Original Research

To the Editor:

ChatGPT (OpenAI), a popular large language model that generates responses to user queries, has attracted substantial attention as a potential resource for patient education.¹ While prior studies have shown that ChatGPT can provide reliable and general patient information, its alignment with the American Academy of Dermatology’s (AAD’s) guidelines for primary cutaneous melanoma (CM) compared to evidence in the recent literature has not been evaluated.^2,3 In this study, we compared ChatGPT’s responses to the 25 evidence-based questions utilized by the AAD to establish its 2019 recommendations for primary CM. Because the 2019 AAD guidelines included literature only through April 2017, we conducted an additional search (May 2017–February 2024) to assess ChatGPT’s alignment with more recent evidence not captured in the guidelines.

On April 17, 2024, 2 authors (D.P. and A.F.) prompted ChatGPT with 25 evidence-based questions from the 2019 AAD guidelines for the management of primary CM.⁴ ChatGPT’s responses were compared with the AAD’s published recommendations and were ­cross-referenced with responses gathered from our own search of PubMed articles indexed for MEDLINE using the phrase melanoma (cutaneous) and treatment, which included studies from May 2017 to February 2024. 

ChatGPT’s answers to 23 of the questions aligned with the AAD’s guidelines (Table 1); in instances when the guidelines were inconclusive regarding pathology, the model provided recommendations supported by our contemporary PubMed literature search. Of the 3 questions related to CM pathology, the AAD guidelines had sufficient evidence to provide recommendations for 2 questions. The first question evaluated the clinical information necessary to help the pathologist improve diagnosis (Table 2). ChatGPT’s response to one question about staged excision and Mohs micrographic surgery for melanoma in situ did not align with the AAD guidelines (Table 3).

Our results showed that ChatGPT provided comprehensive responses aligned with current evidence on CM treatment, except for one surgery question for which its response differed from the AAD guidelines. Our findings are consistent with an observational study that reported board-certified dermatologists rated ChatGPT’s responses on melanoma-related questions as 4.88 on a scale of 1 to 5 (1 indicated completely inaccurate information, 5 indicated complete accuracy for clinical sufficiency in practice). The authors also found that ChatGPT gave vague advice, such as to “get regular skin exams,” which is less specific than dermatologists’ recommendations for annual, biannual, or more frequent examinations.⁵ ChatGPT’s limitations in offering comprehensive answers for some questions aligned with our findings, specifically the omission of key information in the surgical-related question, highlighting the challenge of relying on AI for nuanced clinical guidance.

We found that ChatGPT considered immunosuppression an important risk factor for CM. Similarly, a 2023 cohort study of 93 patients with melanoma and a history of immunosuppression reported that these patients had a higher risk for CM compared with a control group from the National Cancer Institute’s Surveillance, Epidemiology and End Results Program (standardized incidence ratio, 1.53; 95% CI, 1.12-2.04), indicating that incidence of CM in immunocompromised patients was 53% higher than an age- and sex-matched population cohort.⁶

Our findings also demonstrated that both ChatGPT’s responses and the AAD guidelines aligned in indicating that evidence linking pregnancy to an increased risk for CM remains inconclusive and that pregnant women should still undergo surveillance. A 2022 retrospective cohort study of 1406 women comparing pregnancy-associated melanoma to non–pregnancy-associated CM had no difference in overall survival (hazard ratio, 0.75; 95% CI, 0.54-1.05).⁷ However, tumor thickness (2.01-4.00 mm) was greater in postpartum cases compared with cases in nonpregnant women (odds ratio, 1.75; 95% CI, 1.03-2.98), suggesting that pregnancy may affect tumor characteristics.⁷ These findings underscore the importance of using AI tools such as ChatGPT as a supplement to—rather than as a replacement for—expert clinical judgment and up-to-date medical guidelines.

To the Editor:

ChatGPT (OpenAI), a popular large language model that generates responses to user queries, has attracted substantial attention as a potential resource for patient education.¹ While prior studies have shown that ChatGPT can provide reliable and general patient information, its alignment with the American Academy of Dermatology’s (AAD’s) guidelines for primary cutaneous melanoma (CM) compared to evidence in the recent literature has not been evaluated.^2,3 In this study, we compared ChatGPT’s responses to the 25 evidence-based questions utilized by the AAD to establish its 2019 recommendations for primary CM. Because the 2019 AAD guidelines included literature only through April 2017, we conducted an additional search (May 2017–February 2024) to assess ChatGPT’s alignment with more recent evidence not captured in the guidelines.

On April 17, 2024, 2 authors (D.P. and A.F.) prompted ChatGPT with 25 evidence-based questions from the 2019 AAD guidelines for the management of primary CM.⁴ ChatGPT’s responses were compared with the AAD’s published recommendations and were ­cross-referenced with responses gathered from our own search of PubMed articles indexed for MEDLINE using the phrase melanoma (cutaneous) and treatment, which included studies from May 2017 to February 2024. 

ChatGPT’s answers to 23 of the questions aligned with the AAD’s guidelines (Table 1); in instances when the guidelines were inconclusive regarding pathology, the model provided recommendations supported by our contemporary PubMed literature search. Of the 3 questions related to CM pathology, the AAD guidelines had sufficient evidence to provide recommendations for 2 questions. The first question evaluated the clinical information necessary to help the pathologist improve diagnosis (Table 2). ChatGPT’s response to one question about staged excision and Mohs micrographic surgery for melanoma in situ did not align with the AAD guidelines (Table 3).

Our results showed that ChatGPT provided comprehensive responses aligned with current evidence on CM treatment, except for one surgery question for which its response differed from the AAD guidelines. Our findings are consistent with an observational study that reported board-certified dermatologists rated ChatGPT’s responses on melanoma-related questions as 4.88 on a scale of 1 to 5 (1 indicated completely inaccurate information, 5 indicated complete accuracy for clinical sufficiency in practice). The authors also found that ChatGPT gave vague advice, such as to “get regular skin exams,” which is less specific than dermatologists’ recommendations for annual, biannual, or more frequent examinations.⁵ ChatGPT’s limitations in offering comprehensive answers for some questions aligned with our findings, specifically the omission of key information in the surgical-related question, highlighting the challenge of relying on AI for nuanced clinical guidance.

We found that ChatGPT considered immunosuppression an important risk factor for CM. Similarly, a 2023 cohort study of 93 patients with melanoma and a history of immunosuppression reported that these patients had a higher risk for CM compared with a control group from the National Cancer Institute’s Surveillance, Epidemiology and End Results Program (standardized incidence ratio, 1.53; 95% CI, 1.12-2.04), indicating that incidence of CM in immunocompromised patients was 53% higher than an age- and sex-matched population cohort.⁶

Our findings also demonstrated that both ChatGPT’s responses and the AAD guidelines aligned in indicating that evidence linking pregnancy to an increased risk for CM remains inconclusive and that pregnant women should still undergo surveillance. A 2022 retrospective cohort study of 1406 women comparing pregnancy-associated melanoma to non–pregnancy-associated CM had no difference in overall survival (hazard ratio, 0.75; 95% CI, 0.54-1.05).⁷ However, tumor thickness (2.01-4.00 mm) was greater in postpartum cases compared with cases in nonpregnant women (odds ratio, 1.75; 95% CI, 1.03-2.98), suggesting that pregnancy may affect tumor characteristics.⁷ These findings underscore the importance of using AI tools such as ChatGPT as a supplement to—rather than as a replacement for—expert clinical judgment and up-to-date medical guidelines.

To the Editor:

ChatGPT (OpenAI), a popular large language model that generates responses to user queries, has attracted substantial attention as a potential resource for patient education.¹ While prior studies have shown that ChatGPT can provide reliable and general patient information, its alignment with the American Academy of Dermatology’s (AAD’s) guidelines for primary cutaneous melanoma (CM) compared to evidence in the recent literature has not been evaluated.^2,3 In this study, we compared ChatGPT’s responses to the 25 evidence-based questions utilized by the AAD to establish its 2019 recommendations for primary CM. Because the 2019 AAD guidelines included literature only through April 2017, we conducted an additional search (May 2017–February 2024) to assess ChatGPT’s alignment with more recent evidence not captured in the guidelines.

On April 17, 2024, 2 authors (D.P. and A.F.) prompted ChatGPT with 25 evidence-based questions from the 2019 AAD guidelines for the management of primary CM.⁴ ChatGPT’s responses were compared with the AAD’s published recommendations and were ­cross-referenced with responses gathered from our own search of PubMed articles indexed for MEDLINE using the phrase melanoma (cutaneous) and treatment, which included studies from May 2017 to February 2024. 

ChatGPT’s answers to 23 of the questions aligned with the AAD’s guidelines (Table 1); in instances when the guidelines were inconclusive regarding pathology, the model provided recommendations supported by our contemporary PubMed literature search. Of the 3 questions related to CM pathology, the AAD guidelines had sufficient evidence to provide recommendations for 2 questions. The first question evaluated the clinical information necessary to help the pathologist improve diagnosis (Table 2). ChatGPT’s response to one question about staged excision and Mohs micrographic surgery for melanoma in situ did not align with the AAD guidelines (Table 3).

Our results showed that ChatGPT provided comprehensive responses aligned with current evidence on CM treatment, except for one surgery question for which its response differed from the AAD guidelines. Our findings are consistent with an observational study that reported board-certified dermatologists rated ChatGPT’s responses on melanoma-related questions as 4.88 on a scale of 1 to 5 (1 indicated completely inaccurate information, 5 indicated complete accuracy for clinical sufficiency in practice). The authors also found that ChatGPT gave vague advice, such as to “get regular skin exams,” which is less specific than dermatologists’ recommendations for annual, biannual, or more frequent examinations.⁵ ChatGPT’s limitations in offering comprehensive answers for some questions aligned with our findings, specifically the omission of key information in the surgical-related question, highlighting the challenge of relying on AI for nuanced clinical guidance.

We found that ChatGPT considered immunosuppression an important risk factor for CM. Similarly, a 2023 cohort study of 93 patients with melanoma and a history of immunosuppression reported that these patients had a higher risk for CM compared with a control group from the National Cancer Institute’s Surveillance, Epidemiology and End Results Program (standardized incidence ratio, 1.53; 95% CI, 1.12-2.04), indicating that incidence of CM in immunocompromised patients was 53% higher than an age- and sex-matched population cohort.⁶

Our findings also demonstrated that both ChatGPT’s responses and the AAD guidelines aligned in indicating that evidence linking pregnancy to an increased risk for CM remains inconclusive and that pregnant women should still undergo surveillance. A 2022 retrospective cohort study of 1406 women comparing pregnancy-associated melanoma to non–pregnancy-associated CM had no difference in overall survival (hazard ratio, 0.75; 95% CI, 0.54-1.05).⁷ However, tumor thickness (2.01-4.00 mm) was greater in postpartum cases compared with cases in nonpregnant women (odds ratio, 1.75; 95% CI, 1.03-2.98), suggesting that pregnancy may affect tumor characteristics.⁷ These findings underscore the importance of using AI tools such as ChatGPT as a supplement to—rather than as a replacement for—expert clinical judgment and up-to-date medical guidelines.

To the Editor:

Mohs micrographic surgery (MMS) is performed in stages and often requires repeated administration of a local anesthetic, most commonly lidocaine. While generally safe, lidocaine administration carries the potential for cumulative toxicity, particularly in patients who have large or multiple lesions or medical comorbidities or who require extensive repair. Current safety guidelines suggest upper limits of 7 mg/kg (or 500 mg) of lidocaine with epinephrine and 4.5 mg/kg (or 300 mg) without epinephrine for adults.¹ However, concerns have been raised about the relevance of these thresholds to MMS, in which anesthetic administration may be prolonged, cumulative, and influenced by surgical complexity.^2-5 While clinical experience often guides anesthetic planning, limited data exist identifying predictors of lidocaine use during MMS.

We performed an institutional review board–approved retrospective chart review of 149 patients who underwent 170 MMS procedures at a single academic dermatologic surgery center between July 2022 and June 2023. The aim of our study was to identify clinical and surgical predictors of lidocaine volume used during MMS. All procedures were performed by board-certified dermatologic surgeons (including A.J.). All patients received 1% lidocaine with epinephrine as the primary anesthetic agent. We collected patient demographic variables (age, sex, race, weight), procedural characteristics (anatomic site, number of Mohs stages, skin cancer type, number of surgical sites treated in one day, preoperative and postoperative lesion size, surgeon, repair type), comorbid conditions (hypertension, diabetes), and time from diagnosis to surgery. Data were extracted from the institutional REDCap system. We used t tests and analysis of variance for categorical variables and linear regression for continuous predictors, with statistical significance set at P<.05.

Baseline characteristics of the study patients are outlined in Table 1. The mean (SD) age was 74.2 (9.4) years, and most patients (98.7% [147/149]) were White. The mean (SD) weight was 83.1 (19.1) kg. Most lesions were either basal cell carcinoma (BCC)(50.6%) or squamous cell carcinoma (SCC)(44.1%), with 5.3% of lesions representing melanoma. The mean (SD) total lidocaine volume administered was 11.8 (8.3) mL. The majority (123/149 [72.4%]) of cases required one Mohs stage, but a subset required multiple stages, with a maximum of 5.

Several procedural and patient factors were significantly associated with the volume of lidocaine used. As expected, lesion size strongly influenced lidocaine volume. Both preoperative and postoperative lesion sizes were highly significant linear predictors (R²=0.28 and 0.41, respectively; P<.001), and postoperative lesion size demonstrated the strongest correlation of all tested variables. Patient weight was also significantly associated with lidocaine use (R²=.03, P=.0202), though the proportion of explained variance was modest. The operating surgeon also was significantly associated with lidocaine use (P=.006), suggesting potential variation in anesthetic technique or threshold for reinfiltration. The number of surgical sites treated in a single session also was significantly associated with greater lidocaine volume (P<.001).

Skin cancer type was a notable categorical predictor. Melanomas required substantially more lidocaine than BCCs or SCCs, with a mean (SD) volume of 25.6 (12.1) mL compared with 10.8 (6.0) mL for BCC and 11.4 (8.8) mL for SCC (P<.001). This difference may reflect disparities in surgical margin requirements, tumor depth, or intraoperative technique. While lesion location and number of stages were not statistically significant overall, mean lidocaine volumes trended higher in lesions on the trunk (18.2 mL) and in procedures requiring 3 or more stages (up to 22.0 mL for a single 4-stage case), though small sample sizes limited the ability to detect statistically significant differences in these subgroups. Detailed comparisons are presented in Table 2.

Wound repair type also was significantly associated with lidocaine volume requirements. Primary closures required a mean (SD) volume of 12.3 (5.0) mL, whereas flap repairs required 19.3 (10.0) mL and graft repairs required 17.5 (8.2) mL. Secondary-intention healing used the lowest lidocaine volumes (mean [SD], 4.9 [2.0] mL). Differences across repair types were statistically significant (analysis of variance, P<.001). These findings indicate that more complex reconstructions, such as flaps and grafts, are associated with higher anesthetic needs when compared with primary closures or secondary-intention healing.

Several other predictors, including age, time from diagnosis to surgery, and comorbid conditions such as hypertension or diabetes, were not significantly associated with anesthetic volume in our cohort. Time from diagnosis to surgery ranged widely but did not correlate with lesion size or lidocaine use, possibly due to scheduling variability or biopsy technique.

These findings offer practical implications for clinical planning. While most MMS cases fall well within safe limits for lidocaine administration, some patients—­particularly those with melanoma, large lesions, or multiple surgical sites—may approach thresholds at which further monitoring or dose tracking becomes relevant. Anticipating higher anesthetic requirements may help surgical teams plan procedure length, anesthesia restocking, or sequencing of multisite cases. Our analysis also showed that the type of wound repair meaningfully influences anesthetic use, with flap and graft repairs requiring substantially higher lidocaine volumes than primary closures and secondary-intention healing. Considering both tumor characteristics and the planned reconstruction may therefore improve the accuracy of anesthetic forecasting during preoperative planning.

We also observed surgeon-level variation in lidocaine volume despite standardized tumor types and case complexity. This suggests a role for individual technique (eg, depth of field block, number of reinfiltrations) and highlights the need for ongoing education around anesthetic optimization.

Our study was limited by its retrospective design, single-institution setting, and demographically homogeneous population. With 98.8% of patients identifying as White, generalizability to skin of color populations may be limited. In addition, lidocaine metabolism may vary across patient factors not captured here (eg, hepatic or renal function). Finally, although lidocaine volume was the outcome of interest, we did not measure patient-reported pain control, which may further clarify anesthetic adequacy. Nonetheless, our analysis demonstrated that routinely available clinical and procedural data can predict lidocaine volume requirements with reasonable reliability. Although no patient in our cohort approached the maximum recommended lidocaine dose, understanding these predictors may help anticipate scenarios nearing maximum dosing thresholds. In future studies, integrating weight-based thresholds (eg, mL/kg received) or serum lidocaine levels may improve safety monitoring and validate toxicity thresholds in complex cases.

In conclusion, we identified several key factors that predict lidocaine volume during MMS, including lesion size, melanoma diagnosis, number of surgical sites, patient weight, planned reconstruction type, and the operating surgeon. Among these factors, melanoma cases required more than twice the volume of lidocaine compared to BCC and SCC cases, and flap and graft repairs demonstrated the highest anesthetic requirements among closure types. Taken together, these findings reinforce the need for advanced anesthetic planning in aggressive, anatomically complex, or reconstruction-intensive cases and may support more informed intraoperative decision-making.

To the Editor:

Mohs micrographic surgery (MMS) is performed in stages and often requires repeated administration of a local anesthetic, most commonly lidocaine. While generally safe, lidocaine administration carries the potential for cumulative toxicity, particularly in patients who have large or multiple lesions or medical comorbidities or who require extensive repair. Current safety guidelines suggest upper limits of 7 mg/kg (or 500 mg) of lidocaine with epinephrine and 4.5 mg/kg (or 300 mg) without epinephrine for adults.¹ However, concerns have been raised about the relevance of these thresholds to MMS, in which anesthetic administration may be prolonged, cumulative, and influenced by surgical complexity.^2-5 While clinical experience often guides anesthetic planning, limited data exist identifying predictors of lidocaine use during MMS.

We performed an institutional review board–approved retrospective chart review of 149 patients who underwent 170 MMS procedures at a single academic dermatologic surgery center between July 2022 and June 2023. The aim of our study was to identify clinical and surgical predictors of lidocaine volume used during MMS. All procedures were performed by board-certified dermatologic surgeons (including A.J.). All patients received 1% lidocaine with epinephrine as the primary anesthetic agent. We collected patient demographic variables (age, sex, race, weight), procedural characteristics (anatomic site, number of Mohs stages, skin cancer type, number of surgical sites treated in one day, preoperative and postoperative lesion size, surgeon, repair type), comorbid conditions (hypertension, diabetes), and time from diagnosis to surgery. Data were extracted from the institutional REDCap system. We used t tests and analysis of variance for categorical variables and linear regression for continuous predictors, with statistical significance set at P<.05.

Baseline characteristics of the study patients are outlined in Table 1. The mean (SD) age was 74.2 (9.4) years, and most patients (98.7% [147/149]) were White. The mean (SD) weight was 83.1 (19.1) kg. Most lesions were either basal cell carcinoma (BCC)(50.6%) or squamous cell carcinoma (SCC)(44.1%), with 5.3% of lesions representing melanoma. The mean (SD) total lidocaine volume administered was 11.8 (8.3) mL. The majority (123/149 [72.4%]) of cases required one Mohs stage, but a subset required multiple stages, with a maximum of 5.

Several procedural and patient factors were significantly associated with the volume of lidocaine used. As expected, lesion size strongly influenced lidocaine volume. Both preoperative and postoperative lesion sizes were highly significant linear predictors (R²=0.28 and 0.41, respectively; P<.001), and postoperative lesion size demonstrated the strongest correlation of all tested variables. Patient weight was also significantly associated with lidocaine use (R²=.03, P=.0202), though the proportion of explained variance was modest. The operating surgeon also was significantly associated with lidocaine use (P=.006), suggesting potential variation in anesthetic technique or threshold for reinfiltration. The number of surgical sites treated in a single session also was significantly associated with greater lidocaine volume (P<.001).

Skin cancer type was a notable categorical predictor. Melanomas required substantially more lidocaine than BCCs or SCCs, with a mean (SD) volume of 25.6 (12.1) mL compared with 10.8 (6.0) mL for BCC and 11.4 (8.8) mL for SCC (P<.001). This difference may reflect disparities in surgical margin requirements, tumor depth, or intraoperative technique. While lesion location and number of stages were not statistically significant overall, mean lidocaine volumes trended higher in lesions on the trunk (18.2 mL) and in procedures requiring 3 or more stages (up to 22.0 mL for a single 4-stage case), though small sample sizes limited the ability to detect statistically significant differences in these subgroups. Detailed comparisons are presented in Table 2.

Wound repair type also was significantly associated with lidocaine volume requirements. Primary closures required a mean (SD) volume of 12.3 (5.0) mL, whereas flap repairs required 19.3 (10.0) mL and graft repairs required 17.5 (8.2) mL. Secondary-intention healing used the lowest lidocaine volumes (mean [SD], 4.9 [2.0] mL). Differences across repair types were statistically significant (analysis of variance, P<.001). These findings indicate that more complex reconstructions, such as flaps and grafts, are associated with higher anesthetic needs when compared with primary closures or secondary-intention healing.

Several other predictors, including age, time from diagnosis to surgery, and comorbid conditions such as hypertension or diabetes, were not significantly associated with anesthetic volume in our cohort. Time from diagnosis to surgery ranged widely but did not correlate with lesion size or lidocaine use, possibly due to scheduling variability or biopsy technique.

These findings offer practical implications for clinical planning. While most MMS cases fall well within safe limits for lidocaine administration, some patients—­particularly those with melanoma, large lesions, or multiple surgical sites—may approach thresholds at which further monitoring or dose tracking becomes relevant. Anticipating higher anesthetic requirements may help surgical teams plan procedure length, anesthesia restocking, or sequencing of multisite cases. Our analysis also showed that the type of wound repair meaningfully influences anesthetic use, with flap and graft repairs requiring substantially higher lidocaine volumes than primary closures and secondary-intention healing. Considering both tumor characteristics and the planned reconstruction may therefore improve the accuracy of anesthetic forecasting during preoperative planning.

We also observed surgeon-level variation in lidocaine volume despite standardized tumor types and case complexity. This suggests a role for individual technique (eg, depth of field block, number of reinfiltrations) and highlights the need for ongoing education around anesthetic optimization.

Our study was limited by its retrospective design, single-institution setting, and demographically homogeneous population. With 98.8% of patients identifying as White, generalizability to skin of color populations may be limited. In addition, lidocaine metabolism may vary across patient factors not captured here (eg, hepatic or renal function). Finally, although lidocaine volume was the outcome of interest, we did not measure patient-reported pain control, which may further clarify anesthetic adequacy. Nonetheless, our analysis demonstrated that routinely available clinical and procedural data can predict lidocaine volume requirements with reasonable reliability. Although no patient in our cohort approached the maximum recommended lidocaine dose, understanding these predictors may help anticipate scenarios nearing maximum dosing thresholds. In future studies, integrating weight-based thresholds (eg, mL/kg received) or serum lidocaine levels may improve safety monitoring and validate toxicity thresholds in complex cases.

In conclusion, we identified several key factors that predict lidocaine volume during MMS, including lesion size, melanoma diagnosis, number of surgical sites, patient weight, planned reconstruction type, and the operating surgeon. Among these factors, melanoma cases required more than twice the volume of lidocaine compared to BCC and SCC cases, and flap and graft repairs demonstrated the highest anesthetic requirements among closure types. Taken together, these findings reinforce the need for advanced anesthetic planning in aggressive, anatomically complex, or reconstruction-intensive cases and may support more informed intraoperative decision-making.

To the Editor:

Mohs micrographic surgery (MMS) is performed in stages and often requires repeated administration of a local anesthetic, most commonly lidocaine. While generally safe, lidocaine administration carries the potential for cumulative toxicity, particularly in patients who have large or multiple lesions or medical comorbidities or who require extensive repair. Current safety guidelines suggest upper limits of 7 mg/kg (or 500 mg) of lidocaine with epinephrine and 4.5 mg/kg (or 300 mg) without epinephrine for adults.¹ However, concerns have been raised about the relevance of these thresholds to MMS, in which anesthetic administration may be prolonged, cumulative, and influenced by surgical complexity.^2-5 While clinical experience often guides anesthetic planning, limited data exist identifying predictors of lidocaine use during MMS.

We performed an institutional review board–approved retrospective chart review of 149 patients who underwent 170 MMS procedures at a single academic dermatologic surgery center between July 2022 and June 2023. The aim of our study was to identify clinical and surgical predictors of lidocaine volume used during MMS. All procedures were performed by board-certified dermatologic surgeons (including A.J.). All patients received 1% lidocaine with epinephrine as the primary anesthetic agent. We collected patient demographic variables (age, sex, race, weight), procedural characteristics (anatomic site, number of Mohs stages, skin cancer type, number of surgical sites treated in one day, preoperative and postoperative lesion size, surgeon, repair type), comorbid conditions (hypertension, diabetes), and time from diagnosis to surgery. Data were extracted from the institutional REDCap system. We used t tests and analysis of variance for categorical variables and linear regression for continuous predictors, with statistical significance set at P<.05.

Baseline characteristics of the study patients are outlined in Table 1. The mean (SD) age was 74.2 (9.4) years, and most patients (98.7% [147/149]) were White. The mean (SD) weight was 83.1 (19.1) kg. Most lesions were either basal cell carcinoma (BCC)(50.6%) or squamous cell carcinoma (SCC)(44.1%), with 5.3% of lesions representing melanoma. The mean (SD) total lidocaine volume administered was 11.8 (8.3) mL. The majority (123/149 [72.4%]) of cases required one Mohs stage, but a subset required multiple stages, with a maximum of 5.

Several procedural and patient factors were significantly associated with the volume of lidocaine used. As expected, lesion size strongly influenced lidocaine volume. Both preoperative and postoperative lesion sizes were highly significant linear predictors (R²=0.28 and 0.41, respectively; P<.001), and postoperative lesion size demonstrated the strongest correlation of all tested variables. Patient weight was also significantly associated with lidocaine use (R²=.03, P=.0202), though the proportion of explained variance was modest. The operating surgeon also was significantly associated with lidocaine use (P=.006), suggesting potential variation in anesthetic technique or threshold for reinfiltration. The number of surgical sites treated in a single session also was significantly associated with greater lidocaine volume (P<.001).

Skin cancer type was a notable categorical predictor. Melanomas required substantially more lidocaine than BCCs or SCCs, with a mean (SD) volume of 25.6 (12.1) mL compared with 10.8 (6.0) mL for BCC and 11.4 (8.8) mL for SCC (P<.001). This difference may reflect disparities in surgical margin requirements, tumor depth, or intraoperative technique. While lesion location and number of stages were not statistically significant overall, mean lidocaine volumes trended higher in lesions on the trunk (18.2 mL) and in procedures requiring 3 or more stages (up to 22.0 mL for a single 4-stage case), though small sample sizes limited the ability to detect statistically significant differences in these subgroups. Detailed comparisons are presented in Table 2.

Wound repair type also was significantly associated with lidocaine volume requirements. Primary closures required a mean (SD) volume of 12.3 (5.0) mL, whereas flap repairs required 19.3 (10.0) mL and graft repairs required 17.5 (8.2) mL. Secondary-intention healing used the lowest lidocaine volumes (mean [SD], 4.9 [2.0] mL). Differences across repair types were statistically significant (analysis of variance, P<.001). These findings indicate that more complex reconstructions, such as flaps and grafts, are associated with higher anesthetic needs when compared with primary closures or secondary-intention healing.

Several other predictors, including age, time from diagnosis to surgery, and comorbid conditions such as hypertension or diabetes, were not significantly associated with anesthetic volume in our cohort. Time from diagnosis to surgery ranged widely but did not correlate with lesion size or lidocaine use, possibly due to scheduling variability or biopsy technique.

These findings offer practical implications for clinical planning. While most MMS cases fall well within safe limits for lidocaine administration, some patients—­particularly those with melanoma, large lesions, or multiple surgical sites—may approach thresholds at which further monitoring or dose tracking becomes relevant. Anticipating higher anesthetic requirements may help surgical teams plan procedure length, anesthesia restocking, or sequencing of multisite cases. Our analysis also showed that the type of wound repair meaningfully influences anesthetic use, with flap and graft repairs requiring substantially higher lidocaine volumes than primary closures and secondary-intention healing. Considering both tumor characteristics and the planned reconstruction may therefore improve the accuracy of anesthetic forecasting during preoperative planning.

We also observed surgeon-level variation in lidocaine volume despite standardized tumor types and case complexity. This suggests a role for individual technique (eg, depth of field block, number of reinfiltrations) and highlights the need for ongoing education around anesthetic optimization.

Our study was limited by its retrospective design, single-institution setting, and demographically homogeneous population. With 98.8% of patients identifying as White, generalizability to skin of color populations may be limited. In addition, lidocaine metabolism may vary across patient factors not captured here (eg, hepatic or renal function). Finally, although lidocaine volume was the outcome of interest, we did not measure patient-reported pain control, which may further clarify anesthetic adequacy. Nonetheless, our analysis demonstrated that routinely available clinical and procedural data can predict lidocaine volume requirements with reasonable reliability. Although no patient in our cohort approached the maximum recommended lidocaine dose, understanding these predictors may help anticipate scenarios nearing maximum dosing thresholds. In future studies, integrating weight-based thresholds (eg, mL/kg received) or serum lidocaine levels may improve safety monitoring and validate toxicity thresholds in complex cases.

In conclusion, we identified several key factors that predict lidocaine volume during MMS, including lesion size, melanoma diagnosis, number of surgical sites, patient weight, planned reconstruction type, and the operating surgeon. Among these factors, melanoma cases required more than twice the volume of lidocaine compared to BCC and SCC cases, and flap and graft repairs demonstrated the highest anesthetic requirements among closure types. Taken together, these findings reinforce the need for advanced anesthetic planning in aggressive, anatomically complex, or reconstruction-intensive cases and may support more informed intraoperative decision-making.

To the Editor:

Severe cutaneous adverse reactions (SCARs) are rare, life-threatening reactions that include acute generalized exanthematous pustulosis (AGEP), drug reaction with eosinophilia and systemic symptoms (DRESS), and Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN).¹ In addition to being associated with commonly implicated medications, SCARs also may occur in the setting of antineoplastic therapy.^2,3 Although ­antineoplastic-associated SCARs have been described, diagnosis can be difficult due to varying latency periods and atypical clinical features, such as those observed with BRAF inhibitor–related DRESS during immunotherapy.⁴ Severe cutaneous adverse reactions can increase morbidity and mortality in the oncologic patient population due to both the clinical sequelae from the cutaneous reaction and the potential to interrupt cancer treatment.

The aim of this study was to evaluate the clinical characteristics, outcomes, and impact on cancer treatment among patients diagnosed with a SCAR while receiving active therapy for malignancy. We conducted a retrospective chart review of electronic medical records at Yale New Haven Hospital (New Haven, Connecticut) from 2013 to 2023, identifying patients receiving antineoplastic therapy who were diagnosed with a SCAR. Cases were identified through a search of the electronic medical record performed by the joint data analytics team using the keywords DRESS, SJS, TEN, AGEP, and generalized bullous fixed drug eruption, along with spelling variations (both abbreviations and full terms), in addition to manual review by one of the authors (K.V.) of the inpatient dermatology consultation log and dermatopathology database. Only patients for whom an antineoplastic agent was identified as a high-probability culprit by the dermatology and/or oncology teams were included.

In total, 20 patients (11 female, 9 male) were identified as having an antineoplastic-associated SCAR. All patients had metastatic or advanced disease. We identified 2 (10%) cases of AGEP, 16 (80%) cases of DRESS, and 3 (15%) cases of SJS/TEN. One patient on immunotherapy had 2 distinct SCARs (AGEP, DRESS) at different time points. Table 1 describes patient and SCAR characteristics as well as impact on cancer treatment. The median (interquartile range [IQR]) latency period for AGEP was 7.5 (4-11) days. The median (IQR) latency period for 13 of the 16 (81%) DRESS cases was 14 (10-32) days. For 3 DRESS cases with a potential second-hit phenomenon in the setting of current or antecedent immunotherapy,⁵ the median (IQR) latency period was 122 (96-426) days for the immunotherapy drug and 28 (21-52) days for the drug culprit. The median (IQR) latency period for SJS/TEN was 23 (20-27) days.

Patients received treatment with combination systemic corticosteroids and topical corticosteroids in 13 (65%) cases, systemic corticosteroid monotherapy in 6 (30%) cases, or combination systemic corticosteroids and etanercept in 1 (5%) case. All patients experienced resolution of the SCAR and survived to hospital discharge. Most (17/20 [85%]) patients experienced interruption or discontinuation of cancer treatment. Table 2 describes the implicated antineoplastic therapies, which included chemotherapy (3 DRESS, 1 SJS/TEN), hormonal therapy (1 DRESS), immunotherapy (1 AGEP, 4 DRESS), and targeted therapy (1 AGEP, 8 DRESS, 2 SJS/TEN).

Limitations of this study include the retrospective study design, the small sample size, and the challenge of drug culprit identification in oncologic patients on multiple high-probability medications.

Though rare, SCARs can be encountered in patients on antineoplastic therapy with a wide range of drug culprits. In our cohort, SCARs occurred with various antineoplastic agents, including chemotherapy, hormonal therapy, immunotherapy, and targeted therapy. The most common antineoplastic-associated SCAR was DRESS, which had the widest latency period in the setting of a potential second-hit phenomenon with another drug culprit. Although we did not observe any cases of SJS/TEN in the immunotherapy category, it is important to consider progressive immunotherapy-related mucocutaneous eruption in the differential diagnosis. Fortunately, all patients survived to hospital discharge and experienced SCAR resolution with systemic treatment; however, most patients experienced interruption of cancer therapy, which has the potential to affect oncologic outcomes. This interruption is not uncommon, as rechallenge of an antineoplastic agent in patients with a therapy-related SCAR generally is not recommended. The awareness and prompt management of SCARs in a patient on treatment for malignancy are critical in order to minimize negative outcomes in this vulnerable patient population.

To the Editor:

Severe cutaneous adverse reactions (SCARs) are rare, life-threatening reactions that include acute generalized exanthematous pustulosis (AGEP), drug reaction with eosinophilia and systemic symptoms (DRESS), and Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN).¹ In addition to being associated with commonly implicated medications, SCARs also may occur in the setting of antineoplastic therapy.^2,3 Although ­antineoplastic-associated SCARs have been described, diagnosis can be difficult due to varying latency periods and atypical clinical features, such as those observed with BRAF inhibitor–related DRESS during immunotherapy.⁴ Severe cutaneous adverse reactions can increase morbidity and mortality in the oncologic patient population due to both the clinical sequelae from the cutaneous reaction and the potential to interrupt cancer treatment.

The aim of this study was to evaluate the clinical characteristics, outcomes, and impact on cancer treatment among patients diagnosed with a SCAR while receiving active therapy for malignancy. We conducted a retrospective chart review of electronic medical records at Yale New Haven Hospital (New Haven, Connecticut) from 2013 to 2023, identifying patients receiving antineoplastic therapy who were diagnosed with a SCAR. Cases were identified through a search of the electronic medical record performed by the joint data analytics team using the keywords DRESS, SJS, TEN, AGEP, and generalized bullous fixed drug eruption, along with spelling variations (both abbreviations and full terms), in addition to manual review by one of the authors (K.V.) of the inpatient dermatology consultation log and dermatopathology database. Only patients for whom an antineoplastic agent was identified as a high-probability culprit by the dermatology and/or oncology teams were included.

In total, 20 patients (11 female, 9 male) were identified as having an antineoplastic-associated SCAR. All patients had metastatic or advanced disease. We identified 2 (10%) cases of AGEP, 16 (80%) cases of DRESS, and 3 (15%) cases of SJS/TEN. One patient on immunotherapy had 2 distinct SCARs (AGEP, DRESS) at different time points. Table 1 describes patient and SCAR characteristics as well as impact on cancer treatment. The median (interquartile range [IQR]) latency period for AGEP was 7.5 (4-11) days. The median (IQR) latency period for 13 of the 16 (81%) DRESS cases was 14 (10-32) days. For 3 DRESS cases with a potential second-hit phenomenon in the setting of current or antecedent immunotherapy,⁵ the median (IQR) latency period was 122 (96-426) days for the immunotherapy drug and 28 (21-52) days for the drug culprit. The median (IQR) latency period for SJS/TEN was 23 (20-27) days.

Patients received treatment with combination systemic corticosteroids and topical corticosteroids in 13 (65%) cases, systemic corticosteroid monotherapy in 6 (30%) cases, or combination systemic corticosteroids and etanercept in 1 (5%) case. All patients experienced resolution of the SCAR and survived to hospital discharge. Most (17/20 [85%]) patients experienced interruption or discontinuation of cancer treatment. Table 2 describes the implicated antineoplastic therapies, which included chemotherapy (3 DRESS, 1 SJS/TEN), hormonal therapy (1 DRESS), immunotherapy (1 AGEP, 4 DRESS), and targeted therapy (1 AGEP, 8 DRESS, 2 SJS/TEN).

Limitations of this study include the retrospective study design, the small sample size, and the challenge of drug culprit identification in oncologic patients on multiple high-probability medications.

Though rare, SCARs can be encountered in patients on antineoplastic therapy with a wide range of drug culprits. In our cohort, SCARs occurred with various antineoplastic agents, including chemotherapy, hormonal therapy, immunotherapy, and targeted therapy. The most common antineoplastic-associated SCAR was DRESS, which had the widest latency period in the setting of a potential second-hit phenomenon with another drug culprit. Although we did not observe any cases of SJS/TEN in the immunotherapy category, it is important to consider progressive immunotherapy-related mucocutaneous eruption in the differential diagnosis. Fortunately, all patients survived to hospital discharge and experienced SCAR resolution with systemic treatment; however, most patients experienced interruption of cancer therapy, which has the potential to affect oncologic outcomes. This interruption is not uncommon, as rechallenge of an antineoplastic agent in patients with a therapy-related SCAR generally is not recommended. The awareness and prompt management of SCARs in a patient on treatment for malignancy are critical in order to minimize negative outcomes in this vulnerable patient population.

To the Editor:

Severe cutaneous adverse reactions (SCARs) are rare, life-threatening reactions that include acute generalized exanthematous pustulosis (AGEP), drug reaction with eosinophilia and systemic symptoms (DRESS), and Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN).¹ In addition to being associated with commonly implicated medications, SCARs also may occur in the setting of antineoplastic therapy.^2,3 Although ­antineoplastic-associated SCARs have been described, diagnosis can be difficult due to varying latency periods and atypical clinical features, such as those observed with BRAF inhibitor–related DRESS during immunotherapy.⁴ Severe cutaneous adverse reactions can increase morbidity and mortality in the oncologic patient population due to both the clinical sequelae from the cutaneous reaction and the potential to interrupt cancer treatment.

The aim of this study was to evaluate the clinical characteristics, outcomes, and impact on cancer treatment among patients diagnosed with a SCAR while receiving active therapy for malignancy. We conducted a retrospective chart review of electronic medical records at Yale New Haven Hospital (New Haven, Connecticut) from 2013 to 2023, identifying patients receiving antineoplastic therapy who were diagnosed with a SCAR. Cases were identified through a search of the electronic medical record performed by the joint data analytics team using the keywords DRESS, SJS, TEN, AGEP, and generalized bullous fixed drug eruption, along with spelling variations (both abbreviations and full terms), in addition to manual review by one of the authors (K.V.) of the inpatient dermatology consultation log and dermatopathology database. Only patients for whom an antineoplastic agent was identified as a high-probability culprit by the dermatology and/or oncology teams were included.

In total, 20 patients (11 female, 9 male) were identified as having an antineoplastic-associated SCAR. All patients had metastatic or advanced disease. We identified 2 (10%) cases of AGEP, 16 (80%) cases of DRESS, and 3 (15%) cases of SJS/TEN. One patient on immunotherapy had 2 distinct SCARs (AGEP, DRESS) at different time points. Table 1 describes patient and SCAR characteristics as well as impact on cancer treatment. The median (interquartile range [IQR]) latency period for AGEP was 7.5 (4-11) days. The median (IQR) latency period for 13 of the 16 (81%) DRESS cases was 14 (10-32) days. For 3 DRESS cases with a potential second-hit phenomenon in the setting of current or antecedent immunotherapy,⁵ the median (IQR) latency period was 122 (96-426) days for the immunotherapy drug and 28 (21-52) days for the drug culprit. The median (IQR) latency period for SJS/TEN was 23 (20-27) days.

Patients received treatment with combination systemic corticosteroids and topical corticosteroids in 13 (65%) cases, systemic corticosteroid monotherapy in 6 (30%) cases, or combination systemic corticosteroids and etanercept in 1 (5%) case. All patients experienced resolution of the SCAR and survived to hospital discharge. Most (17/20 [85%]) patients experienced interruption or discontinuation of cancer treatment. Table 2 describes the implicated antineoplastic therapies, which included chemotherapy (3 DRESS, 1 SJS/TEN), hormonal therapy (1 DRESS), immunotherapy (1 AGEP, 4 DRESS), and targeted therapy (1 AGEP, 8 DRESS, 2 SJS/TEN).

Limitations of this study include the retrospective study design, the small sample size, and the challenge of drug culprit identification in oncologic patients on multiple high-probability medications.

Though rare, SCARs can be encountered in patients on antineoplastic therapy with a wide range of drug culprits. In our cohort, SCARs occurred with various antineoplastic agents, including chemotherapy, hormonal therapy, immunotherapy, and targeted therapy. The most common antineoplastic-associated SCAR was DRESS, which had the widest latency period in the setting of a potential second-hit phenomenon with another drug culprit. Although we did not observe any cases of SJS/TEN in the immunotherapy category, it is important to consider progressive immunotherapy-related mucocutaneous eruption in the differential diagnosis. Fortunately, all patients survived to hospital discharge and experienced SCAR resolution with systemic treatment; however, most patients experienced interruption of cancer therapy, which has the potential to affect oncologic outcomes. This interruption is not uncommon, as rechallenge of an antineoplastic agent in patients with a therapy-related SCAR generally is not recommended. The awareness and prompt management of SCARs in a patient on treatment for malignancy are critical in order to minimize negative outcomes in this vulnerable patient population.

To the Editor:

At-home microcurrent facial devices have gained rapid popularity for cosmetic rejuvenation, promising improvements in skin tone, contour, and collagen production.¹ In particular, the post–COVID-19 era has seen a surge in at-home beauty practices driven by social media influence, with the global microcurrent facial market estimated at $372.9 million in 2022 and projected to grow at a compound annual growth rate of 7.3% through 2030.¹ Microcurrent devices deliver low-level electrical currents to the skin and underlying muscles. Given the limited exploration of the long-term safety, we aimed to collate existing data and identify trends in reports of adverse events (AEs) associated with these microcurrent devices.

On April 15, 2025, the US Food and Drug Administration’s Manufacturer and User Facility Device Experience (MAUDE) database was queried for medical device reports from January 1, 2013, through March 31, 2025, using product names and keywords including NuFACE, TheraFace, FOREO, and microcurrent device. Search terms were limited to brands for which complaint data existed in the MAUDE database at the time of query. To ensure accuracy, reports were manually reviewed to eliminate duplicates and irrelevant entries.

A total of 28 unique AE reports associated with at-home microcurrent devices were identified (eTable). The majority involved NuFACE devices (ie, NuFACE Trinity, NuFACE Mini, and NuFACE Trinity+)(NuFACE)(n=25), followed by the TheraFace PRO (Therabody, Inc)(n=2) and the FOREO BEAR (FOREO)(n=1). The most frequently documented AEs associated with the NuFACE devices included arrhythmia (7/25 [28%]), pain (6/25 [24%]), dizziness (4/25 [16%]), headache (4/25 [16%]), and inflammation (4/25 [16%]). There was 1 (4%) case of retinal detachment. The TheraFace PRO was associated with device overheating (2/2 [100%]), and the FOREO BEAR was associated with facial deformity/disfigurement (1/1 [100%]).

While microcurrent therapy is widely marketed to consumers through social media influencers and at-home beauty platforms,¹ randomized controlled trials (RCTs) evaluating AEs related to use of this technology are lacking, possibly due to nonstringent regulation of nonprescription cosmetic devices.² Contrary to our findings, RCTs of microcurrent devices have reported minimal or no AEs; for instance, an RCT evaluating 56 participants treated 5 times weekly for 12 weeks with a microcurrent device that was not included in our analysis reported only mild erythema in all experimental group participants.² In another RCT of 30 participants, 15 of whom were treated with a microcurrent device and 15 with placebo for 30 minutes once daily over a period of 10 days, no AEs were reported.³ A cohort analysis of 34 patients also provided preliminary evidence supporting the use of microcurrent therapy for chronic back and neck pain, beyond its cosmetic applications.⁴ Despite the lack of reported AEs in the literature, there is a notable absence of large-scale, rigorous studies on this topic.

Our analysis was subject to the limitations of the MAUDE database, in which reports of severe AEs are more likely to be reported than transient ones. Additionally, the small sample size and lack of a known denominator make it difficult to compare frequencies of AEs among different microcurrent tools. The products chosen for this study were the select few that reported complaint data, but there is a large existing market of devices that may be associated with AEs that have yet to be reported, potentially because of their novelty.

Our findings suggest that, despite their over-the-counter availability, microcurrent facial devices may carry major risks—particularly in at-home settings. While short-term studies have highlighted potential benefits, the small sample sizes and limited follow-up make it difficult to comprehensively characterize long-term safety risks. Among available studies on microcurrent beauty treatments, the longest follow-up was only 12 weeks.² Our findings support the need for further large-scale and longitudinal studies to evaluate both the efficacy and safety of at-home microcurrent therapy, especially with increasing consumer interest. The diversity of the products available adds to the challenge of broad safety guidelines, in addition to the lack of long-term clinical studies.

To the Editor:

At-home microcurrent facial devices have gained rapid popularity for cosmetic rejuvenation, promising improvements in skin tone, contour, and collagen production.¹ In particular, the post–COVID-19 era has seen a surge in at-home beauty practices driven by social media influence, with the global microcurrent facial market estimated at $372.9 million in 2022 and projected to grow at a compound annual growth rate of 7.3% through 2030.¹ Microcurrent devices deliver low-level electrical currents to the skin and underlying muscles. Given the limited exploration of the long-term safety, we aimed to collate existing data and identify trends in reports of adverse events (AEs) associated with these microcurrent devices.

On April 15, 2025, the US Food and Drug Administration’s Manufacturer and User Facility Device Experience (MAUDE) database was queried for medical device reports from January 1, 2013, through March 31, 2025, using product names and keywords including NuFACE, TheraFace, FOREO, and microcurrent device. Search terms were limited to brands for which complaint data existed in the MAUDE database at the time of query. To ensure accuracy, reports were manually reviewed to eliminate duplicates and irrelevant entries.

A total of 28 unique AE reports associated with at-home microcurrent devices were identified (eTable). The majority involved NuFACE devices (ie, NuFACE Trinity, NuFACE Mini, and NuFACE Trinity+)(NuFACE)(n=25), followed by the TheraFace PRO (Therabody, Inc)(n=2) and the FOREO BEAR (FOREO)(n=1). The most frequently documented AEs associated with the NuFACE devices included arrhythmia (7/25 [28%]), pain (6/25 [24%]), dizziness (4/25 [16%]), headache (4/25 [16%]), and inflammation (4/25 [16%]). There was 1 (4%) case of retinal detachment. The TheraFace PRO was associated with device overheating (2/2 [100%]), and the FOREO BEAR was associated with facial deformity/disfigurement (1/1 [100%]).

While microcurrent therapy is widely marketed to consumers through social media influencers and at-home beauty platforms,¹ randomized controlled trials (RCTs) evaluating AEs related to use of this technology are lacking, possibly due to nonstringent regulation of nonprescription cosmetic devices.² Contrary to our findings, RCTs of microcurrent devices have reported minimal or no AEs; for instance, an RCT evaluating 56 participants treated 5 times weekly for 12 weeks with a microcurrent device that was not included in our analysis reported only mild erythema in all experimental group participants.² In another RCT of 30 participants, 15 of whom were treated with a microcurrent device and 15 with placebo for 30 minutes once daily over a period of 10 days, no AEs were reported.³ A cohort analysis of 34 patients also provided preliminary evidence supporting the use of microcurrent therapy for chronic back and neck pain, beyond its cosmetic applications.⁴ Despite the lack of reported AEs in the literature, there is a notable absence of large-scale, rigorous studies on this topic.

Our analysis was subject to the limitations of the MAUDE database, in which reports of severe AEs are more likely to be reported than transient ones. Additionally, the small sample size and lack of a known denominator make it difficult to compare frequencies of AEs among different microcurrent tools. The products chosen for this study were the select few that reported complaint data, but there is a large existing market of devices that may be associated with AEs that have yet to be reported, potentially because of their novelty.

Our findings suggest that, despite their over-the-counter availability, microcurrent facial devices may carry major risks—particularly in at-home settings. While short-term studies have highlighted potential benefits, the small sample sizes and limited follow-up make it difficult to comprehensively characterize long-term safety risks. Among available studies on microcurrent beauty treatments, the longest follow-up was only 12 weeks.² Our findings support the need for further large-scale and longitudinal studies to evaluate both the efficacy and safety of at-home microcurrent therapy, especially with increasing consumer interest. The diversity of the products available adds to the challenge of broad safety guidelines, in addition to the lack of long-term clinical studies.

To the Editor:

At-home microcurrent facial devices have gained rapid popularity for cosmetic rejuvenation, promising improvements in skin tone, contour, and collagen production.¹ In particular, the post–COVID-19 era has seen a surge in at-home beauty practices driven by social media influence, with the global microcurrent facial market estimated at $372.9 million in 2022 and projected to grow at a compound annual growth rate of 7.3% through 2030.¹ Microcurrent devices deliver low-level electrical currents to the skin and underlying muscles. Given the limited exploration of the long-term safety, we aimed to collate existing data and identify trends in reports of adverse events (AEs) associated with these microcurrent devices.

On April 15, 2025, the US Food and Drug Administration’s Manufacturer and User Facility Device Experience (MAUDE) database was queried for medical device reports from January 1, 2013, through March 31, 2025, using product names and keywords including NuFACE, TheraFace, FOREO, and microcurrent device. Search terms were limited to brands for which complaint data existed in the MAUDE database at the time of query. To ensure accuracy, reports were manually reviewed to eliminate duplicates and irrelevant entries.

A total of 28 unique AE reports associated with at-home microcurrent devices were identified (eTable). The majority involved NuFACE devices (ie, NuFACE Trinity, NuFACE Mini, and NuFACE Trinity+)(NuFACE)(n=25), followed by the TheraFace PRO (Therabody, Inc)(n=2) and the FOREO BEAR (FOREO)(n=1). The most frequently documented AEs associated with the NuFACE devices included arrhythmia (7/25 [28%]), pain (6/25 [24%]), dizziness (4/25 [16%]), headache (4/25 [16%]), and inflammation (4/25 [16%]). There was 1 (4%) case of retinal detachment. The TheraFace PRO was associated with device overheating (2/2 [100%]), and the FOREO BEAR was associated with facial deformity/disfigurement (1/1 [100%]).

While microcurrent therapy is widely marketed to consumers through social media influencers and at-home beauty platforms,¹ randomized controlled trials (RCTs) evaluating AEs related to use of this technology are lacking, possibly due to nonstringent regulation of nonprescription cosmetic devices.² Contrary to our findings, RCTs of microcurrent devices have reported minimal or no AEs; for instance, an RCT evaluating 56 participants treated 5 times weekly for 12 weeks with a microcurrent device that was not included in our analysis reported only mild erythema in all experimental group participants.² In another RCT of 30 participants, 15 of whom were treated with a microcurrent device and 15 with placebo for 30 minutes once daily over a period of 10 days, no AEs were reported.³ A cohort analysis of 34 patients also provided preliminary evidence supporting the use of microcurrent therapy for chronic back and neck pain, beyond its cosmetic applications.⁴ Despite the lack of reported AEs in the literature, there is a notable absence of large-scale, rigorous studies on this topic.

Our analysis was subject to the limitations of the MAUDE database, in which reports of severe AEs are more likely to be reported than transient ones. Additionally, the small sample size and lack of a known denominator make it difficult to compare frequencies of AEs among different microcurrent tools. The products chosen for this study were the select few that reported complaint data, but there is a large existing market of devices that may be associated with AEs that have yet to be reported, potentially because of their novelty.

Our findings suggest that, despite their over-the-counter availability, microcurrent facial devices may carry major risks—particularly in at-home settings. While short-term studies have highlighted potential benefits, the small sample sizes and limited follow-up make it difficult to comprehensively characterize long-term safety risks. Among available studies on microcurrent beauty treatments, the longest follow-up was only 12 weeks.² Our findings support the need for further large-scale and longitudinal studies to evaluate both the efficacy and safety of at-home microcurrent therapy, especially with increasing consumer interest. The diversity of the products available adds to the challenge of broad safety guidelines, in addition to the lack of long-term clinical studies.

The Public Health and Welfare Act of 1988 prohibited the use of federal funds to “provide individuals with hypodermic needles or syringes so that such individuals may use illegal drugs.”¹ Although the Act included the caveat that the US Surgeon General may determine that “a demonstration needle exchange program would be effective in reducing drug abuse,” and thus federal funds could be used, the legislation prohibited federal, state, and local agencies from funding syringe services programs (SSPs). SSPs use various harm reduction tools to improve public safety and reduce the potential harmful consequences of risky behaviors, similar to how using a seat belt while driving reduces the risk of injury or death.² SSPs are rooted in evidence-based practices, and several studies, according to the Centers for Disease Control and Prevention, have found that people who use drugs (PWUDs) who use community-based SSPs are 5 times more likely to enter treatment than those who do not use these programs. Additionally, these programs have shown an estimated 50% reduction in HIV and hepatitis C infections.³

Amid a 2015 HIV outbreak in Indiana among individuals sharing needles for injection drug use, Congress passed an omnibus spending bill that partially lifted the federal funding restriction. Federal funds now may be used for operational costs that support SSPs but may not be used to purchase syringes themselves.⁴

Following the 2015 legislation, federal agencies began implementing SSPs. The Veterans Health Administration (VHA) established SSPs at 3 medical centers in 2017.⁵ Veterans who participated in the programs were able to access supplies (eg, syringes, fentanyl test strips, wound care kits, and condoms) through donations to US Department of Veterans Affairs (VA) medical centers (VAMCs). The success of these programs laid the foundation for the VHA to implement SSPs nationally. VHA SSPs provided access to naloxone (an opioid overdose reversal medication), fentanyl test strips, condoms, sterile syringe distribution, testing for blood-borne viruses, HIV pre-exposure prophylaxis, as well as educational materials and resources, and low-barrier access to drug treatment (eg, medications for opioid use disorder [OUD]).

In 2020, the Biden Administration outlined 7 drug policy priorities, which included enhancing evidence-based harm reduction efforts. ⁶ This policy also discussed mandates for federal agencies to remove barriers to federal funding for purchasing syringes and other harm reduction supplies. The VHA responded to the policy by publishing guidance that recommended VAMCs develop and/or ensure veterans have access to harm reduction services in the community, where state law is not legally more stringent.⁷

In 2025 the Trump administration Statement of Drug Policy Priorities encouraged local jurisdictions to increase the availability of drug test strips and naloxone.⁸ These significant policy shifts moved SSPs from being housed mostly in local public health departments and community-based organizations to also being available at health care facilities. ⁹ VAMCs have unique opportunities to provide universal health care that includes both prevention services and other medical management to PWUD.

One study assessed staff perceptions of PWUD at a VAMC in preparation for a training program about harm reduction. The results indicated an overall positive staff perception of PWUD, although only the Drug and Drug Problems Perceptions Questionnaire (DDPPQ) was administered, which assessed comfort of working with this population and not explicitly the use of harm reduction.¹⁰ Another study interviewed clinical pharmacists, primary care clinicians, social workers, and directors of addiction and mental health services to determine barriers and facilitators (ie, potential opportunities to promote change) to implementing harm reduction at the VHA. The study identified barriers to be a lack of knowledge, time, and comfort, while suggesting opportunities for improvement were engagement of champions, communication and educational strategies, and adaptation of existing infrastructure.¹¹

While these findings are insightful for the VHA to disseminate a harm reduction program, there remains a gap in assessing staff willingness to provide harm reduction services. Evidence on harm reduction services among veterans is limited and more research is needed to better understand the role of these services and acceptance among enrolled veterans and VHA staff. Specifically, more research is needed on health care practitioners’ (HCPs) perceptions of harm reduction use.

Mental health care practitioners frequently treat patients with substance use disorders (SUDs), making them an ideal initial cohort to assess willingness to provide harm reduction to this population. By analyzing mental HCPs’ perceptions, additional interventions could be identified, implemented, and evaluated to improve their willingness to provide harm reduction tools.

This project focused on mental health clinicians with prescribing privileges: physicians (allopathic and osteopathic physicians), nurse practitioners, physician assistants, and clinical pharmacist practitioners. Mental health prescribers were selected because they are uniquely positioned at the intersection of prevention and treatment in drug use. Furthermore, mental health prescribers at the VAMCs included in this study are usually the primary point of entry to SUD clinics. This mixed-methods study used an anonymous online survey and voluntary postsurvey discussions with mental health care prescribers to elaborate on their beliefs and attitudes, providing deeper insight into their responses regarding harm reduction.

Methods

This project was conducted by the Veterans Integrated Services Network (VISN) 5 academic detailing team. VISN 5 serves veterans from economically and demographically diverse areas in Maryland; Washington, DC; West Virginia; and portions of Virginia, Pennsylvania, Ohio, and Kentucky. VAMCs in Baltimore, Maryland, and Washington, DC, serve a largely urban population while the 4 West Virginia facilities in Martinsburg, Huntington, Beckley, and Clarksburg, serve a largely rural population. West Virginia has been the epicenter of the opioid crisis and consistently has the highest drug overdose deaths per capita in the United States.¹² Among cities, Baltimore, Maryland, has the highest number of drug overdose deaths per capita with 174.1 per 100,000 people.^12,13

At the time of this project, the 6 VISN 5 VAMCs had established overdose education and naloxone distribution (OEND) programs. Although OEND programs have existed since 2013, VISN 5 SSPs and harm reduction services that provided fentanyl test strips were only available at the Martinsburg, Beckley, and Huntington VAMCs. All 6 VAMCs had substance use treatment programs with a variety of inpatient and outpatient mental health services. The Washington, DC and Baltimore VAMCs had opioid treatment programs that provided methadone maintenance.

The VISN 5 academic detailing team consists of 7 clinical pharmacists. These academic detailers plan annual systematic interventions to provide medical knowledge translation services on health-related campaigns. Academic detailers are trained in change management and motivational interviewing. They uniquely facilitate conversations with HCPs on various topics or campaigns, aiming for quality improvement and behavioral change through positive relationships and sharing resources.¹⁴ Academic detailing conversations and relationships with HCPs involve assessing and understanding HCP behaviors, including barriers and readiness to change to align with the goal of improving patient outcomes. Academic detailing has improved practice behaviors around providing OEND in VHA.¹⁵

To prepare for a harm reduction campaign, the academic detailers sought to gain insight from target VISN 5 mental health prescribers. Figure 1 outlines the project timeline, which started with emails inviting mental health prescribers to complete an anonymous online survey. Academic detailers from each site emailed mental health prescribers who completed the survey to determine interest in expanding on survey findings. Mental health prescribers who completed the survey could participate in a postsurvey discussion.

Surveys

Between January 29, 2024, and February 22, 2024, the academic detailers emailed facility mental health prescribers (N = 156) a link to an anonymous 15-question survey. The email informed recipients of the survey’s purpose: to gain a better understanding of prescriber perceptions of veterans with SUD and harm reduction programs and their willingness to provide harm reduction tools, to better determine interventions that could be implemented.

The survey collected prescriber demographic data and their perceptions of PWUD and harm reduction tools and education. Survey questions were extrapolated from validated surveys (eg, DDPPQ) and survey-based implicit association test.^16,17 The survey used multiple choice and 5-point Likert scale questions. Mental health prescribers were asked about their role at the VHA, years in practice, medical center affiliation, type of SUDs treated (eg, opioid, stimulant, alcohol, cannabis, or other), and whether they had previously met with academic detailers about harm reduction.

Respondents read statements about patients with or without SUD and provided Likert scale responses describing their regard, level of comfort, and preferences. The survey included Likert scale questions about respondents’ comfort in providing harm reduction education and supplies. Respondents also noted whether they believed harm reduction reduced substance use, harm reduction tools encourage people with SUD to continue using drugs, and whether HCPs can impact clinical change.

Postsurvey interviews with predetermined questions were conducted in-person or via video conference with ≥ 1 prescriber at each VAMC by an academic detailer. The postsurvey discussion offered an opportunity for respondents to further elaborate and describe previous experiences and current beliefs that may affect their attitudes toward people with SUD and their views on harm reduction. Participants received no compensation for survey completion or interviews.

Analysis

The Washington VAMC Institutional Review Board reviewed and approved this project as quality improvement with potential publication. No inferential statistics were calculated. Survey participant demographics were reported using frequencies and proportions reported for categorical variables. Notes from follow-up interviews were analyzed using the Prosci Awareness, Desire, Knowledge, Ability, and Reinforcement (ADKAR) Model for Change Management.¹⁸ This framework is used by academic detailers to determine a prescriber’s stage of change, which helps select the appropriate resources to move the clinician along a change framework. Completed postsurvey interview sheets, including notes written by the academic detailer, were analyzed by the project lead (NJ) who reviewed each interview sheet and analysis with the academic detailer who led the discussion.

Results

Sixty-six respondents completed the online survey (42% response rate), and 7 mental health prescribers participated in a postsurvey discussion. Thirty-one participants (47%) were physicians and 17 (26%) were in practice for > 20 years. Response rates reflected the size of mental health staff at each VAMC at the time of the survey: 17 respondents (26%) worked at each of the Martinsburg and Baltimore VAMCs, with fewer at the other VAMCs (Table 1). Alcohol use disorder was the most commonly reported SUD treated (n = 62; 33%), followed by cannabis use disorder (n = 40; 21%), OUD (n = 38; 20%), and stimulant use disorder (n = 37; 20%).

Respondents felt comfortable and confident educating patients on ways to reduce harm related to substance use (91%; mean [SD], 4.24 [0.84]). Most prescribers surveyed (97%; mean [SD], 1.59 [0.68]) disagreed or strongly disagreed that harm reduction encourages patients with SUD to continue using drugs, and all prescribers surveyed disagreed that there is nothing they can do to encourage harm reduction. Survey results were mixed for personal comfort in working with people with SUD vs people without SUD (Figure 2). Respondents were most willing to provide naloxone (95%; mean [SD], 4.71 [0.78]), compared to fentanyl test strips (61%; mean [SD], 3.61 [1.41]) or syringes (39%; mean [SD], 3.18 [1.39]). Respondents were neutral or least willing to provide syringes (Figure 3).

Seven postsurvey interviews were completed between academic detailers and mental health clinicians across the 6 VAMCs. Respondents included 1 physician assistant, 1 nurse practitioner, 1 pharmacist, and 4 physicians. Notes were analyzed using the ADKAR Change Competency Model to organize clinician stages of change (Table 2).

Barriers identified by interviewees included lack of mobile services, lack of confidence and awareness of the availability of harm reduction at their respective medical center, lack of time to discuss harm reduction, negative sentiments toward providing SUD-related harm reduction, discomfort with harm reduction products, and lack of knowledge and time to learn about harm reduction services. Opportunities identified to drive change in practice included additional time allotted during patient appointments, educational discussions and presentations to increase knowledge of and comfort with harm reduction tools, a clear clinical patient care workflow and process for harm reduction services, and reinforcement strategies to recognize success.

Discussion

This project investigated mental health prescribers’ perceptions of harm reduction at VAMCs in West Virginia, Maryland, and Washington, DC. While previous studies have demonstrated the efficacy of harm reduction tools, there is a lack of research on HCPs willingness to use these resources. This study suggests that while most respondents feel confident in and see the value of offering harm reduction resources to patients, a disparity exists between which resources HCPs are more likely to use and factors that would further enhance their ability to integrate harm reduction into practice. The follow-up interviews provided additional insight into the survey results.

Most respondents met the awareness and desire stage and moved to the knowledge, ability, or reinforcement ADKAR stage. It would be reasonable to extrapolate that most of the respondents felt comfortable with and were very likely to offer certain harm reduction tools. In the ADKAR interview analysis, the most common factors needed to drive change included having more time during patient appointments, additional education, clear processes for harm reduction services, and reinforcement strategies to sustain change. Respondents noted that harm reduction discussions took extra time in their already limited appointments with patients, which may have limited time for discussions surrounding all other mental health concerns. These discussions often necessitate in-depth conversations to accurately understand the patients’ needs. Given HCP time constraints, they may view harm reduction as lower in urgency and priority relative to other concerns. While most respondents were in the reinforcement phase, it is important to note the ADKAR model is fluid, and therefore an HCP could move forward or backward. This movement can be noted in the postsurvey interviews where, for example, prescriber 6 was determined to be in the reinforcement stage since they had already discussed harm reduction with patients. However, prescriber 6 also noted a barrier of unfamiliarity with local laws, which could shift them to the ADKAR knowledge stage.

Respondents noted that education through didactic sessions could lead to better incorporation of harm reduction into patient care. While harm reduction has evidence supporting its effectiveness, the respondents noted willingness to discuss harm reduction when treatment fails or the patient refuses treatment or referrals. Respondents expressed mixed opinions on use of harm reduction tools among patients with SUDs as some prescribers viewed harm reduction as part of a treatment plan and others viewed a return to drug use as a failure of treatment. Furthermore, respondents expressed hesitancy surrounding certain harm reduction tools, such as fentanyl test strips or syringes, and perceived these supplies as intended for medical use rather than harm reduction. HCPs may feel uncomfortable offering these supplies for drug use, despite their use for reducing risk.

Most responses were received from VAMCs with large mental health substance use programs. Respondents at larger, urban facilities (Washington, DC, and Baltimore, Maryland) expressed more hesitancy around using harm reduction tools despite having more harm reduction resources available compared to smaller or rural sites. These results align with previous studies that found no difference in prescribers providing medications for OUD in rural and urban VAMCs, showing urban sites, despite more resources, are not more willing to provide harm reduction or other addiction services.¹⁹ This evidence might indicate that urban sites may not use available resources (eg, methadone clinics) or that rural sites can provide just as robust medications for OUD care as urban sites.

Follow-up interview analysis indicated that HCPs lack knowledge of certain harm reduction tools. One-on-one peer discussions, like academic detailing, can facilitate discussions around a prescriber’s role in harm reduction, address gaps in knowledge by sharing what is available at the facilities for harm reduction, and suggest conversation points to help prescribers start harm reduction discussions with patients unwilling to begin treatment. Additionally, academic detailing can connect prescribers to available resources in the community to provide pragmatic approaches and suggestions. A clear and consistent treatment process may reduce barriers by reassuring prescribers they have support and by providing consistent directions so that prescribers do not waste time.

Reinforcement is important for sustaining change. VAMCs could consider positive feedback and other evidence-based reinforcement strategies (eg, social recognition, continuing education) to communicate that these changes are noticed and appreciated.²⁰ Late adopters may also be influenced by seeing positive feedback and results for peers. Systematic changes can be the catalyst for and sustain individual change.

Shifting perceptions and adopting change may be challenging, especially for SUD, which can be highly stigmatized. Promotion of successful change should be multifaceted and include both system and individual approaches. VHA systemic changes that could contribute to positive change include provision of time and access to SUD treatment training, a clear and sustainable treatment process, and reinforcement by recognizing success. In addition, facility leadership could provide support through dedicated time and resources during the workday for SUD treatment and harm reduction training. Support could empower HCPs and convey leadership support for harm reduction. This dedicated time could be used for didactic lecture sessions or individual meetings with academic detailers who can tailor discussions to the prescriber’s practice.

Strengths and Limitations

This survey included prescribers from a range of mental health care practice settings (eg, inpatient, outpatient clinic, rural, urban) and varied years of experience. This variety resulted in diverse perspectives and knowledge bases. Postsurvey interviews allowed academic detailers to gain deeper insight into answers in the survey, which can guide future interventions. Postsurvey interviews and application of the ADKAR model provided additional viewpoints on harm reduction.

A limitation of this project is the absence of an assessment of respondents’ harm reduction knowledge accuracy. Although respondents reported confidence in discussing harm reduction with patients, the survey did not assess whether their knowledge was accurate. Additionally, the survey did not ask about the availability of syringes and test strips at the prescribers’ VAMC, which could explain discrepancies in responses between naloxone and other forms of harm reduction (drug test strips and syringes were not available to all HCPs in the VISN). This lack of availability may have skewed responses. West Virginia SSPs, for example, were closed following legislative changes, which may contribute to stigma.²¹

Not all respondents were asked to do a follow-up interview, which limited the perspectives included in this study. Each site had ≥ 1 follow-up interview to limit the academic detailer’s workload. The initial survey included the phrase clean syringe, which can be stigmatizing and insinuate that PWUD are not clean. The preferred term would have been sterile syringe.²²

Conclusions

This survey of mental health prescribers found that most respondents are comfortable treating patients with SUD and confident in educating patients on harm reduction. Additionally, most respondents were more willing to provide naloxone vs fentanyl test strips or sterile syringes. A lack of time and awareness was the most frequently cited barrier to harm reduction services. As the VHA continues to expand access to harm reduction programs, which have proven to increase treatment rates and reduce disease, it will be imperative for HCPs, including mental health prescribers, to recognize the benefit of these programs for veterans with SUD. Future interventions should be designed and evaluated in collaboration with all HCPs and patients. This project determined ways to promote change for prescribers, but it will be important for further research to continue those conversations and incorporate patient perspectives.

The Public Health and Welfare Act of 1988 prohibited the use of federal funds to “provide individuals with hypodermic needles or syringes so that such individuals may use illegal drugs.”¹ Although the Act included the caveat that the US Surgeon General may determine that “a demonstration needle exchange program would be effective in reducing drug abuse,” and thus federal funds could be used, the legislation prohibited federal, state, and local agencies from funding syringe services programs (SSPs). SSPs use various harm reduction tools to improve public safety and reduce the potential harmful consequences of risky behaviors, similar to how using a seat belt while driving reduces the risk of injury or death.² SSPs are rooted in evidence-based practices, and several studies, according to the Centers for Disease Control and Prevention, have found that people who use drugs (PWUDs) who use community-based SSPs are 5 times more likely to enter treatment than those who do not use these programs. Additionally, these programs have shown an estimated 50% reduction in HIV and hepatitis C infections.³

Amid a 2015 HIV outbreak in Indiana among individuals sharing needles for injection drug use, Congress passed an omnibus spending bill that partially lifted the federal funding restriction. Federal funds now may be used for operational costs that support SSPs but may not be used to purchase syringes themselves.⁴

Following the 2015 legislation, federal agencies began implementing SSPs. The Veterans Health Administration (VHA) established SSPs at 3 medical centers in 2017.⁵ Veterans who participated in the programs were able to access supplies (eg, syringes, fentanyl test strips, wound care kits, and condoms) through donations to US Department of Veterans Affairs (VA) medical centers (VAMCs). The success of these programs laid the foundation for the VHA to implement SSPs nationally. VHA SSPs provided access to naloxone (an opioid overdose reversal medication), fentanyl test strips, condoms, sterile syringe distribution, testing for blood-borne viruses, HIV pre-exposure prophylaxis, as well as educational materials and resources, and low-barrier access to drug treatment (eg, medications for opioid use disorder [OUD]).

In 2020, the Biden Administration outlined 7 drug policy priorities, which included enhancing evidence-based harm reduction efforts. ⁶ This policy also discussed mandates for federal agencies to remove barriers to federal funding for purchasing syringes and other harm reduction supplies. The VHA responded to the policy by publishing guidance that recommended VAMCs develop and/or ensure veterans have access to harm reduction services in the community, where state law is not legally more stringent.⁷

In 2025 the Trump administration Statement of Drug Policy Priorities encouraged local jurisdictions to increase the availability of drug test strips and naloxone.⁸ These significant policy shifts moved SSPs from being housed mostly in local public health departments and community-based organizations to also being available at health care facilities. ⁹ VAMCs have unique opportunities to provide universal health care that includes both prevention services and other medical management to PWUD.

One study assessed staff perceptions of PWUD at a VAMC in preparation for a training program about harm reduction. The results indicated an overall positive staff perception of PWUD, although only the Drug and Drug Problems Perceptions Questionnaire (DDPPQ) was administered, which assessed comfort of working with this population and not explicitly the use of harm reduction.¹⁰ Another study interviewed clinical pharmacists, primary care clinicians, social workers, and directors of addiction and mental health services to determine barriers and facilitators (ie, potential opportunities to promote change) to implementing harm reduction at the VHA. The study identified barriers to be a lack of knowledge, time, and comfort, while suggesting opportunities for improvement were engagement of champions, communication and educational strategies, and adaptation of existing infrastructure.¹¹

While these findings are insightful for the VHA to disseminate a harm reduction program, there remains a gap in assessing staff willingness to provide harm reduction services. Evidence on harm reduction services among veterans is limited and more research is needed to better understand the role of these services and acceptance among enrolled veterans and VHA staff. Specifically, more research is needed on health care practitioners’ (HCPs) perceptions of harm reduction use.

Mental health care practitioners frequently treat patients with substance use disorders (SUDs), making them an ideal initial cohort to assess willingness to provide harm reduction to this population. By analyzing mental HCPs’ perceptions, additional interventions could be identified, implemented, and evaluated to improve their willingness to provide harm reduction tools.

This project focused on mental health clinicians with prescribing privileges: physicians (allopathic and osteopathic physicians), nurse practitioners, physician assistants, and clinical pharmacist practitioners. Mental health prescribers were selected because they are uniquely positioned at the intersection of prevention and treatment in drug use. Furthermore, mental health prescribers at the VAMCs included in this study are usually the primary point of entry to SUD clinics. This mixed-methods study used an anonymous online survey and voluntary postsurvey discussions with mental health care prescribers to elaborate on their beliefs and attitudes, providing deeper insight into their responses regarding harm reduction.

Methods

This project was conducted by the Veterans Integrated Services Network (VISN) 5 academic detailing team. VISN 5 serves veterans from economically and demographically diverse areas in Maryland; Washington, DC; West Virginia; and portions of Virginia, Pennsylvania, Ohio, and Kentucky. VAMCs in Baltimore, Maryland, and Washington, DC, serve a largely urban population while the 4 West Virginia facilities in Martinsburg, Huntington, Beckley, and Clarksburg, serve a largely rural population. West Virginia has been the epicenter of the opioid crisis and consistently has the highest drug overdose deaths per capita in the United States.¹² Among cities, Baltimore, Maryland, has the highest number of drug overdose deaths per capita with 174.1 per 100,000 people.^12,13

At the time of this project, the 6 VISN 5 VAMCs had established overdose education and naloxone distribution (OEND) programs. Although OEND programs have existed since 2013, VISN 5 SSPs and harm reduction services that provided fentanyl test strips were only available at the Martinsburg, Beckley, and Huntington VAMCs. All 6 VAMCs had substance use treatment programs with a variety of inpatient and outpatient mental health services. The Washington, DC and Baltimore VAMCs had opioid treatment programs that provided methadone maintenance.

The VISN 5 academic detailing team consists of 7 clinical pharmacists. These academic detailers plan annual systematic interventions to provide medical knowledge translation services on health-related campaigns. Academic detailers are trained in change management and motivational interviewing. They uniquely facilitate conversations with HCPs on various topics or campaigns, aiming for quality improvement and behavioral change through positive relationships and sharing resources.¹⁴ Academic detailing conversations and relationships with HCPs involve assessing and understanding HCP behaviors, including barriers and readiness to change to align with the goal of improving patient outcomes. Academic detailing has improved practice behaviors around providing OEND in VHA.¹⁵

To prepare for a harm reduction campaign, the academic detailers sought to gain insight from target VISN 5 mental health prescribers. Figure 1 outlines the project timeline, which started with emails inviting mental health prescribers to complete an anonymous online survey. Academic detailers from each site emailed mental health prescribers who completed the survey to determine interest in expanding on survey findings. Mental health prescribers who completed the survey could participate in a postsurvey discussion.

Surveys

Between January 29, 2024, and February 22, 2024, the academic detailers emailed facility mental health prescribers (N = 156) a link to an anonymous 15-question survey. The email informed recipients of the survey’s purpose: to gain a better understanding of prescriber perceptions of veterans with SUD and harm reduction programs and their willingness to provide harm reduction tools, to better determine interventions that could be implemented.

The survey collected prescriber demographic data and their perceptions of PWUD and harm reduction tools and education. Survey questions were extrapolated from validated surveys (eg, DDPPQ) and survey-based implicit association test.^16,17 The survey used multiple choice and 5-point Likert scale questions. Mental health prescribers were asked about their role at the VHA, years in practice, medical center affiliation, type of SUDs treated (eg, opioid, stimulant, alcohol, cannabis, or other), and whether they had previously met with academic detailers about harm reduction.

Respondents read statements about patients with or without SUD and provided Likert scale responses describing their regard, level of comfort, and preferences. The survey included Likert scale questions about respondents’ comfort in providing harm reduction education and supplies. Respondents also noted whether they believed harm reduction reduced substance use, harm reduction tools encourage people with SUD to continue using drugs, and whether HCPs can impact clinical change.

Postsurvey interviews with predetermined questions were conducted in-person or via video conference with ≥ 1 prescriber at each VAMC by an academic detailer. The postsurvey discussion offered an opportunity for respondents to further elaborate and describe previous experiences and current beliefs that may affect their attitudes toward people with SUD and their views on harm reduction. Participants received no compensation for survey completion or interviews.

Analysis

The Washington VAMC Institutional Review Board reviewed and approved this project as quality improvement with potential publication. No inferential statistics were calculated. Survey participant demographics were reported using frequencies and proportions reported for categorical variables. Notes from follow-up interviews were analyzed using the Prosci Awareness, Desire, Knowledge, Ability, and Reinforcement (ADKAR) Model for Change Management.¹⁸ This framework is used by academic detailers to determine a prescriber’s stage of change, which helps select the appropriate resources to move the clinician along a change framework. Completed postsurvey interview sheets, including notes written by the academic detailer, were analyzed by the project lead (NJ) who reviewed each interview sheet and analysis with the academic detailer who led the discussion.

Results

Sixty-six respondents completed the online survey (42% response rate), and 7 mental health prescribers participated in a postsurvey discussion. Thirty-one participants (47%) were physicians and 17 (26%) were in practice for > 20 years. Response rates reflected the size of mental health staff at each VAMC at the time of the survey: 17 respondents (26%) worked at each of the Martinsburg and Baltimore VAMCs, with fewer at the other VAMCs (Table 1). Alcohol use disorder was the most commonly reported SUD treated (n = 62; 33%), followed by cannabis use disorder (n = 40; 21%), OUD (n = 38; 20%), and stimulant use disorder (n = 37; 20%).

Respondents felt comfortable and confident educating patients on ways to reduce harm related to substance use (91%; mean [SD], 4.24 [0.84]). Most prescribers surveyed (97%; mean [SD], 1.59 [0.68]) disagreed or strongly disagreed that harm reduction encourages patients with SUD to continue using drugs, and all prescribers surveyed disagreed that there is nothing they can do to encourage harm reduction. Survey results were mixed for personal comfort in working with people with SUD vs people without SUD (Figure 2). Respondents were most willing to provide naloxone (95%; mean [SD], 4.71 [0.78]), compared to fentanyl test strips (61%; mean [SD], 3.61 [1.41]) or syringes (39%; mean [SD], 3.18 [1.39]). Respondents were neutral or least willing to provide syringes (Figure 3).

Seven postsurvey interviews were completed between academic detailers and mental health clinicians across the 6 VAMCs. Respondents included 1 physician assistant, 1 nurse practitioner, 1 pharmacist, and 4 physicians. Notes were analyzed using the ADKAR Change Competency Model to organize clinician stages of change (Table 2).

Barriers identified by interviewees included lack of mobile services, lack of confidence and awareness of the availability of harm reduction at their respective medical center, lack of time to discuss harm reduction, negative sentiments toward providing SUD-related harm reduction, discomfort with harm reduction products, and lack of knowledge and time to learn about harm reduction services. Opportunities identified to drive change in practice included additional time allotted during patient appointments, educational discussions and presentations to increase knowledge of and comfort with harm reduction tools, a clear clinical patient care workflow and process for harm reduction services, and reinforcement strategies to recognize success.

Discussion

This project investigated mental health prescribers’ perceptions of harm reduction at VAMCs in West Virginia, Maryland, and Washington, DC. While previous studies have demonstrated the efficacy of harm reduction tools, there is a lack of research on HCPs willingness to use these resources. This study suggests that while most respondents feel confident in and see the value of offering harm reduction resources to patients, a disparity exists between which resources HCPs are more likely to use and factors that would further enhance their ability to integrate harm reduction into practice. The follow-up interviews provided additional insight into the survey results.

Most respondents met the awareness and desire stage and moved to the knowledge, ability, or reinforcement ADKAR stage. It would be reasonable to extrapolate that most of the respondents felt comfortable with and were very likely to offer certain harm reduction tools. In the ADKAR interview analysis, the most common factors needed to drive change included having more time during patient appointments, additional education, clear processes for harm reduction services, and reinforcement strategies to sustain change. Respondents noted that harm reduction discussions took extra time in their already limited appointments with patients, which may have limited time for discussions surrounding all other mental health concerns. These discussions often necessitate in-depth conversations to accurately understand the patients’ needs. Given HCP time constraints, they may view harm reduction as lower in urgency and priority relative to other concerns. While most respondents were in the reinforcement phase, it is important to note the ADKAR model is fluid, and therefore an HCP could move forward or backward. This movement can be noted in the postsurvey interviews where, for example, prescriber 6 was determined to be in the reinforcement stage since they had already discussed harm reduction with patients. However, prescriber 6 also noted a barrier of unfamiliarity with local laws, which could shift them to the ADKAR knowledge stage.

Respondents noted that education through didactic sessions could lead to better incorporation of harm reduction into patient care. While harm reduction has evidence supporting its effectiveness, the respondents noted willingness to discuss harm reduction when treatment fails or the patient refuses treatment or referrals. Respondents expressed mixed opinions on use of harm reduction tools among patients with SUDs as some prescribers viewed harm reduction as part of a treatment plan and others viewed a return to drug use as a failure of treatment. Furthermore, respondents expressed hesitancy surrounding certain harm reduction tools, such as fentanyl test strips or syringes, and perceived these supplies as intended for medical use rather than harm reduction. HCPs may feel uncomfortable offering these supplies for drug use, despite their use for reducing risk.

Most responses were received from VAMCs with large mental health substance use programs. Respondents at larger, urban facilities (Washington, DC, and Baltimore, Maryland) expressed more hesitancy around using harm reduction tools despite having more harm reduction resources available compared to smaller or rural sites. These results align with previous studies that found no difference in prescribers providing medications for OUD in rural and urban VAMCs, showing urban sites, despite more resources, are not more willing to provide harm reduction or other addiction services.¹⁹ This evidence might indicate that urban sites may not use available resources (eg, methadone clinics) or that rural sites can provide just as robust medications for OUD care as urban sites.

Follow-up interview analysis indicated that HCPs lack knowledge of certain harm reduction tools. One-on-one peer discussions, like academic detailing, can facilitate discussions around a prescriber’s role in harm reduction, address gaps in knowledge by sharing what is available at the facilities for harm reduction, and suggest conversation points to help prescribers start harm reduction discussions with patients unwilling to begin treatment. Additionally, academic detailing can connect prescribers to available resources in the community to provide pragmatic approaches and suggestions. A clear and consistent treatment process may reduce barriers by reassuring prescribers they have support and by providing consistent directions so that prescribers do not waste time.

Reinforcement is important for sustaining change. VAMCs could consider positive feedback and other evidence-based reinforcement strategies (eg, social recognition, continuing education) to communicate that these changes are noticed and appreciated.²⁰ Late adopters may also be influenced by seeing positive feedback and results for peers. Systematic changes can be the catalyst for and sustain individual change.

Shifting perceptions and adopting change may be challenging, especially for SUD, which can be highly stigmatized. Promotion of successful change should be multifaceted and include both system and individual approaches. VHA systemic changes that could contribute to positive change include provision of time and access to SUD treatment training, a clear and sustainable treatment process, and reinforcement by recognizing success. In addition, facility leadership could provide support through dedicated time and resources during the workday for SUD treatment and harm reduction training. Support could empower HCPs and convey leadership support for harm reduction. This dedicated time could be used for didactic lecture sessions or individual meetings with academic detailers who can tailor discussions to the prescriber’s practice.

Strengths and Limitations

This survey included prescribers from a range of mental health care practice settings (eg, inpatient, outpatient clinic, rural, urban) and varied years of experience. This variety resulted in diverse perspectives and knowledge bases. Postsurvey interviews allowed academic detailers to gain deeper insight into answers in the survey, which can guide future interventions. Postsurvey interviews and application of the ADKAR model provided additional viewpoints on harm reduction.

A limitation of this project is the absence of an assessment of respondents’ harm reduction knowledge accuracy. Although respondents reported confidence in discussing harm reduction with patients, the survey did not assess whether their knowledge was accurate. Additionally, the survey did not ask about the availability of syringes and test strips at the prescribers’ VAMC, which could explain discrepancies in responses between naloxone and other forms of harm reduction (drug test strips and syringes were not available to all HCPs in the VISN). This lack of availability may have skewed responses. West Virginia SSPs, for example, were closed following legislative changes, which may contribute to stigma.²¹

Not all respondents were asked to do a follow-up interview, which limited the perspectives included in this study. Each site had ≥ 1 follow-up interview to limit the academic detailer’s workload. The initial survey included the phrase clean syringe, which can be stigmatizing and insinuate that PWUD are not clean. The preferred term would have been sterile syringe.²²

Conclusions

This survey of mental health prescribers found that most respondents are comfortable treating patients with SUD and confident in educating patients on harm reduction. Additionally, most respondents were more willing to provide naloxone vs fentanyl test strips or sterile syringes. A lack of time and awareness was the most frequently cited barrier to harm reduction services. As the VHA continues to expand access to harm reduction programs, which have proven to increase treatment rates and reduce disease, it will be imperative for HCPs, including mental health prescribers, to recognize the benefit of these programs for veterans with SUD. Future interventions should be designed and evaluated in collaboration with all HCPs and patients. This project determined ways to promote change for prescribers, but it will be important for further research to continue those conversations and incorporate patient perspectives.

The Public Health and Welfare Act of 1988 prohibited the use of federal funds to “provide individuals with hypodermic needles or syringes so that such individuals may use illegal drugs.”¹ Although the Act included the caveat that the US Surgeon General may determine that “a demonstration needle exchange program would be effective in reducing drug abuse,” and thus federal funds could be used, the legislation prohibited federal, state, and local agencies from funding syringe services programs (SSPs). SSPs use various harm reduction tools to improve public safety and reduce the potential harmful consequences of risky behaviors, similar to how using a seat belt while driving reduces the risk of injury or death.² SSPs are rooted in evidence-based practices, and several studies, according to the Centers for Disease Control and Prevention, have found that people who use drugs (PWUDs) who use community-based SSPs are 5 times more likely to enter treatment than those who do not use these programs. Additionally, these programs have shown an estimated 50% reduction in HIV and hepatitis C infections.³

Amid a 2015 HIV outbreak in Indiana among individuals sharing needles for injection drug use, Congress passed an omnibus spending bill that partially lifted the federal funding restriction. Federal funds now may be used for operational costs that support SSPs but may not be used to purchase syringes themselves.⁴

Following the 2015 legislation, federal agencies began implementing SSPs. The Veterans Health Administration (VHA) established SSPs at 3 medical centers in 2017.⁵ Veterans who participated in the programs were able to access supplies (eg, syringes, fentanyl test strips, wound care kits, and condoms) through donations to US Department of Veterans Affairs (VA) medical centers (VAMCs). The success of these programs laid the foundation for the VHA to implement SSPs nationally. VHA SSPs provided access to naloxone (an opioid overdose reversal medication), fentanyl test strips, condoms, sterile syringe distribution, testing for blood-borne viruses, HIV pre-exposure prophylaxis, as well as educational materials and resources, and low-barrier access to drug treatment (eg, medications for opioid use disorder [OUD]).

In 2020, the Biden Administration outlined 7 drug policy priorities, which included enhancing evidence-based harm reduction efforts. ⁶ This policy also discussed mandates for federal agencies to remove barriers to federal funding for purchasing syringes and other harm reduction supplies. The VHA responded to the policy by publishing guidance that recommended VAMCs develop and/or ensure veterans have access to harm reduction services in the community, where state law is not legally more stringent.⁷

In 2025 the Trump administration Statement of Drug Policy Priorities encouraged local jurisdictions to increase the availability of drug test strips and naloxone.⁸ These significant policy shifts moved SSPs from being housed mostly in local public health departments and community-based organizations to also being available at health care facilities. ⁹ VAMCs have unique opportunities to provide universal health care that includes both prevention services and other medical management to PWUD.

One study assessed staff perceptions of PWUD at a VAMC in preparation for a training program about harm reduction. The results indicated an overall positive staff perception of PWUD, although only the Drug and Drug Problems Perceptions Questionnaire (DDPPQ) was administered, which assessed comfort of working with this population and not explicitly the use of harm reduction.¹⁰ Another study interviewed clinical pharmacists, primary care clinicians, social workers, and directors of addiction and mental health services to determine barriers and facilitators (ie, potential opportunities to promote change) to implementing harm reduction at the VHA. The study identified barriers to be a lack of knowledge, time, and comfort, while suggesting opportunities for improvement were engagement of champions, communication and educational strategies, and adaptation of existing infrastructure.¹¹

While these findings are insightful for the VHA to disseminate a harm reduction program, there remains a gap in assessing staff willingness to provide harm reduction services. Evidence on harm reduction services among veterans is limited and more research is needed to better understand the role of these services and acceptance among enrolled veterans and VHA staff. Specifically, more research is needed on health care practitioners’ (HCPs) perceptions of harm reduction use.

Mental health care practitioners frequently treat patients with substance use disorders (SUDs), making them an ideal initial cohort to assess willingness to provide harm reduction to this population. By analyzing mental HCPs’ perceptions, additional interventions could be identified, implemented, and evaluated to improve their willingness to provide harm reduction tools.

This project focused on mental health clinicians with prescribing privileges: physicians (allopathic and osteopathic physicians), nurse practitioners, physician assistants, and clinical pharmacist practitioners. Mental health prescribers were selected because they are uniquely positioned at the intersection of prevention and treatment in drug use. Furthermore, mental health prescribers at the VAMCs included in this study are usually the primary point of entry to SUD clinics. This mixed-methods study used an anonymous online survey and voluntary postsurvey discussions with mental health care prescribers to elaborate on their beliefs and attitudes, providing deeper insight into their responses regarding harm reduction.

Methods

This project was conducted by the Veterans Integrated Services Network (VISN) 5 academic detailing team. VISN 5 serves veterans from economically and demographically diverse areas in Maryland; Washington, DC; West Virginia; and portions of Virginia, Pennsylvania, Ohio, and Kentucky. VAMCs in Baltimore, Maryland, and Washington, DC, serve a largely urban population while the 4 West Virginia facilities in Martinsburg, Huntington, Beckley, and Clarksburg, serve a largely rural population. West Virginia has been the epicenter of the opioid crisis and consistently has the highest drug overdose deaths per capita in the United States.¹² Among cities, Baltimore, Maryland, has the highest number of drug overdose deaths per capita with 174.1 per 100,000 people.^12,13

At the time of this project, the 6 VISN 5 VAMCs had established overdose education and naloxone distribution (OEND) programs. Although OEND programs have existed since 2013, VISN 5 SSPs and harm reduction services that provided fentanyl test strips were only available at the Martinsburg, Beckley, and Huntington VAMCs. All 6 VAMCs had substance use treatment programs with a variety of inpatient and outpatient mental health services. The Washington, DC and Baltimore VAMCs had opioid treatment programs that provided methadone maintenance.

The VISN 5 academic detailing team consists of 7 clinical pharmacists. These academic detailers plan annual systematic interventions to provide medical knowledge translation services on health-related campaigns. Academic detailers are trained in change management and motivational interviewing. They uniquely facilitate conversations with HCPs on various topics or campaigns, aiming for quality improvement and behavioral change through positive relationships and sharing resources.¹⁴ Academic detailing conversations and relationships with HCPs involve assessing and understanding HCP behaviors, including barriers and readiness to change to align with the goal of improving patient outcomes. Academic detailing has improved practice behaviors around providing OEND in VHA.¹⁵

To prepare for a harm reduction campaign, the academic detailers sought to gain insight from target VISN 5 mental health prescribers. Figure 1 outlines the project timeline, which started with emails inviting mental health prescribers to complete an anonymous online survey. Academic detailers from each site emailed mental health prescribers who completed the survey to determine interest in expanding on survey findings. Mental health prescribers who completed the survey could participate in a postsurvey discussion.

Surveys

Between January 29, 2024, and February 22, 2024, the academic detailers emailed facility mental health prescribers (N = 156) a link to an anonymous 15-question survey. The email informed recipients of the survey’s purpose: to gain a better understanding of prescriber perceptions of veterans with SUD and harm reduction programs and their willingness to provide harm reduction tools, to better determine interventions that could be implemented.

The survey collected prescriber demographic data and their perceptions of PWUD and harm reduction tools and education. Survey questions were extrapolated from validated surveys (eg, DDPPQ) and survey-based implicit association test.^16,17 The survey used multiple choice and 5-point Likert scale questions. Mental health prescribers were asked about their role at the VHA, years in practice, medical center affiliation, type of SUDs treated (eg, opioid, stimulant, alcohol, cannabis, or other), and whether they had previously met with academic detailers about harm reduction.

Respondents read statements about patients with or without SUD and provided Likert scale responses describing their regard, level of comfort, and preferences. The survey included Likert scale questions about respondents’ comfort in providing harm reduction education and supplies. Respondents also noted whether they believed harm reduction reduced substance use, harm reduction tools encourage people with SUD to continue using drugs, and whether HCPs can impact clinical change.

Postsurvey interviews with predetermined questions were conducted in-person or via video conference with ≥ 1 prescriber at each VAMC by an academic detailer. The postsurvey discussion offered an opportunity for respondents to further elaborate and describe previous experiences and current beliefs that may affect their attitudes toward people with SUD and their views on harm reduction. Participants received no compensation for survey completion or interviews.

Analysis

The Washington VAMC Institutional Review Board reviewed and approved this project as quality improvement with potential publication. No inferential statistics were calculated. Survey participant demographics were reported using frequencies and proportions reported for categorical variables. Notes from follow-up interviews were analyzed using the Prosci Awareness, Desire, Knowledge, Ability, and Reinforcement (ADKAR) Model for Change Management.¹⁸ This framework is used by academic detailers to determine a prescriber’s stage of change, which helps select the appropriate resources to move the clinician along a change framework. Completed postsurvey interview sheets, including notes written by the academic detailer, were analyzed by the project lead (NJ) who reviewed each interview sheet and analysis with the academic detailer who led the discussion.

Results

Sixty-six respondents completed the online survey (42% response rate), and 7 mental health prescribers participated in a postsurvey discussion. Thirty-one participants (47%) were physicians and 17 (26%) were in practice for > 20 years. Response rates reflected the size of mental health staff at each VAMC at the time of the survey: 17 respondents (26%) worked at each of the Martinsburg and Baltimore VAMCs, with fewer at the other VAMCs (Table 1). Alcohol use disorder was the most commonly reported SUD treated (n = 62; 33%), followed by cannabis use disorder (n = 40; 21%), OUD (n = 38; 20%), and stimulant use disorder (n = 37; 20%).

Respondents felt comfortable and confident educating patients on ways to reduce harm related to substance use (91%; mean [SD], 4.24 [0.84]). Most prescribers surveyed (97%; mean [SD], 1.59 [0.68]) disagreed or strongly disagreed that harm reduction encourages patients with SUD to continue using drugs, and all prescribers surveyed disagreed that there is nothing they can do to encourage harm reduction. Survey results were mixed for personal comfort in working with people with SUD vs people without SUD (Figure 2). Respondents were most willing to provide naloxone (95%; mean [SD], 4.71 [0.78]), compared to fentanyl test strips (61%; mean [SD], 3.61 [1.41]) or syringes (39%; mean [SD], 3.18 [1.39]). Respondents were neutral or least willing to provide syringes (Figure 3).

Seven postsurvey interviews were completed between academic detailers and mental health clinicians across the 6 VAMCs. Respondents included 1 physician assistant, 1 nurse practitioner, 1 pharmacist, and 4 physicians. Notes were analyzed using the ADKAR Change Competency Model to organize clinician stages of change (Table 2).

Barriers identified by interviewees included lack of mobile services, lack of confidence and awareness of the availability of harm reduction at their respective medical center, lack of time to discuss harm reduction, negative sentiments toward providing SUD-related harm reduction, discomfort with harm reduction products, and lack of knowledge and time to learn about harm reduction services. Opportunities identified to drive change in practice included additional time allotted during patient appointments, educational discussions and presentations to increase knowledge of and comfort with harm reduction tools, a clear clinical patient care workflow and process for harm reduction services, and reinforcement strategies to recognize success.

Discussion

This project investigated mental health prescribers’ perceptions of harm reduction at VAMCs in West Virginia, Maryland, and Washington, DC. While previous studies have demonstrated the efficacy of harm reduction tools, there is a lack of research on HCPs willingness to use these resources. This study suggests that while most respondents feel confident in and see the value of offering harm reduction resources to patients, a disparity exists between which resources HCPs are more likely to use and factors that would further enhance their ability to integrate harm reduction into practice. The follow-up interviews provided additional insight into the survey results.

Most respondents met the awareness and desire stage and moved to the knowledge, ability, or reinforcement ADKAR stage. It would be reasonable to extrapolate that most of the respondents felt comfortable with and were very likely to offer certain harm reduction tools. In the ADKAR interview analysis, the most common factors needed to drive change included having more time during patient appointments, additional education, clear processes for harm reduction services, and reinforcement strategies to sustain change. Respondents noted that harm reduction discussions took extra time in their already limited appointments with patients, which may have limited time for discussions surrounding all other mental health concerns. These discussions often necessitate in-depth conversations to accurately understand the patients’ needs. Given HCP time constraints, they may view harm reduction as lower in urgency and priority relative to other concerns. While most respondents were in the reinforcement phase, it is important to note the ADKAR model is fluid, and therefore an HCP could move forward or backward. This movement can be noted in the postsurvey interviews where, for example, prescriber 6 was determined to be in the reinforcement stage since they had already discussed harm reduction with patients. However, prescriber 6 also noted a barrier of unfamiliarity with local laws, which could shift them to the ADKAR knowledge stage.

Respondents noted that education through didactic sessions could lead to better incorporation of harm reduction into patient care. While harm reduction has evidence supporting its effectiveness, the respondents noted willingness to discuss harm reduction when treatment fails or the patient refuses treatment or referrals. Respondents expressed mixed opinions on use of harm reduction tools among patients with SUDs as some prescribers viewed harm reduction as part of a treatment plan and others viewed a return to drug use as a failure of treatment. Furthermore, respondents expressed hesitancy surrounding certain harm reduction tools, such as fentanyl test strips or syringes, and perceived these supplies as intended for medical use rather than harm reduction. HCPs may feel uncomfortable offering these supplies for drug use, despite their use for reducing risk.

Most responses were received from VAMCs with large mental health substance use programs. Respondents at larger, urban facilities (Washington, DC, and Baltimore, Maryland) expressed more hesitancy around using harm reduction tools despite having more harm reduction resources available compared to smaller or rural sites. These results align with previous studies that found no difference in prescribers providing medications for OUD in rural and urban VAMCs, showing urban sites, despite more resources, are not more willing to provide harm reduction or other addiction services.¹⁹ This evidence might indicate that urban sites may not use available resources (eg, methadone clinics) or that rural sites can provide just as robust medications for OUD care as urban sites.

Follow-up interview analysis indicated that HCPs lack knowledge of certain harm reduction tools. One-on-one peer discussions, like academic detailing, can facilitate discussions around a prescriber’s role in harm reduction, address gaps in knowledge by sharing what is available at the facilities for harm reduction, and suggest conversation points to help prescribers start harm reduction discussions with patients unwilling to begin treatment. Additionally, academic detailing can connect prescribers to available resources in the community to provide pragmatic approaches and suggestions. A clear and consistent treatment process may reduce barriers by reassuring prescribers they have support and by providing consistent directions so that prescribers do not waste time.

Reinforcement is important for sustaining change. VAMCs could consider positive feedback and other evidence-based reinforcement strategies (eg, social recognition, continuing education) to communicate that these changes are noticed and appreciated.²⁰ Late adopters may also be influenced by seeing positive feedback and results for peers. Systematic changes can be the catalyst for and sustain individual change.

Shifting perceptions and adopting change may be challenging, especially for SUD, which can be highly stigmatized. Promotion of successful change should be multifaceted and include both system and individual approaches. VHA systemic changes that could contribute to positive change include provision of time and access to SUD treatment training, a clear and sustainable treatment process, and reinforcement by recognizing success. In addition, facility leadership could provide support through dedicated time and resources during the workday for SUD treatment and harm reduction training. Support could empower HCPs and convey leadership support for harm reduction. This dedicated time could be used for didactic lecture sessions or individual meetings with academic detailers who can tailor discussions to the prescriber’s practice.

Strengths and Limitations

This survey included prescribers from a range of mental health care practice settings (eg, inpatient, outpatient clinic, rural, urban) and varied years of experience. This variety resulted in diverse perspectives and knowledge bases. Postsurvey interviews allowed academic detailers to gain deeper insight into answers in the survey, which can guide future interventions. Postsurvey interviews and application of the ADKAR model provided additional viewpoints on harm reduction.

A limitation of this project is the absence of an assessment of respondents’ harm reduction knowledge accuracy. Although respondents reported confidence in discussing harm reduction with patients, the survey did not assess whether their knowledge was accurate. Additionally, the survey did not ask about the availability of syringes and test strips at the prescribers’ VAMC, which could explain discrepancies in responses between naloxone and other forms of harm reduction (drug test strips and syringes were not available to all HCPs in the VISN). This lack of availability may have skewed responses. West Virginia SSPs, for example, were closed following legislative changes, which may contribute to stigma.²¹

Not all respondents were asked to do a follow-up interview, which limited the perspectives included in this study. Each site had ≥ 1 follow-up interview to limit the academic detailer’s workload. The initial survey included the phrase clean syringe, which can be stigmatizing and insinuate that PWUD are not clean. The preferred term would have been sterile syringe.²²

Conclusions

This survey of mental health prescribers found that most respondents are comfortable treating patients with SUD and confident in educating patients on harm reduction. Additionally, most respondents were more willing to provide naloxone vs fentanyl test strips or sterile syringes. A lack of time and awareness was the most frequently cited barrier to harm reduction services. As the VHA continues to expand access to harm reduction programs, which have proven to increase treatment rates and reduce disease, it will be imperative for HCPs, including mental health prescribers, to recognize the benefit of these programs for veterans with SUD. Future interventions should be designed and evaluated in collaboration with all HCPs and patients. This project determined ways to promote change for prescribers, but it will be important for further research to continue those conversations and incorporate patient perspectives.

Growing recognition of the limitations of long-term opioid therapy for chronic noncancer pain has highlighted the importance of nonpharmacologic approaches to pain treatment.^1,2 These treatments are varied and may include psychological and behavioral therapies (eg, cognitive behavioral therapy for chronic pain), exercise and movement therapies (eg, yoga), and manual therapies (eg, chiropractic). Body surface cold therapy, while predominantly used to reduce postoperative pain and inflammation,^3,4 pain subsequent to acute musculoskeletal injury,⁵ and pain related to muscle soreness,⁶ is a nonpharmacologic treatment that has shown pain-reducing benefits for chronic low back pain and knee osteoarthritis, as has its counterpart, superficial heat therapy.^7-9 Heat therapy has also been shown to improve strength, flexibility, and activities of daily living in patients with chronic low back pain.^10,11 Cold and heat therapies are commonly used complementarily. Cold therapies aim to reduce blood flow and inflammation and are often used immediately following trauma to an affected area, whereas heat therapies increase blood flow and metabolic activity and are commonly used to promote healing.⁵

Heat and cold therapies (also known as thermal therapies) benefit resource-limited health care systems, as most devices require a single expenditure and can be self-administered by patients at home as part of their pain self-management plan. In addition, these pain self-management tools may attenuate the need for more expensive specialty pain care and ongoing analgesic pharmacotherapy. Despite their potential, few studies have characterized the benefits of thermal therapies for patients with heterogeneous chronic pain syndromes.

The purpose of this retrospective cohort study was to examine the potential clinical and health system benefits of patient-administered thermal therapy. Our primary hypothesis was that patients with chronic musculoskeletal pain who received a thermal therapy self-management device would have fewer days of opioid prescriptions compared with a sample of matched control patients. Secondarily, we hypothesized that patients who received a thermal therapy device would have lower utilization of specialty pain care, fewer potentially hazardous opioid prescriptions (eg, high-dose opioid therapy and concurrent opioid and benzodiazepine prescriptions), fewer prescriptions for nonopioid analgesic medications, and decreased pain intensity when compared with matched controls.

Methods

This retrospective cohort study compared pain pharmacotherapy, pain treatment utilization, and pain intensity outcomes between patients who received a thermal therapy device and matched patients who did not. The study was approved by the US Department of Veterans Affairs (VA) Portland Health Care System Institutional Review Board and was granted a waiver of informed consent to access patient electronic health records (EHRs).

Pain Care

The VA uses ThermaZone thermal therapy devices (Innovative Medical Equipment) for chronic pain treatment. The device uses thermoelectric technology to provide point-of-contact cooling and heating therapy through site-specific pads (eg, ankle, knee, hip, back, elbow, shoulder). Patients place pads on sites where they experience pain, and temperature regulated water circulates through the device and to the pad, providing consistent, localized thermal therapy. The pads range in temperature from 1 °C to 52 °C, and temperatures are self-monitored and controlled by the patient.

Standard pain care in this study followed the VA stepped model of pain care, which builds on a foundation of patient education for pain self-management approaches (eg, exercise, mindfulness, relaxation, social support).¹² According to the VA stepped model of pain care, all patients with chronic pain should engage in these foundational self-management approaches. However, some patients require more intensive care. The VA stepped-up treatment engages primary and specialty care services—such as physical therapy, pharmacy, complementary and integrative health approaches, mental health, and substance use services—and, when indicated, may escalate treatment to interdisciplinary pain teams or tertiary pain centers. In this retrospective cohort study, treatment patients received a thermal therapy device and standard of care, whereas control patients received standard care only.

Sample Selection

Eligible patients were aged ≥ 18 years, had a musculoskeletal pain diagnosis documented in the EHR in the year prior to thermal therapy device receipt (or during the same period for a treatment patient’s matched control), and were enrolled in VA health care during the study period. Patients who died during the study period were excluded. Treatment patients received a thermal therapy device from the VA between January 1, 2017, and December 31, 2018, when complete data on thermal therapy devices were available. For control patients, the VA Corporate Data Warehouse (CDW) was used to identify VA patients with characteristics similar to those of treated patients.

We modeled the probability that a patient would receive a thermal therapy device using logistic regression. Predictor variables were measured in the year prior to device receipt and included variables associated with pain treatment utilization and analgesic pharmacotherapy receipt, as recommended by Brookhart et al.¹³ These included age, sex, race, ethnicity, VA service-connected disability status, comorbidities, receipt of medications for opioid use disorder, pain diagnoses, mental health diagnoses, and substance use disorder diagnoses.^14-19

The resulting propensity scores (eg, predicted probabilities) were used to match treatment patients 1:1 with control patients using a nearest-neighbor matching algorithm.²⁰ This procedure matches a control patient with the closest propensity score to that of the corresponding treatment patient. An exact match on VA facility was required (eg, treatment patients and matched controls received care at the same VA facility). Standardized differences were used to assess covariate balance between the matched groups, and kernel density plots of propensity scores tested for sufficient overlap.²¹ Control patients were selected from a pool of 1,150,149 patients.

Study Variables

The index date was the date the thermal therapy device was released to treatment patients or the same date for the matched controls. Data were extracted from the CDW over a 24-month period: 12 months prior to the index date through 12 months afterwards. Collecting data in the 12 months prior to treatment initiation allowed us to adjust for covariates and provided greater precision, as recommended for observational study designs.²²

Treatment conditions were defined dichotomously as receipt vs nonreceipt of a thermal therapy device. The primary outcome was the number of days of opioid use in the 12 months following the index date. Additional outcomes included days of high-dose opioid therapy (≥ 50 mg morphine equivalent [MME] daily), concurrent opioid and benzodiazepine prescriptions, and nonopioid analgesic pharmacotherapy (eg, nonsteroidal anti-inflammatory drugs, acetaminophen, muscle relaxants). All prescription data were based on medication fills obtained from VA pharmacy records. Additional outcomes included the number of visits to physical therapy, occupational therapy, physical medicine and rehabilitation, and interdisciplinary pain clinics, including interventional pain medicine.

Pain intensity ratings were collected as part of routine VA care using a numeric scale from 0 (no pain) to 10 (worst possible pain). Pain intensity trajectories were computed using all available EHR-derived pain intensity score data for each patient in the 12 months prior to and following the index date.

Covariates were extracted from the EHR and evaluated in the year prior to the index date, unless otherwise noted. They included age at the index date; self-reported sex, and race and ethnicity; service-connected disability status (disability awarded as a result of military service-related trauma or injury); Charlson Comorbidity Index; and diagnoses of opioid use disorder, alcohol use disorder, other substance use disorder, mood disorder, posttraumatic stress disorder, other anxiety disorder, psychotic disorder, neuropathic pain, and headache pain.²³ All diagnoses were coded as yes if identified in the EHR as a focus of treatment during any clinical encounter in the year prior to the index date or no if not identified as a focus of treatment.

The number of days patients had been living with pain was calculated and defined as the number of days from the first pain diagnosis available in a patient’s EHR to the index date. Finally, the number of pain-related surgeries (eg, surgeries with ≥ 1 pain diagnoses associated with the clinical encounter) and average pain intensity were computed for the pre- and postindex date evaluation periods and included as model covariates.

Statistical Analyses

For the 4 pharmacotherapy and 4 nonpharmacologic treatment usage outcomes, we tested the fit of linear models and several models with count distributions using the Bayesian information criterion.²⁴ Count distributions included Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial. With 1 exception (days of high-dose opioid use), a negative binomial distribution best fit the data. For days of high-dose opioid use, a Gaussian distribution best fit the data.

Eight separate mixed-effects regression analyses compared changes in each outcome from the 12-month preindex period through the 12-month postindex period between treatment and control patients by testing the Time × Treatment interaction. This approach statistically accounts for observed pretreatment differences in outcome variables. Statistics for the main effects of time and treatment are also presented. To reduce bias, models controlled for covariates specified previously.

For pain intensity, we used random-effects growth modeling to quantify both fixed and random effects of pain intensity at the index date (eg, the model intercept), which estimates pain at the time of treatment initiation, and change in pain during the 12 months following the index date (eg, the model slope), which characterizes the trajectory of pain intensity ratings.²⁵ The model included piecewise components of pain score trajectories in the 12 months prior to the index date and in the 12 months following the index date. Several types of change for the 12-month postindex observation period were explored—including quadratic and cubic curvilinear change. A linear model for change in pain over time provided the best fit based on the Bayesian information criterion and parsimony of model parameters.²⁶ We report estimates of change in pain over time in monthly intervals for ease of interpretation. However, models used all individual pain scores rather than computing monthly averages when > 1 pain score was available within a month, as recommended in previous research.²⁷ This approach makes optimal use of all available data. Both random effects (intercept and slope) were regressed onto the set of covariates described previously.

This study used data available in the EHR over the 24-month observation period. We characterize the density (eg, frequency) of all outcome variables by treatment condition in the Appendix. Because the hypotheses were directional, the authors used a 2-sided α = 0.10 and applied a Bonferroni correction for the 9 statistical tests performed, resulting in an adjusted α of 0.01. Treatment utilization and pharmacotherapy outcome analyses were performed in Stata, version 16.1. Random-effects growth modeling of pain score trajectories was performed using Mplus, version 8.8.

Results

There were 2182 patients in the treatment group and 2182 matched controls. The mean (SD) age was 54 (15) years; 81% were male, and about two-thirds (68%) identified as White and non-Hispanic. Mental health comorbidities were common, with > 40% of the sample having diagnoses of a mood disorder and/or posttraumatic stress disorder. Nearly all patients (90%) had VA service-connected disability ratings. Among patients with chronic musculoskeletal pain, 8% had comorbid neuropathic pain and 14% had headache. The mean (SD) duration of chronic pain across both patient groups was 3416 (2016) days, or about 9.4 years (Table 1).

Pharmacotherapy

High-dose opioid use (> 50 MME daily dose), days of opioid use, and concurrent opioid and benzodiazepine use decreased for all patients from the pre- to posttreatment period (Table 2). However, high-dose opioid use (Time × Treatment interaction, 3.24; 99% CI, 0.34 to 6.14) (Figure 1A) and concurrent opioid and benzodiazepine use (Time × Treatment interaction, 0.76; 99% CI, 0.67 to 0.86) (Figure 1B) had a larger decrease for the treatment vs matched control group. Treatment and matched control patients had comparable reductions in days of opioid use (Time × Treatment interaction, 0.98; 99% CI, 0.91 to 1.05) (Figure 1C). Neither group showed changes in nonopioid analgesic pharmacotherapy over time (main effect of time, incidence rate ratio [IRR], 1.03; 99% CI, 0.99 to 1.07; Time × Treatment interaction, 0.95; 99% CI, 0.90 to 1.01) (Figure 1D).

Nonpharmacologic Pain Treatment

The number of physical therapy and pain clinic visits declined for treatment patients and increased slightly for matched control patients (Figure 2A and 2B). For occupational therapy visits, neither group showed changes over time (main effect of time IRR, 1.03; 99% CI, 0.83 to 1.22; Time × Treatment interaction, 1.20; 99% CI, 0.93 to 1.46) (Figure 2C). For physical medicine and rehabilitation visits, both groups decreased use over time (main effect of time IRR, 0.78; 99% CI, 0.66 to 0.90), but this decrease did not differ between treatment and control patients (Time × Treatment interaction, 1.16; 99% CI, 0.94 to 1.37) (Figure 2D).

Pain Intensity

Pain intensity decreased for both groups by an estimated 0.02 points per month (99% CI, -0.04 to -0.01; P < .01), or 0.24 points over the 12-month postindex follow-up period (Figure 3). There were no statistically significant differences between treatment and control patients in pain intensity changes over the follow-up period (B = -0.02; 99% CI, -0.06 to 0.02; P = .15).

Discussion

Patients with musculoskeletal pain who received a thermal therapy device showed a larger decline in use of some specialty pain treatments, including physical therapy and specialty pain clinic services, when compared with matched control patients. One possible explanation is that patients who used the device may have had their pain adequately managed and thus required less specialty pain care. However, the absence of clinically significant changes in pain intensity over time suggests that pain intensity alone did not account for the observed changes in pain treatment use. We were unable to measure constructs of pain interference or functioning, which may be better predictors of functional restoration, as these data are not routinely collected within the VA and documented in the EHR. Future studies could clarify these findings by including measures of pain interference and functioning.

Although the overall declines in physical therapy and specialty pain clinic use associated with thermal therapy were modest (< 1 visit per patient), the impact of these reductions can be profound. In resource-limited health care settings, even small reductions in high-cost care utilization could be of great value in that health systems could offset costs associated with treating chronic pain without compromising quality of care or key clinical outcomes, such as pain intensity. This study, however, did not include a cost analysis. Future studies should incorporate formal cost analyses to quantify cost offsets that may result from decreased specialty pain treatment use.

Patients who received thermal therapy devices did not show clinically meaningful reductions in pain intensity over time, defined as reductions in pain intensity of 1.7 to 2.0 on a 0 to 10 scale.^28,29 This finding is consistent with prior research that demonstrates relatively stable pain intensity self-ratings longitudinally by patients with chronic pain diagnoses, when assessed in the context of usual clinical care.³⁰ This finding, however, is inconsistent with prior literature that demonstrates pain-reducing benefits of thermal therapy for low back pain and knee osteoarthritis.^7-9

In this study, pain intensity ratings were derived from the EHR during routine outpatient clinical encounters and not at the time thermal therapy was self-administered, as has been done in prior clinical trials.^7-9 Pain location was not specified at the time of pain ratings, and it is possible that patients may have been endorsing pain in areas of the body that had not been treated by thermal therapy. Patient-level variability in pain intensity ratings (eg, within-patient range over time) was not examined, although prior research indicates substantial variability.³⁰ While average pain intensity ratings in the current study did not change, an examination of patient variability warrants further study, as a narrowing of pain intensity ratings can be perceived, by patients, as demonstrable improvement and has been associated with improved physical and psychological outcomes.³¹ Furthermore, pain intensity does not characterize physical or emotional functioning that can be captured with more comprehensive validated measures, some of which are recommended outcomes in pain clinical trials.³²

Our findings point to reductions in all forms of opioid use across both treatment and control patients. Data from the VA and in the US more generally point to downward trends in opioid prescribing during the study period.³³ This decline is likely due to increased use of risk mitigation approaches, such as routine urine drug screens and review of prescription drug monitoring databases.³⁴ These state-level databases track prescribing of controlled substances, including opioids and benzodiazepines, within a state. Implementation of these practices has been associated with declines in higher risk opioid prescribing.³⁴ Findings from this study further point to associations of reduced higher risk opioid use among patients who received thermal therapy devices. In the full sample of patients, reductions in days of opioid use, high-dose opioid use, and co-use of opioids and benzodiazepines were observed across all patients, with greater reductions observed in high-dose opioid use and concurrent opioid and benzodiazepine use among patients who received a thermal therapy device. Experimental studies indicate that the endogenous opioid system is implicated in pain relief and activated by thermal therapies. ³⁵ Differential reductions in higher-risk opioid use among patients who received thermal therapy devices in our study may be associated with endogenous opioid activation, though this was not specifically measured. It is also unclear whether thermal therapy was provided by clinicians in the context of opioid tapering or other risk mitigation efforts, or patients reduced higher risk opioid use of their own volition. Prior research has identified both patient- and clinician-initiated opioid tapering and discontinuation.³⁶ While a thorough explication of opioid dose reduction was beyond the scope of this study, future qualitative work could help explain potential benefits of thermal therapy in the context of analgesic pharmacotherapy use, including opioid medications.

Limitations

The extent to which patients used the thermal therapy device could not be measured; therefore, device receipt was used as a proxy for use. However, it was not possible to determine whether the frequency and duration of device use was associated with study outcomes. Treatment and control groups demonstrated some differences in outcome variables at the index date. Potential known biases were addressed using propensity score matching procedures and statistical procedures that controlled for patient demographic and clinical characteristics, as well as pretreatment values of all outcome variables. Nevertheless, as an observational study, this analysis cannot account for all known and unknown confounders, and a randomized controlled trial is needed to make claims of causality. The study population consisted of US veterans and included a low proportion of women. As a result, the findings may not be generalizable to other patient populations. Finally, prescription dispensing data, used as a proxy for pharmacotherapy use, do not necessarily reflect actual medication use.

Conclusions

This study is among the first to examine associations between thermal therapy and specialty pain treatment and analgesic pharmacotherapy use among US veterans. Although the retrospective cohort study design does not allow causal inferences regarding the efficacy of thermal therapy for veterans with chronic musculoskeletal pain, confidence in the findings is strengthened by methodological and statistical control of known confounders. Future trials employing experimental designs are needed to further clarify the clinical and health systems benefits of thermal therapy for musculoskeletal pain syndromes.

Growing recognition of the limitations of long-term opioid therapy for chronic noncancer pain has highlighted the importance of nonpharmacologic approaches to pain treatment.^1,2 These treatments are varied and may include psychological and behavioral therapies (eg, cognitive behavioral therapy for chronic pain), exercise and movement therapies (eg, yoga), and manual therapies (eg, chiropractic). Body surface cold therapy, while predominantly used to reduce postoperative pain and inflammation,^3,4 pain subsequent to acute musculoskeletal injury,⁵ and pain related to muscle soreness,⁶ is a nonpharmacologic treatment that has shown pain-reducing benefits for chronic low back pain and knee osteoarthritis, as has its counterpart, superficial heat therapy.^7-9 Heat therapy has also been shown to improve strength, flexibility, and activities of daily living in patients with chronic low back pain.^10,11 Cold and heat therapies are commonly used complementarily. Cold therapies aim to reduce blood flow and inflammation and are often used immediately following trauma to an affected area, whereas heat therapies increase blood flow and metabolic activity and are commonly used to promote healing.⁵

Heat and cold therapies (also known as thermal therapies) benefit resource-limited health care systems, as most devices require a single expenditure and can be self-administered by patients at home as part of their pain self-management plan. In addition, these pain self-management tools may attenuate the need for more expensive specialty pain care and ongoing analgesic pharmacotherapy. Despite their potential, few studies have characterized the benefits of thermal therapies for patients with heterogeneous chronic pain syndromes.

The purpose of this retrospective cohort study was to examine the potential clinical and health system benefits of patient-administered thermal therapy. Our primary hypothesis was that patients with chronic musculoskeletal pain who received a thermal therapy self-management device would have fewer days of opioid prescriptions compared with a sample of matched control patients. Secondarily, we hypothesized that patients who received a thermal therapy device would have lower utilization of specialty pain care, fewer potentially hazardous opioid prescriptions (eg, high-dose opioid therapy and concurrent opioid and benzodiazepine prescriptions), fewer prescriptions for nonopioid analgesic medications, and decreased pain intensity when compared with matched controls.

Methods

This retrospective cohort study compared pain pharmacotherapy, pain treatment utilization, and pain intensity outcomes between patients who received a thermal therapy device and matched patients who did not. The study was approved by the US Department of Veterans Affairs (VA) Portland Health Care System Institutional Review Board and was granted a waiver of informed consent to access patient electronic health records (EHRs).

Pain Care

The VA uses ThermaZone thermal therapy devices (Innovative Medical Equipment) for chronic pain treatment. The device uses thermoelectric technology to provide point-of-contact cooling and heating therapy through site-specific pads (eg, ankle, knee, hip, back, elbow, shoulder). Patients place pads on sites where they experience pain, and temperature regulated water circulates through the device and to the pad, providing consistent, localized thermal therapy. The pads range in temperature from 1 °C to 52 °C, and temperatures are self-monitored and controlled by the patient.

Standard pain care in this study followed the VA stepped model of pain care, which builds on a foundation of patient education for pain self-management approaches (eg, exercise, mindfulness, relaxation, social support).¹² According to the VA stepped model of pain care, all patients with chronic pain should engage in these foundational self-management approaches. However, some patients require more intensive care. The VA stepped-up treatment engages primary and specialty care services—such as physical therapy, pharmacy, complementary and integrative health approaches, mental health, and substance use services—and, when indicated, may escalate treatment to interdisciplinary pain teams or tertiary pain centers. In this retrospective cohort study, treatment patients received a thermal therapy device and standard of care, whereas control patients received standard care only.

Sample Selection

Eligible patients were aged ≥ 18 years, had a musculoskeletal pain diagnosis documented in the EHR in the year prior to thermal therapy device receipt (or during the same period for a treatment patient’s matched control), and were enrolled in VA health care during the study period. Patients who died during the study period were excluded. Treatment patients received a thermal therapy device from the VA between January 1, 2017, and December 31, 2018, when complete data on thermal therapy devices were available. For control patients, the VA Corporate Data Warehouse (CDW) was used to identify VA patients with characteristics similar to those of treated patients.

We modeled the probability that a patient would receive a thermal therapy device using logistic regression. Predictor variables were measured in the year prior to device receipt and included variables associated with pain treatment utilization and analgesic pharmacotherapy receipt, as recommended by Brookhart et al.¹³ These included age, sex, race, ethnicity, VA service-connected disability status, comorbidities, receipt of medications for opioid use disorder, pain diagnoses, mental health diagnoses, and substance use disorder diagnoses.^14-19

The resulting propensity scores (eg, predicted probabilities) were used to match treatment patients 1:1 with control patients using a nearest-neighbor matching algorithm.²⁰ This procedure matches a control patient with the closest propensity score to that of the corresponding treatment patient. An exact match on VA facility was required (eg, treatment patients and matched controls received care at the same VA facility). Standardized differences were used to assess covariate balance between the matched groups, and kernel density plots of propensity scores tested for sufficient overlap.²¹ Control patients were selected from a pool of 1,150,149 patients.

Study Variables

The index date was the date the thermal therapy device was released to treatment patients or the same date for the matched controls. Data were extracted from the CDW over a 24-month period: 12 months prior to the index date through 12 months afterwards. Collecting data in the 12 months prior to treatment initiation allowed us to adjust for covariates and provided greater precision, as recommended for observational study designs.²²

Treatment conditions were defined dichotomously as receipt vs nonreceipt of a thermal therapy device. The primary outcome was the number of days of opioid use in the 12 months following the index date. Additional outcomes included days of high-dose opioid therapy (≥ 50 mg morphine equivalent [MME] daily), concurrent opioid and benzodiazepine prescriptions, and nonopioid analgesic pharmacotherapy (eg, nonsteroidal anti-inflammatory drugs, acetaminophen, muscle relaxants). All prescription data were based on medication fills obtained from VA pharmacy records. Additional outcomes included the number of visits to physical therapy, occupational therapy, physical medicine and rehabilitation, and interdisciplinary pain clinics, including interventional pain medicine.

Pain intensity ratings were collected as part of routine VA care using a numeric scale from 0 (no pain) to 10 (worst possible pain). Pain intensity trajectories were computed using all available EHR-derived pain intensity score data for each patient in the 12 months prior to and following the index date.

Covariates were extracted from the EHR and evaluated in the year prior to the index date, unless otherwise noted. They included age at the index date; self-reported sex, and race and ethnicity; service-connected disability status (disability awarded as a result of military service-related trauma or injury); Charlson Comorbidity Index; and diagnoses of opioid use disorder, alcohol use disorder, other substance use disorder, mood disorder, posttraumatic stress disorder, other anxiety disorder, psychotic disorder, neuropathic pain, and headache pain.²³ All diagnoses were coded as yes if identified in the EHR as a focus of treatment during any clinical encounter in the year prior to the index date or no if not identified as a focus of treatment.

The number of days patients had been living with pain was calculated and defined as the number of days from the first pain diagnosis available in a patient’s EHR to the index date. Finally, the number of pain-related surgeries (eg, surgeries with ≥ 1 pain diagnoses associated with the clinical encounter) and average pain intensity were computed for the pre- and postindex date evaluation periods and included as model covariates.

Statistical Analyses

For the 4 pharmacotherapy and 4 nonpharmacologic treatment usage outcomes, we tested the fit of linear models and several models with count distributions using the Bayesian information criterion.²⁴ Count distributions included Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial. With 1 exception (days of high-dose opioid use), a negative binomial distribution best fit the data. For days of high-dose opioid use, a Gaussian distribution best fit the data.

Eight separate mixed-effects regression analyses compared changes in each outcome from the 12-month preindex period through the 12-month postindex period between treatment and control patients by testing the Time × Treatment interaction. This approach statistically accounts for observed pretreatment differences in outcome variables. Statistics for the main effects of time and treatment are also presented. To reduce bias, models controlled for covariates specified previously.

For pain intensity, we used random-effects growth modeling to quantify both fixed and random effects of pain intensity at the index date (eg, the model intercept), which estimates pain at the time of treatment initiation, and change in pain during the 12 months following the index date (eg, the model slope), which characterizes the trajectory of pain intensity ratings.²⁵ The model included piecewise components of pain score trajectories in the 12 months prior to the index date and in the 12 months following the index date. Several types of change for the 12-month postindex observation period were explored—including quadratic and cubic curvilinear change. A linear model for change in pain over time provided the best fit based on the Bayesian information criterion and parsimony of model parameters.²⁶ We report estimates of change in pain over time in monthly intervals for ease of interpretation. However, models used all individual pain scores rather than computing monthly averages when > 1 pain score was available within a month, as recommended in previous research.²⁷ This approach makes optimal use of all available data. Both random effects (intercept and slope) were regressed onto the set of covariates described previously.

This study used data available in the EHR over the 24-month observation period. We characterize the density (eg, frequency) of all outcome variables by treatment condition in the Appendix. Because the hypotheses were directional, the authors used a 2-sided α = 0.10 and applied a Bonferroni correction for the 9 statistical tests performed, resulting in an adjusted α of 0.01. Treatment utilization and pharmacotherapy outcome analyses were performed in Stata, version 16.1. Random-effects growth modeling of pain score trajectories was performed using Mplus, version 8.8.

Results

There were 2182 patients in the treatment group and 2182 matched controls. The mean (SD) age was 54 (15) years; 81% were male, and about two-thirds (68%) identified as White and non-Hispanic. Mental health comorbidities were common, with > 40% of the sample having diagnoses of a mood disorder and/or posttraumatic stress disorder. Nearly all patients (90%) had VA service-connected disability ratings. Among patients with chronic musculoskeletal pain, 8% had comorbid neuropathic pain and 14% had headache. The mean (SD) duration of chronic pain across both patient groups was 3416 (2016) days, or about 9.4 years (Table 1).

Pharmacotherapy

High-dose opioid use (> 50 MME daily dose), days of opioid use, and concurrent opioid and benzodiazepine use decreased for all patients from the pre- to posttreatment period (Table 2). However, high-dose opioid use (Time × Treatment interaction, 3.24; 99% CI, 0.34 to 6.14) (Figure 1A) and concurrent opioid and benzodiazepine use (Time × Treatment interaction, 0.76; 99% CI, 0.67 to 0.86) (Figure 1B) had a larger decrease for the treatment vs matched control group. Treatment and matched control patients had comparable reductions in days of opioid use (Time × Treatment interaction, 0.98; 99% CI, 0.91 to 1.05) (Figure 1C). Neither group showed changes in nonopioid analgesic pharmacotherapy over time (main effect of time, incidence rate ratio [IRR], 1.03; 99% CI, 0.99 to 1.07; Time × Treatment interaction, 0.95; 99% CI, 0.90 to 1.01) (Figure 1D).

Nonpharmacologic Pain Treatment

The number of physical therapy and pain clinic visits declined for treatment patients and increased slightly for matched control patients (Figure 2A and 2B). For occupational therapy visits, neither group showed changes over time (main effect of time IRR, 1.03; 99% CI, 0.83 to 1.22; Time × Treatment interaction, 1.20; 99% CI, 0.93 to 1.46) (Figure 2C). For physical medicine and rehabilitation visits, both groups decreased use over time (main effect of time IRR, 0.78; 99% CI, 0.66 to 0.90), but this decrease did not differ between treatment and control patients (Time × Treatment interaction, 1.16; 99% CI, 0.94 to 1.37) (Figure 2D).

Pain Intensity

Pain intensity decreased for both groups by an estimated 0.02 points per month (99% CI, -0.04 to -0.01; P < .01), or 0.24 points over the 12-month postindex follow-up period (Figure 3). There were no statistically significant differences between treatment and control patients in pain intensity changes over the follow-up period (B = -0.02; 99% CI, -0.06 to 0.02; P = .15).

Discussion

Patients with musculoskeletal pain who received a thermal therapy device showed a larger decline in use of some specialty pain treatments, including physical therapy and specialty pain clinic services, when compared with matched control patients. One possible explanation is that patients who used the device may have had their pain adequately managed and thus required less specialty pain care. However, the absence of clinically significant changes in pain intensity over time suggests that pain intensity alone did not account for the observed changes in pain treatment use. We were unable to measure constructs of pain interference or functioning, which may be better predictors of functional restoration, as these data are not routinely collected within the VA and documented in the EHR. Future studies could clarify these findings by including measures of pain interference and functioning.

Although the overall declines in physical therapy and specialty pain clinic use associated with thermal therapy were modest (< 1 visit per patient), the impact of these reductions can be profound. In resource-limited health care settings, even small reductions in high-cost care utilization could be of great value in that health systems could offset costs associated with treating chronic pain without compromising quality of care or key clinical outcomes, such as pain intensity. This study, however, did not include a cost analysis. Future studies should incorporate formal cost analyses to quantify cost offsets that may result from decreased specialty pain treatment use.

Patients who received thermal therapy devices did not show clinically meaningful reductions in pain intensity over time, defined as reductions in pain intensity of 1.7 to 2.0 on a 0 to 10 scale.^28,29 This finding is consistent with prior research that demonstrates relatively stable pain intensity self-ratings longitudinally by patients with chronic pain diagnoses, when assessed in the context of usual clinical care.³⁰ This finding, however, is inconsistent with prior literature that demonstrates pain-reducing benefits of thermal therapy for low back pain and knee osteoarthritis.^7-9

In this study, pain intensity ratings were derived from the EHR during routine outpatient clinical encounters and not at the time thermal therapy was self-administered, as has been done in prior clinical trials.^7-9 Pain location was not specified at the time of pain ratings, and it is possible that patients may have been endorsing pain in areas of the body that had not been treated by thermal therapy. Patient-level variability in pain intensity ratings (eg, within-patient range over time) was not examined, although prior research indicates substantial variability.³⁰ While average pain intensity ratings in the current study did not change, an examination of patient variability warrants further study, as a narrowing of pain intensity ratings can be perceived, by patients, as demonstrable improvement and has been associated with improved physical and psychological outcomes.³¹ Furthermore, pain intensity does not characterize physical or emotional functioning that can be captured with more comprehensive validated measures, some of which are recommended outcomes in pain clinical trials.³²

Our findings point to reductions in all forms of opioid use across both treatment and control patients. Data from the VA and in the US more generally point to downward trends in opioid prescribing during the study period.³³ This decline is likely due to increased use of risk mitigation approaches, such as routine urine drug screens and review of prescription drug monitoring databases.³⁴ These state-level databases track prescribing of controlled substances, including opioids and benzodiazepines, within a state. Implementation of these practices has been associated with declines in higher risk opioid prescribing.³⁴ Findings from this study further point to associations of reduced higher risk opioid use among patients who received thermal therapy devices. In the full sample of patients, reductions in days of opioid use, high-dose opioid use, and co-use of opioids and benzodiazepines were observed across all patients, with greater reductions observed in high-dose opioid use and concurrent opioid and benzodiazepine use among patients who received a thermal therapy device. Experimental studies indicate that the endogenous opioid system is implicated in pain relief and activated by thermal therapies. ³⁵ Differential reductions in higher-risk opioid use among patients who received thermal therapy devices in our study may be associated with endogenous opioid activation, though this was not specifically measured. It is also unclear whether thermal therapy was provided by clinicians in the context of opioid tapering or other risk mitigation efforts, or patients reduced higher risk opioid use of their own volition. Prior research has identified both patient- and clinician-initiated opioid tapering and discontinuation.³⁶ While a thorough explication of opioid dose reduction was beyond the scope of this study, future qualitative work could help explain potential benefits of thermal therapy in the context of analgesic pharmacotherapy use, including opioid medications.

Limitations

The extent to which patients used the thermal therapy device could not be measured; therefore, device receipt was used as a proxy for use. However, it was not possible to determine whether the frequency and duration of device use was associated with study outcomes. Treatment and control groups demonstrated some differences in outcome variables at the index date. Potential known biases were addressed using propensity score matching procedures and statistical procedures that controlled for patient demographic and clinical characteristics, as well as pretreatment values of all outcome variables. Nevertheless, as an observational study, this analysis cannot account for all known and unknown confounders, and a randomized controlled trial is needed to make claims of causality. The study population consisted of US veterans and included a low proportion of women. As a result, the findings may not be generalizable to other patient populations. Finally, prescription dispensing data, used as a proxy for pharmacotherapy use, do not necessarily reflect actual medication use.

Conclusions

This study is among the first to examine associations between thermal therapy and specialty pain treatment and analgesic pharmacotherapy use among US veterans. Although the retrospective cohort study design does not allow causal inferences regarding the efficacy of thermal therapy for veterans with chronic musculoskeletal pain, confidence in the findings is strengthened by methodological and statistical control of known confounders. Future trials employing experimental designs are needed to further clarify the clinical and health systems benefits of thermal therapy for musculoskeletal pain syndromes.

Growing recognition of the limitations of long-term opioid therapy for chronic noncancer pain has highlighted the importance of nonpharmacologic approaches to pain treatment.^1,2 These treatments are varied and may include psychological and behavioral therapies (eg, cognitive behavioral therapy for chronic pain), exercise and movement therapies (eg, yoga), and manual therapies (eg, chiropractic). Body surface cold therapy, while predominantly used to reduce postoperative pain and inflammation,^3,4 pain subsequent to acute musculoskeletal injury,⁵ and pain related to muscle soreness,⁶ is a nonpharmacologic treatment that has shown pain-reducing benefits for chronic low back pain and knee osteoarthritis, as has its counterpart, superficial heat therapy.^7-9 Heat therapy has also been shown to improve strength, flexibility, and activities of daily living in patients with chronic low back pain.^10,11 Cold and heat therapies are commonly used complementarily. Cold therapies aim to reduce blood flow and inflammation and are often used immediately following trauma to an affected area, whereas heat therapies increase blood flow and metabolic activity and are commonly used to promote healing.⁵

Heat and cold therapies (also known as thermal therapies) benefit resource-limited health care systems, as most devices require a single expenditure and can be self-administered by patients at home as part of their pain self-management plan. In addition, these pain self-management tools may attenuate the need for more expensive specialty pain care and ongoing analgesic pharmacotherapy. Despite their potential, few studies have characterized the benefits of thermal therapies for patients with heterogeneous chronic pain syndromes.

The purpose of this retrospective cohort study was to examine the potential clinical and health system benefits of patient-administered thermal therapy. Our primary hypothesis was that patients with chronic musculoskeletal pain who received a thermal therapy self-management device would have fewer days of opioid prescriptions compared with a sample of matched control patients. Secondarily, we hypothesized that patients who received a thermal therapy device would have lower utilization of specialty pain care, fewer potentially hazardous opioid prescriptions (eg, high-dose opioid therapy and concurrent opioid and benzodiazepine prescriptions), fewer prescriptions for nonopioid analgesic medications, and decreased pain intensity when compared with matched controls.

Methods

This retrospective cohort study compared pain pharmacotherapy, pain treatment utilization, and pain intensity outcomes between patients who received a thermal therapy device and matched patients who did not. The study was approved by the US Department of Veterans Affairs (VA) Portland Health Care System Institutional Review Board and was granted a waiver of informed consent to access patient electronic health records (EHRs).

Pain Care

The VA uses ThermaZone thermal therapy devices (Innovative Medical Equipment) for chronic pain treatment. The device uses thermoelectric technology to provide point-of-contact cooling and heating therapy through site-specific pads (eg, ankle, knee, hip, back, elbow, shoulder). Patients place pads on sites where they experience pain, and temperature regulated water circulates through the device and to the pad, providing consistent, localized thermal therapy. The pads range in temperature from 1 °C to 52 °C, and temperatures are self-monitored and controlled by the patient.

Standard pain care in this study followed the VA stepped model of pain care, which builds on a foundation of patient education for pain self-management approaches (eg, exercise, mindfulness, relaxation, social support).¹² According to the VA stepped model of pain care, all patients with chronic pain should engage in these foundational self-management approaches. However, some patients require more intensive care. The VA stepped-up treatment engages primary and specialty care services—such as physical therapy, pharmacy, complementary and integrative health approaches, mental health, and substance use services—and, when indicated, may escalate treatment to interdisciplinary pain teams or tertiary pain centers. In this retrospective cohort study, treatment patients received a thermal therapy device and standard of care, whereas control patients received standard care only.

Sample Selection

Eligible patients were aged ≥ 18 years, had a musculoskeletal pain diagnosis documented in the EHR in the year prior to thermal therapy device receipt (or during the same period for a treatment patient’s matched control), and were enrolled in VA health care during the study period. Patients who died during the study period were excluded. Treatment patients received a thermal therapy device from the VA between January 1, 2017, and December 31, 2018, when complete data on thermal therapy devices were available. For control patients, the VA Corporate Data Warehouse (CDW) was used to identify VA patients with characteristics similar to those of treated patients.

We modeled the probability that a patient would receive a thermal therapy device using logistic regression. Predictor variables were measured in the year prior to device receipt and included variables associated with pain treatment utilization and analgesic pharmacotherapy receipt, as recommended by Brookhart et al.¹³ These included age, sex, race, ethnicity, VA service-connected disability status, comorbidities, receipt of medications for opioid use disorder, pain diagnoses, mental health diagnoses, and substance use disorder diagnoses.^14-19

The resulting propensity scores (eg, predicted probabilities) were used to match treatment patients 1:1 with control patients using a nearest-neighbor matching algorithm.²⁰ This procedure matches a control patient with the closest propensity score to that of the corresponding treatment patient. An exact match on VA facility was required (eg, treatment patients and matched controls received care at the same VA facility). Standardized differences were used to assess covariate balance between the matched groups, and kernel density plots of propensity scores tested for sufficient overlap.²¹ Control patients were selected from a pool of 1,150,149 patients.

Study Variables

The index date was the date the thermal therapy device was released to treatment patients or the same date for the matched controls. Data were extracted from the CDW over a 24-month period: 12 months prior to the index date through 12 months afterwards. Collecting data in the 12 months prior to treatment initiation allowed us to adjust for covariates and provided greater precision, as recommended for observational study designs.²²

Treatment conditions were defined dichotomously as receipt vs nonreceipt of a thermal therapy device. The primary outcome was the number of days of opioid use in the 12 months following the index date. Additional outcomes included days of high-dose opioid therapy (≥ 50 mg morphine equivalent [MME] daily), concurrent opioid and benzodiazepine prescriptions, and nonopioid analgesic pharmacotherapy (eg, nonsteroidal anti-inflammatory drugs, acetaminophen, muscle relaxants). All prescription data were based on medication fills obtained from VA pharmacy records. Additional outcomes included the number of visits to physical therapy, occupational therapy, physical medicine and rehabilitation, and interdisciplinary pain clinics, including interventional pain medicine.

Pain intensity ratings were collected as part of routine VA care using a numeric scale from 0 (no pain) to 10 (worst possible pain). Pain intensity trajectories were computed using all available EHR-derived pain intensity score data for each patient in the 12 months prior to and following the index date.

Covariates were extracted from the EHR and evaluated in the year prior to the index date, unless otherwise noted. They included age at the index date; self-reported sex, and race and ethnicity; service-connected disability status (disability awarded as a result of military service-related trauma or injury); Charlson Comorbidity Index; and diagnoses of opioid use disorder, alcohol use disorder, other substance use disorder, mood disorder, posttraumatic stress disorder, other anxiety disorder, psychotic disorder, neuropathic pain, and headache pain.²³ All diagnoses were coded as yes if identified in the EHR as a focus of treatment during any clinical encounter in the year prior to the index date or no if not identified as a focus of treatment.

The number of days patients had been living with pain was calculated and defined as the number of days from the first pain diagnosis available in a patient’s EHR to the index date. Finally, the number of pain-related surgeries (eg, surgeries with ≥ 1 pain diagnoses associated with the clinical encounter) and average pain intensity were computed for the pre- and postindex date evaluation periods and included as model covariates.

Statistical Analyses

For the 4 pharmacotherapy and 4 nonpharmacologic treatment usage outcomes, we tested the fit of linear models and several models with count distributions using the Bayesian information criterion.²⁴ Count distributions included Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial. With 1 exception (days of high-dose opioid use), a negative binomial distribution best fit the data. For days of high-dose opioid use, a Gaussian distribution best fit the data.

Eight separate mixed-effects regression analyses compared changes in each outcome from the 12-month preindex period through the 12-month postindex period between treatment and control patients by testing the Time × Treatment interaction. This approach statistically accounts for observed pretreatment differences in outcome variables. Statistics for the main effects of time and treatment are also presented. To reduce bias, models controlled for covariates specified previously.

For pain intensity, we used random-effects growth modeling to quantify both fixed and random effects of pain intensity at the index date (eg, the model intercept), which estimates pain at the time of treatment initiation, and change in pain during the 12 months following the index date (eg, the model slope), which characterizes the trajectory of pain intensity ratings.²⁵ The model included piecewise components of pain score trajectories in the 12 months prior to the index date and in the 12 months following the index date. Several types of change for the 12-month postindex observation period were explored—including quadratic and cubic curvilinear change. A linear model for change in pain over time provided the best fit based on the Bayesian information criterion and parsimony of model parameters.²⁶ We report estimates of change in pain over time in monthly intervals for ease of interpretation. However, models used all individual pain scores rather than computing monthly averages when > 1 pain score was available within a month, as recommended in previous research.²⁷ This approach makes optimal use of all available data. Both random effects (intercept and slope) were regressed onto the set of covariates described previously.

This study used data available in the EHR over the 24-month observation period. We characterize the density (eg, frequency) of all outcome variables by treatment condition in the Appendix. Because the hypotheses were directional, the authors used a 2-sided α = 0.10 and applied a Bonferroni correction for the 9 statistical tests performed, resulting in an adjusted α of 0.01. Treatment utilization and pharmacotherapy outcome analyses were performed in Stata, version 16.1. Random-effects growth modeling of pain score trajectories was performed using Mplus, version 8.8.

Results

There were 2182 patients in the treatment group and 2182 matched controls. The mean (SD) age was 54 (15) years; 81% were male, and about two-thirds (68%) identified as White and non-Hispanic. Mental health comorbidities were common, with > 40% of the sample having diagnoses of a mood disorder and/or posttraumatic stress disorder. Nearly all patients (90%) had VA service-connected disability ratings. Among patients with chronic musculoskeletal pain, 8% had comorbid neuropathic pain and 14% had headache. The mean (SD) duration of chronic pain across both patient groups was 3416 (2016) days, or about 9.4 years (Table 1).

Pharmacotherapy

High-dose opioid use (> 50 MME daily dose), days of opioid use, and concurrent opioid and benzodiazepine use decreased for all patients from the pre- to posttreatment period (Table 2). However, high-dose opioid use (Time × Treatment interaction, 3.24; 99% CI, 0.34 to 6.14) (Figure 1A) and concurrent opioid and benzodiazepine use (Time × Treatment interaction, 0.76; 99% CI, 0.67 to 0.86) (Figure 1B) had a larger decrease for the treatment vs matched control group. Treatment and matched control patients had comparable reductions in days of opioid use (Time × Treatment interaction, 0.98; 99% CI, 0.91 to 1.05) (Figure 1C). Neither group showed changes in nonopioid analgesic pharmacotherapy over time (main effect of time, incidence rate ratio [IRR], 1.03; 99% CI, 0.99 to 1.07; Time × Treatment interaction, 0.95; 99% CI, 0.90 to 1.01) (Figure 1D).

Nonpharmacologic Pain Treatment

The number of physical therapy and pain clinic visits declined for treatment patients and increased slightly for matched control patients (Figure 2A and 2B). For occupational therapy visits, neither group showed changes over time (main effect of time IRR, 1.03; 99% CI, 0.83 to 1.22; Time × Treatment interaction, 1.20; 99% CI, 0.93 to 1.46) (Figure 2C). For physical medicine and rehabilitation visits, both groups decreased use over time (main effect of time IRR, 0.78; 99% CI, 0.66 to 0.90), but this decrease did not differ between treatment and control patients (Time × Treatment interaction, 1.16; 99% CI, 0.94 to 1.37) (Figure 2D).

Pain Intensity

Pain intensity decreased for both groups by an estimated 0.02 points per month (99% CI, -0.04 to -0.01; P < .01), or 0.24 points over the 12-month postindex follow-up period (Figure 3). There were no statistically significant differences between treatment and control patients in pain intensity changes over the follow-up period (B = -0.02; 99% CI, -0.06 to 0.02; P = .15).

Discussion

Patients with musculoskeletal pain who received a thermal therapy device showed a larger decline in use of some specialty pain treatments, including physical therapy and specialty pain clinic services, when compared with matched control patients. One possible explanation is that patients who used the device may have had their pain adequately managed and thus required less specialty pain care. However, the absence of clinically significant changes in pain intensity over time suggests that pain intensity alone did not account for the observed changes in pain treatment use. We were unable to measure constructs of pain interference or functioning, which may be better predictors of functional restoration, as these data are not routinely collected within the VA and documented in the EHR. Future studies could clarify these findings by including measures of pain interference and functioning.

Although the overall declines in physical therapy and specialty pain clinic use associated with thermal therapy were modest (< 1 visit per patient), the impact of these reductions can be profound. In resource-limited health care settings, even small reductions in high-cost care utilization could be of great value in that health systems could offset costs associated with treating chronic pain without compromising quality of care or key clinical outcomes, such as pain intensity. This study, however, did not include a cost analysis. Future studies should incorporate formal cost analyses to quantify cost offsets that may result from decreased specialty pain treatment use.

Patients who received thermal therapy devices did not show clinically meaningful reductions in pain intensity over time, defined as reductions in pain intensity of 1.7 to 2.0 on a 0 to 10 scale.^28,29 This finding is consistent with prior research that demonstrates relatively stable pain intensity self-ratings longitudinally by patients with chronic pain diagnoses, when assessed in the context of usual clinical care.³⁰ This finding, however, is inconsistent with prior literature that demonstrates pain-reducing benefits of thermal therapy for low back pain and knee osteoarthritis.^7-9

In this study, pain intensity ratings were derived from the EHR during routine outpatient clinical encounters and not at the time thermal therapy was self-administered, as has been done in prior clinical trials.^7-9 Pain location was not specified at the time of pain ratings, and it is possible that patients may have been endorsing pain in areas of the body that had not been treated by thermal therapy. Patient-level variability in pain intensity ratings (eg, within-patient range over time) was not examined, although prior research indicates substantial variability.³⁰ While average pain intensity ratings in the current study did not change, an examination of patient variability warrants further study, as a narrowing of pain intensity ratings can be perceived, by patients, as demonstrable improvement and has been associated with improved physical and psychological outcomes.³¹ Furthermore, pain intensity does not characterize physical or emotional functioning that can be captured with more comprehensive validated measures, some of which are recommended outcomes in pain clinical trials.³²

Our findings point to reductions in all forms of opioid use across both treatment and control patients. Data from the VA and in the US more generally point to downward trends in opioid prescribing during the study period.³³ This decline is likely due to increased use of risk mitigation approaches, such as routine urine drug screens and review of prescription drug monitoring databases.³⁴ These state-level databases track prescribing of controlled substances, including opioids and benzodiazepines, within a state. Implementation of these practices has been associated with declines in higher risk opioid prescribing.³⁴ Findings from this study further point to associations of reduced higher risk opioid use among patients who received thermal therapy devices. In the full sample of patients, reductions in days of opioid use, high-dose opioid use, and co-use of opioids and benzodiazepines were observed across all patients, with greater reductions observed in high-dose opioid use and concurrent opioid and benzodiazepine use among patients who received a thermal therapy device. Experimental studies indicate that the endogenous opioid system is implicated in pain relief and activated by thermal therapies. ³⁵ Differential reductions in higher-risk opioid use among patients who received thermal therapy devices in our study may be associated with endogenous opioid activation, though this was not specifically measured. It is also unclear whether thermal therapy was provided by clinicians in the context of opioid tapering or other risk mitigation efforts, or patients reduced higher risk opioid use of their own volition. Prior research has identified both patient- and clinician-initiated opioid tapering and discontinuation.³⁶ While a thorough explication of opioid dose reduction was beyond the scope of this study, future qualitative work could help explain potential benefits of thermal therapy in the context of analgesic pharmacotherapy use, including opioid medications.

Limitations

The extent to which patients used the thermal therapy device could not be measured; therefore, device receipt was used as a proxy for use. However, it was not possible to determine whether the frequency and duration of device use was associated with study outcomes. Treatment and control groups demonstrated some differences in outcome variables at the index date. Potential known biases were addressed using propensity score matching procedures and statistical procedures that controlled for patient demographic and clinical characteristics, as well as pretreatment values of all outcome variables. Nevertheless, as an observational study, this analysis cannot account for all known and unknown confounders, and a randomized controlled trial is needed to make claims of causality. The study population consisted of US veterans and included a low proportion of women. As a result, the findings may not be generalizable to other patient populations. Finally, prescription dispensing data, used as a proxy for pharmacotherapy use, do not necessarily reflect actual medication use.

Conclusions

This study is among the first to examine associations between thermal therapy and specialty pain treatment and analgesic pharmacotherapy use among US veterans. Although the retrospective cohort study design does not allow causal inferences regarding the efficacy of thermal therapy for veterans with chronic musculoskeletal pain, confidence in the findings is strengthened by methodological and statistical control of known confounders. Future trials employing experimental designs are needed to further clarify the clinical and health systems benefits of thermal therapy for musculoskeletal pain syndromes.

Self-reported penicillin allergies are common, with a prevalence of about 10% of patients, according to the Centers for Disease Control and Prevention (CDC).¹ However, only about 1% of patients have a true immunoglobulin E (IgE)-mediated allergy. This issue is often further complicated by inaccurate classification of nonallergic adverse effects as an allergy, resulting in incomplete allergy documentation in the electronic health record (EHR). The cross-reactivity rate with cephalosporins (Β-lactam antibiotics) in patients reporting a penicillin allergy is < 1%, which suggests that many patients with reported penicillin allergies can safely receive them.² Despite this, patients with self-reported penicillin allergies often receive non–Β-lactam antibiotic agents, which may be associated with an increased risk of adverse drug reactions (ADRs), increased health care costs, and inferior clinical outcomes.³

Several strategies are recommended to assess patients with self-reported penicillin allergies. According to the CDC, evaluating a patient who reports a penicillin or other Β-lactam antibiotic allergy involves 3 steps: (1) obtaining a thorough medical history, including previous exposures to penicillin or other Β-lactam antibiotic; (2) performing a skin test using the penicillin major and minor determinants; and (3) among those who have a negative penicillin skin test, performing an observed oral challenge with 250 mg amoxicillin before proceeding directly to treatment with the indicated Β-lactam therapy.⁴

Most existing clinical guidance for assessing patients with self-reported penicillin allergies stems from site-specific policies and primarily focuses on oral amoxicillin challenges or penicillin skin testing (PST). However, performing these tests may not be feasible at all facilities due to time constraints and lack of allergists. Therefore, alternative strategies are necessary, such as conducting detailed patient interviews. Few studies have evaluated switching to Β-lactam agents following a penicillin allergy interview alone. However, with thorough patient histories and detailed interviews, patients with reported penicillin allergies can safely use Β-lactam antibiotics.⁵ Implementing this procedure provides a cost-savings opportunity by not having to administer additional antibiotics for testing in addition to improving antibiotic stewardship.

The Memphis Veterans Affairs Medical Center (MVAMC) created the Allergy to Β-Lactam Evaluation (ABLE) process to clarify and remove penicillin allergies. The process involves conducting a thorough chart review and patient interview followed by completion of a note template that provides recommendations about patient allergies and Β-lactam prescribing. Mitchell et al found that the pharmacist-led process to be beneficial for addressing Β-lactam allergy clearance.⁶ As a result, the ABLE process was implemented at several other US Department of Veterans Affairs (VA) medical centers (VAMCs). Using the ABLE template, the purpose of this study was to evaluate the impact of a pharmacist-led penicillin allergy initiative on penicillin allergy delabeling with an interview process alone.

Methods

Prior to ABLE process implementation, there were no standardized procedures for documenting allergy histories. ABLE was implemented at the Robley Rex VAMC (RRVAMC) in November 2022. During the interview phase, patients were initially identified during admission via TheraDoc as having either a penicillin allergy or ADR. The infectious disease pharmacist or pharmacy resident interviewed patients with documented penicillin allergies or ADRs using a standardized questionnaire (eAppendix 1). Not all identified patients could be interviewed. Patients currently receiving an antibiotic were prioritized for interviews. Patients were excluded if they declined or were unable to be interviewed, although a patient’s caregiver(s) could be interviewed in person or via telephone, if the patient was not available.

Following the interview, pharmacists used guidance from the ABLE process in addition to a detailed EHR review to determine whether the patient was eligible for an allergy update or removal and/or switch to a Β-lactam antibiotic (Figure). If eligible for modification, the interviewing pharmacist made the necessary changes. A templated process note with patient-specific recommendations was entered into the Computerized Patient Record System (CPRS) and the primary care team attending physician was added as an additional signer to be alerted in the system note (eAppendix 2).

This single-center, retrospective cohort study involved review of CPRS notes and clinical interviews in the interviewed group. Hospitalized patients at the RRVAMC aged ≥ 18 years with a documented penicillin allergy or ADR were included. The historical control group consisted of patients admitted between October 31, 2019, and October 31, 2022, and the intervention group consisted of patients admitted between November 1, 2022, and March 1, 2023. Patients in the historical control group were matched 1:1 to the intervention group for penicillin allergy severity (allergy [IgE-mediated], unknown, adverse effect, severe cutaneous or other non–IgE-mediated reaction) and whether they received a noncarbapenem non–Β-lactam antibiotic.

The primary outcome was the number of patient allergies/ADRs removed or changed on patient profiles regardless of whether their antibiotic regimen was changed. This outcome was further assessed by evaluating the number of patient allergies or ADRs removed or changed on patient profiles with or without a change in antibiotic regimen. Primary outcomes were analyzed using χ² and/ or Fisher exact tests, as appropriate to determine statistically significant differences between the interviewed group and the historical control.

Results

Seventy patients were included: 35 patients in the interviewed group and 35 patients in the historical control group, respectively. Both groups had a mean age of 72 years and predominantly included White male patients (Table 1). Following the interview, the allergy profile was modified for 6 patients (17%) in the interview group vs 0 patients in the control group (P = .03) (Table 2). The primary outcome was analyzed separately regardless of an antibiotic regimen change. There was not a statistically significant difference between groups when assessing patients for change in therapy (P > .99). All 6 patients with an allergy profile modification had no change in antibiotic regimen.

Discussion

This study suggests the ABLE process may be a valuable tool for adjusting penicillin allergies or ADRs within patient EHRs. In the interview group, allergies were modified in 6 (17%) patients while no patients in the control group had allergy modifications. Of the 6 allergy profile modifications, 4 allergy labels were changed from an allergy to an ADR. These patients were cleared to receive future Β-lactam antibiotics after clinicians recognized the lack of a true IgE-mediated allergic reaction. In addition, 2 of the modified allergy profiles removed the allergy designation. Although this represents a small subset of interviewed patients, it illustrates the clinical effectiveness of an interview process alone to remove penicillin allergy designations.

Previous research has assessed the impact of pharmacist intervention on penicillin allergy clarification. Mitchell et al implemented a pharmacist-driven Β-lactam allergy assessment and penicillin allergy clinic (PAC) at the MVAMC with the goal of evaluating its impact on allergy clearance. In their study, clinical pharmacy specialists evaluated patients with Β-lactam allergies, and those deemed eligible were later seen in the PAC. Among the 246 patients evaluated using the Β-lactam allergy assessment alone and who were not seen in the PAC, 25% had their penicillin allergy removed following a detailed assessment.⁶

Song et al evaluated the effectiveness and feasibility of a pharmacist-driven penicillin allergy delabeling pilot program without skin testing or oral challenges. Patients with penicillin allergies were interviewed by a pharmacy resident using a standardized checklist. Among the 66 patients interviewed, 12 (18%) met the criteria for delabeling and consented to removal of their allergy.⁷ The delabeling rates in these 2 studies are similar to the 17% rate of allergy modification in our study, although this study is the only one to compare results to a historical control group.

Harper et al evaluated the impact of a penicillin allergy assessment, including penicillin skin testing and oral amoxicillin challenges, on delabeling penicillin allergies. Pharmacists completed a penicillin allergy assessment and performed penicillin skin testing and/or oral amoxicillin challenges for eligible patients. Of 35 patients, 31 (89%) had their penicillin allergies delabeled in the EHR.⁸ The rate of penicillin allergy delabeling in Harper et al was likely higher than that seen in our study due to the use of oral challenge and skin testing. Regardless, a detailed penicillin allergy interview alone was effective at RRVAMC, resulting in a significant rate of allergy removal or change. This supports the use of detailed penicillin allergy assessments in settings where penicillin skin testing or oral challenges may not be feasible.

Mann et al demonstrated the effectiveness of penicillin allergy assessments in switching eligible patients to Β-lactam antibiotics. Their single-center, prospective study assessed the impact of a pharmacist-driven detailed penicillin allergy interview initiative. Interviews that evaluated potential changes to allergy profiles were conducted with 175 patients. Of these patients, 135 (77.1%) were on antimicrobial therapy and 42 (31.1%) patients receiving therapy met criteria to switch to a noncarbapenem Β-lactam antibiotic. Thirty-one patients (73.8%) switched with no signs or symptoms of intolerance demonstrating that an interview can be a valuable tool for antibiotic optimization, specifically in patients with penicillin allergy.⁹ No patients in our study switched antibiotic therapy, likely because only a small number of patients were eligible for transition to a noncarbapenem Β-lactam antibiotic. In the Mann et al study, non–Β-lactam antibiotics, such as fluoroquinolones and carbapenems, accounted for > 75% of the antibiotics used.

Limitations

The sample size of this study was small and its duration was short. There is a risk for selection bias as not all identified patients were able to be interviewed while admitted, but patients on antibiotics were prioritized as they were most likely to directly benefit during their current admission from a modification of their allergy. Most patients in the study were White and male, which may limit the generalizability of the results. Additionally, recommendations regarding antibiotic changes were primarily communicated to the treatment team based on a templated note in CPRS alone. Therefore, implementation of these recommendations largely relied upon nonverbal communication. Direct pharmacist-physician communication could have led to a larger impact on antimicrobial therapy changes. The interviewer’s participation in daily rounds with time allotted to discuss this topic can be considered in the future to improve these processes.

Conclusions

This study found that the ABLE process identified patients for penicillin allergy delabeling. With the high prevalence of inaccurate penicillin allergy documentation, this tool offers VA health care systems a way to empower pharmacists in allergy clarification, leading to improvements in antibiotic stewardship. Although the sample size was small, the ABLE process may provide a framework for VA clinicians. Future research has the potential to demonstrate the practicality and effectiveness this pharmacist-led penicillin allergy interview process can offer clinicians.

Self-reported penicillin allergies are common, with a prevalence of about 10% of patients, according to the Centers for Disease Control and Prevention (CDC).¹ However, only about 1% of patients have a true immunoglobulin E (IgE)-mediated allergy. This issue is often further complicated by inaccurate classification of nonallergic adverse effects as an allergy, resulting in incomplete allergy documentation in the electronic health record (EHR). The cross-reactivity rate with cephalosporins (Β-lactam antibiotics) in patients reporting a penicillin allergy is < 1%, which suggests that many patients with reported penicillin allergies can safely receive them.² Despite this, patients with self-reported penicillin allergies often receive non–Β-lactam antibiotic agents, which may be associated with an increased risk of adverse drug reactions (ADRs), increased health care costs, and inferior clinical outcomes.³

Several strategies are recommended to assess patients with self-reported penicillin allergies. According to the CDC, evaluating a patient who reports a penicillin or other Β-lactam antibiotic allergy involves 3 steps: (1) obtaining a thorough medical history, including previous exposures to penicillin or other Β-lactam antibiotic; (2) performing a skin test using the penicillin major and minor determinants; and (3) among those who have a negative penicillin skin test, performing an observed oral challenge with 250 mg amoxicillin before proceeding directly to treatment with the indicated Β-lactam therapy.⁴

Most existing clinical guidance for assessing patients with self-reported penicillin allergies stems from site-specific policies and primarily focuses on oral amoxicillin challenges or penicillin skin testing (PST). However, performing these tests may not be feasible at all facilities due to time constraints and lack of allergists. Therefore, alternative strategies are necessary, such as conducting detailed patient interviews. Few studies have evaluated switching to Β-lactam agents following a penicillin allergy interview alone. However, with thorough patient histories and detailed interviews, patients with reported penicillin allergies can safely use Β-lactam antibiotics.⁵ Implementing this procedure provides a cost-savings opportunity by not having to administer additional antibiotics for testing in addition to improving antibiotic stewardship.

The Memphis Veterans Affairs Medical Center (MVAMC) created the Allergy to Β-Lactam Evaluation (ABLE) process to clarify and remove penicillin allergies. The process involves conducting a thorough chart review and patient interview followed by completion of a note template that provides recommendations about patient allergies and Β-lactam prescribing. Mitchell et al found that the pharmacist-led process to be beneficial for addressing Β-lactam allergy clearance.⁶ As a result, the ABLE process was implemented at several other US Department of Veterans Affairs (VA) medical centers (VAMCs). Using the ABLE template, the purpose of this study was to evaluate the impact of a pharmacist-led penicillin allergy initiative on penicillin allergy delabeling with an interview process alone.

Methods

Prior to ABLE process implementation, there were no standardized procedures for documenting allergy histories. ABLE was implemented at the Robley Rex VAMC (RRVAMC) in November 2022. During the interview phase, patients were initially identified during admission via TheraDoc as having either a penicillin allergy or ADR. The infectious disease pharmacist or pharmacy resident interviewed patients with documented penicillin allergies or ADRs using a standardized questionnaire (eAppendix 1). Not all identified patients could be interviewed. Patients currently receiving an antibiotic were prioritized for interviews. Patients were excluded if they declined or were unable to be interviewed, although a patient’s caregiver(s) could be interviewed in person or via telephone, if the patient was not available.

Following the interview, pharmacists used guidance from the ABLE process in addition to a detailed EHR review to determine whether the patient was eligible for an allergy update or removal and/or switch to a Β-lactam antibiotic (Figure). If eligible for modification, the interviewing pharmacist made the necessary changes. A templated process note with patient-specific recommendations was entered into the Computerized Patient Record System (CPRS) and the primary care team attending physician was added as an additional signer to be alerted in the system note (eAppendix 2).

This single-center, retrospective cohort study involved review of CPRS notes and clinical interviews in the interviewed group. Hospitalized patients at the RRVAMC aged ≥ 18 years with a documented penicillin allergy or ADR were included. The historical control group consisted of patients admitted between October 31, 2019, and October 31, 2022, and the intervention group consisted of patients admitted between November 1, 2022, and March 1, 2023. Patients in the historical control group were matched 1:1 to the intervention group for penicillin allergy severity (allergy [IgE-mediated], unknown, adverse effect, severe cutaneous or other non–IgE-mediated reaction) and whether they received a noncarbapenem non–Β-lactam antibiotic.

The primary outcome was the number of patient allergies/ADRs removed or changed on patient profiles regardless of whether their antibiotic regimen was changed. This outcome was further assessed by evaluating the number of patient allergies or ADRs removed or changed on patient profiles with or without a change in antibiotic regimen. Primary outcomes were analyzed using χ² and/ or Fisher exact tests, as appropriate to determine statistically significant differences between the interviewed group and the historical control.

Results

Seventy patients were included: 35 patients in the interviewed group and 35 patients in the historical control group, respectively. Both groups had a mean age of 72 years and predominantly included White male patients (Table 1). Following the interview, the allergy profile was modified for 6 patients (17%) in the interview group vs 0 patients in the control group (P = .03) (Table 2). The primary outcome was analyzed separately regardless of an antibiotic regimen change. There was not a statistically significant difference between groups when assessing patients for change in therapy (P > .99). All 6 patients with an allergy profile modification had no change in antibiotic regimen.

Discussion

This study suggests the ABLE process may be a valuable tool for adjusting penicillin allergies or ADRs within patient EHRs. In the interview group, allergies were modified in 6 (17%) patients while no patients in the control group had allergy modifications. Of the 6 allergy profile modifications, 4 allergy labels were changed from an allergy to an ADR. These patients were cleared to receive future Β-lactam antibiotics after clinicians recognized the lack of a true IgE-mediated allergic reaction. In addition, 2 of the modified allergy profiles removed the allergy designation. Although this represents a small subset of interviewed patients, it illustrates the clinical effectiveness of an interview process alone to remove penicillin allergy designations.

Previous research has assessed the impact of pharmacist intervention on penicillin allergy clarification. Mitchell et al implemented a pharmacist-driven Β-lactam allergy assessment and penicillin allergy clinic (PAC) at the MVAMC with the goal of evaluating its impact on allergy clearance. In their study, clinical pharmacy specialists evaluated patients with Β-lactam allergies, and those deemed eligible were later seen in the PAC. Among the 246 patients evaluated using the Β-lactam allergy assessment alone and who were not seen in the PAC, 25% had their penicillin allergy removed following a detailed assessment.⁶

Song et al evaluated the effectiveness and feasibility of a pharmacist-driven penicillin allergy delabeling pilot program without skin testing or oral challenges. Patients with penicillin allergies were interviewed by a pharmacy resident using a standardized checklist. Among the 66 patients interviewed, 12 (18%) met the criteria for delabeling and consented to removal of their allergy.⁷ The delabeling rates in these 2 studies are similar to the 17% rate of allergy modification in our study, although this study is the only one to compare results to a historical control group.

Harper et al evaluated the impact of a penicillin allergy assessment, including penicillin skin testing and oral amoxicillin challenges, on delabeling penicillin allergies. Pharmacists completed a penicillin allergy assessment and performed penicillin skin testing and/or oral amoxicillin challenges for eligible patients. Of 35 patients, 31 (89%) had their penicillin allergies delabeled in the EHR.⁸ The rate of penicillin allergy delabeling in Harper et al was likely higher than that seen in our study due to the use of oral challenge and skin testing. Regardless, a detailed penicillin allergy interview alone was effective at RRVAMC, resulting in a significant rate of allergy removal or change. This supports the use of detailed penicillin allergy assessments in settings where penicillin skin testing or oral challenges may not be feasible.

Mann et al demonstrated the effectiveness of penicillin allergy assessments in switching eligible patients to Β-lactam antibiotics. Their single-center, prospective study assessed the impact of a pharmacist-driven detailed penicillin allergy interview initiative. Interviews that evaluated potential changes to allergy profiles were conducted with 175 patients. Of these patients, 135 (77.1%) were on antimicrobial therapy and 42 (31.1%) patients receiving therapy met criteria to switch to a noncarbapenem Β-lactam antibiotic. Thirty-one patients (73.8%) switched with no signs or symptoms of intolerance demonstrating that an interview can be a valuable tool for antibiotic optimization, specifically in patients with penicillin allergy.⁹ No patients in our study switched antibiotic therapy, likely because only a small number of patients were eligible for transition to a noncarbapenem Β-lactam antibiotic. In the Mann et al study, non–Β-lactam antibiotics, such as fluoroquinolones and carbapenems, accounted for > 75% of the antibiotics used.

Limitations

The sample size of this study was small and its duration was short. There is a risk for selection bias as not all identified patients were able to be interviewed while admitted, but patients on antibiotics were prioritized as they were most likely to directly benefit during their current admission from a modification of their allergy. Most patients in the study were White and male, which may limit the generalizability of the results. Additionally, recommendations regarding antibiotic changes were primarily communicated to the treatment team based on a templated note in CPRS alone. Therefore, implementation of these recommendations largely relied upon nonverbal communication. Direct pharmacist-physician communication could have led to a larger impact on antimicrobial therapy changes. The interviewer’s participation in daily rounds with time allotted to discuss this topic can be considered in the future to improve these processes.

Conclusions

This study found that the ABLE process identified patients for penicillin allergy delabeling. With the high prevalence of inaccurate penicillin allergy documentation, this tool offers VA health care systems a way to empower pharmacists in allergy clarification, leading to improvements in antibiotic stewardship. Although the sample size was small, the ABLE process may provide a framework for VA clinicians. Future research has the potential to demonstrate the practicality and effectiveness this pharmacist-led penicillin allergy interview process can offer clinicians.

Self-reported penicillin allergies are common, with a prevalence of about 10% of patients, according to the Centers for Disease Control and Prevention (CDC).¹ However, only about 1% of patients have a true immunoglobulin E (IgE)-mediated allergy. This issue is often further complicated by inaccurate classification of nonallergic adverse effects as an allergy, resulting in incomplete allergy documentation in the electronic health record (EHR). The cross-reactivity rate with cephalosporins (Β-lactam antibiotics) in patients reporting a penicillin allergy is < 1%, which suggests that many patients with reported penicillin allergies can safely receive them.² Despite this, patients with self-reported penicillin allergies often receive non–Β-lactam antibiotic agents, which may be associated with an increased risk of adverse drug reactions (ADRs), increased health care costs, and inferior clinical outcomes.³

Several strategies are recommended to assess patients with self-reported penicillin allergies. According to the CDC, evaluating a patient who reports a penicillin or other Β-lactam antibiotic allergy involves 3 steps: (1) obtaining a thorough medical history, including previous exposures to penicillin or other Β-lactam antibiotic; (2) performing a skin test using the penicillin major and minor determinants; and (3) among those who have a negative penicillin skin test, performing an observed oral challenge with 250 mg amoxicillin before proceeding directly to treatment with the indicated Β-lactam therapy.⁴

Most existing clinical guidance for assessing patients with self-reported penicillin allergies stems from site-specific policies and primarily focuses on oral amoxicillin challenges or penicillin skin testing (PST). However, performing these tests may not be feasible at all facilities due to time constraints and lack of allergists. Therefore, alternative strategies are necessary, such as conducting detailed patient interviews. Few studies have evaluated switching to Β-lactam agents following a penicillin allergy interview alone. However, with thorough patient histories and detailed interviews, patients with reported penicillin allergies can safely use Β-lactam antibiotics.⁵ Implementing this procedure provides a cost-savings opportunity by not having to administer additional antibiotics for testing in addition to improving antibiotic stewardship.

The Memphis Veterans Affairs Medical Center (MVAMC) created the Allergy to Β-Lactam Evaluation (ABLE) process to clarify and remove penicillin allergies. The process involves conducting a thorough chart review and patient interview followed by completion of a note template that provides recommendations about patient allergies and Β-lactam prescribing. Mitchell et al found that the pharmacist-led process to be beneficial for addressing Β-lactam allergy clearance.⁶ As a result, the ABLE process was implemented at several other US Department of Veterans Affairs (VA) medical centers (VAMCs). Using the ABLE template, the purpose of this study was to evaluate the impact of a pharmacist-led penicillin allergy initiative on penicillin allergy delabeling with an interview process alone.

Methods

Prior to ABLE process implementation, there were no standardized procedures for documenting allergy histories. ABLE was implemented at the Robley Rex VAMC (RRVAMC) in November 2022. During the interview phase, patients were initially identified during admission via TheraDoc as having either a penicillin allergy or ADR. The infectious disease pharmacist or pharmacy resident interviewed patients with documented penicillin allergies or ADRs using a standardized questionnaire (eAppendix 1). Not all identified patients could be interviewed. Patients currently receiving an antibiotic were prioritized for interviews. Patients were excluded if they declined or were unable to be interviewed, although a patient’s caregiver(s) could be interviewed in person or via telephone, if the patient was not available.

Following the interview, pharmacists used guidance from the ABLE process in addition to a detailed EHR review to determine whether the patient was eligible for an allergy update or removal and/or switch to a Β-lactam antibiotic (Figure). If eligible for modification, the interviewing pharmacist made the necessary changes. A templated process note with patient-specific recommendations was entered into the Computerized Patient Record System (CPRS) and the primary care team attending physician was added as an additional signer to be alerted in the system note (eAppendix 2).

This single-center, retrospective cohort study involved review of CPRS notes and clinical interviews in the interviewed group. Hospitalized patients at the RRVAMC aged ≥ 18 years with a documented penicillin allergy or ADR were included. The historical control group consisted of patients admitted between October 31, 2019, and October 31, 2022, and the intervention group consisted of patients admitted between November 1, 2022, and March 1, 2023. Patients in the historical control group were matched 1:1 to the intervention group for penicillin allergy severity (allergy [IgE-mediated], unknown, adverse effect, severe cutaneous or other non–IgE-mediated reaction) and whether they received a noncarbapenem non–Β-lactam antibiotic.

The primary outcome was the number of patient allergies/ADRs removed or changed on patient profiles regardless of whether their antibiotic regimen was changed. This outcome was further assessed by evaluating the number of patient allergies or ADRs removed or changed on patient profiles with or without a change in antibiotic regimen. Primary outcomes were analyzed using χ² and/ or Fisher exact tests, as appropriate to determine statistically significant differences between the interviewed group and the historical control.

Results

Seventy patients were included: 35 patients in the interviewed group and 35 patients in the historical control group, respectively. Both groups had a mean age of 72 years and predominantly included White male patients (Table 1). Following the interview, the allergy profile was modified for 6 patients (17%) in the interview group vs 0 patients in the control group (P = .03) (Table 2). The primary outcome was analyzed separately regardless of an antibiotic regimen change. There was not a statistically significant difference between groups when assessing patients for change in therapy (P > .99). All 6 patients with an allergy profile modification had no change in antibiotic regimen.

Discussion

This study suggests the ABLE process may be a valuable tool for adjusting penicillin allergies or ADRs within patient EHRs. In the interview group, allergies were modified in 6 (17%) patients while no patients in the control group had allergy modifications. Of the 6 allergy profile modifications, 4 allergy labels were changed from an allergy to an ADR. These patients were cleared to receive future Β-lactam antibiotics after clinicians recognized the lack of a true IgE-mediated allergic reaction. In addition, 2 of the modified allergy profiles removed the allergy designation. Although this represents a small subset of interviewed patients, it illustrates the clinical effectiveness of an interview process alone to remove penicillin allergy designations.

Previous research has assessed the impact of pharmacist intervention on penicillin allergy clarification. Mitchell et al implemented a pharmacist-driven Β-lactam allergy assessment and penicillin allergy clinic (PAC) at the MVAMC with the goal of evaluating its impact on allergy clearance. In their study, clinical pharmacy specialists evaluated patients with Β-lactam allergies, and those deemed eligible were later seen in the PAC. Among the 246 patients evaluated using the Β-lactam allergy assessment alone and who were not seen in the PAC, 25% had their penicillin allergy removed following a detailed assessment.⁶

Song et al evaluated the effectiveness and feasibility of a pharmacist-driven penicillin allergy delabeling pilot program without skin testing or oral challenges. Patients with penicillin allergies were interviewed by a pharmacy resident using a standardized checklist. Among the 66 patients interviewed, 12 (18%) met the criteria for delabeling and consented to removal of their allergy.⁷ The delabeling rates in these 2 studies are similar to the 17% rate of allergy modification in our study, although this study is the only one to compare results to a historical control group.

Harper et al evaluated the impact of a penicillin allergy assessment, including penicillin skin testing and oral amoxicillin challenges, on delabeling penicillin allergies. Pharmacists completed a penicillin allergy assessment and performed penicillin skin testing and/or oral amoxicillin challenges for eligible patients. Of 35 patients, 31 (89%) had their penicillin allergies delabeled in the EHR.⁸ The rate of penicillin allergy delabeling in Harper et al was likely higher than that seen in our study due to the use of oral challenge and skin testing. Regardless, a detailed penicillin allergy interview alone was effective at RRVAMC, resulting in a significant rate of allergy removal or change. This supports the use of detailed penicillin allergy assessments in settings where penicillin skin testing or oral challenges may not be feasible.

Mann et al demonstrated the effectiveness of penicillin allergy assessments in switching eligible patients to Β-lactam antibiotics. Their single-center, prospective study assessed the impact of a pharmacist-driven detailed penicillin allergy interview initiative. Interviews that evaluated potential changes to allergy profiles were conducted with 175 patients. Of these patients, 135 (77.1%) were on antimicrobial therapy and 42 (31.1%) patients receiving therapy met criteria to switch to a noncarbapenem Β-lactam antibiotic. Thirty-one patients (73.8%) switched with no signs or symptoms of intolerance demonstrating that an interview can be a valuable tool for antibiotic optimization, specifically in patients with penicillin allergy.⁹ No patients in our study switched antibiotic therapy, likely because only a small number of patients were eligible for transition to a noncarbapenem Β-lactam antibiotic. In the Mann et al study, non–Β-lactam antibiotics, such as fluoroquinolones and carbapenems, accounted for > 75% of the antibiotics used.

Limitations

The sample size of this study was small and its duration was short. There is a risk for selection bias as not all identified patients were able to be interviewed while admitted, but patients on antibiotics were prioritized as they were most likely to directly benefit during their current admission from a modification of their allergy. Most patients in the study were White and male, which may limit the generalizability of the results. Additionally, recommendations regarding antibiotic changes were primarily communicated to the treatment team based on a templated note in CPRS alone. Therefore, implementation of these recommendations largely relied upon nonverbal communication. Direct pharmacist-physician communication could have led to a larger impact on antimicrobial therapy changes. The interviewer’s participation in daily rounds with time allotted to discuss this topic can be considered in the future to improve these processes.

Conclusions

This study found that the ABLE process identified patients for penicillin allergy delabeling. With the high prevalence of inaccurate penicillin allergy documentation, this tool offers VA health care systems a way to empower pharmacists in allergy clarification, leading to improvements in antibiotic stewardship. Although the sample size was small, the ABLE process may provide a framework for VA clinicians. Future research has the potential to demonstrate the practicality and effectiveness this pharmacist-led penicillin allergy interview process can offer clinicians.

Given its safety profile and bactericidal activity against the predominant organisms causing surgical site infections (SSIs), cefazolin remains the most popular choice for surgical prophylaxis.¹ Cefazolin offers protection against the pathogens most likely to contaminate the surgical site while minimizing inappropriate methicillin- resistant Staphylococcus aureus coverage that occurs with alternatives such as vancomycin and clindamycin. Documented allergies to Β-lactam antibiotics have historically forced clinicians to avoid the use of cephalosporins due to the potential risk of cross-reactivity. True type 1 (immunoglobin E [IgE]-mediated) cross-allergic reactions between penicillin and cephalosporins are rare, and previously reported data indicate cross-reactivity as a result of antibody recognition is more closely related to the side-chain identity rather than the Β-lactam ring.^2,3

About 10% of US patients report having a penicillin allergy; however, < 1% of the population has a true IgE-mediated allergic reaction.⁴ Previous research that has challenged penicillin allergies with cefazolin for surgical prophylaxis has reported minimal rates of allergic reactions.^2-5

In previous trials, patients with a history of delayed skin reactions, such as Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and drug reaction with eosinophilia and systemic symptoms (DRESS), were excluded. Additionally, patients with an allergy to cefazolin including those with urticaria, angioedema, bronchospasm, or anaphylaxis, were excluded from perioperative retrial of cefazolin. Grant et al found that cefazolin can be safely given to patients with IgE-mediated reactions to penicillin and other cephalosporins due to a structurally different side chain.³

In January 2023, the Veteran Health Indiana (VHI) pharmacy team in conjunction with surgery, infectious disease, and anesthesiology, implemented a screening tool as an amendment to perioperative antibiotic guidance to help determine which patients with a documented penicillin allergy could be candidates for perioperative cefazolin. The implemented screening tool (Allergy Clarification for Cefazolin Evidence-Based Prescribing Tool) has been described by Lam et al, who reported that an increased proportion of patients with documented penicillin allergy received cefazolin without more adverse drug reactions (ADRs).⁵ Patients with a Β-lactam allergy were eligible to receive cefazolin unless the ADR was SJS, TEN, or DRESS, or the offending agent was cefazolin and the patient experienced urticaria, angioedema, bronchospasm, or anaphylaxis. If the reaction was not from cefazolin or was unknown, patients were eligible to receive cefazolin (Figure).

To date, minimal data exist to evaluate the incidence of ADRs when cefazolin is given perioperatively to patients with a previously documented penicillin allergy. The purpose of this study was to evaluate the incidence of allergic ADRs in patients who had a documented penicillin allergy and received periprocedural antibiotics.

Methods

This single-center, retrospective chart review used the US Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) to identify patients with a documented penicillin allergy who underwent an operation and received periprocedural antibiotics between February 1, 2023, and January 31, 2024. This study was reviewed and approved by the Indiana University Health Institutional Review Board and the VHI Research and Development Committee.

Patients were enrolled if they were aged ≥ 18 years, had a documented penicillin allergy, underwent a surgical intervention, and received perioperative antibiotics during the study period. Patients were excluded if they had a documented penicillin allergy resulting in severe delayed skin reactions (ie, SJS, TEN, or DRESS). These criteria produced 197 surgical procedures. Data were collected for each surgical procedure, so patients could be included more than once. Patient history of allergic reaction to penicillin was obtained through CPRS.

The primary endpoint was the percentage of allergic ADRs in patients with penicillin allergies receiving cefazolin perioperatively. Secondary outcomes included the appropriateness of the antibiotic regimen in congruence with American System of Health Pharmacists (ASHP) recommendations, incidence of SSIs within 30 days of the procedure, incidence of ADRs in those with a history of anaphylaxis vs nonanaphylaxis allergy, incidence of allergic reaction requiring pharmacologic and nonpharmacologic interventions, and incidence of acute kidney injury (AKI). AKI was defined as an increase in serum creatinine by ≥ 0.3 mg/dL within 48 hours or an increase in serum creatinine to ≥ 1.5 times baseline.

Demographic data included sex, age, race, preoperative serum creatinine, and postoperative serum creatinine. Anaphylaxis was defined as an acute onset of illness (within minutes to several hours) with involvement of skin, mucosal tissue, or both involving either respiratory compromise or reduced blood pressures. Allergic reactions were defined as facial, tongue, throat, airway, lip, mouth, periorbital, or eye swelling, urticaria, angioedema, dyspnea, anaphylaxis, or a positive penicillin skin test. Additionally, data collected included the description and severity of postprophylactic antibiotic reaction, antibiotic choice, interventions required for the allergic reaction, SSI occurrence, date of SSI, operating specialty, and postoperative change in renal function.

Descriptive statistics, including mean, SD, and percentages were reported for baseline characteristics of the study population. Percentages were used to demonstrate the differences in primary and secondary outcomes for each study group. Fisher exact tests were used for incidence of ADRs in patients with penicillin allergy who received cefazolin and reported incidence of SSIs.

Results

A total of 197 surgical procedures in patients with a documented penicillin allergy were included; 127 procedures used cefazolin perioperatively, 3 procedures used cefazolin plus gentamicin, and 67 procedures used other antibiotics. Most patients were White (n = 160; 81.2%), male (n = 158; 80.2%), and had a mean age of 64.9 years. Urology was the most common surgical specialty (n = 59; 29.9%) (Table 1). Of the 16 patients with documented penicillin anaphylaxis reaction, 8 received cefazolin and 8 received a different antibiotic. A total of 181 patients reported a nonanaphylaxis allergy. One hundred fifty-one patients (68.6%) reported a reaction history of hives, rash, or swelling (Table 2). Patients could report ≥ 1 reaction. The most prevalent antibiotics used were cefazolin, which was used by 130 patients (61.3%), and clindamycin which was used by 33 patients (15.6%) (Table 3). Patients could receive ≥ 1 antibiotic.

For the primary outcome, the incidence of allergic reactions in patients allergic to penicillin, there was no incidence of allergic reactions in either the cefazolin or other group. Given the absence of reactions, no interventions were required.

There were no ADRs in those with history of anaphylaxis or nonanaphylaxis allergy. In the cefazolin group, 126 of 127 surgical procedure regimens (99.2%) were congruent with ASHP recommendations, all 3 surgical procedures regimens in the cefazolin plus gentamicin group were congruent with ASHP recommendations, and 58 of 67 surgical procedure regimens (86.6%) in the other antibiotic group were congruent with ASHP recommendations. None of the 127 patients in the cefazolin group or of the 3 patients in the cefazolin plus gentamicin group reported an SSI, and 3 of 67 patients (4.5%) had an SSI in the other antibiotic group. One procedure that resulted in SSI was not congruent with ASHP recommendations. Twenty-four patients had 2 serum creatinine levels drawn within 48 hours of surgery. One of 12 patients (8.3%) and 0 of 12 patients had an AKI in the cefazolin and other antibiotic group, respectively (Table 4).

Discussion

Implementation of a screening tool at VHI allowed patients with documented penicillin allergy, including anaphylaxis, to receive cefazolin perioperatively. Broad spectrum antibiotics such as vancomycin, clindamycin, and fluoroquinolones are frequently used in patients allergic to penicillin, which can increase health care costs, risk of toxicity, and antimicrobial resistance.⁴ There was no incidence of allergic reactions noted in patients allergic to penicillin who received cefazolin. When comparing the incidence of observed allergic reactions to received perioperative antibiotics in the cefazolin group to previously published literature, no difference in allergy rates (P = .09) was found.³ Most antibiotics administered were congruent with ASHP guideline recommendations, and most patients eligible for cefazolin received it perioperatively.

Similar to this study, Goodman et al concluded that cefazolin appears to be a safe regimen in patients with documented penicillin anaphylactic reaction for surgical prophylaxis with only 1 (0.2%) potential allergic reaction.⁶ Patients who received cefazolin perioperatively had a statistically significant decrease in SSI rates. There were no clinically or statistically significant differences found between the proportion of allergic reactions or ADRs when compared to alternative antibiotics. Lessard et al concluded that a pharmacist-led interdisciplinary collaborative practice agreement increased cefazolin use in patients allergic to penicillin, including those with urticaria and anaphylaxis, with no reported ADRs.⁷ This study further demonstrated the safety of cefazolin use in patients with anaphylaxis to penicillin.

Limitations

This study’s single-center, retrospective design, patient population, and small sample size limit the generalizability of its results. The data collected are dependent on documentation in the chart. No ADRs were reported from the antibiotics patients received perioperatively. When considering safety data, information such as serum creatinine were available only in CPRS and some patients did not receive a postprocedure serum creatinine level. Additionally, this study did not investigate whether there was an increase in preferred preoperative antimicrobial prophylaxis after implementation of this protocol.

Conclusions

The results of this study support the use of cefazolin perioperatively in patients allergic to penicillin, including those with a history of anaphylaxis. Additional research should be conducted to validate data given the low incidence of ADRs. The primary outcome did not reach statistical significance, but the results may be clinically significant from a stewardship and safety perspective. VHI continues to use the screening tool described in this article.

Given its safety profile and bactericidal activity against the predominant organisms causing surgical site infections (SSIs), cefazolin remains the most popular choice for surgical prophylaxis.¹ Cefazolin offers protection against the pathogens most likely to contaminate the surgical site while minimizing inappropriate methicillin- resistant Staphylococcus aureus coverage that occurs with alternatives such as vancomycin and clindamycin. Documented allergies to Β-lactam antibiotics have historically forced clinicians to avoid the use of cephalosporins due to the potential risk of cross-reactivity. True type 1 (immunoglobin E [IgE]-mediated) cross-allergic reactions between penicillin and cephalosporins are rare, and previously reported data indicate cross-reactivity as a result of antibody recognition is more closely related to the side-chain identity rather than the Β-lactam ring.^2,3

About 10% of US patients report having a penicillin allergy; however, < 1% of the population has a true IgE-mediated allergic reaction.⁴ Previous research that has challenged penicillin allergies with cefazolin for surgical prophylaxis has reported minimal rates of allergic reactions.^2-5

In previous trials, patients with a history of delayed skin reactions, such as Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and drug reaction with eosinophilia and systemic symptoms (DRESS), were excluded. Additionally, patients with an allergy to cefazolin including those with urticaria, angioedema, bronchospasm, or anaphylaxis, were excluded from perioperative retrial of cefazolin. Grant et al found that cefazolin can be safely given to patients with IgE-mediated reactions to penicillin and other cephalosporins due to a structurally different side chain.³

In January 2023, the Veteran Health Indiana (VHI) pharmacy team in conjunction with surgery, infectious disease, and anesthesiology, implemented a screening tool as an amendment to perioperative antibiotic guidance to help determine which patients with a documented penicillin allergy could be candidates for perioperative cefazolin. The implemented screening tool (Allergy Clarification for Cefazolin Evidence-Based Prescribing Tool) has been described by Lam et al, who reported that an increased proportion of patients with documented penicillin allergy received cefazolin without more adverse drug reactions (ADRs).⁵ Patients with a Β-lactam allergy were eligible to receive cefazolin unless the ADR was SJS, TEN, or DRESS, or the offending agent was cefazolin and the patient experienced urticaria, angioedema, bronchospasm, or anaphylaxis. If the reaction was not from cefazolin or was unknown, patients were eligible to receive cefazolin (Figure).

To date, minimal data exist to evaluate the incidence of ADRs when cefazolin is given perioperatively to patients with a previously documented penicillin allergy. The purpose of this study was to evaluate the incidence of allergic ADRs in patients who had a documented penicillin allergy and received periprocedural antibiotics.

Methods

This single-center, retrospective chart review used the US Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) to identify patients with a documented penicillin allergy who underwent an operation and received periprocedural antibiotics between February 1, 2023, and January 31, 2024. This study was reviewed and approved by the Indiana University Health Institutional Review Board and the VHI Research and Development Committee.

Patients were enrolled if they were aged ≥ 18 years, had a documented penicillin allergy, underwent a surgical intervention, and received perioperative antibiotics during the study period. Patients were excluded if they had a documented penicillin allergy resulting in severe delayed skin reactions (ie, SJS, TEN, or DRESS). These criteria produced 197 surgical procedures. Data were collected for each surgical procedure, so patients could be included more than once. Patient history of allergic reaction to penicillin was obtained through CPRS.

The primary endpoint was the percentage of allergic ADRs in patients with penicillin allergies receiving cefazolin perioperatively. Secondary outcomes included the appropriateness of the antibiotic regimen in congruence with American System of Health Pharmacists (ASHP) recommendations, incidence of SSIs within 30 days of the procedure, incidence of ADRs in those with a history of anaphylaxis vs nonanaphylaxis allergy, incidence of allergic reaction requiring pharmacologic and nonpharmacologic interventions, and incidence of acute kidney injury (AKI). AKI was defined as an increase in serum creatinine by ≥ 0.3 mg/dL within 48 hours or an increase in serum creatinine to ≥ 1.5 times baseline.

Demographic data included sex, age, race, preoperative serum creatinine, and postoperative serum creatinine. Anaphylaxis was defined as an acute onset of illness (within minutes to several hours) with involvement of skin, mucosal tissue, or both involving either respiratory compromise or reduced blood pressures. Allergic reactions were defined as facial, tongue, throat, airway, lip, mouth, periorbital, or eye swelling, urticaria, angioedema, dyspnea, anaphylaxis, or a positive penicillin skin test. Additionally, data collected included the description and severity of postprophylactic antibiotic reaction, antibiotic choice, interventions required for the allergic reaction, SSI occurrence, date of SSI, operating specialty, and postoperative change in renal function.

Descriptive statistics, including mean, SD, and percentages were reported for baseline characteristics of the study population. Percentages were used to demonstrate the differences in primary and secondary outcomes for each study group. Fisher exact tests were used for incidence of ADRs in patients with penicillin allergy who received cefazolin and reported incidence of SSIs.

Results

A total of 197 surgical procedures in patients with a documented penicillin allergy were included; 127 procedures used cefazolin perioperatively, 3 procedures used cefazolin plus gentamicin, and 67 procedures used other antibiotics. Most patients were White (n = 160; 81.2%), male (n = 158; 80.2%), and had a mean age of 64.9 years. Urology was the most common surgical specialty (n = 59; 29.9%) (Table 1). Of the 16 patients with documented penicillin anaphylaxis reaction, 8 received cefazolin and 8 received a different antibiotic. A total of 181 patients reported a nonanaphylaxis allergy. One hundred fifty-one patients (68.6%) reported a reaction history of hives, rash, or swelling (Table 2). Patients could report ≥ 1 reaction. The most prevalent antibiotics used were cefazolin, which was used by 130 patients (61.3%), and clindamycin which was used by 33 patients (15.6%) (Table 3). Patients could receive ≥ 1 antibiotic.

For the primary outcome, the incidence of allergic reactions in patients allergic to penicillin, there was no incidence of allergic reactions in either the cefazolin or other group. Given the absence of reactions, no interventions were required.

There were no ADRs in those with history of anaphylaxis or nonanaphylaxis allergy. In the cefazolin group, 126 of 127 surgical procedure regimens (99.2%) were congruent with ASHP recommendations, all 3 surgical procedures regimens in the cefazolin plus gentamicin group were congruent with ASHP recommendations, and 58 of 67 surgical procedure regimens (86.6%) in the other antibiotic group were congruent with ASHP recommendations. None of the 127 patients in the cefazolin group or of the 3 patients in the cefazolin plus gentamicin group reported an SSI, and 3 of 67 patients (4.5%) had an SSI in the other antibiotic group. One procedure that resulted in SSI was not congruent with ASHP recommendations. Twenty-four patients had 2 serum creatinine levels drawn within 48 hours of surgery. One of 12 patients (8.3%) and 0 of 12 patients had an AKI in the cefazolin and other antibiotic group, respectively (Table 4).

Discussion

Implementation of a screening tool at VHI allowed patients with documented penicillin allergy, including anaphylaxis, to receive cefazolin perioperatively. Broad spectrum antibiotics such as vancomycin, clindamycin, and fluoroquinolones are frequently used in patients allergic to penicillin, which can increase health care costs, risk of toxicity, and antimicrobial resistance.⁴ There was no incidence of allergic reactions noted in patients allergic to penicillin who received cefazolin. When comparing the incidence of observed allergic reactions to received perioperative antibiotics in the cefazolin group to previously published literature, no difference in allergy rates (P = .09) was found.³ Most antibiotics administered were congruent with ASHP guideline recommendations, and most patients eligible for cefazolin received it perioperatively.

Similar to this study, Goodman et al concluded that cefazolin appears to be a safe regimen in patients with documented penicillin anaphylactic reaction for surgical prophylaxis with only 1 (0.2%) potential allergic reaction.⁶ Patients who received cefazolin perioperatively had a statistically significant decrease in SSI rates. There were no clinically or statistically significant differences found between the proportion of allergic reactions or ADRs when compared to alternative antibiotics. Lessard et al concluded that a pharmacist-led interdisciplinary collaborative practice agreement increased cefazolin use in patients allergic to penicillin, including those with urticaria and anaphylaxis, with no reported ADRs.⁷ This study further demonstrated the safety of cefazolin use in patients with anaphylaxis to penicillin.

Limitations

This study’s single-center, retrospective design, patient population, and small sample size limit the generalizability of its results. The data collected are dependent on documentation in the chart. No ADRs were reported from the antibiotics patients received perioperatively. When considering safety data, information such as serum creatinine were available only in CPRS and some patients did not receive a postprocedure serum creatinine level. Additionally, this study did not investigate whether there was an increase in preferred preoperative antimicrobial prophylaxis after implementation of this protocol.

Conclusions

The results of this study support the use of cefazolin perioperatively in patients allergic to penicillin, including those with a history of anaphylaxis. Additional research should be conducted to validate data given the low incidence of ADRs. The primary outcome did not reach statistical significance, but the results may be clinically significant from a stewardship and safety perspective. VHI continues to use the screening tool described in this article.

Given its safety profile and bactericidal activity against the predominant organisms causing surgical site infections (SSIs), cefazolin remains the most popular choice for surgical prophylaxis.¹ Cefazolin offers protection against the pathogens most likely to contaminate the surgical site while minimizing inappropriate methicillin- resistant Staphylococcus aureus coverage that occurs with alternatives such as vancomycin and clindamycin. Documented allergies to Β-lactam antibiotics have historically forced clinicians to avoid the use of cephalosporins due to the potential risk of cross-reactivity. True type 1 (immunoglobin E [IgE]-mediated) cross-allergic reactions between penicillin and cephalosporins are rare, and previously reported data indicate cross-reactivity as a result of antibody recognition is more closely related to the side-chain identity rather than the Β-lactam ring.^2,3

About 10% of US patients report having a penicillin allergy; however, < 1% of the population has a true IgE-mediated allergic reaction.⁴ Previous research that has challenged penicillin allergies with cefazolin for surgical prophylaxis has reported minimal rates of allergic reactions.^2-5

In previous trials, patients with a history of delayed skin reactions, such as Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and drug reaction with eosinophilia and systemic symptoms (DRESS), were excluded. Additionally, patients with an allergy to cefazolin including those with urticaria, angioedema, bronchospasm, or anaphylaxis, were excluded from perioperative retrial of cefazolin. Grant et al found that cefazolin can be safely given to patients with IgE-mediated reactions to penicillin and other cephalosporins due to a structurally different side chain.³

In January 2023, the Veteran Health Indiana (VHI) pharmacy team in conjunction with surgery, infectious disease, and anesthesiology, implemented a screening tool as an amendment to perioperative antibiotic guidance to help determine which patients with a documented penicillin allergy could be candidates for perioperative cefazolin. The implemented screening tool (Allergy Clarification for Cefazolin Evidence-Based Prescribing Tool) has been described by Lam et al, who reported that an increased proportion of patients with documented penicillin allergy received cefazolin without more adverse drug reactions (ADRs).⁵ Patients with a Β-lactam allergy were eligible to receive cefazolin unless the ADR was SJS, TEN, or DRESS, or the offending agent was cefazolin and the patient experienced urticaria, angioedema, bronchospasm, or anaphylaxis. If the reaction was not from cefazolin or was unknown, patients were eligible to receive cefazolin (Figure).

To date, minimal data exist to evaluate the incidence of ADRs when cefazolin is given perioperatively to patients with a previously documented penicillin allergy. The purpose of this study was to evaluate the incidence of allergic ADRs in patients who had a documented penicillin allergy and received periprocedural antibiotics.

Methods

This single-center, retrospective chart review used the US Department of Veterans Affairs (VA) Computerized Patient Record System (CPRS) to identify patients with a documented penicillin allergy who underwent an operation and received periprocedural antibiotics between February 1, 2023, and January 31, 2024. This study was reviewed and approved by the Indiana University Health Institutional Review Board and the VHI Research and Development Committee.

Patients were enrolled if they were aged ≥ 18 years, had a documented penicillin allergy, underwent a surgical intervention, and received perioperative antibiotics during the study period. Patients were excluded if they had a documented penicillin allergy resulting in severe delayed skin reactions (ie, SJS, TEN, or DRESS). These criteria produced 197 surgical procedures. Data were collected for each surgical procedure, so patients could be included more than once. Patient history of allergic reaction to penicillin was obtained through CPRS.

The primary endpoint was the percentage of allergic ADRs in patients with penicillin allergies receiving cefazolin perioperatively. Secondary outcomes included the appropriateness of the antibiotic regimen in congruence with American System of Health Pharmacists (ASHP) recommendations, incidence of SSIs within 30 days of the procedure, incidence of ADRs in those with a history of anaphylaxis vs nonanaphylaxis allergy, incidence of allergic reaction requiring pharmacologic and nonpharmacologic interventions, and incidence of acute kidney injury (AKI). AKI was defined as an increase in serum creatinine by ≥ 0.3 mg/dL within 48 hours or an increase in serum creatinine to ≥ 1.5 times baseline.

Demographic data included sex, age, race, preoperative serum creatinine, and postoperative serum creatinine. Anaphylaxis was defined as an acute onset of illness (within minutes to several hours) with involvement of skin, mucosal tissue, or both involving either respiratory compromise or reduced blood pressures. Allergic reactions were defined as facial, tongue, throat, airway, lip, mouth, periorbital, or eye swelling, urticaria, angioedema, dyspnea, anaphylaxis, or a positive penicillin skin test. Additionally, data collected included the description and severity of postprophylactic antibiotic reaction, antibiotic choice, interventions required for the allergic reaction, SSI occurrence, date of SSI, operating specialty, and postoperative change in renal function.

Descriptive statistics, including mean, SD, and percentages were reported for baseline characteristics of the study population. Percentages were used to demonstrate the differences in primary and secondary outcomes for each study group. Fisher exact tests were used for incidence of ADRs in patients with penicillin allergy who received cefazolin and reported incidence of SSIs.

Results

A total of 197 surgical procedures in patients with a documented penicillin allergy were included; 127 procedures used cefazolin perioperatively, 3 procedures used cefazolin plus gentamicin, and 67 procedures used other antibiotics. Most patients were White (n = 160; 81.2%), male (n = 158; 80.2%), and had a mean age of 64.9 years. Urology was the most common surgical specialty (n = 59; 29.9%) (Table 1). Of the 16 patients with documented penicillin anaphylaxis reaction, 8 received cefazolin and 8 received a different antibiotic. A total of 181 patients reported a nonanaphylaxis allergy. One hundred fifty-one patients (68.6%) reported a reaction history of hives, rash, or swelling (Table 2). Patients could report ≥ 1 reaction. The most prevalent antibiotics used were cefazolin, which was used by 130 patients (61.3%), and clindamycin which was used by 33 patients (15.6%) (Table 3). Patients could receive ≥ 1 antibiotic.

For the primary outcome, the incidence of allergic reactions in patients allergic to penicillin, there was no incidence of allergic reactions in either the cefazolin or other group. Given the absence of reactions, no interventions were required.

There were no ADRs in those with history of anaphylaxis or nonanaphylaxis allergy. In the cefazolin group, 126 of 127 surgical procedure regimens (99.2%) were congruent with ASHP recommendations, all 3 surgical procedures regimens in the cefazolin plus gentamicin group were congruent with ASHP recommendations, and 58 of 67 surgical procedure regimens (86.6%) in the other antibiotic group were congruent with ASHP recommendations. None of the 127 patients in the cefazolin group or of the 3 patients in the cefazolin plus gentamicin group reported an SSI, and 3 of 67 patients (4.5%) had an SSI in the other antibiotic group. One procedure that resulted in SSI was not congruent with ASHP recommendations. Twenty-four patients had 2 serum creatinine levels drawn within 48 hours of surgery. One of 12 patients (8.3%) and 0 of 12 patients had an AKI in the cefazolin and other antibiotic group, respectively (Table 4).

Discussion

Implementation of a screening tool at VHI allowed patients with documented penicillin allergy, including anaphylaxis, to receive cefazolin perioperatively. Broad spectrum antibiotics such as vancomycin, clindamycin, and fluoroquinolones are frequently used in patients allergic to penicillin, which can increase health care costs, risk of toxicity, and antimicrobial resistance.⁴ There was no incidence of allergic reactions noted in patients allergic to penicillin who received cefazolin. When comparing the incidence of observed allergic reactions to received perioperative antibiotics in the cefazolin group to previously published literature, no difference in allergy rates (P = .09) was found.³ Most antibiotics administered were congruent with ASHP guideline recommendations, and most patients eligible for cefazolin received it perioperatively.

Similar to this study, Goodman et al concluded that cefazolin appears to be a safe regimen in patients with documented penicillin anaphylactic reaction for surgical prophylaxis with only 1 (0.2%) potential allergic reaction.⁶ Patients who received cefazolin perioperatively had a statistically significant decrease in SSI rates. There were no clinically or statistically significant differences found between the proportion of allergic reactions or ADRs when compared to alternative antibiotics. Lessard et al concluded that a pharmacist-led interdisciplinary collaborative practice agreement increased cefazolin use in patients allergic to penicillin, including those with urticaria and anaphylaxis, with no reported ADRs.⁷ This study further demonstrated the safety of cefazolin use in patients with anaphylaxis to penicillin.

Limitations

This study’s single-center, retrospective design, patient population, and small sample size limit the generalizability of its results. The data collected are dependent on documentation in the chart. No ADRs were reported from the antibiotics patients received perioperatively. When considering safety data, information such as serum creatinine were available only in CPRS and some patients did not receive a postprocedure serum creatinine level. Additionally, this study did not investigate whether there was an increase in preferred preoperative antimicrobial prophylaxis after implementation of this protocol.

Conclusions

The results of this study support the use of cefazolin perioperatively in patients allergic to penicillin, including those with a history of anaphylaxis. Additional research should be conducted to validate data given the low incidence of ADRs. The primary outcome did not reach statistical significance, but the results may be clinically significant from a stewardship and safety perspective. VHI continues to use the screening tool described in this article.

Roughly 8.6% of the 17.4 million US veterans live in poverty. About 11.1% are considered food insecure (ie, unable to acquire adequate food for ≥1 household members), with another 5.3% considered very food insecure (ie, eating patterns of ≥1 household members were disrupted and their food intake was reduced at least some time during the year). Compared with nonveterans, veterans are 7.4% more likely to be food insecure.¹ This high prevalence of food insecurity and poverty has a negative impact on veteran diets.

Veterans’ diets contained more added sugars and solid fats and scored lower compared with nonveterans when assessed for diet quality with the Healthy Eating Index.² Veterans have a higher prevalence of diet-related chronic disease, including diabetes, hypertension, and obesity compared with the nonveterans.^3-5 Given the critical role of diet in health and disease risk, enhancing diet quality among veterans has garnered significant attention and calls to action.^2,6,7 While there are many factors that contribute to diet, any veteran can receive a consultation or self-refer to receive nutrition counseling effective for improving diet quality, within the US Department of Veterans Affairs (VA).

The NOVA food classification system describes diet quality by categorizing food items by processing methods and ingredients into 4 food groups.⁸ The first is unprocessed and minimally processed items (MPFs) such as fresh fruits, vegetables, and meats. MPFs consist of whole foods which can also be minimally processed (eg, chopping, drying, grinding, heating, chilling). Culinary processed foods (CPFs) are processed foods for cooking (eg, salt, butter, and vinegar) and are typically eaten in small quantities along with MPFs. Processed foods (PRFs) include canned and smoked foods, while ultra-processed foods (UPFs) are distinguished by industrial ingredients, requiring specialized tools and processing techniques, and hyper-palatability related to color, flavor, and packaging.⁸ Examples of UPFs include mass-produced breads found at grocery stores, prepackaged snacks and meals, and hydrogenated oils. UPF consumption is associated with higher risk for negative cardiometabolic outcomes, common mental disorders, and all-cause mortality.⁹ To date, only a study by Powell et al has used the NOVA classification system in a veteran population, and it was limited to a comparison of the price of UPFs and veteran body mass index (BMI).¹⁰ Therefore, it remains unknown what percentage of total energy intake (TEI) comes from UPFs in the diets of veterans.

This study sought to quantify the proportion of TEI from UPFs among a sample of patients from the VA Phoenix Health Care System (VAPHCS). Results from a 2021 global meta-analysis reveal that the US and United Kingdom have the highest intakes of UPFs in the world.¹¹ Specifically, within the US, 15 studies with 234,890 participants reveal that the majority of TEI (about 55%) comes from UPFs.¹¹ We hypothesized that this veteran sample would have a higher proportion of TEI from UPFs, possibly due to a higher prevalence of poverty and food insecurity among veterans compared with nonveterans.¹ If the percentage of TEI coming from UPF is higher or even similar to nonveterans, further efforts to increase veterans’ use of the available nutritional services would be warranted to minimize nutrition-related disease among veterans.

Methods

This is a cross-sectional, secondary data analysis of baseline 3-day food records collected from 2017 to 2020 from 92 patients recruited at VAPHCS to participate in a whole-food plant-based diet study.¹² The original study was reviewed and approved by the VAPHCS Institutional Review Board (1593830). Recruitment methods included clinician recommendation, a recorded advertisement played while phone calls were on hold, and flyers distributed throughout VAPHCS. Patients were included if they were aged 18 to 90 years, had a BMI 25.1 to 39.9, had a diagnosis of nutrition-related chronic disease (hypertension, diabetes, or hyperlipidemia), an interest and desire to make a lifestyle change, active telephone contact information (either landline or cell phone), no contraindication to be on a whole-food plant-based diet, access to transportation and a functioning kitchen, ability to prepare meals independently, access to a computer or tablet with internet access, and a digital camera or smartphone. Exclusion criteria included significant unplanned weight loss within 6 months, uncontrolled insulin-dependent diabetes with a current hemoglobin A_1c > 9%, pregnancy/lactation, taking prescribed weight loss medication, currently following a diet (eg, plant-based diet, vegan, or medical weight loss program diet), celiac disease diagnosed within 6 months, end-stage hepatic disease or renal disease requiring dialysis, active cancer or receiving chemotherapy or radiation therapy, active alcohol or substance use disorder, history of eating disorders, fasting triglyceride level > 350 mg/dL, any psychological issues that prevent adherence, inability to speak English, limited mobility, and homeless or in housing with limited kitchen access. A baseline 3-day food record was collected from the participants and used in this secondary analysis.

Diet Analysis

Food records were analyzed using Esha Research Food Processor 4.0 to identify calorie and macronutrient information. To limit bias, food items were coded independently by 2 researchers into 4 food processing groups determined by the NOVA classification: MPF, CPF, PRF, and UPF.⁸ When possible, specific ingredient information was collected using internet searches for brand product websites. Initial coding had an 89% agreement rate for food item coding between the 2 researchers. As coding was done in duplicate, a third researcher resolved disagreements. The number of food items for each processing group was determined and the mean (SD) percentage of TEI for each NOVA group was provided across participants. A 1-way analysis of variance and Tukey Multiple Comparisons Test were used to determine significance between groups with an α = .05 using Prism V9.

Results

Of the 92 participants in the original study, only 79 met inclusion criteria and had baseline diet data. The 79 veterans had a mean (SD) age of 61 (13) years and 59 (75%) were male (Table 1). Mean (SD) TEI was 1921 (815) kcal. The mean (SD) percentage of calories from carbohydrate, fat, and protein were 46% (21%), 39% (20%), and 16% (6%), respectively (Table 2).

A mean (SD) of 36 (12) food items were analyzed from the 3-day food records. The majority of food items were UPFs (56%), 33% were MPFs, 8% were PRFs, and 3% were CPFs. In total, 75% of TEI came from UPFs (P < .001); only 14% of TEI came from minimally processed foods (Figure).

Discussion

To our knowledge, this is the first analysis of UPF consumption among US veterans. TEIs coming from UPFs appear to be about 20% higher among veterans compared to nonveterans: 75% vs 55%.¹¹ Coupled with high UPF consumption, MPFs (14%) and PRFs (9%) represent smaller sources of TEI among surveyed veterans. Top caloric sources of UPFs in the US include sandwiches (including burgers), sweet bakery products, savory snacks, pizza, sweetened beverages, and breads, rolls, and tortillas, and likely reflect the major sources of UPFs in the veteran diet.¹³ As the statistical comparison between the veteran data and nonveteran data is not feasible in the present study, a future study with a much larger sample size would be needed for a direct comparison.

While the exact cause of higher UPF consumption among sampled veterans remains unknown and likely multifactorial (eg, cost, food insecurity, access, cooking skills, nutrition knowledge), veterans can receive a consult or self-refer to a registered dietitian nutritionist (RDN) for nutrition education. Counseling has been shown to be an effective way to improve diet quality and increase daily fruit and vegetable intake.¹⁴ High consumption of UPFs, which are generally energy-dense and nutrient-poor, contributes to the low diet quality observed in veterans, and future research examining the relationship between UPF intake and overall diet quality among veterans is warranted.^2,15 As nutrition knowledge is associated with higher diet quality among veterans, increased use of nutrition services (ie, nutrition education or food supplement programs) has the potential to influence consumption of MPFs and decrease consumption of UPFs.¹⁶ Subsequently, UPF-targeted interventions developed by VA RDNs hold the promise to decrease consumption of UPFs and increase intake of MPFs and PRFs.

Veterans have a high prevalence of diabetes, hypertension, and obesity.⁹ The high UPF intake observed in this sample of veterans may increase the risk for these chronic diseases and overall mortality. The high percentage of TEI from UPFs among veterans is also of concern not only due to potential negative health outcomes, but also associated costs of treating veterans with multimorbidities.¹⁷ Targeting UPF intake via nutritional education may promote health and decrease the financial burden needed to support the health of veterans.

Improving veteran health and well-being, including enhancing health care accessibility in underserved areas, are pivotal objectives of the VA strategic plan for 2026 to 2030. Public policy aims to tackle food insecurity within the veteran population during the first 5 years of civilian life.¹⁸ In alignment with the White House Strategy on Hunger, Nutrition, and Health, VA established a Food Security Office (FSO) in 2023. The FSO mission is to use an interdisciplinary approach to provide resources to ensure veteran food security and create an environment where all veterans are food and nutrition secure.

Limitations

This study has several limitations. As the Food Processor software database does not include all brand items, similar brands were used to mirror the nutrient profile. While food records are common among veteran diet studies, accuracy may be reduced due to self-reporting bias.¹⁹ Different interpretation of the NOVA classification designation for various food items is possible, however, 89% of foods were coded the same by the research team which suggests high accuracy in food coding. Specific ingredient information was not collected from the 3-day food records; thus, these records were not produced in such a way to improve the accuracy of the NOVA classification designation. This study was limited by its small sample size (N = 79); although, this analysis is larger than other studies of UPF consumption in the US.^20,21 In addition, the generalizability of this study is limited as this population sample was from a single VA hospital and may not reflect the overall veteran population. Participants in this study were recruited only from those receiving VA care, thus their diet quality may not represent the quality consumed by veterans not participating in VA services. Further research on UPF consumption among veterans is warranted with a larger, more representative study sample size.

Conclusions

As this is the highest observed UPF intake documented in the US, these results should be of concern for the VA and its RDNs. More research is needed to better understand why UPF consumption is so high among veterans, what barriers veterans face to decreasing UPF consumption, and what intervention(s) veterans would welcome to improve their diet quality. Presently, veterans are provided with access to a variety of effective nutrition education and counseling options and should be encouraged to use these services. VA RDNs should be aware of the high intake of UPFs in the veteran population and familiarize themselves with education and counseling strategies that promote behavior change to replace UPFs with more nutrient-dense foods choices.

Roughly 8.6% of the 17.4 million US veterans live in poverty. About 11.1% are considered food insecure (ie, unable to acquire adequate food for ≥1 household members), with another 5.3% considered very food insecure (ie, eating patterns of ≥1 household members were disrupted and their food intake was reduced at least some time during the year). Compared with nonveterans, veterans are 7.4% more likely to be food insecure.¹ This high prevalence of food insecurity and poverty has a negative impact on veteran diets.

Veterans’ diets contained more added sugars and solid fats and scored lower compared with nonveterans when assessed for diet quality with the Healthy Eating Index.² Veterans have a higher prevalence of diet-related chronic disease, including diabetes, hypertension, and obesity compared with the nonveterans.^3-5 Given the critical role of diet in health and disease risk, enhancing diet quality among veterans has garnered significant attention and calls to action.^2,6,7 While there are many factors that contribute to diet, any veteran can receive a consultation or self-refer to receive nutrition counseling effective for improving diet quality, within the US Department of Veterans Affairs (VA).

The NOVA food classification system describes diet quality by categorizing food items by processing methods and ingredients into 4 food groups.⁸ The first is unprocessed and minimally processed items (MPFs) such as fresh fruits, vegetables, and meats. MPFs consist of whole foods which can also be minimally processed (eg, chopping, drying, grinding, heating, chilling). Culinary processed foods (CPFs) are processed foods for cooking (eg, salt, butter, and vinegar) and are typically eaten in small quantities along with MPFs. Processed foods (PRFs) include canned and smoked foods, while ultra-processed foods (UPFs) are distinguished by industrial ingredients, requiring specialized tools and processing techniques, and hyper-palatability related to color, flavor, and packaging.⁸ Examples of UPFs include mass-produced breads found at grocery stores, prepackaged snacks and meals, and hydrogenated oils. UPF consumption is associated with higher risk for negative cardiometabolic outcomes, common mental disorders, and all-cause mortality.⁹ To date, only a study by Powell et al has used the NOVA classification system in a veteran population, and it was limited to a comparison of the price of UPFs and veteran body mass index (BMI).¹⁰ Therefore, it remains unknown what percentage of total energy intake (TEI) comes from UPFs in the diets of veterans.

This study sought to quantify the proportion of TEI from UPFs among a sample of patients from the VA Phoenix Health Care System (VAPHCS). Results from a 2021 global meta-analysis reveal that the US and United Kingdom have the highest intakes of UPFs in the world.¹¹ Specifically, within the US, 15 studies with 234,890 participants reveal that the majority of TEI (about 55%) comes from UPFs.¹¹ We hypothesized that this veteran sample would have a higher proportion of TEI from UPFs, possibly due to a higher prevalence of poverty and food insecurity among veterans compared with nonveterans.¹ If the percentage of TEI coming from UPF is higher or even similar to nonveterans, further efforts to increase veterans’ use of the available nutritional services would be warranted to minimize nutrition-related disease among veterans.

Methods

This is a cross-sectional, secondary data analysis of baseline 3-day food records collected from 2017 to 2020 from 92 patients recruited at VAPHCS to participate in a whole-food plant-based diet study.¹² The original study was reviewed and approved by the VAPHCS Institutional Review Board (1593830). Recruitment methods included clinician recommendation, a recorded advertisement played while phone calls were on hold, and flyers distributed throughout VAPHCS. Patients were included if they were aged 18 to 90 years, had a BMI 25.1 to 39.9, had a diagnosis of nutrition-related chronic disease (hypertension, diabetes, or hyperlipidemia), an interest and desire to make a lifestyle change, active telephone contact information (either landline or cell phone), no contraindication to be on a whole-food plant-based diet, access to transportation and a functioning kitchen, ability to prepare meals independently, access to a computer or tablet with internet access, and a digital camera or smartphone. Exclusion criteria included significant unplanned weight loss within 6 months, uncontrolled insulin-dependent diabetes with a current hemoglobin A_1c > 9%, pregnancy/lactation, taking prescribed weight loss medication, currently following a diet (eg, plant-based diet, vegan, or medical weight loss program diet), celiac disease diagnosed within 6 months, end-stage hepatic disease or renal disease requiring dialysis, active cancer or receiving chemotherapy or radiation therapy, active alcohol or substance use disorder, history of eating disorders, fasting triglyceride level > 350 mg/dL, any psychological issues that prevent adherence, inability to speak English, limited mobility, and homeless or in housing with limited kitchen access. A baseline 3-day food record was collected from the participants and used in this secondary analysis.

Diet Analysis

Food records were analyzed using Esha Research Food Processor 4.0 to identify calorie and macronutrient information. To limit bias, food items were coded independently by 2 researchers into 4 food processing groups determined by the NOVA classification: MPF, CPF, PRF, and UPF.⁸ When possible, specific ingredient information was collected using internet searches for brand product websites. Initial coding had an 89% agreement rate for food item coding between the 2 researchers. As coding was done in duplicate, a third researcher resolved disagreements. The number of food items for each processing group was determined and the mean (SD) percentage of TEI for each NOVA group was provided across participants. A 1-way analysis of variance and Tukey Multiple Comparisons Test were used to determine significance between groups with an α = .05 using Prism V9.

Results

Of the 92 participants in the original study, only 79 met inclusion criteria and had baseline diet data. The 79 veterans had a mean (SD) age of 61 (13) years and 59 (75%) were male (Table 1). Mean (SD) TEI was 1921 (815) kcal. The mean (SD) percentage of calories from carbohydrate, fat, and protein were 46% (21%), 39% (20%), and 16% (6%), respectively (Table 2).

A mean (SD) of 36 (12) food items were analyzed from the 3-day food records. The majority of food items were UPFs (56%), 33% were MPFs, 8% were PRFs, and 3% were CPFs. In total, 75% of TEI came from UPFs (P < .001); only 14% of TEI came from minimally processed foods (Figure).

Discussion

To our knowledge, this is the first analysis of UPF consumption among US veterans. TEIs coming from UPFs appear to be about 20% higher among veterans compared to nonveterans: 75% vs 55%.¹¹ Coupled with high UPF consumption, MPFs (14%) and PRFs (9%) represent smaller sources of TEI among surveyed veterans. Top caloric sources of UPFs in the US include sandwiches (including burgers), sweet bakery products, savory snacks, pizza, sweetened beverages, and breads, rolls, and tortillas, and likely reflect the major sources of UPFs in the veteran diet.¹³ As the statistical comparison between the veteran data and nonveteran data is not feasible in the present study, a future study with a much larger sample size would be needed for a direct comparison.

While the exact cause of higher UPF consumption among sampled veterans remains unknown and likely multifactorial (eg, cost, food insecurity, access, cooking skills, nutrition knowledge), veterans can receive a consult or self-refer to a registered dietitian nutritionist (RDN) for nutrition education. Counseling has been shown to be an effective way to improve diet quality and increase daily fruit and vegetable intake.¹⁴ High consumption of UPFs, which are generally energy-dense and nutrient-poor, contributes to the low diet quality observed in veterans, and future research examining the relationship between UPF intake and overall diet quality among veterans is warranted.^2,15 As nutrition knowledge is associated with higher diet quality among veterans, increased use of nutrition services (ie, nutrition education or food supplement programs) has the potential to influence consumption of MPFs and decrease consumption of UPFs.¹⁶ Subsequently, UPF-targeted interventions developed by VA RDNs hold the promise to decrease consumption of UPFs and increase intake of MPFs and PRFs.

Veterans have a high prevalence of diabetes, hypertension, and obesity.⁹ The high UPF intake observed in this sample of veterans may increase the risk for these chronic diseases and overall mortality. The high percentage of TEI from UPFs among veterans is also of concern not only due to potential negative health outcomes, but also associated costs of treating veterans with multimorbidities.¹⁷ Targeting UPF intake via nutritional education may promote health and decrease the financial burden needed to support the health of veterans.

Improving veteran health and well-being, including enhancing health care accessibility in underserved areas, are pivotal objectives of the VA strategic plan for 2026 to 2030. Public policy aims to tackle food insecurity within the veteran population during the first 5 years of civilian life.¹⁸ In alignment with the White House Strategy on Hunger, Nutrition, and Health, VA established a Food Security Office (FSO) in 2023. The FSO mission is to use an interdisciplinary approach to provide resources to ensure veteran food security and create an environment where all veterans are food and nutrition secure.

Limitations

This study has several limitations. As the Food Processor software database does not include all brand items, similar brands were used to mirror the nutrient profile. While food records are common among veteran diet studies, accuracy may be reduced due to self-reporting bias.¹⁹ Different interpretation of the NOVA classification designation for various food items is possible, however, 89% of foods were coded the same by the research team which suggests high accuracy in food coding. Specific ingredient information was not collected from the 3-day food records; thus, these records were not produced in such a way to improve the accuracy of the NOVA classification designation. This study was limited by its small sample size (N = 79); although, this analysis is larger than other studies of UPF consumption in the US.^20,21 In addition, the generalizability of this study is limited as this population sample was from a single VA hospital and may not reflect the overall veteran population. Participants in this study were recruited only from those receiving VA care, thus their diet quality may not represent the quality consumed by veterans not participating in VA services. Further research on UPF consumption among veterans is warranted with a larger, more representative study sample size.

Conclusions

As this is the highest observed UPF intake documented in the US, these results should be of concern for the VA and its RDNs. More research is needed to better understand why UPF consumption is so high among veterans, what barriers veterans face to decreasing UPF consumption, and what intervention(s) veterans would welcome to improve their diet quality. Presently, veterans are provided with access to a variety of effective nutrition education and counseling options and should be encouraged to use these services. VA RDNs should be aware of the high intake of UPFs in the veteran population and familiarize themselves with education and counseling strategies that promote behavior change to replace UPFs with more nutrient-dense foods choices.

Roughly 8.6% of the 17.4 million US veterans live in poverty. About 11.1% are considered food insecure (ie, unable to acquire adequate food for ≥1 household members), with another 5.3% considered very food insecure (ie, eating patterns of ≥1 household members were disrupted and their food intake was reduced at least some time during the year). Compared with nonveterans, veterans are 7.4% more likely to be food insecure.¹ This high prevalence of food insecurity and poverty has a negative impact on veteran diets.

Veterans’ diets contained more added sugars and solid fats and scored lower compared with nonveterans when assessed for diet quality with the Healthy Eating Index.² Veterans have a higher prevalence of diet-related chronic disease, including diabetes, hypertension, and obesity compared with the nonveterans.^3-5 Given the critical role of diet in health and disease risk, enhancing diet quality among veterans has garnered significant attention and calls to action.^2,6,7 While there are many factors that contribute to diet, any veteran can receive a consultation or self-refer to receive nutrition counseling effective for improving diet quality, within the US Department of Veterans Affairs (VA).

The NOVA food classification system describes diet quality by categorizing food items by processing methods and ingredients into 4 food groups.⁸ The first is unprocessed and minimally processed items (MPFs) such as fresh fruits, vegetables, and meats. MPFs consist of whole foods which can also be minimally processed (eg, chopping, drying, grinding, heating, chilling). Culinary processed foods (CPFs) are processed foods for cooking (eg, salt, butter, and vinegar) and are typically eaten in small quantities along with MPFs. Processed foods (PRFs) include canned and smoked foods, while ultra-processed foods (UPFs) are distinguished by industrial ingredients, requiring specialized tools and processing techniques, and hyper-palatability related to color, flavor, and packaging.⁸ Examples of UPFs include mass-produced breads found at grocery stores, prepackaged snacks and meals, and hydrogenated oils. UPF consumption is associated with higher risk for negative cardiometabolic outcomes, common mental disorders, and all-cause mortality.⁹ To date, only a study by Powell et al has used the NOVA classification system in a veteran population, and it was limited to a comparison of the price of UPFs and veteran body mass index (BMI).¹⁰ Therefore, it remains unknown what percentage of total energy intake (TEI) comes from UPFs in the diets of veterans.

This study sought to quantify the proportion of TEI from UPFs among a sample of patients from the VA Phoenix Health Care System (VAPHCS). Results from a 2021 global meta-analysis reveal that the US and United Kingdom have the highest intakes of UPFs in the world.¹¹ Specifically, within the US, 15 studies with 234,890 participants reveal that the majority of TEI (about 55%) comes from UPFs.¹¹ We hypothesized that this veteran sample would have a higher proportion of TEI from UPFs, possibly due to a higher prevalence of poverty and food insecurity among veterans compared with nonveterans.¹ If the percentage of TEI coming from UPF is higher or even similar to nonveterans, further efforts to increase veterans’ use of the available nutritional services would be warranted to minimize nutrition-related disease among veterans.

Methods

This is a cross-sectional, secondary data analysis of baseline 3-day food records collected from 2017 to 2020 from 92 patients recruited at VAPHCS to participate in a whole-food plant-based diet study.¹² The original study was reviewed and approved by the VAPHCS Institutional Review Board (1593830). Recruitment methods included clinician recommendation, a recorded advertisement played while phone calls were on hold, and flyers distributed throughout VAPHCS. Patients were included if they were aged 18 to 90 years, had a BMI 25.1 to 39.9, had a diagnosis of nutrition-related chronic disease (hypertension, diabetes, or hyperlipidemia), an interest and desire to make a lifestyle change, active telephone contact information (either landline or cell phone), no contraindication to be on a whole-food plant-based diet, access to transportation and a functioning kitchen, ability to prepare meals independently, access to a computer or tablet with internet access, and a digital camera or smartphone. Exclusion criteria included significant unplanned weight loss within 6 months, uncontrolled insulin-dependent diabetes with a current hemoglobin A_1c > 9%, pregnancy/lactation, taking prescribed weight loss medication, currently following a diet (eg, plant-based diet, vegan, or medical weight loss program diet), celiac disease diagnosed within 6 months, end-stage hepatic disease or renal disease requiring dialysis, active cancer or receiving chemotherapy or radiation therapy, active alcohol or substance use disorder, history of eating disorders, fasting triglyceride level > 350 mg/dL, any psychological issues that prevent adherence, inability to speak English, limited mobility, and homeless or in housing with limited kitchen access. A baseline 3-day food record was collected from the participants and used in this secondary analysis.

Diet Analysis

Food records were analyzed using Esha Research Food Processor 4.0 to identify calorie and macronutrient information. To limit bias, food items were coded independently by 2 researchers into 4 food processing groups determined by the NOVA classification: MPF, CPF, PRF, and UPF.⁸ When possible, specific ingredient information was collected using internet searches for brand product websites. Initial coding had an 89% agreement rate for food item coding between the 2 researchers. As coding was done in duplicate, a third researcher resolved disagreements. The number of food items for each processing group was determined and the mean (SD) percentage of TEI for each NOVA group was provided across participants. A 1-way analysis of variance and Tukey Multiple Comparisons Test were used to determine significance between groups with an α = .05 using Prism V9.

Results

Of the 92 participants in the original study, only 79 met inclusion criteria and had baseline diet data. The 79 veterans had a mean (SD) age of 61 (13) years and 59 (75%) were male (Table 1). Mean (SD) TEI was 1921 (815) kcal. The mean (SD) percentage of calories from carbohydrate, fat, and protein were 46% (21%), 39% (20%), and 16% (6%), respectively (Table 2).

A mean (SD) of 36 (12) food items were analyzed from the 3-day food records. The majority of food items were UPFs (56%), 33% were MPFs, 8% were PRFs, and 3% were CPFs. In total, 75% of TEI came from UPFs (P < .001); only 14% of TEI came from minimally processed foods (Figure).

Discussion

To our knowledge, this is the first analysis of UPF consumption among US veterans. TEIs coming from UPFs appear to be about 20% higher among veterans compared to nonveterans: 75% vs 55%.¹¹ Coupled with high UPF consumption, MPFs (14%) and PRFs (9%) represent smaller sources of TEI among surveyed veterans. Top caloric sources of UPFs in the US include sandwiches (including burgers), sweet bakery products, savory snacks, pizza, sweetened beverages, and breads, rolls, and tortillas, and likely reflect the major sources of UPFs in the veteran diet.¹³ As the statistical comparison between the veteran data and nonveteran data is not feasible in the present study, a future study with a much larger sample size would be needed for a direct comparison.

While the exact cause of higher UPF consumption among sampled veterans remains unknown and likely multifactorial (eg, cost, food insecurity, access, cooking skills, nutrition knowledge), veterans can receive a consult or self-refer to a registered dietitian nutritionist (RDN) for nutrition education. Counseling has been shown to be an effective way to improve diet quality and increase daily fruit and vegetable intake.¹⁴ High consumption of UPFs, which are generally energy-dense and nutrient-poor, contributes to the low diet quality observed in veterans, and future research examining the relationship between UPF intake and overall diet quality among veterans is warranted.^2,15 As nutrition knowledge is associated with higher diet quality among veterans, increased use of nutrition services (ie, nutrition education or food supplement programs) has the potential to influence consumption of MPFs and decrease consumption of UPFs.¹⁶ Subsequently, UPF-targeted interventions developed by VA RDNs hold the promise to decrease consumption of UPFs and increase intake of MPFs and PRFs.

Veterans have a high prevalence of diabetes, hypertension, and obesity.⁹ The high UPF intake observed in this sample of veterans may increase the risk for these chronic diseases and overall mortality. The high percentage of TEI from UPFs among veterans is also of concern not only due to potential negative health outcomes, but also associated costs of treating veterans with multimorbidities.¹⁷ Targeting UPF intake via nutritional education may promote health and decrease the financial burden needed to support the health of veterans.

Improving veteran health and well-being, including enhancing health care accessibility in underserved areas, are pivotal objectives of the VA strategic plan for 2026 to 2030. Public policy aims to tackle food insecurity within the veteran population during the first 5 years of civilian life.¹⁸ In alignment with the White House Strategy on Hunger, Nutrition, and Health, VA established a Food Security Office (FSO) in 2023. The FSO mission is to use an interdisciplinary approach to provide resources to ensure veteran food security and create an environment where all veterans are food and nutrition secure.

Limitations

This study has several limitations. As the Food Processor software database does not include all brand items, similar brands were used to mirror the nutrient profile. While food records are common among veteran diet studies, accuracy may be reduced due to self-reporting bias.¹⁹ Different interpretation of the NOVA classification designation for various food items is possible, however, 89% of foods were coded the same by the research team which suggests high accuracy in food coding. Specific ingredient information was not collected from the 3-day food records; thus, these records were not produced in such a way to improve the accuracy of the NOVA classification designation. This study was limited by its small sample size (N = 79); although, this analysis is larger than other studies of UPF consumption in the US.^20,21 In addition, the generalizability of this study is limited as this population sample was from a single VA hospital and may not reflect the overall veteran population. Participants in this study were recruited only from those receiving VA care, thus their diet quality may not represent the quality consumed by veterans not participating in VA services. Further research on UPF consumption among veterans is warranted with a larger, more representative study sample size.

Conclusions

As this is the highest observed UPF intake documented in the US, these results should be of concern for the VA and its RDNs. More research is needed to better understand why UPF consumption is so high among veterans, what barriers veterans face to decreasing UPF consumption, and what intervention(s) veterans would welcome to improve their diet quality. Presently, veterans are provided with access to a variety of effective nutrition education and counseling options and should be encouraged to use these services. VA RDNs should be aware of the high intake of UPFs in the veteran population and familiarize themselves with education and counseling strategies that promote behavior change to replace UPFs with more nutrient-dense foods choices.