly timed troughs. Secondarily, this study sought to compare the rate of repeated vancomycin trough levels in early vs correctly timed measurements.

Methods

This retrospective cohort analysis compared the outcomes of early and correctly timed vancomycin troughs. This study was approved by the Edward Hines, Jr. VA Hospital and JALFHCC Institutional Review Board. Veteran patients aged ≥ 18 years, hospitalized at JALFHCC, and receiving IV vancomycin at dosing intervals of 8, 12, 24, and 48 hours with measured trough levels between July 1, 2009, and July 1, 2013, were included in this study. Patients were excluded from analysis if vancomycin was given at any schedule other than the previously stated frequencies, they received hemodialysis during the treatment period, or their insurance coverage was through TRICARE (these patients had either active-duty or retired active-duty status).

Potentially eligible patients were identified via a Computerized Patient Records System (CPRS) search for laboratory vancomycin level measurements. The search supplied the researcher with the patient name, vancomycin level date and time, type of vancomycin level (trough or random), and vancomycin concentration. With this information, further data were gathered through CPRS: demographics, type of clinical infection, desired trough level (inferred if not listed in CPRS note), and vancomycin administration time (through the bar code medication administration system [BCMA] in CPRS). This analysis was of troughs, and multiple troughs may have originated from the same patient.

An early trough was defined as a trough taken more than 2 hours earlier than the next theoretical administration time or anytime before the third dose. After a trough was determined to be early or on time, the clinical actions taken during the dosing interval following sample collection were documented. A dose was considered to be held if stated in the BCMA or in a CPRS provider note. A dose was considered to be decreased with a change in frequency or strength that resulted in an overall daily dose decrease. A recollected vancomycin trough was counted within 24 hours of the trough or per a note in CPRS. Finally, observations that noted trends in vancomycin trough management were recorded.

The chi-square test with a significance criterion of 0.05 was used to compare early and on time troughs. Based on the results from the Boston, Massachusetts, study and 1 other study, about 780 vancomycin troughs would be required to meet significance in the primary outcome.^5,6

Results

A total of 474 patient charts were reviewed, and 278 met inclusion criteria (196 were excluded). Of the included patients, 799 trough levels were analyzed. Of these, 377 (42.2%) were drawn early. There was no significant difference in the baseline characteristics of the early group vs the correctly timed group (Table 1). Of the early troughs, 190 (56.3%) were drawn prior to the third dose of vancomycin. It was observed that a large portion of these troughs occurred after a vancomycin dose adjustment.

Clinical actions taken after sampling occurred at a rate of 14.5% in the early group and 22.9% in the correctly timed group (P = .003; Table 2). Early troughs led to a 7.7% rate of trough recollection, which was significantly greater than the 1.5% rate in the correctly timed group (P < .001). An analysis of each factor resulting in a clinical action illustrated that the rates of daily dose decrease and discontinued dose were similar between the groups (Table 3). However, the rate of held doses was 8.3% in the early group and 17.1% in the correctly timed group.

This research process yielded some observations. Occasionally a trough was drawn after vancomycin therapy was discontinued and when there was no concern for nephrotoxicity. After the guidelines were published, providers continued to document in CPRS notes to check troughs before the third dose. This incidence decreased over time. Troughs were taken often in patients who were receiving a short course of therapy or who were hemodynamically stable. Finally, documentation of vancomycin regimen changes occasionally did not match the record in the BCMA (in these situations, the BCMA record was used for this study).

Related: Assessment of High Staphylococcus aureus MIC and Poor Patient Outcomes

Discussion

A large portion of trough levels at the JALFHCC were drawn early and did not adhere to the 2009 consensus guidelines. The rates of early troughs in this study and in the Boston, Massachusetts, study are similar.⁵ However, the 2 studies differed in 1 significant aspect: Clinical actions were taken less often in the early group at JALFHCC, whereas they were taken more often in the early group in the Boston, Massachusetts, study. This dissimilarity could be attributed to a difference in software between the hospitals. In the previous study, trough levels and the time that they were drawn were not displayed together. Thus, clinicians may have been less likely to gauge whether a trough was early. Since this information is available at the JALFHCC, clinicians may have been aware that the trough was early and avoided adjusting treatment (such as holding a dose, as illustrated in the data) based on a falsely elevated trough. This point is further supported by significantly greater amounts of recollected troughs in the early group, suggesting an understanding that the trough was early.

The low trough recollection rate of 7.7% of all early samples could be due to several factors that would prevent a trough redraw. First, medication discontinuation resulting from course completion or sensitivity results would not require further trough monitoring. Second, practitioners may assess the early sample as insignificantly different from a correctly timed one and elect not to redraw the trough. Sometimes a trough was drawn at the correct time, but the time was recorded incorrectly. In this situation, a new trough level would not be necessary. Finally, a lack of sufficient staffing during nights and weekends may result in a delay in interpreting results leading to a missed opportunity for recollection. Additionally, some troughs may not have been redrawn based on a practitioner’s opinion that a trough was not significantly early and did not represent skewed results. Sometimes an incorrect recording of trough draw time reflected that it was taken after vancomycin dosing when it was not.

Specific observations regarding the timing of the trough indicate other possible concerns and areas for improvement. First, providers must cancel future trough orders concurrently with canceling treatment. Second, at the time of publication of the consensus, some providers were slow adopters of the new guidelines. Finally, the IDSA guidelines state that frequent monitoring for short course, lower intensity therapy, or in patients who are hemodynamically stable is not recommended.³ However, troughs were sometimes measured 2 to 3 times weekly in these patients.

Related: Results mixed in hospital efforts to tackle antimicrobial resistance

The data and observations lead to the conclusion that although providers may be able to discern between early and correctly timed troughs, they were not consistently adherent to the 2009 IDSA guidelines. It has been shown that pharmacy involvement of Medicare patients with infections in the intensive care unit has led to better clinical and monetary outcomes.⁸ Therefore, continued efforts by clinical pharmacists to monitor trough timing can be used to improve adherence and decrease costs (each trough is estimated to cost $16.97).

A study conducted in Australia demonstrated that pharmacist-led education of vancomycin dosing and monitoring (including when to measure a trough level) among prescribers and nurses led to improved adherence to the current guidelines and a greater number of patients treated within desired therapeutic ranges.⁹ In addition, a small study at the Atlanta VAMC in Georgia demonstrated that education of nurses, lab personnel, residents, ward clerks, and pharmacists led to a greater number of appropriately timed vancomycin and aminoglycoside levels.¹⁰ Thus, an interdisciplinary review of the current IDSA guidelines and review on the publication of the anticipated updated vancomycin guidelines should be provided to hospital personnel to aid in adoption of current dosing and monitoring recommendations.¹¹

Limitations

This study is limited by the 4-year span of time that it encompassed, which may give a skewed depiction of current practices. Another limitation is that patients with fluctuating renal function were included in the analysis. Instead of selecting a random level order, a trough level order was sometimes selected for these patients. This could lead to a lower actual rate of early troughs. A third limitation is that this was a small and unblinded study. Also, the actual trough levels and the resulting changes that were made to specific regimens were not recorded. Thus, these data do not indicate whether the changes that were made reflected guideline recommendations. Finally, some clinical actions were taken after the dosing interval following the trough. This was often a result of off-hours lab results or waiting on attending physician or infectious disease guidance. These data were not included in the analysis.

Conclusion

Vancomycin troughs were often drawn too early and resulted in an increased rate of trough recollection. In an attempt to improve adherence to the current and the upcoming revised version of the IDSA consensus statement, it is recommended to educate and reeducate providers through interdisciplinary-led review sessions.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Vancomycin was isolated in the 1950s, but due to impurities causing adverse events and semisynthetic penicillin production, its use was greatly reduced.^1,2 However, this medication gained in popularity 30 years later as a first-line treatment for methicillin-resistant Staphylococcus aureus infections.

In 2009 the Infectious Diseases Society of America (IDSA), American Society of Health System Pharmacists, and Society of Infectious Diseases Pharmacists developed a consensus review of the therapeutic monitoring and dosing of vancomycin in adult patients.³ Trough serum concentration levels are recommended as the most accurate and convenient method to monitor vancomycin. Per IDSA guidelines, an optimal trough is intended to be high enough to clear infections (> 10 mg/L) and prevent the development of vancomycin intermediate and resistant bacteria. Troughs should be obtained just before the next dose in steady-state conditions (starting just before the fourth dose) in patients with normal renal function.

Since the development of these guidelines, vancomycin trough levels are often drawn early.^4-7 This may lead to an overestimation of the true trough concentration. A study by Morrison and colleagues in Boston, Massachusetts, found that 41.3% of vancomycin troughs were drawn early, and this resulted in statistically significant increases in the vancomycin concentrations, the rate of vancomycin regimen adjustments (decrease, discontinuation, or holding of dose), and the repeat vancomycin level orders compared with correctly timed troughs.⁵ It was noted by the study authors that lowering the daily dose of vancomycin based on early trough levels could lead to an underdosing of vancomycin and an increase in intermediate or resistant bacteria.

Related: IDWEEK: Antibiotic ‘time-out’ cut vancomycin use 

The prevalence and implications of early trough samples have been measured at only 1 facility, and it is unknown whether these data can be reproduced elsewhere.⁵ Thus, this study sought to determine the prevalence and corresponding clinical actions of early trough levels at the Captain James A. Lovell Federal Health Care Center (JALFHCC). This is a unique facility that in 2010 combined a VA hospital with a DoD hospital. This facility cares for 67,000 military and retiree beneficiaries each year from southwestern Wisconsin and northwestern Illinois.The primary objective of this study was to measure the rate of early troughs drawn and their resultant effect on vancomycin regimens compared with correctly timed troughs. Secondarily, this study sought to compare the rate of repeated vancomycin trough levels in early vs correctly timed measurements.

Methods

This retrospective cohort analysis compared the outcomes of early and correctly timed vancomycin troughs. This study was approved by the Edward Hines, Jr. VA Hospital and JALFHCC Institutional Review Board. Veteran patients aged ≥ 18 years, hospitalized at JALFHCC, and receiving IV vancomycin at dosing intervals of 8, 12, 24, and 48 hours with measured trough levels between July 1, 2009, and July 1, 2013, were included in this study. Patients were excluded from analysis if vancomycin was given at any schedule other than the previously stated frequencies, they received hemodialysis during the treatment period, or their insurance coverage was through TRICARE (these patients had either active-duty or retired active-duty status).

Potentially eligible patients were identified via a Computerized Patient Records System (CPRS) search for laboratory vancomycin level measurements. The search supplied the researcher with the patient name, vancomycin level date and time, type of vancomycin level (trough or random), and vancomycin concentration. With this information, further data were gathered through CPRS: demographics, type of clinical infection, desired trough level (inferred if not listed in CPRS note), and vancomycin administration time (through the bar code medication administration system [BCMA] in CPRS). This analysis was of troughs, and multiple troughs may have originated from the same patient.

An early trough was defined as a trough taken more than 2 hours earlier than the next theoretical administration time or anytime before the third dose. After a trough was determined to be early or on time, the clinical actions taken during the dosing interval following sample collection were documented. A dose was considered to be held if stated in the BCMA or in a CPRS provider note. A dose was considered to be decreased with a change in frequency or strength that resulted in an overall daily dose decrease. A recollected vancomycin trough was counted within 24 hours of the trough or per a note in CPRS. Finally, observations that noted trends in vancomycin trough management were recorded.

The chi-square test with a significance criterion of 0.05 was used to compare early and on time troughs. Based on the results from the Boston, Massachusetts, study and 1 other study, about 780 vancomycin troughs would be required to meet significance in the primary outcome.^5,6

Results

A total of 474 patient charts were reviewed, and 278 met inclusion criteria (196 were excluded). Of the included patients, 799 trough levels were analyzed. Of these, 377 (42.2%) were drawn early. There was no significant difference in the baseline characteristics of the early group vs the correctly timed group (Table 1). Of the early troughs, 190 (56.3%) were drawn prior to the third dose of vancomycin. It was observed that a large portion of these troughs occurred after a vancomycin dose adjustment.

Clinical actions taken after sampling occurred at a rate of 14.5% in the early group and 22.9% in the correctly timed group (P = .003; Table 2). Early troughs led to a 7.7% rate of trough recollection, which was significantly greater than the 1.5% rate in the correctly timed group (P < .001). An analysis of each factor resulting in a clinical action illustrated that the rates of daily dose decrease and discontinued dose were similar between the groups (Table 3). However, the rate of held doses was 8.3% in the early group and 17.1% in the correctly timed group.

This research process yielded some observations. Occasionally a trough was drawn after vancomycin therapy was discontinued and when there was no concern for nephrotoxicity. After the guidelines were published, providers continued to document in CPRS notes to check troughs before the third dose. This incidence decreased over time. Troughs were taken often in patients who were receiving a short course of therapy or who were hemodynamically stable. Finally, documentation of vancomycin regimen changes occasionally did not match the record in the BCMA (in these situations, the BCMA record was used for this study).

Related: Assessment of High Staphylococcus aureus MIC and Poor Patient Outcomes

Discussion

A large portion of trough levels at the JALFHCC were drawn early and did not adhere to the 2009 consensus guidelines. The rates of early troughs in this study and in the Boston, Massachusetts, study are similar.⁵ However, the 2 studies differed in 1 significant aspect: Clinical actions were taken less often in the early group at JALFHCC, whereas they were taken more often in the early group in the Boston, Massachusetts, study. This dissimilarity could be attributed to a difference in software between the hospitals. In the previous study, trough levels and the time that they were drawn were not displayed together. Thus, clinicians may have been less likely to gauge whether a trough was early. Since this information is available at the JALFHCC, clinicians may have been aware that the trough was early and avoided adjusting treatment (such as holding a dose, as illustrated in the data) based on a falsely elevated trough. This point is further supported by significantly greater amounts of recollected troughs in the early group, suggesting an understanding that the trough was early.

The low trough recollection rate of 7.7% of all early samples could be due to several factors that would prevent a trough redraw. First, medication discontinuation resulting from course completion or sensitivity results would not require further trough monitoring. Second, practitioners may assess the early sample as insignificantly different from a correctly timed one and elect not to redraw the trough. Sometimes a trough was drawn at the correct time, but the time was recorded incorrectly. In this situation, a new trough level would not be necessary. Finally, a lack of sufficient staffing during nights and weekends may result in a delay in interpreting results leading to a missed opportunity for recollection. Additionally, some troughs may not have been redrawn based on a practitioner’s opinion that a trough was not significantly early and did not represent skewed results. Sometimes an incorrect recording of trough draw time reflected that it was taken after vancomycin dosing when it was not.

Specific observations regarding the timing of the trough indicate other possible concerns and areas for improvement. First, providers must cancel future trough orders concurrently with canceling treatment. Second, at the time of publication of the consensus, some providers were slow adopters of the new guidelines. Finally, the IDSA guidelines state that frequent monitoring for short course, lower intensity therapy, or in patients who are hemodynamically stable is not recommended.³ However, troughs were sometimes measured 2 to 3 times weekly in these patients.

Related: Results mixed in hospital efforts to tackle antimicrobial resistance

The data and observations lead to the conclusion that although providers may be able to discern between early and correctly timed troughs, they were not consistently adherent to the 2009 IDSA guidelines. It has been shown that pharmacy involvement of Medicare patients with infections in the intensive care unit has led to better clinical and monetary outcomes.⁸ Therefore, continued efforts by clinical pharmacists to monitor trough timing can be used to improve adherence and decrease costs (each trough is estimated to cost $16.97).

A study conducted in Australia demonstrated that pharmacist-led education of vancomycin dosing and monitoring (including when to measure a trough level) among prescribers and nurses led to improved adherence to the current guidelines and a greater number of patients treated within desired therapeutic ranges.⁹ In addition, a small study at the Atlanta VAMC in Georgia demonstrated that education of nurses, lab personnel, residents, ward clerks, and pharmacists led to a greater number of appropriately timed vancomycin and aminoglycoside levels.¹⁰ Thus, an interdisciplinary review of the current IDSA guidelines and review on the publication of the anticipated updated vancomycin guidelines should be provided to hospital personnel to aid in adoption of current dosing and monitoring recommendations.¹¹

Limitations

This study is limited by the 4-year span of time that it encompassed, which may give a skewed depiction of current practices. Another limitation is that patients with fluctuating renal function were included in the analysis. Instead of selecting a random level order, a trough level order was sometimes selected for these patients. This could lead to a lower actual rate of early troughs. A third limitation is that this was a small and unblinded study. Also, the actual trough levels and the resulting changes that were made to specific regimens were not recorded. Thus, these data do not indicate whether the changes that were made reflected guideline recommendations. Finally, some clinical actions were taken after the dosing interval following the trough. This was often a result of off-hours lab results or waiting on attending physician or infectious disease guidance. These data were not included in the analysis.

Conclusion

Vancomycin troughs were often drawn too early and resulted in an increased rate of trough recollection. In an attempt to improve adherence to the current and the upcoming revised version of the IDSA consensus statement, it is recommended to educate and reeducate providers through interdisciplinary-led review sessions.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Vancomycin was isolated in the 1950s, but due to impurities causing adverse events and semisynthetic penicillin production, its use was greatly reduced.^1,2 However, this medication gained in popularity 30 years later as a first-line treatment for methicillin-resistant Staphylococcus aureus infections.

In 2009 the Infectious Diseases Society of America (IDSA), American Society of Health System Pharmacists, and Society of Infectious Diseases Pharmacists developed a consensus review of the therapeutic monitoring and dosing of vancomycin in adult patients.³ Trough serum concentration levels are recommended as the most accurate and convenient method to monitor vancomycin. Per IDSA guidelines, an optimal trough is intended to be high enough to clear infections (> 10 mg/L) and prevent the development of vancomycin intermediate and resistant bacteria. Troughs should be obtained just before the next dose in steady-state conditions (starting just before the fourth dose) in patients with normal renal function.

Since the development of these guidelines, vancomycin trough levels are often drawn early.^4-7 This may lead to an overestimation of the true trough concentration. A study by Morrison and colleagues in Boston, Massachusetts, found that 41.3% of vancomycin troughs were drawn early, and this resulted in statistically significant increases in the vancomycin concentrations, the rate of vancomycin regimen adjustments (decrease, discontinuation, or holding of dose), and the repeat vancomycin level orders compared with correctly timed troughs.⁵ It was noted by the study authors that lowering the daily dose of vancomycin based on early trough levels could lead to an underdosing of vancomycin and an increase in intermediate or resistant bacteria.

Related: IDWEEK: Antibiotic ‘time-out’ cut vancomycin use 

The prevalence and implications of early trough samples have been measured at only 1 facility, and it is unknown whether these data can be reproduced elsewhere.⁵ Thus, this study sought to determine the prevalence and corresponding clinical actions of early trough levels at the Captain James A. Lovell Federal Health Care Center (JALFHCC). This is a unique facility that in 2010 combined a VA hospital with a DoD hospital. This facility cares for 67,000 military and retiree beneficiaries each year from southwestern Wisconsin and northwestern Illinois.The primary objective of this study was to measure the rate of early troughs drawn and their resultant effect on vancomycin regimens compared with correctly timed troughs. Secondarily, this study sought to compare the rate of repeated vancomycin trough levels in early vs correctly timed measurements.

Methods

This retrospective cohort analysis compared the outcomes of early and correctly timed vancomycin troughs. This study was approved by the Edward Hines, Jr. VA Hospital and JALFHCC Institutional Review Board. Veteran patients aged ≥ 18 years, hospitalized at JALFHCC, and receiving IV vancomycin at dosing intervals of 8, 12, 24, and 48 hours with measured trough levels between July 1, 2009, and July 1, 2013, were included in this study. Patients were excluded from analysis if vancomycin was given at any schedule other than the previously stated frequencies, they received hemodialysis during the treatment period, or their insurance coverage was through TRICARE (these patients had either active-duty or retired active-duty status).

Potentially eligible patients were identified via a Computerized Patient Records System (CPRS) search for laboratory vancomycin level measurements. The search supplied the researcher with the patient name, vancomycin level date and time, type of vancomycin level (trough or random), and vancomycin concentration. With this information, further data were gathered through CPRS: demographics, type of clinical infection, desired trough level (inferred if not listed in CPRS note), and vancomycin administration time (through the bar code medication administration system [BCMA] in CPRS). This analysis was of troughs, and multiple troughs may have originated from the same patient.

An early trough was defined as a trough taken more than 2 hours earlier than the next theoretical administration time or anytime before the third dose. After a trough was determined to be early or on time, the clinical actions taken during the dosing interval following sample collection were documented. A dose was considered to be held if stated in the BCMA or in a CPRS provider note. A dose was considered to be decreased with a change in frequency or strength that resulted in an overall daily dose decrease. A recollected vancomycin trough was counted within 24 hours of the trough or per a note in CPRS. Finally, observations that noted trends in vancomycin trough management were recorded.

The chi-square test with a significance criterion of 0.05 was used to compare early and on time troughs. Based on the results from the Boston, Massachusetts, study and 1 other study, about 780 vancomycin troughs would be required to meet significance in the primary outcome.^5,6

Results

A total of 474 patient charts were reviewed, and 278 met inclusion criteria (196 were excluded). Of the included patients, 799 trough levels were analyzed. Of these, 377 (42.2%) were drawn early. There was no significant difference in the baseline characteristics of the early group vs the correctly timed group (Table 1). Of the early troughs, 190 (56.3%) were drawn prior to the third dose of vancomycin. It was observed that a large portion of these troughs occurred after a vancomycin dose adjustment.

Clinical actions taken after sampling occurred at a rate of 14.5% in the early group and 22.9% in the correctly timed group (P = .003; Table 2). Early troughs led to a 7.7% rate of trough recollection, which was significantly greater than the 1.5% rate in the correctly timed group (P < .001). An analysis of each factor resulting in a clinical action illustrated that the rates of daily dose decrease and discontinued dose were similar between the groups (Table 3). However, the rate of held doses was 8.3% in the early group and 17.1% in the correctly timed group.

This research process yielded some observations. Occasionally a trough was drawn after vancomycin therapy was discontinued and when there was no concern for nephrotoxicity. After the guidelines were published, providers continued to document in CPRS notes to check troughs before the third dose. This incidence decreased over time. Troughs were taken often in patients who were receiving a short course of therapy or who were hemodynamically stable. Finally, documentation of vancomycin regimen changes occasionally did not match the record in the BCMA (in these situations, the BCMA record was used for this study).

Related: Assessment of High Staphylococcus aureus MIC and Poor Patient Outcomes

Discussion

A large portion of trough levels at the JALFHCC were drawn early and did not adhere to the 2009 consensus guidelines. The rates of early troughs in this study and in the Boston, Massachusetts, study are similar.⁵ However, the 2 studies differed in 1 significant aspect: Clinical actions were taken less often in the early group at JALFHCC, whereas they were taken more often in the early group in the Boston, Massachusetts, study. This dissimilarity could be attributed to a difference in software between the hospitals. In the previous study, trough levels and the time that they were drawn were not displayed together. Thus, clinicians may have been less likely to gauge whether a trough was early. Since this information is available at the JALFHCC, clinicians may have been aware that the trough was early and avoided adjusting treatment (such as holding a dose, as illustrated in the data) based on a falsely elevated trough. This point is further supported by significantly greater amounts of recollected troughs in the early group, suggesting an understanding that the trough was early.

The low trough recollection rate of 7.7% of all early samples could be due to several factors that would prevent a trough redraw. First, medication discontinuation resulting from course completion or sensitivity results would not require further trough monitoring. Second, practitioners may assess the early sample as insignificantly different from a correctly timed one and elect not to redraw the trough. Sometimes a trough was drawn at the correct time, but the time was recorded incorrectly. In this situation, a new trough level would not be necessary. Finally, a lack of sufficient staffing during nights and weekends may result in a delay in interpreting results leading to a missed opportunity for recollection. Additionally, some troughs may not have been redrawn based on a practitioner’s opinion that a trough was not significantly early and did not represent skewed results. Sometimes an incorrect recording of trough draw time reflected that it was taken after vancomycin dosing when it was not.

Specific observations regarding the timing of the trough indicate other possible concerns and areas for improvement. First, providers must cancel future trough orders concurrently with canceling treatment. Second, at the time of publication of the consensus, some providers were slow adopters of the new guidelines. Finally, the IDSA guidelines state that frequent monitoring for short course, lower intensity therapy, or in patients who are hemodynamically stable is not recommended.³ However, troughs were sometimes measured 2 to 3 times weekly in these patients.

Related: Results mixed in hospital efforts to tackle antimicrobial resistance

The data and observations lead to the conclusion that although providers may be able to discern between early and correctly timed troughs, they were not consistently adherent to the 2009 IDSA guidelines. It has been shown that pharmacy involvement of Medicare patients with infections in the intensive care unit has led to better clinical and monetary outcomes.⁸ Therefore, continued efforts by clinical pharmacists to monitor trough timing can be used to improve adherence and decrease costs (each trough is estimated to cost $16.97).

A study conducted in Australia demonstrated that pharmacist-led education of vancomycin dosing and monitoring (including when to measure a trough level) among prescribers and nurses led to improved adherence to the current guidelines and a greater number of patients treated within desired therapeutic ranges.⁹ In addition, a small study at the Atlanta VAMC in Georgia demonstrated that education of nurses, lab personnel, residents, ward clerks, and pharmacists led to a greater number of appropriately timed vancomycin and aminoglycoside levels.¹⁰ Thus, an interdisciplinary review of the current IDSA guidelines and review on the publication of the anticipated updated vancomycin guidelines should be provided to hospital personnel to aid in adoption of current dosing and monitoring recommendations.¹¹

Limitations

This study is limited by the 4-year span of time that it encompassed, which may give a skewed depiction of current practices. Another limitation is that patients with fluctuating renal function were included in the analysis. Instead of selecting a random level order, a trough level order was sometimes selected for these patients. This could lead to a lower actual rate of early troughs. A third limitation is that this was a small and unblinded study. Also, the actual trough levels and the resulting changes that were made to specific regimens were not recorded. Thus, these data do not indicate whether the changes that were made reflected guideline recommendations. Finally, some clinical actions were taken after the dosing interval following the trough. This was often a result of off-hours lab results or waiting on attending physician or infectious disease guidance. These data were not included in the analysis.

Conclusion

Vancomycin troughs were often drawn too early and resulted in an increased rate of trough recollection. In an attempt to improve adherence to the current and the upcoming revised version of the IDSA consensus statement, it is recommended to educate and reeducate providers through interdisciplinary-led review sessions.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

In 2012, 9.3% of the U.S. population had diabetes mellitus (DM).¹ According to the American Diabetes Association, in 2012, the total cost of diagnosed DM in the U.S. was $245 billion.² Diabetes mellitus is a leading cause of blindness, end-stage renal disease, and amputation in the U.S.³ Up to 80% of patients with DM will develop or die of macrovascular disease, such as heart attack or stroke.³

Diabetes mellitus is a chronic disease of epidemic proportion with management complexity that threatens to overwhelm providers in the acute care and primary care settings. Limited specialist availability and increased wait times continue to afflict the VA health care system, prompting efforts to increase health care provider (HCP) access and improve clinic efficiency.⁴ One of the methods proposed to increase HCP access and maximize clinic efficiency is the shared medical appointment (SMA).^5,6

The SMA was designed to improve access and quality of care through enhanced education and support. With the number of people living with chronic diseases on the rise, the current patient-provider model is unrealistic in today’s health care environment. Shared medical appointments offer a unique format for providing evidence-based chronic disease management in which patients and a multidisciplinary team of providers collaborate toward education, discussion, and medication management in a supportive environment.⁷ Research has suggested that SMAs are a successful way to manage type 2 DM (T2DM).^8,9 However, there is uncertainty regarding the optimal model design. The goals of this study were to evaluate whether the diabetes SMA at the Adam Benjamin, Jr. (ABJ) community-based outpatient clinic (CBOC) was an effective practice model for achieving improvements in glycemic control and to use subgroup analyses to elucidate unique characteristics about SMAs that may have been correlated with clinical success. This study may provide valuable information for other facilities considering SMAs.

Overview

The Jesse Brown VAMC (JBVAMC) and the ABJ CBOC implemented a T2DM-focused SMA in 2011. The ABJ CBOC multidisciplinary SMA team consisted of a medical administration service clerk, a registered dietician, a certified DM educator, a registered nurse, a nurse practitioner (NP), and a clinical pharmacy specialist (CPS). This team collaborated to deliver high-quality care to patients with poorly controlled T2DM to improve their glycemic control as well as clinical knowledge of their disease. A private conference room at the ABJ CBOC served as the location for the SMAs. This room was divided into 2 adjacent areas: One area with tables was organized in a semicircle to promote group discussion as well as minimize isolated conversations; the other area had computer terminals to facilitate individualized medication management. Other equipment included a scale for obtaining patient weights and various audio-visual devices.

Related: Efficacy of Patient Aligned Care Team Pharmacist Services in Reaching Diabetes and Hyperlipidemia Treatment Goals

The ABJ CBOC offered monthly SMAs. The team made several attempts to maximize SMA show rates, as previous studies indicated that low SMA show rates were a barrier to success.^3,4,7-9 One review reported no-show rates as high as 70% in certain group visit models.⁴ About 2 weeks prior to a session, prospective SMA patients received automated and customized preappointment letters. Automated and customized phone call reminders were made to prospective SMA patients a few days before each session. As many as 18 patients participated in a single ABJ SMA.

The ABJ SMAs lasted from 60 to 90 minutes, depending on the level of patient participation and the size of the group. The first half of the SMA was dedicated to a group discussion, which involved the SMA team, the patient, and the patient’s family (if desired). The topic of conversation was typically guided by patient curiosity and knowledge deficits in a spontaneous and free-flowing manner; for this reason, these sessions were considered to be open.

The team also engaged in more structured focused sessions, which limited the spontaneous flow of conservation and narrowed the scope to provide targeted education about various aspects of T2DM care. During focused sessions, services such as dental, optometry, podiatry, MOVE! (a VA self-management weight reduction program), and nutrition also participated. Focused sessions addressed topics such as hypoglycemia management, eating around the holidays, sick-day management of T2DM, grocery shopping, exercise, oral health, eye care, and foot care. The specialty services were encouraged to be creative and interactive during the SMA. Many of these services used supportive literature, demonstrations, diagrams, and props to enrich the educational experience. Group discussion typically lasted 30 to 40 minutes; after which patients met individually with either a CPS or NP for medication management.

Medication management focused on optimizing T2DM therapy (both oral and injectable) to improve glycemic control. Interventions outside of T2DM therapy (eg, cholesterol, hypertension, and other risk reduction modalities) were not made, due to time constraints. Once a patient demonstrated improved working knowledge of T2DM and a clinically significant reduction in their glycosylated hemoglobin A_1c (A_1c) they were discharged from SMAs at the discretion of the SMA team. There was no set minimum or maximum duration for the SMAs.

Methods

This study was a retrospective chart review conducted at the JBVAMC and was approved by the institutional review board and the research and development committee. Patient confidentiality was maintained by identifying patients by means other than name or unique identifiers. Protected health information was accessible only by the aforementioned investigators. There was no direct patient contact during this study.

Patient lists were generated from the computerized patient record system (CPRS). Patients were tracked up to 6 months after SMA discharge or until the last SMA in which they participated. The control group was matched according to location, age, glycemic control, and time. The control group never attended an ABJ SMA but may have received regular care through their primary care provider, CPS, or endocrinologist. Prospective control group patients were randomized and reviewed sequentially to obtain the matched cohort.

The study took place at ABJ, an outpatient clinic serving veterans in northwest Indiana and surrounding areas. Inclusion criteria for the SMA group were patients with T2DM, aged ≥ 45 years, with an A_1c ≥ 8.5% seen at ABJ for T2DM from May 1, 2011, to June 30, 2013. The control group included patients with T2DM, aged ≥ 45 years, with an A_1c > 9% who never attended SMAs but may have received regular care at ABJ during the study period. The SMA group’s inclusion criteria threshold for A_1c was lower in order to maximize sample size. The control group’s inclusion criteria threshold for A_1c was higher due to use of a default reminder report called “A_1c > 9%” to generate patient lists. Patients were excluded from the study if they did not meet inclusion criteria.

Baseline datum was the most recent parameter available in CPRS prior to enrollment. The endpoint datum was the parameter nearest the time of SMA discharge or the first available parameter within 6 months from the date of discharge. In the control group, the baseline datum was the initial parameter during the study period and the endpoint datum was the closest measure to 4 months after baseline. Four months was chosen to allow for at least 1 A_1c measurement during the study period. In addition, it was estimated (prior to collecting any data) that 4 months was the average time a patient participated in SMAs. Serial A_1c measurements were defined as values obtained at SMA discharge and 3- and 6-months postdischarge. These parameters were used to evaluate the sustainability of improvements in glycemic control. All values falling outside of these defined parameters were excluded.

Related: Experiences of Veterans With Diabetes From Shared Medical Appointments

The data analysis compared A_1c change from baseline to endpoint for the SMA and control groups. Data collection included baseline characteristics, SMA show rate, number of SMA patients seen by a CPS or NP, number and type of SMA interventions made by a CPS or NP, and the number and type of non-SMA interventions made during the study period. Intervention types were medications: added, discontinued, or titrated; and other, defined as referrals made to specialty services (such as dental, optometry, and podiatry).

Secondary endpoints included the number of SMAs and glycemic improvement, SMA format style (open- vs focused session) and glycemic improvement, SMA provider (CPS vs NP) and glycemic improvement, the change in A_1c stratified by baseline A_1c (A_1c ≥ 10% vs < 10%), the change in actual body weight (ABW) and body mass index (BMI), and maintenance of A_1c (3- and 6-months postdischarge).

The primary endpoint was evaluated using a 2-sample Student t test. Secondary endpoints were evaluated using the independent t test. Statistical significance was defined as P < .05.

Results

A total of 129 unique patients were scheduled for SMAs, 62 of which met inclusion criteria and were included in the SMA group. During enrollment, 67 patients were excluded: 55 never participated in SMAs, 6 had baseline A_1c values < 8.5%, 4 had insufficient data, and 2 were aged < 45 years. A total of 29 SMAs were conducted during the study period, and patients attended an average of 3.15 ± 2.14 (SD) SMAs. The average attendance at each SMA was 7.1 ± 2.62 (SD) patients. For the control group, 754 unique patients were identified and randomized. A total of 90 charts were sequentially reviewed in order to obtain the 62 patients for the control group.

Baseline characteristics were balanced between groups. However, there were more women in the SMA group vs the control group (Table 1). Within the control group, there were a total of 107 appointments that addressed T2DM, which averaged 1.72 ± 1.51 (SD) appointments per patient. The total number of interventions made in the SMA group was 192: 64.6% (124) by a CPS and 35.4% (68) by a NP. For the CPS, the most frequent intervention was medication titration (69.5%), followed by other (23.5%), medication addition (4%), and medication discontinuation (3%). Of note, 53.2% (33) of the SMA patients were seen an average of 1.2 times by non-SMA providers. The SMA patients had a total of 45 non-SMA interventions (0.73 per patient) during the study period.

For the primary endpoint, the SMA group had a 1.48% ± 0.02 (SD) reduction in A_1c compared with a 0.6% ± 0.02 (SD) decrease in the control group (P = .01). When evaluating mean changes in A_1c by the number of SMAs attended, it was noted that participation in ≥ 6 SMAs led to the greatest reduction in A_1c of 2.08%. In the SMA group, it was noted that patients with higher A_1c values at baseline demonstrated greater improvements in glycemic control compared with patients with lower baseline A_1c values. The mean change in A_1c, stratified by baseline A_1c, was -2.26% for those with baseline A_1c values ≥ 10% and -0.87% for those with baseline A_1c values < 10%.

In evaluating the format style, open- vs focused-session, it was observed that participation in focused sessions led to greater improvements in glycemic control. Furthermore, when stratified by provider, greater improvements in glycemic control were demonstrated when medication management was completed by a CPS vs a NP (Table 2). The average number of interventions per SMA patient was 3.1 ± 2.22 (SD). For the control group, the total number of interventions made was 86, with an average of 1.37 ± 1.51 (SD) per patient. The overall show rate was 49% ± 16 (SD), 52% ± 16 (SD) for open visits, and 46% ± 15 (SD) for focused visits. The mean change in ABW and BMI from baseline to endpoint was no different between the SMA and control groups (Table 3). The SMA group participants demonstrated a decrease in A_1c at 3 months postdischarge, and a moderate increase in A_1c was noted at 6 months postdischarge.

Discussion

Shared medical appointments provide an effective alternative to standards of care in order to obtain improvements in glycemic control. Consistent with previous studies, this study reported significant improvements in glycemic control in the SMA group vs the control group. This study also elucidated unique characteristics about SMAs that may have been correlated with clinical success.

Although the greatest improvements in glycemic control were noted for those who participated in ≥ 6 SMAs, it was observed that participation in only 1 SMA also led to improvements. For a site with limited staff and a high volume of patients waiting to participate in SMAs, it may be mutually beneficial to offer only 1 SMA per patient. In addition, patients with ≥ 10% A_1c at baseline demonstrated greater improvements in glycemic control compared to those with < 10% A_1c at baseline. The reasons the higher baseline A_1c subgroup responded to interventions more robustly are unclear and likely multifactorial. Nonetheless, factors such as psychosocial influences (eg, peer pressure to get healthy) may have increased motivation to prevent complications and improved medication adherence in the setting of closer follow-up. Additionally, hyperresponsiveness to drug therapy may have played a role. Regardless, for new SMA programs interested in making an immediate impact, it may be advantageous to initially select patients with very poorly controlled DM.

A unique aspect of the ABJ SMA was the variety of focused sessions offered. Previous studies did not demonstrate such a variety of focused sessions, nor did they evaluate the impact of a focused visit on the patient’s T2DM control. Participation in focused ABJ SMA sessions may have led to improved T2DM control, which may be attributed to the value patients assigned to specialty care and an increased motivation to get healthy.

Related: SGLT2 Inhibitors for Type 2 Diabetes Mellitus Treatment

Another factor that may have led to improved T2DM control was CPS involvement with medication management. The presence of a NP was highly valued, both from a group discussion and medication management standpoint; still, it is a good idea to involve a CPS who has a strong command of DM pharmacotherapy. One shortcoming of this SMA program was the inability for patients to maintain glycemic improvements 6 months after discharge. This pitfall was likely the result of suboptimal coordination of care after SMA discharge and may be avoided by asking the medical administration service clerk to promptly schedule discharged SMA patients for a general medicine clinic T2DM follow-up.The SMA patients had more T2DM interventions within the same time frame compared with the control patients. Although not causative, the increased number of interventions in addition to the bolstered support of the SMA may have correlated with glycemic improvements.

An important finding of this study was the SMA show rate and how it compared with attendance rates found in other group models. The favorable ABJ SMA show rate could have been due to the rigorous attention paid to reminder letters and phone calls. The literature has not established a standard approach to increasing SMA show rates; however, the current data suggest that increased reminders may have increased attendance.

Limitations

This study had several limitations. The external validity was weakened by the modest sample size and the homogenous baseline characteristics of those enrolled. Another limitation was inconsistent documentation of laboratory parameters. The inability to obtain A_1c values exactly at enrollment and discharge could have potentially skewed the results. In addition, incomplete documentation of interventions for dual-care patients (ie, those who obtained care outside of the VA) was an unavoidable challenge. Last, this study did not perform an assessment of SMA patient satisfaction, cost-benefit, or safety.

Conclusion

The ABJ SMA was an effective addition to standards of care in order to achieve improvements in glycemic control in a veteran population with poorly controlled T2DM. Furthermore,the data suggest that a successful program should be multidisciplinary, select poorly controlled patients, offer focused sessions, have a CPS participate in medication management, and encourage patients to complete ≥ 6 sessions. Future studies should be conducted to include more diverse patients to see whether the efficacy of this SMA format is maintained.

A safety analysis should also be conducted to ensure that the SMA format is not only effective, but also a safe means to manage medical conditions. In addition, the scope of the ABJ SMAs should be expanded to allow for evaluation of other diseases. An evaluation of patient satisfaction and cost-benefit could provide additional support for the implementation of SMAs, as improvements in quality of life and cost savings are endpoints to be desired.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

In 2012, 9.3% of the U.S. population had diabetes mellitus (DM).¹ According to the American Diabetes Association, in 2012, the total cost of diagnosed DM in the U.S. was $245 billion.² Diabetes mellitus is a leading cause of blindness, end-stage renal disease, and amputation in the U.S.³ Up to 80% of patients with DM will develop or die of macrovascular disease, such as heart attack or stroke.³

Diabetes mellitus is a chronic disease of epidemic proportion with management complexity that threatens to overwhelm providers in the acute care and primary care settings. Limited specialist availability and increased wait times continue to afflict the VA health care system, prompting efforts to increase health care provider (HCP) access and improve clinic efficiency.⁴ One of the methods proposed to increase HCP access and maximize clinic efficiency is the shared medical appointment (SMA).^5,6

The SMA was designed to improve access and quality of care through enhanced education and support. With the number of people living with chronic diseases on the rise, the current patient-provider model is unrealistic in today’s health care environment. Shared medical appointments offer a unique format for providing evidence-based chronic disease management in which patients and a multidisciplinary team of providers collaborate toward education, discussion, and medication management in a supportive environment.⁷ Research has suggested that SMAs are a successful way to manage type 2 DM (T2DM).^8,9 However, there is uncertainty regarding the optimal model design. The goals of this study were to evaluate whether the diabetes SMA at the Adam Benjamin, Jr. (ABJ) community-based outpatient clinic (CBOC) was an effective practice model for achieving improvements in glycemic control and to use subgroup analyses to elucidate unique characteristics about SMAs that may have been correlated with clinical success. This study may provide valuable information for other facilities considering SMAs.

Overview

The Jesse Brown VAMC (JBVAMC) and the ABJ CBOC implemented a T2DM-focused SMA in 2011. The ABJ CBOC multidisciplinary SMA team consisted of a medical administration service clerk, a registered dietician, a certified DM educator, a registered nurse, a nurse practitioner (NP), and a clinical pharmacy specialist (CPS). This team collaborated to deliver high-quality care to patients with poorly controlled T2DM to improve their glycemic control as well as clinical knowledge of their disease. A private conference room at the ABJ CBOC served as the location for the SMAs. This room was divided into 2 adjacent areas: One area with tables was organized in a semicircle to promote group discussion as well as minimize isolated conversations; the other area had computer terminals to facilitate individualized medication management. Other equipment included a scale for obtaining patient weights and various audio-visual devices.

Related: Efficacy of Patient Aligned Care Team Pharmacist Services in Reaching Diabetes and Hyperlipidemia Treatment Goals

The ABJ CBOC offered monthly SMAs. The team made several attempts to maximize SMA show rates, as previous studies indicated that low SMA show rates were a barrier to success.^3,4,7-9 One review reported no-show rates as high as 70% in certain group visit models.⁴ About 2 weeks prior to a session, prospective SMA patients received automated and customized preappointment letters. Automated and customized phone call reminders were made to prospective SMA patients a few days before each session. As many as 18 patients participated in a single ABJ SMA.

The ABJ SMAs lasted from 60 to 90 minutes, depending on the level of patient participation and the size of the group. The first half of the SMA was dedicated to a group discussion, which involved the SMA team, the patient, and the patient’s family (if desired). The topic of conversation was typically guided by patient curiosity and knowledge deficits in a spontaneous and free-flowing manner; for this reason, these sessions were considered to be open.

The team also engaged in more structured focused sessions, which limited the spontaneous flow of conservation and narrowed the scope to provide targeted education about various aspects of T2DM care. During focused sessions, services such as dental, optometry, podiatry, MOVE! (a VA self-management weight reduction program), and nutrition also participated. Focused sessions addressed topics such as hypoglycemia management, eating around the holidays, sick-day management of T2DM, grocery shopping, exercise, oral health, eye care, and foot care. The specialty services were encouraged to be creative and interactive during the SMA. Many of these services used supportive literature, demonstrations, diagrams, and props to enrich the educational experience. Group discussion typically lasted 30 to 40 minutes; after which patients met individually with either a CPS or NP for medication management.

Medication management focused on optimizing T2DM therapy (both oral and injectable) to improve glycemic control. Interventions outside of T2DM therapy (eg, cholesterol, hypertension, and other risk reduction modalities) were not made, due to time constraints. Once a patient demonstrated improved working knowledge of T2DM and a clinically significant reduction in their glycosylated hemoglobin A_1c (A_1c) they were discharged from SMAs at the discretion of the SMA team. There was no set minimum or maximum duration for the SMAs.

Methods

This study was a retrospective chart review conducted at the JBVAMC and was approved by the institutional review board and the research and development committee. Patient confidentiality was maintained by identifying patients by means other than name or unique identifiers. Protected health information was accessible only by the aforementioned investigators. There was no direct patient contact during this study.

Patient lists were generated from the computerized patient record system (CPRS). Patients were tracked up to 6 months after SMA discharge or until the last SMA in which they participated. The control group was matched according to location, age, glycemic control, and time. The control group never attended an ABJ SMA but may have received regular care through their primary care provider, CPS, or endocrinologist. Prospective control group patients were randomized and reviewed sequentially to obtain the matched cohort.

The study took place at ABJ, an outpatient clinic serving veterans in northwest Indiana and surrounding areas. Inclusion criteria for the SMA group were patients with T2DM, aged ≥ 45 years, with an A_1c ≥ 8.5% seen at ABJ for T2DM from May 1, 2011, to June 30, 2013. The control group included patients with T2DM, aged ≥ 45 years, with an A_1c > 9% who never attended SMAs but may have received regular care at ABJ during the study period. The SMA group’s inclusion criteria threshold for A_1c was lower in order to maximize sample size. The control group’s inclusion criteria threshold for A_1c was higher due to use of a default reminder report called “A_1c > 9%” to generate patient lists. Patients were excluded from the study if they did not meet inclusion criteria.

Baseline datum was the most recent parameter available in CPRS prior to enrollment. The endpoint datum was the parameter nearest the time of SMA discharge or the first available parameter within 6 months from the date of discharge. In the control group, the baseline datum was the initial parameter during the study period and the endpoint datum was the closest measure to 4 months after baseline. Four months was chosen to allow for at least 1 A_1c measurement during the study period. In addition, it was estimated (prior to collecting any data) that 4 months was the average time a patient participated in SMAs. Serial A_1c measurements were defined as values obtained at SMA discharge and 3- and 6-months postdischarge. These parameters were used to evaluate the sustainability of improvements in glycemic control. All values falling outside of these defined parameters were excluded.

Related: Experiences of Veterans With Diabetes From Shared Medical Appointments

The data analysis compared A_1c change from baseline to endpoint for the SMA and control groups. Data collection included baseline characteristics, SMA show rate, number of SMA patients seen by a CPS or NP, number and type of SMA interventions made by a CPS or NP, and the number and type of non-SMA interventions made during the study period. Intervention types were medications: added, discontinued, or titrated; and other, defined as referrals made to specialty services (such as dental, optometry, and podiatry).

Secondary endpoints included the number of SMAs and glycemic improvement, SMA format style (open- vs focused session) and glycemic improvement, SMA provider (CPS vs NP) and glycemic improvement, the change in A_1c stratified by baseline A_1c (A_1c ≥ 10% vs < 10%), the change in actual body weight (ABW) and body mass index (BMI), and maintenance of A_1c (3- and 6-months postdischarge).

The primary endpoint was evaluated using a 2-sample Student t test. Secondary endpoints were evaluated using the independent t test. Statistical significance was defined as P < .05.

Results

A total of 129 unique patients were scheduled for SMAs, 62 of which met inclusion criteria and were included in the SMA group. During enrollment, 67 patients were excluded: 55 never participated in SMAs, 6 had baseline A_1c values < 8.5%, 4 had insufficient data, and 2 were aged < 45 years. A total of 29 SMAs were conducted during the study period, and patients attended an average of 3.15 ± 2.14 (SD) SMAs. The average attendance at each SMA was 7.1 ± 2.62 (SD) patients. For the control group, 754 unique patients were identified and randomized. A total of 90 charts were sequentially reviewed in order to obtain the 62 patients for the control group.

Baseline characteristics were balanced between groups. However, there were more women in the SMA group vs the control group (Table 1). Within the control group, there were a total of 107 appointments that addressed T2DM, which averaged 1.72 ± 1.51 (SD) appointments per patient. The total number of interventions made in the SMA group was 192: 64.6% (124) by a CPS and 35.4% (68) by a NP. For the CPS, the most frequent intervention was medication titration (69.5%), followed by other (23.5%), medication addition (4%), and medication discontinuation (3%). Of note, 53.2% (33) of the SMA patients were seen an average of 1.2 times by non-SMA providers. The SMA patients had a total of 45 non-SMA interventions (0.73 per patient) during the study period.

For the primary endpoint, the SMA group had a 1.48% ± 0.02 (SD) reduction in A_1c compared with a 0.6% ± 0.02 (SD) decrease in the control group (P = .01). When evaluating mean changes in A_1c by the number of SMAs attended, it was noted that participation in ≥ 6 SMAs led to the greatest reduction in A_1c of 2.08%. In the SMA group, it was noted that patients with higher A_1c values at baseline demonstrated greater improvements in glycemic control compared with patients with lower baseline A_1c values. The mean change in A_1c, stratified by baseline A_1c, was -2.26% for those with baseline A_1c values ≥ 10% and -0.87% for those with baseline A_1c values < 10%.

In evaluating the format style, open- vs focused-session, it was observed that participation in focused sessions led to greater improvements in glycemic control. Furthermore, when stratified by provider, greater improvements in glycemic control were demonstrated when medication management was completed by a CPS vs a NP (Table 2). The average number of interventions per SMA patient was 3.1 ± 2.22 (SD). For the control group, the total number of interventions made was 86, with an average of 1.37 ± 1.51 (SD) per patient. The overall show rate was 49% ± 16 (SD), 52% ± 16 (SD) for open visits, and 46% ± 15 (SD) for focused visits. The mean change in ABW and BMI from baseline to endpoint was no different between the SMA and control groups (Table 3). The SMA group participants demonstrated a decrease in A_1c at 3 months postdischarge, and a moderate increase in A_1c was noted at 6 months postdischarge.

Discussion

Shared medical appointments provide an effective alternative to standards of care in order to obtain improvements in glycemic control. Consistent with previous studies, this study reported significant improvements in glycemic control in the SMA group vs the control group. This study also elucidated unique characteristics about SMAs that may have been correlated with clinical success.

Although the greatest improvements in glycemic control were noted for those who participated in ≥ 6 SMAs, it was observed that participation in only 1 SMA also led to improvements. For a site with limited staff and a high volume of patients waiting to participate in SMAs, it may be mutually beneficial to offer only 1 SMA per patient. In addition, patients with ≥ 10% A_1c at baseline demonstrated greater improvements in glycemic control compared to those with < 10% A_1c at baseline. The reasons the higher baseline A_1c subgroup responded to interventions more robustly are unclear and likely multifactorial. Nonetheless, factors such as psychosocial influences (eg, peer pressure to get healthy) may have increased motivation to prevent complications and improved medication adherence in the setting of closer follow-up. Additionally, hyperresponsiveness to drug therapy may have played a role. Regardless, for new SMA programs interested in making an immediate impact, it may be advantageous to initially select patients with very poorly controlled DM.

A unique aspect of the ABJ SMA was the variety of focused sessions offered. Previous studies did not demonstrate such a variety of focused sessions, nor did they evaluate the impact of a focused visit on the patient’s T2DM control. Participation in focused ABJ SMA sessions may have led to improved T2DM control, which may be attributed to the value patients assigned to specialty care and an increased motivation to get healthy.

Related: SGLT2 Inhibitors for Type 2 Diabetes Mellitus Treatment

Another factor that may have led to improved T2DM control was CPS involvement with medication management. The presence of a NP was highly valued, both from a group discussion and medication management standpoint; still, it is a good idea to involve a CPS who has a strong command of DM pharmacotherapy. One shortcoming of this SMA program was the inability for patients to maintain glycemic improvements 6 months after discharge. This pitfall was likely the result of suboptimal coordination of care after SMA discharge and may be avoided by asking the medical administration service clerk to promptly schedule discharged SMA patients for a general medicine clinic T2DM follow-up.The SMA patients had more T2DM interventions within the same time frame compared with the control patients. Although not causative, the increased number of interventions in addition to the bolstered support of the SMA may have correlated with glycemic improvements.

An important finding of this study was the SMA show rate and how it compared with attendance rates found in other group models. The favorable ABJ SMA show rate could have been due to the rigorous attention paid to reminder letters and phone calls. The literature has not established a standard approach to increasing SMA show rates; however, the current data suggest that increased reminders may have increased attendance.

Limitations

This study had several limitations. The external validity was weakened by the modest sample size and the homogenous baseline characteristics of those enrolled. Another limitation was inconsistent documentation of laboratory parameters. The inability to obtain A_1c values exactly at enrollment and discharge could have potentially skewed the results. In addition, incomplete documentation of interventions for dual-care patients (ie, those who obtained care outside of the VA) was an unavoidable challenge. Last, this study did not perform an assessment of SMA patient satisfaction, cost-benefit, or safety.

Conclusion

The ABJ SMA was an effective addition to standards of care in order to achieve improvements in glycemic control in a veteran population with poorly controlled T2DM. Furthermore,the data suggest that a successful program should be multidisciplinary, select poorly controlled patients, offer focused sessions, have a CPS participate in medication management, and encourage patients to complete ≥ 6 sessions. Future studies should be conducted to include more diverse patients to see whether the efficacy of this SMA format is maintained.

A safety analysis should also be conducted to ensure that the SMA format is not only effective, but also a safe means to manage medical conditions. In addition, the scope of the ABJ SMAs should be expanded to allow for evaluation of other diseases. An evaluation of patient satisfaction and cost-benefit could provide additional support for the implementation of SMAs, as improvements in quality of life and cost savings are endpoints to be desired.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

In 2012, 9.3% of the U.S. population had diabetes mellitus (DM).¹ According to the American Diabetes Association, in 2012, the total cost of diagnosed DM in the U.S. was $245 billion.² Diabetes mellitus is a leading cause of blindness, end-stage renal disease, and amputation in the U.S.³ Up to 80% of patients with DM will develop or die of macrovascular disease, such as heart attack or stroke.³

Diabetes mellitus is a chronic disease of epidemic proportion with management complexity that threatens to overwhelm providers in the acute care and primary care settings. Limited specialist availability and increased wait times continue to afflict the VA health care system, prompting efforts to increase health care provider (HCP) access and improve clinic efficiency.⁴ One of the methods proposed to increase HCP access and maximize clinic efficiency is the shared medical appointment (SMA).^5,6

The SMA was designed to improve access and quality of care through enhanced education and support. With the number of people living with chronic diseases on the rise, the current patient-provider model is unrealistic in today’s health care environment. Shared medical appointments offer a unique format for providing evidence-based chronic disease management in which patients and a multidisciplinary team of providers collaborate toward education, discussion, and medication management in a supportive environment.⁷ Research has suggested that SMAs are a successful way to manage type 2 DM (T2DM).^8,9 However, there is uncertainty regarding the optimal model design. The goals of this study were to evaluate whether the diabetes SMA at the Adam Benjamin, Jr. (ABJ) community-based outpatient clinic (CBOC) was an effective practice model for achieving improvements in glycemic control and to use subgroup analyses to elucidate unique characteristics about SMAs that may have been correlated with clinical success. This study may provide valuable information for other facilities considering SMAs.

Overview

The Jesse Brown VAMC (JBVAMC) and the ABJ CBOC implemented a T2DM-focused SMA in 2011. The ABJ CBOC multidisciplinary SMA team consisted of a medical administration service clerk, a registered dietician, a certified DM educator, a registered nurse, a nurse practitioner (NP), and a clinical pharmacy specialist (CPS). This team collaborated to deliver high-quality care to patients with poorly controlled T2DM to improve their glycemic control as well as clinical knowledge of their disease. A private conference room at the ABJ CBOC served as the location for the SMAs. This room was divided into 2 adjacent areas: One area with tables was organized in a semicircle to promote group discussion as well as minimize isolated conversations; the other area had computer terminals to facilitate individualized medication management. Other equipment included a scale for obtaining patient weights and various audio-visual devices.

Related: Efficacy of Patient Aligned Care Team Pharmacist Services in Reaching Diabetes and Hyperlipidemia Treatment Goals

The ABJ CBOC offered monthly SMAs. The team made several attempts to maximize SMA show rates, as previous studies indicated that low SMA show rates were a barrier to success.^3,4,7-9 One review reported no-show rates as high as 70% in certain group visit models.⁴ About 2 weeks prior to a session, prospective SMA patients received automated and customized preappointment letters. Automated and customized phone call reminders were made to prospective SMA patients a few days before each session. As many as 18 patients participated in a single ABJ SMA.

The ABJ SMAs lasted from 60 to 90 minutes, depending on the level of patient participation and the size of the group. The first half of the SMA was dedicated to a group discussion, which involved the SMA team, the patient, and the patient’s family (if desired). The topic of conversation was typically guided by patient curiosity and knowledge deficits in a spontaneous and free-flowing manner; for this reason, these sessions were considered to be open.

The team also engaged in more structured focused sessions, which limited the spontaneous flow of conservation and narrowed the scope to provide targeted education about various aspects of T2DM care. During focused sessions, services such as dental, optometry, podiatry, MOVE! (a VA self-management weight reduction program), and nutrition also participated. Focused sessions addressed topics such as hypoglycemia management, eating around the holidays, sick-day management of T2DM, grocery shopping, exercise, oral health, eye care, and foot care. The specialty services were encouraged to be creative and interactive during the SMA. Many of these services used supportive literature, demonstrations, diagrams, and props to enrich the educational experience. Group discussion typically lasted 30 to 40 minutes; after which patients met individually with either a CPS or NP for medication management.

Medication management focused on optimizing T2DM therapy (both oral and injectable) to improve glycemic control. Interventions outside of T2DM therapy (eg, cholesterol, hypertension, and other risk reduction modalities) were not made, due to time constraints. Once a patient demonstrated improved working knowledge of T2DM and a clinically significant reduction in their glycosylated hemoglobin A_1c (A_1c) they were discharged from SMAs at the discretion of the SMA team. There was no set minimum or maximum duration for the SMAs.

Methods

This study was a retrospective chart review conducted at the JBVAMC and was approved by the institutional review board and the research and development committee. Patient confidentiality was maintained by identifying patients by means other than name or unique identifiers. Protected health information was accessible only by the aforementioned investigators. There was no direct patient contact during this study.

Patient lists were generated from the computerized patient record system (CPRS). Patients were tracked up to 6 months after SMA discharge or until the last SMA in which they participated. The control group was matched according to location, age, glycemic control, and time. The control group never attended an ABJ SMA but may have received regular care through their primary care provider, CPS, or endocrinologist. Prospective control group patients were randomized and reviewed sequentially to obtain the matched cohort.

The study took place at ABJ, an outpatient clinic serving veterans in northwest Indiana and surrounding areas. Inclusion criteria for the SMA group were patients with T2DM, aged ≥ 45 years, with an A_1c ≥ 8.5% seen at ABJ for T2DM from May 1, 2011, to June 30, 2013. The control group included patients with T2DM, aged ≥ 45 years, with an A_1c > 9% who never attended SMAs but may have received regular care at ABJ during the study period. The SMA group’s inclusion criteria threshold for A_1c was lower in order to maximize sample size. The control group’s inclusion criteria threshold for A_1c was higher due to use of a default reminder report called “A_1c > 9%” to generate patient lists. Patients were excluded from the study if they did not meet inclusion criteria.

Baseline datum was the most recent parameter available in CPRS prior to enrollment. The endpoint datum was the parameter nearest the time of SMA discharge or the first available parameter within 6 months from the date of discharge. In the control group, the baseline datum was the initial parameter during the study period and the endpoint datum was the closest measure to 4 months after baseline. Four months was chosen to allow for at least 1 A_1c measurement during the study period. In addition, it was estimated (prior to collecting any data) that 4 months was the average time a patient participated in SMAs. Serial A_1c measurements were defined as values obtained at SMA discharge and 3- and 6-months postdischarge. These parameters were used to evaluate the sustainability of improvements in glycemic control. All values falling outside of these defined parameters were excluded.

Related: Experiences of Veterans With Diabetes From Shared Medical Appointments

The data analysis compared A_1c change from baseline to endpoint for the SMA and control groups. Data collection included baseline characteristics, SMA show rate, number of SMA patients seen by a CPS or NP, number and type of SMA interventions made by a CPS or NP, and the number and type of non-SMA interventions made during the study period. Intervention types were medications: added, discontinued, or titrated; and other, defined as referrals made to specialty services (such as dental, optometry, and podiatry).

Secondary endpoints included the number of SMAs and glycemic improvement, SMA format style (open- vs focused session) and glycemic improvement, SMA provider (CPS vs NP) and glycemic improvement, the change in A_1c stratified by baseline A_1c (A_1c ≥ 10% vs < 10%), the change in actual body weight (ABW) and body mass index (BMI), and maintenance of A_1c (3- and 6-months postdischarge).

The primary endpoint was evaluated using a 2-sample Student t test. Secondary endpoints were evaluated using the independent t test. Statistical significance was defined as P < .05.

Results

A total of 129 unique patients were scheduled for SMAs, 62 of which met inclusion criteria and were included in the SMA group. During enrollment, 67 patients were excluded: 55 never participated in SMAs, 6 had baseline A_1c values < 8.5%, 4 had insufficient data, and 2 were aged < 45 years. A total of 29 SMAs were conducted during the study period, and patients attended an average of 3.15 ± 2.14 (SD) SMAs. The average attendance at each SMA was 7.1 ± 2.62 (SD) patients. For the control group, 754 unique patients were identified and randomized. A total of 90 charts were sequentially reviewed in order to obtain the 62 patients for the control group.

Baseline characteristics were balanced between groups. However, there were more women in the SMA group vs the control group (Table 1). Within the control group, there were a total of 107 appointments that addressed T2DM, which averaged 1.72 ± 1.51 (SD) appointments per patient. The total number of interventions made in the SMA group was 192: 64.6% (124) by a CPS and 35.4% (68) by a NP. For the CPS, the most frequent intervention was medication titration (69.5%), followed by other (23.5%), medication addition (4%), and medication discontinuation (3%). Of note, 53.2% (33) of the SMA patients were seen an average of 1.2 times by non-SMA providers. The SMA patients had a total of 45 non-SMA interventions (0.73 per patient) during the study period.

For the primary endpoint, the SMA group had a 1.48% ± 0.02 (SD) reduction in A_1c compared with a 0.6% ± 0.02 (SD) decrease in the control group (P = .01). When evaluating mean changes in A_1c by the number of SMAs attended, it was noted that participation in ≥ 6 SMAs led to the greatest reduction in A_1c of 2.08%. In the SMA group, it was noted that patients with higher A_1c values at baseline demonstrated greater improvements in glycemic control compared with patients with lower baseline A_1c values. The mean change in A_1c, stratified by baseline A_1c, was -2.26% for those with baseline A_1c values ≥ 10% and -0.87% for those with baseline A_1c values < 10%.

In evaluating the format style, open- vs focused-session, it was observed that participation in focused sessions led to greater improvements in glycemic control. Furthermore, when stratified by provider, greater improvements in glycemic control were demonstrated when medication management was completed by a CPS vs a NP (Table 2). The average number of interventions per SMA patient was 3.1 ± 2.22 (SD). For the control group, the total number of interventions made was 86, with an average of 1.37 ± 1.51 (SD) per patient. The overall show rate was 49% ± 16 (SD), 52% ± 16 (SD) for open visits, and 46% ± 15 (SD) for focused visits. The mean change in ABW and BMI from baseline to endpoint was no different between the SMA and control groups (Table 3). The SMA group participants demonstrated a decrease in A_1c at 3 months postdischarge, and a moderate increase in A_1c was noted at 6 months postdischarge.

Discussion

Shared medical appointments provide an effective alternative to standards of care in order to obtain improvements in glycemic control. Consistent with previous studies, this study reported significant improvements in glycemic control in the SMA group vs the control group. This study also elucidated unique characteristics about SMAs that may have been correlated with clinical success.

Although the greatest improvements in glycemic control were noted for those who participated in ≥ 6 SMAs, it was observed that participation in only 1 SMA also led to improvements. For a site with limited staff and a high volume of patients waiting to participate in SMAs, it may be mutually beneficial to offer only 1 SMA per patient. In addition, patients with ≥ 10% A_1c at baseline demonstrated greater improvements in glycemic control compared to those with < 10% A_1c at baseline. The reasons the higher baseline A_1c subgroup responded to interventions more robustly are unclear and likely multifactorial. Nonetheless, factors such as psychosocial influences (eg, peer pressure to get healthy) may have increased motivation to prevent complications and improved medication adherence in the setting of closer follow-up. Additionally, hyperresponsiveness to drug therapy may have played a role. Regardless, for new SMA programs interested in making an immediate impact, it may be advantageous to initially select patients with very poorly controlled DM.

A unique aspect of the ABJ SMA was the variety of focused sessions offered. Previous studies did not demonstrate such a variety of focused sessions, nor did they evaluate the impact of a focused visit on the patient’s T2DM control. Participation in focused ABJ SMA sessions may have led to improved T2DM control, which may be attributed to the value patients assigned to specialty care and an increased motivation to get healthy.

Related: SGLT2 Inhibitors for Type 2 Diabetes Mellitus Treatment

Another factor that may have led to improved T2DM control was CPS involvement with medication management. The presence of a NP was highly valued, both from a group discussion and medication management standpoint; still, it is a good idea to involve a CPS who has a strong command of DM pharmacotherapy. One shortcoming of this SMA program was the inability for patients to maintain glycemic improvements 6 months after discharge. This pitfall was likely the result of suboptimal coordination of care after SMA discharge and may be avoided by asking the medical administration service clerk to promptly schedule discharged SMA patients for a general medicine clinic T2DM follow-up.The SMA patients had more T2DM interventions within the same time frame compared with the control patients. Although not causative, the increased number of interventions in addition to the bolstered support of the SMA may have correlated with glycemic improvements.

An important finding of this study was the SMA show rate and how it compared with attendance rates found in other group models. The favorable ABJ SMA show rate could have been due to the rigorous attention paid to reminder letters and phone calls. The literature has not established a standard approach to increasing SMA show rates; however, the current data suggest that increased reminders may have increased attendance.

Limitations

This study had several limitations. The external validity was weakened by the modest sample size and the homogenous baseline characteristics of those enrolled. Another limitation was inconsistent documentation of laboratory parameters. The inability to obtain A_1c values exactly at enrollment and discharge could have potentially skewed the results. In addition, incomplete documentation of interventions for dual-care patients (ie, those who obtained care outside of the VA) was an unavoidable challenge. Last, this study did not perform an assessment of SMA patient satisfaction, cost-benefit, or safety.

Conclusion

The ABJ SMA was an effective addition to standards of care in order to achieve improvements in glycemic control in a veteran population with poorly controlled T2DM. Furthermore,the data suggest that a successful program should be multidisciplinary, select poorly controlled patients, offer focused sessions, have a CPS participate in medication management, and encourage patients to complete ≥ 6 sessions. Future studies should be conducted to include more diverse patients to see whether the efficacy of this SMA format is maintained.

A safety analysis should also be conducted to ensure that the SMA format is not only effective, but also a safe means to manage medical conditions. In addition, the scope of the ABJ SMAs should be expanded to allow for evaluation of other diseases. An evaluation of patient satisfaction and cost-benefit could provide additional support for the implementation of SMAs, as improvements in quality of life and cost savings are endpoints to be desired.

Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Atopic dermatitis (AD) is a chronic inflammatory skin disease that typically begins in early childhood (Figure). It is one of the most commonly diagnosed dermatologic conditions, affecting up to 25% of children and 2% to 3% of adults in the United States.^1,2 The mainstays of treatment for AD are topical emollients and topical medications, of which corticosteroids are most commonly prescribed.³ Although treatments for AD generally are straightforward and efficacious when used correctly, poor adherence to treatment often prevents patients from achieving disease control.⁴ Patient adherence to therapy is a familiar challenge in dermatology, especially for diseases like AD that require long-term treatment with topical medications.^4,5 In some instances, poor adherence may be misconstrued as poor response to treatment, which may lead to escalation to more powerful and potentially dangerous systemic medications.⁶ Ensuring good adherence to treatment leads to better outcomes and disease control, averts unnecessary treatment, prevents disease complications, improves quality of life, and decreases treatment cost.^4,5 This article provides a review of the literature on patient adherence to topical therapies for AD as well as a discussion of methods to improve patient adherence to treatment in the clinical setting.

Methods

A PubMed search of articles indexed for MEDLINE from January 2005 to May 2015 was conducted to identify studies that focused on treatment adherence in AD using the search terms atopic dermatitis and medication adherence and atopic dermatitis and patient compliance After excluding duplicate results and those that were not in the English language, a final list of clinical trials that investigated patient adherence/compliance to topical medications for the treatment of AD was extracted for evaluation.

Results

Our review of the literature yielded 7 quantitative studies that evaluated adherence to topical medications in AD using electronic monitoring and/or self-reporting (Table).^7-13 Participant demographics, disease severity, drug and vehicle used, duration of treatment, and number of follow-up visits varied. All studies used medication event monitoring system caps on medication jars to objectively track patient adherence by recording the date and time when the cap was removed. To assess disease response, the studies used such measures as the Investigator Global Assessment scale, Eczema Area and Severity Index score, or other visual analog scales.

In all of the studies, treatment proved effective and disease severity declined from baseline regardless of the rate of adherence, with benefit continuing after treatment had ended.^7-13 Some results suggested that better adherence increased treatment efficacy and reduced disease severity.^8,9 However, one 10-day trial found no difference in severity and efficacy among participants who applied the medication at least once daily, missed applications some days, or applied the medication more than twice daily.¹³

Study participants typically overestimated their adherence to treatment compared to actual adherence rates, with most reporting near 100% adherence.^7-9,11,12 Average measured adherence rates ranged from 32% to 93% (Table). Adherence rates typically were highest at the beginning of the study and decreased as the study continued.^7-13 The study with the best average adherence rate of 93% had the shortest treatment period of 3 days,¹¹ and the study with the lowest average adherence rate of 32% had the longest treatment period of 8 weeks.⁷ The study with the lowest adherence rate was the only study wherein participants were blinded to their enrollment in the study, which would most closely mimic adherence rates in clinical practice.⁷ The participants in the other studies were not aware that their adherence was being monitored, but their behavior may have been influenced since they were aware of their enrollment in the study.

Many variables affect treatment adherence in patients with AD. Average adherence rates were significantly higher (P=.03) in participants with greater disease severity.⁷ There is conflicting evidence regarding the role of medication vehicle in treatment adherence. While Wilson et al⁹ did not find any difference in adherence based on medication vehicle, Yentzer et al¹² found vehicle characteristics and medication side effects were among patients’ top-ranked concerns about using topical medications. Sagransky et al¹⁰ compared treatment adherence between 2 groups of AD patients: one control group received a standard-of-care 4-week follow-up, and an active group received an additional 1-week follow-up. The mean adherence rate of the treatment group was 69% compared with 54% in the control group.¹⁰

Comment

Poor adherence to treatment is a pervasive problem in patients with AD. Our review of the literature confirmed that patients generally are not accurate historians of their medication usage, often reporting near-perfect treatment adherence even when actual adherence is poor. Rates of adherence from clinical trials are likely higher than those seen in clinical practice due in part to study incentives and differences between how patients in a study are treated compared to those in a physician’s clinic; for example, research study participants often have additional follow-up visits compared to those being treated in the clinical population and by virtue of being enrolled in a study are aware that their behavior is being monitored, which can increase treatment adherence.⁷

The dogma suggesting that tachyphylaxis can occur with long-term use of topical corticosteroids is not supported by clinical trials.¹⁴ Furthermore, in our review of the literature patient adherence was highest in the shortest study¹¹ and lowest in the longest study.⁷ Given that AD patients cannot benefit from a treatment if they do not use it, the supposed decrease in efficacy of topical corticosteroids over time may be because patients fail to use them consistently.

Our review of the literature was limited by the small body of research that exists on treatment adherence in AD patients, especially relating to topical medications, and did not reveal any studies evaluating systemic medications in AD. Of the studies we examined, sample sizes were small and treatment and follow-up periods were short. Our review only covered adherence to prescribed topical medications in AD, chiefly corticosteroids; thus, we did not evaluate adherence to other therapies (eg, emollients) in this patient population.

The existing research also is limited by the relative paucity of data showing a correlation between improved adherence to topical treatment and improved disease outcomes, which may be due to the methodological limitations of the study designs that have been used; for instance, studies may use objective monitors to describe daily adherence to treatment, but disease severity typically is measured over longer periods of time, usually every few weeks or months. Short-term data may not be an accurate demonstration of how participants’ actual treatment adherence impacts disease outcome, as the data does not account for more complex adherence factors; for example, participants who achieve good disease control using topical corticosteroids for an 8-week study period may actually demonstrate poor treatment adherence overall, as topical corticosteroids have good short-term efficacy and the patient may have stopped using the product after the first few weeks of the treatment period. In contrast, poorly adherent patients may never use the medication well enough to achieve improvement and may continue low-level use throughout the study period. Therefore, studies that measure disease severity at more regular intervals are required to show the true effect of treatment adherence on disease outcomes.

Since AD mainly affects children, family issues can pose special challenges to attaining good treatment adherence.^15,16 The physician–patient (or parent) relationship and the family’s perception of the patient’s disease severity are strong predictors of adherence to topical treatment.¹⁶ Potential barriers to adherence in the pediatric population are caregivers with negative beliefs about treatment, the time-consuming nature of applying topical therapies, or a child who is uncooperative.^15,17 In the treatment of infants, critical factors are caregiver availability and beliefs and fears about medications and their side effects, while in the teenage population, the desire to “fit in” and oppositional behavior can lead to poor adherence to treatment.¹⁷ Regardless of age, other barriers to treatment adherence are forgetfulness, belief that the drug is not working, and the messiness of treatment.¹⁷

Educational tools (eg, action plans, instructions about how to apply topical medications correctly) may be underutilized in patients with AD. If consistently implemented, these tools could have a positive impact on adherence to medication in patients with AD. For example, written action plans pioneered in the asthma community have shown to improve quality of life and reduce disease severity and may offer the same benefits for AD patients due to the similarities of the diseases.¹⁸ Since AD patients and their caregivers often are not well versed in how to apply topical medications correctly, efforts to educate patients could potentially increase adherence to treatment. In one study, AD patients began to use medications more effectively after applying a fluorescent cream to reveal affected areas they had missed, and clinicians were able to provide additional instruction based on the findings.¹⁹

Adherence to topical treatments among AD patients is a multifactorial issue. Regimens often are complex and inconvenient due to the need for multiple medications, the topical nature of the products, and the need for frequent application. To optimize prescription treatments, patients also must be diligent with preventive measures such as application of topical emollients and use of bathing techniques (eg, bleach baths). A way to overcome treatment complexity and increase adherence may be to provide a written action plan and involve the patient and caregiver in the plan’s development. If a drug formulation is not aesthetically acceptable to the patient (eg, the greasiness of an ointment), allowing the patient to choose the medication vehicle may increase satisfaction and use.¹² Fear of steroid side effects also is common among patients and caregivers and could be overcome with education about the product.²⁰

Conclusion

Treatment adherence can have a dramatic effect on diseases outcomes and can be particularly challenging in AD due to the use of topical medications with complex treatment regimens. Additionally, a large majority of patients with AD are children, from infants to teenagers, adding another layer of treatment challenges. Further research is needed to more definitively develop effective methods for enhancing treatment adherence in this patient population. Although enormous amounts of money are being spent to develop improved treatments for AD, we may be able to achieve far more benefit at a much lower cost by figuring out how to get patients to adhere to the treatments that are already available.

Atopic dermatitis (AD) is a chronic inflammatory skin disease that typically begins in early childhood (Figure). It is one of the most commonly diagnosed dermatologic conditions, affecting up to 25% of children and 2% to 3% of adults in the United States.^1,2 The mainstays of treatment for AD are topical emollients and topical medications, of which corticosteroids are most commonly prescribed.³ Although treatments for AD generally are straightforward and efficacious when used correctly, poor adherence to treatment often prevents patients from achieving disease control.⁴ Patient adherence to therapy is a familiar challenge in dermatology, especially for diseases like AD that require long-term treatment with topical medications.^4,5 In some instances, poor adherence may be misconstrued as poor response to treatment, which may lead to escalation to more powerful and potentially dangerous systemic medications.⁶ Ensuring good adherence to treatment leads to better outcomes and disease control, averts unnecessary treatment, prevents disease complications, improves quality of life, and decreases treatment cost.^4,5 This article provides a review of the literature on patient adherence to topical therapies for AD as well as a discussion of methods to improve patient adherence to treatment in the clinical setting.

Methods

A PubMed search of articles indexed for MEDLINE from January 2005 to May 2015 was conducted to identify studies that focused on treatment adherence in AD using the search terms atopic dermatitis and medication adherence and atopic dermatitis and patient compliance After excluding duplicate results and those that were not in the English language, a final list of clinical trials that investigated patient adherence/compliance to topical medications for the treatment of AD was extracted for evaluation.

Results

Our review of the literature yielded 7 quantitative studies that evaluated adherence to topical medications in AD using electronic monitoring and/or self-reporting (Table).^7-13 Participant demographics, disease severity, drug and vehicle used, duration of treatment, and number of follow-up visits varied. All studies used medication event monitoring system caps on medication jars to objectively track patient adherence by recording the date and time when the cap was removed. To assess disease response, the studies used such measures as the Investigator Global Assessment scale, Eczema Area and Severity Index score, or other visual analog scales.

In all of the studies, treatment proved effective and disease severity declined from baseline regardless of the rate of adherence, with benefit continuing after treatment had ended.^7-13 Some results suggested that better adherence increased treatment efficacy and reduced disease severity.^8,9 However, one 10-day trial found no difference in severity and efficacy among participants who applied the medication at least once daily, missed applications some days, or applied the medication more than twice daily.¹³

Study participants typically overestimated their adherence to treatment compared to actual adherence rates, with most reporting near 100% adherence.^7-9,11,12 Average measured adherence rates ranged from 32% to 93% (Table). Adherence rates typically were highest at the beginning of the study and decreased as the study continued.^7-13 The study with the best average adherence rate of 93% had the shortest treatment period of 3 days,¹¹ and the study with the lowest average adherence rate of 32% had the longest treatment period of 8 weeks.⁷ The study with the lowest adherence rate was the only study wherein participants were blinded to their enrollment in the study, which would most closely mimic adherence rates in clinical practice.⁷ The participants in the other studies were not aware that their adherence was being monitored, but their behavior may have been influenced since they were aware of their enrollment in the study.

Many variables affect treatment adherence in patients with AD. Average adherence rates were significantly higher (P=.03) in participants with greater disease severity.⁷ There is conflicting evidence regarding the role of medication vehicle in treatment adherence. While Wilson et al⁹ did not find any difference in adherence based on medication vehicle, Yentzer et al¹² found vehicle characteristics and medication side effects were among patients’ top-ranked concerns about using topical medications. Sagransky et al¹⁰ compared treatment adherence between 2 groups of AD patients: one control group received a standard-of-care 4-week follow-up, and an active group received an additional 1-week follow-up. The mean adherence rate of the treatment group was 69% compared with 54% in the control group.¹⁰

Comment

Poor adherence to treatment is a pervasive problem in patients with AD. Our review of the literature confirmed that patients generally are not accurate historians of their medication usage, often reporting near-perfect treatment adherence even when actual adherence is poor. Rates of adherence from clinical trials are likely higher than those seen in clinical practice due in part to study incentives and differences between how patients in a study are treated compared to those in a physician’s clinic; for example, research study participants often have additional follow-up visits compared to those being treated in the clinical population and by virtue of being enrolled in a study are aware that their behavior is being monitored, which can increase treatment adherence.⁷

The dogma suggesting that tachyphylaxis can occur with long-term use of topical corticosteroids is not supported by clinical trials.¹⁴ Furthermore, in our review of the literature patient adherence was highest in the shortest study¹¹ and lowest in the longest study.⁷ Given that AD patients cannot benefit from a treatment if they do not use it, the supposed decrease in efficacy of topical corticosteroids over time may be because patients fail to use them consistently.

Our review of the literature was limited by the small body of research that exists on treatment adherence in AD patients, especially relating to topical medications, and did not reveal any studies evaluating systemic medications in AD. Of the studies we examined, sample sizes were small and treatment and follow-up periods were short. Our review only covered adherence to prescribed topical medications in AD, chiefly corticosteroids; thus, we did not evaluate adherence to other therapies (eg, emollients) in this patient population.

The existing research also is limited by the relative paucity of data showing a correlation between improved adherence to topical treatment and improved disease outcomes, which may be due to the methodological limitations of the study designs that have been used; for instance, studies may use objective monitors to describe daily adherence to treatment, but disease severity typically is measured over longer periods of time, usually every few weeks or months. Short-term data may not be an accurate demonstration of how participants’ actual treatment adherence impacts disease outcome, as the data does not account for more complex adherence factors; for example, participants who achieve good disease control using topical corticosteroids for an 8-week study period may actually demonstrate poor treatment adherence overall, as topical corticosteroids have good short-term efficacy and the patient may have stopped using the product after the first few weeks of the treatment period. In contrast, poorly adherent patients may never use the medication well enough to achieve improvement and may continue low-level use throughout the study period. Therefore, studies that measure disease severity at more regular intervals are required to show the true effect of treatment adherence on disease outcomes.

Since AD mainly affects children, family issues can pose special challenges to attaining good treatment adherence.^15,16 The physician–patient (or parent) relationship and the family’s perception of the patient’s disease severity are strong predictors of adherence to topical treatment.¹⁶ Potential barriers to adherence in the pediatric population are caregivers with negative beliefs about treatment, the time-consuming nature of applying topical therapies, or a child who is uncooperative.^15,17 In the treatment of infants, critical factors are caregiver availability and beliefs and fears about medications and their side effects, while in the teenage population, the desire to “fit in” and oppositional behavior can lead to poor adherence to treatment.¹⁷ Regardless of age, other barriers to treatment adherence are forgetfulness, belief that the drug is not working, and the messiness of treatment.¹⁷

Educational tools (eg, action plans, instructions about how to apply topical medications correctly) may be underutilized in patients with AD. If consistently implemented, these tools could have a positive impact on adherence to medication in patients with AD. For example, written action plans pioneered in the asthma community have shown to improve quality of life and reduce disease severity and may offer the same benefits for AD patients due to the similarities of the diseases.¹⁸ Since AD patients and their caregivers often are not well versed in how to apply topical medications correctly, efforts to educate patients could potentially increase adherence to treatment. In one study, AD patients began to use medications more effectively after applying a fluorescent cream to reveal affected areas they had missed, and clinicians were able to provide additional instruction based on the findings.¹⁹

Adherence to topical treatments among AD patients is a multifactorial issue. Regimens often are complex and inconvenient due to the need for multiple medications, the topical nature of the products, and the need for frequent application. To optimize prescription treatments, patients also must be diligent with preventive measures such as application of topical emollients and use of bathing techniques (eg, bleach baths). A way to overcome treatment complexity and increase adherence may be to provide a written action plan and involve the patient and caregiver in the plan’s development. If a drug formulation is not aesthetically acceptable to the patient (eg, the greasiness of an ointment), allowing the patient to choose the medication vehicle may increase satisfaction and use.¹² Fear of steroid side effects also is common among patients and caregivers and could be overcome with education about the product.²⁰

Conclusion

Treatment adherence can have a dramatic effect on diseases outcomes and can be particularly challenging in AD due to the use of topical medications with complex treatment regimens. Additionally, a large majority of patients with AD are children, from infants to teenagers, adding another layer of treatment challenges. Further research is needed to more definitively develop effective methods for enhancing treatment adherence in this patient population. Although enormous amounts of money are being spent to develop improved treatments for AD, we may be able to achieve far more benefit at a much lower cost by figuring out how to get patients to adhere to the treatments that are already available.

Atopic dermatitis (AD) is a chronic inflammatory skin disease that typically begins in early childhood (Figure). It is one of the most commonly diagnosed dermatologic conditions, affecting up to 25% of children and 2% to 3% of adults in the United States.^1,2 The mainstays of treatment for AD are topical emollients and topical medications, of which corticosteroids are most commonly prescribed.³ Although treatments for AD generally are straightforward and efficacious when used correctly, poor adherence to treatment often prevents patients from achieving disease control.⁴ Patient adherence to therapy is a familiar challenge in dermatology, especially for diseases like AD that require long-term treatment with topical medications.^4,5 In some instances, poor adherence may be misconstrued as poor response to treatment, which may lead to escalation to more powerful and potentially dangerous systemic medications.⁶ Ensuring good adherence to treatment leads to better outcomes and disease control, averts unnecessary treatment, prevents disease complications, improves quality of life, and decreases treatment cost.^4,5 This article provides a review of the literature on patient adherence to topical therapies for AD as well as a discussion of methods to improve patient adherence to treatment in the clinical setting.

Methods

A PubMed search of articles indexed for MEDLINE from January 2005 to May 2015 was conducted to identify studies that focused on treatment adherence in AD using the search terms atopic dermatitis and medication adherence and atopic dermatitis and patient compliance After excluding duplicate results and those that were not in the English language, a final list of clinical trials that investigated patient adherence/compliance to topical medications for the treatment of AD was extracted for evaluation.

Results

Our review of the literature yielded 7 quantitative studies that evaluated adherence to topical medications in AD using electronic monitoring and/or self-reporting (Table).^7-13 Participant demographics, disease severity, drug and vehicle used, duration of treatment, and number of follow-up visits varied. All studies used medication event monitoring system caps on medication jars to objectively track patient adherence by recording the date and time when the cap was removed. To assess disease response, the studies used such measures as the Investigator Global Assessment scale, Eczema Area and Severity Index score, or other visual analog scales.

In all of the studies, treatment proved effective and disease severity declined from baseline regardless of the rate of adherence, with benefit continuing after treatment had ended.^7-13 Some results suggested that better adherence increased treatment efficacy and reduced disease severity.^8,9 However, one 10-day trial found no difference in severity and efficacy among participants who applied the medication at least once daily, missed applications some days, or applied the medication more than twice daily.¹³

Study participants typically overestimated their adherence to treatment compared to actual adherence rates, with most reporting near 100% adherence.^7-9,11,12 Average measured adherence rates ranged from 32% to 93% (Table). Adherence rates typically were highest at the beginning of the study and decreased as the study continued.^7-13 The study with the best average adherence rate of 93% had the shortest treatment period of 3 days,¹¹ and the study with the lowest average adherence rate of 32% had the longest treatment period of 8 weeks.⁷ The study with the lowest adherence rate was the only study wherein participants were blinded to their enrollment in the study, which would most closely mimic adherence rates in clinical practice.⁷ The participants in the other studies were not aware that their adherence was being monitored, but their behavior may have been influenced since they were aware of their enrollment in the study.

Many variables affect treatment adherence in patients with AD. Average adherence rates were significantly higher (P=.03) in participants with greater disease severity.⁷ There is conflicting evidence regarding the role of medication vehicle in treatment adherence. While Wilson et al⁹ did not find any difference in adherence based on medication vehicle, Yentzer et al¹² found vehicle characteristics and medication side effects were among patients’ top-ranked concerns about using topical medications. Sagransky et al¹⁰ compared treatment adherence between 2 groups of AD patients: one control group received a standard-of-care 4-week follow-up, and an active group received an additional 1-week follow-up. The mean adherence rate of the treatment group was 69% compared with 54% in the control group.¹⁰

Comment

Poor adherence to treatment is a pervasive problem in patients with AD. Our review of the literature confirmed that patients generally are not accurate historians of their medication usage, often reporting near-perfect treatment adherence even when actual adherence is poor. Rates of adherence from clinical trials are likely higher than those seen in clinical practice due in part to study incentives and differences between how patients in a study are treated compared to those in a physician’s clinic; for example, research study participants often have additional follow-up visits compared to those being treated in the clinical population and by virtue of being enrolled in a study are aware that their behavior is being monitored, which can increase treatment adherence.⁷

The dogma suggesting that tachyphylaxis can occur with long-term use of topical corticosteroids is not supported by clinical trials.¹⁴ Furthermore, in our review of the literature patient adherence was highest in the shortest study¹¹ and lowest in the longest study.⁷ Given that AD patients cannot benefit from a treatment if they do not use it, the supposed decrease in efficacy of topical corticosteroids over time may be because patients fail to use them consistently.

Our review of the literature was limited by the small body of research that exists on treatment adherence in AD patients, especially relating to topical medications, and did not reveal any studies evaluating systemic medications in AD. Of the studies we examined, sample sizes were small and treatment and follow-up periods were short. Our review only covered adherence to prescribed topical medications in AD, chiefly corticosteroids; thus, we did not evaluate adherence to other therapies (eg, emollients) in this patient population.

The existing research also is limited by the relative paucity of data showing a correlation between improved adherence to topical treatment and improved disease outcomes, which may be due to the methodological limitations of the study designs that have been used; for instance, studies may use objective monitors to describe daily adherence to treatment, but disease severity typically is measured over longer periods of time, usually every few weeks or months. Short-term data may not be an accurate demonstration of how participants’ actual treatment adherence impacts disease outcome, as the data does not account for more complex adherence factors; for example, participants who achieve good disease control using topical corticosteroids for an 8-week study period may actually demonstrate poor treatment adherence overall, as topical corticosteroids have good short-term efficacy and the patient may have stopped using the product after the first few weeks of the treatment period. In contrast, poorly adherent patients may never use the medication well enough to achieve improvement and may continue low-level use throughout the study period. Therefore, studies that measure disease severity at more regular intervals are required to show the true effect of treatment adherence on disease outcomes.

Since AD mainly affects children, family issues can pose special challenges to attaining good treatment adherence.^15,16 The physician–patient (or parent) relationship and the family’s perception of the patient’s disease severity are strong predictors of adherence to topical treatment.¹⁶ Potential barriers to adherence in the pediatric population are caregivers with negative beliefs about treatment, the time-consuming nature of applying topical therapies, or a child who is uncooperative.^15,17 In the treatment of infants, critical factors are caregiver availability and beliefs and fears about medications and their side effects, while in the teenage population, the desire to “fit in” and oppositional behavior can lead to poor adherence to treatment.¹⁷ Regardless of age, other barriers to treatment adherence are forgetfulness, belief that the drug is not working, and the messiness of treatment.¹⁷

Educational tools (eg, action plans, instructions about how to apply topical medications correctly) may be underutilized in patients with AD. If consistently implemented, these tools could have a positive impact on adherence to medication in patients with AD. For example, written action plans pioneered in the asthma community have shown to improve quality of life and reduce disease severity and may offer the same benefits for AD patients due to the similarities of the diseases.¹⁸ Since AD patients and their caregivers often are not well versed in how to apply topical medications correctly, efforts to educate patients could potentially increase adherence to treatment. In one study, AD patients began to use medications more effectively after applying a fluorescent cream to reveal affected areas they had missed, and clinicians were able to provide additional instruction based on the findings.¹⁹

Adherence to topical treatments among AD patients is a multifactorial issue. Regimens often are complex and inconvenient due to the need for multiple medications, the topical nature of the products, and the need for frequent application. To optimize prescription treatments, patients also must be diligent with preventive measures such as application of topical emollients and use of bathing techniques (eg, bleach baths). A way to overcome treatment complexity and increase adherence may be to provide a written action plan and involve the patient and caregiver in the plan’s development. If a drug formulation is not aesthetically acceptable to the patient (eg, the greasiness of an ointment), allowing the patient to choose the medication vehicle may increase satisfaction and use.¹² Fear of steroid side effects also is common among patients and caregivers and could be overcome with education about the product.²⁰

Conclusion

Treatment adherence can have a dramatic effect on diseases outcomes and can be particularly challenging in AD due to the use of topical medications with complex treatment regimens. Additionally, a large majority of patients with AD are children, from infants to teenagers, adding another layer of treatment challenges. Further research is needed to more definitively develop effective methods for enhancing treatment adherence in this patient population. Although enormous amounts of money are being spent to develop improved treatments for AD, we may be able to achieve far more benefit at a much lower cost by figuring out how to get patients to adhere to the treatments that are already available.