Original Research

Veterans have a high burden of digestive diseases, and gastroenterologists are needed for the diagnosis and management of these conditions.^1-4 According to the Veterans Health Administration (VHA) Workforce Management and Consulting (WMC) office, the physician specialties with the greatest shortages are psychiatry, primary care, and gastroenterology.⁵ The VHA estimates it must hire 70 new gastroenterologists annually between fiscal years 2023 and 2027 to provide timely digestive care.⁵

Filling these positions will be increasingly difficult as competition for gastroenterologists is fierce. A recent Merritt Hawkins review states, “Gastroenterologists were the most in-demand type of provider during the 2022 review period.”⁶ In 2022, the median annual salary for US gastroenterologists was reported to be $561,375.⁷ Currently, the US Department of Veterans Affairs (VA) has an aggregate annual pay limit of $400,000 for all federal employees and cannot compete based on salary alone.

Retention of existing VA gastroenterologists also is challenging. The WMC has reported that 21.6% of VA gastroenterologists are eligible to retire, and in 2021, 8.2% left the VA to retire or seek non-VA positions.⁵ While not specific to the VA, a survey of practicing gastroenterologists conducted by the American College of Gastroenterology found a 49% burnout rate among respondents.⁸ Factors contributing to burnout at all career stages included administrative nonclinical work and a lack of clinical support staff.⁸ Burnout is also linked with higher rates of medical errors, interpersonal conflicts, and patient dissatisfaction. Burnout is more common among those with an innate strong sense of purpose and responsibility for their patients, characteristics we have observed in our VA colleagues.⁹

As members of the Section Chief Subcommittee of the VA Gastroenterology Field Advisory Board (GI FAB), we are passionate about providing outstanding gastroenterology care to US veterans, and we are alarmed at the struggles we are observing with recruiting and retaining a qualified national gastroenterology physician workforce. As such, we set out to survey the VA gastroenterology section chief community to gain insights into recruitment and retention challenges they have faced and identify potential solutions to these problems.

Methods

The GI FAB Section Chief Subcommittee developed a survey on gastroenterologist recruitment and retention using Microsoft Forms (Appendix). A link to the survey, which included 11 questions about facility location, current vacancies, and free text responses on barriers to recruitment and retention and potential solutions, was sent via email to all gastroenterology section chiefs on the National Gastroenterology and Hepatology Program Office’s email list of section chiefs on January 31, 2023. A reminder to complete the survey was sent to all section chiefs on February 8, 2023. Survey responses were aggregated and analyzed by the authors using descriptive statistics.

The VA gastroenterologist recruitment and retention survey was emailed to 131 gastroenterology section chiefs and completed by 55 respondents (42%) (Figure). Of the responding section chiefs, 36 (65%) reported gastroenterologist vacancies at their facilities. Seventeen respondents (47%) reported a single vacancy, 12 (33%) reported 2 vacancies, 4 (11%) reported 3 vacancies, and 3 (8%) reported 4 vacancies. Of the sites with reported vacancies, 32 (89%) reported a need for a general gastroenterologist, 12 (33%) reported a need for a hepatologist, 11 (31%) reported a need for an advanced endoscopist, 9 (25%) reported a need for a gastroenterologist with specialized expertise in inflammatory bowel diseases, and 1 (3%) reported a need for a gastrointestinal motility specialist.

Numerous barriers to the recruitment and retention of gastroenterologists were reported. Given the large number of respondents that reported a unique barrier (ie, being the only respondents to report the barrier), a decision was made to include only barriers to recruitment and retention that were reported by at least 2 sites (Table). While there were some common themes, the reported barriers to retention differed from those to recruitment. The most reported barriers to recruitment were 46 respondents who noted salary, 23 reported human resources-related challenges, and 12 reported location. Respondents also noted various retention barriers, including 32 respondents who reported salary barriers; 13 reported administrative burden barriers, 6 reported medical center leadership, and 4 reported burnout.

Survey respondents provided multiple recommendations on how the VA can best support the recruitment and retention of gastroenterologists. The most frequent recommendations were to increase financial compensation by increasing the current aggregate salary cap to > $400,000, increasing the use of recruitment and retention incentives, and ensuring that gastroenterology is on the national Educational Debt Reduction Program (EDRP) list, which facilitates student loan repayment. It was recommended that a third-party company assist with hiring to overcome perceived issues with human resources. Additionally, there were multiple recommendations for improving administrative and clinical support. These included mandating how many support staff should be assigned to each gastroenterologist and providing best practice recommendations for support staff so that gastroenterologists can focus on physician-level work. Recommendations also included having a dedicated gastroenterology practice manager, nurse care coordinators, a colorectal cancer screening/surveillance coordinator, sufficient medical support assistants, and quality improvement personnel tracking ongoing professional practice evaluation data. Survey respondents also highlighted specific suggestions for recruiting recent graduates. These included offering a 4-day work week, as recent graduates place a premium on work-life balance, and ensuring gastroenterologists have individual offices. One respondent commented that gastroenterology fellows seeing VA gastroenterology attendings in cramped, shared offices, contrasted with private practice gastroenterologists in large private offices, may contribute to choosing private practice over joining the VA.

Discussion

Gastroenterology is currently listed by VHA WMC as 1 of the top 3 medical specialties in the VA with the most physician shortages.⁵ Working as a physician in the VA has long been recognized to have many benefits. First and foremost, many physicians are motivated by the VA mission to serve veterans, as this offers personal fulfillment and other intangible benefits. In addition, the VA can provide work-life balance, which is often not possible in fee-for-service settings, with patient panels and call volumes typically lower than in comparable private hospital settings. Moreover, VA physicians have outstanding teaching opportunities, as the VA is the largest supporter of medical education, with postgraduate trainees rotating through > 150 VA medical centers. Likewise, the VA offers a variety of student loan repayment programs (eg, the Specialty Education Loan Repayment Program and the EDRP). The VA offers research funding such as the Cooperative Studies Programs or program project funding, and rewards in parallel with the National Institute of Health (eg, career development awards, or merit review awards) and other grants. VA researchers have conducted many landmark studies that continue to shape the practice of gastroenterology and hepatology. From the earliest studies to demonstrate the effectiveness of screening colonoscopy, to the largest ongoing clinical trial in US history to assess the effectiveness of fecal immunochemical testing (FIT) vs screening colonoscopy.^10-12 The VA has also led the field in the study of gastroesophageal reflux disease, hepatitis C treatment, and liver cancer screening.^13-15 VA physicians also benefit from participation in the Federal Employee Retirement System, including its pension system.

These benefits apply to all medical specialties, making the VA a potentially appealing workplace for gastroenterologists. However, recent trends indicate that recruitment and retention of gastroenterologists is increasingly challenging, as the VA gastroenterology workforce grew by 5.0% in fiscal year (FY) 2020 and 1.8% in FY 2021. However, it was on track to end FY 2022 with a loss (-1.1%).⁵ It must be noted that this trend is not limited to the VA, and the National Center for Health Workforce Analysis predicts that gastroenterology will remain among the highest projected specialty shortages. Driven by increased demand for digestive health care services, more physicians nearing traditional retirement age, and substantially higher rates of burnout after the COVID-19 pandemic.¹⁶ All these factors are likely to result in an increasingly competitive market for gastroenterology, highlight the growing differences between VA and non-VA positions, and may augment the impact of differences for the individual gastroenterologist weighing employment options within and outside the VA.

Suboptimal human resources were the second most commonly cited impediment to recruiting gastroenterologists. Many section chiefs expressed frustration with the inefficient and slow administrative process of onboarding new gastroenterologists, which may take many months and not infrequently results in losing candidates to competing entities. While this issue is specific to recruitment, recurring and long-standing vacancies can increase work burdens, complicate logistics for remaining faculty, and may also negatively impact retention. One potential opportunity to improve VHA competitiveness is to streamline the administrative component of recruitment and optimize human resources support. The use of a third-party hiring company also should be considered.

Survey responses also indicated that administrative burden and insufficient support staff were significant retention challenges. Several respondents described a lack of efficient endoscopy workflow and delegation of simple administrative tasks to gastroenterologists as more likely in units without proper task distribution. Importantly, these shortcomings occur at the expense of workload-generating activities and career-enhancing opportunities.

While burnout rates among VA gastroenterologists have not been documented systematically, they likely correlate with workplace frustration and jeopardizegastroenterologist retention. Successful retention of gastroenterologists as highly trained medical professionals is more likely in workplaces that are vertically organized, efficient, and use physicians at the top of their skill level.

Conclusions

The VA offers the opportunity for a rewarding lifelong career in gastroenterology. The fulfillment of serving veterans, teaching future health care leaders, performing impactful research, and having job security is invaluable. Despite the tremendous benefits, this survey supports improving VA recruitment and retention strategies for the high-demand gastroenterology specialty. Improved salary parity is needed for workforce maintenance and recruitment, as is improved administrative and clinical support to maintain the high level of care our veterans deserve.

Veterans have a high burden of digestive diseases, and gastroenterologists are needed for the diagnosis and management of these conditions.^1-4 According to the Veterans Health Administration (VHA) Workforce Management and Consulting (WMC) office, the physician specialties with the greatest shortages are psychiatry, primary care, and gastroenterology.⁵ The VHA estimates it must hire 70 new gastroenterologists annually between fiscal years 2023 and 2027 to provide timely digestive care.⁵

Filling these positions will be increasingly difficult as competition for gastroenterologists is fierce. A recent Merritt Hawkins review states, “Gastroenterologists were the most in-demand type of provider during the 2022 review period.”⁶ In 2022, the median annual salary for US gastroenterologists was reported to be $561,375.⁷ Currently, the US Department of Veterans Affairs (VA) has an aggregate annual pay limit of $400,000 for all federal employees and cannot compete based on salary alone.

Retention of existing VA gastroenterologists also is challenging. The WMC has reported that 21.6% of VA gastroenterologists are eligible to retire, and in 2021, 8.2% left the VA to retire or seek non-VA positions.⁵ While not specific to the VA, a survey of practicing gastroenterologists conducted by the American College of Gastroenterology found a 49% burnout rate among respondents.⁸ Factors contributing to burnout at all career stages included administrative nonclinical work and a lack of clinical support staff.⁸ Burnout is also linked with higher rates of medical errors, interpersonal conflicts, and patient dissatisfaction. Burnout is more common among those with an innate strong sense of purpose and responsibility for their patients, characteristics we have observed in our VA colleagues.⁹

As members of the Section Chief Subcommittee of the VA Gastroenterology Field Advisory Board (GI FAB), we are passionate about providing outstanding gastroenterology care to US veterans, and we are alarmed at the struggles we are observing with recruiting and retaining a qualified national gastroenterology physician workforce. As such, we set out to survey the VA gastroenterology section chief community to gain insights into recruitment and retention challenges they have faced and identify potential solutions to these problems.

Methods

The GI FAB Section Chief Subcommittee developed a survey on gastroenterologist recruitment and retention using Microsoft Forms (Appendix). A link to the survey, which included 11 questions about facility location, current vacancies, and free text responses on barriers to recruitment and retention and potential solutions, was sent via email to all gastroenterology section chiefs on the National Gastroenterology and Hepatology Program Office’s email list of section chiefs on January 31, 2023. A reminder to complete the survey was sent to all section chiefs on February 8, 2023. Survey responses were aggregated and analyzed by the authors using descriptive statistics.

The VA gastroenterologist recruitment and retention survey was emailed to 131 gastroenterology section chiefs and completed by 55 respondents (42%) (Figure). Of the responding section chiefs, 36 (65%) reported gastroenterologist vacancies at their facilities. Seventeen respondents (47%) reported a single vacancy, 12 (33%) reported 2 vacancies, 4 (11%) reported 3 vacancies, and 3 (8%) reported 4 vacancies. Of the sites with reported vacancies, 32 (89%) reported a need for a general gastroenterologist, 12 (33%) reported a need for a hepatologist, 11 (31%) reported a need for an advanced endoscopist, 9 (25%) reported a need for a gastroenterologist with specialized expertise in inflammatory bowel diseases, and 1 (3%) reported a need for a gastrointestinal motility specialist.

Numerous barriers to the recruitment and retention of gastroenterologists were reported. Given the large number of respondents that reported a unique barrier (ie, being the only respondents to report the barrier), a decision was made to include only barriers to recruitment and retention that were reported by at least 2 sites (Table). While there were some common themes, the reported barriers to retention differed from those to recruitment. The most reported barriers to recruitment were 46 respondents who noted salary, 23 reported human resources-related challenges, and 12 reported location. Respondents also noted various retention barriers, including 32 respondents who reported salary barriers; 13 reported administrative burden barriers, 6 reported medical center leadership, and 4 reported burnout.

Survey respondents provided multiple recommendations on how the VA can best support the recruitment and retention of gastroenterologists. The most frequent recommendations were to increase financial compensation by increasing the current aggregate salary cap to > $400,000, increasing the use of recruitment and retention incentives, and ensuring that gastroenterology is on the national Educational Debt Reduction Program (EDRP) list, which facilitates student loan repayment. It was recommended that a third-party company assist with hiring to overcome perceived issues with human resources. Additionally, there were multiple recommendations for improving administrative and clinical support. These included mandating how many support staff should be assigned to each gastroenterologist and providing best practice recommendations for support staff so that gastroenterologists can focus on physician-level work. Recommendations also included having a dedicated gastroenterology practice manager, nurse care coordinators, a colorectal cancer screening/surveillance coordinator, sufficient medical support assistants, and quality improvement personnel tracking ongoing professional practice evaluation data. Survey respondents also highlighted specific suggestions for recruiting recent graduates. These included offering a 4-day work week, as recent graduates place a premium on work-life balance, and ensuring gastroenterologists have individual offices. One respondent commented that gastroenterology fellows seeing VA gastroenterology attendings in cramped, shared offices, contrasted with private practice gastroenterologists in large private offices, may contribute to choosing private practice over joining the VA.

Discussion

Gastroenterology is currently listed by VHA WMC as 1 of the top 3 medical specialties in the VA with the most physician shortages.⁵ Working as a physician in the VA has long been recognized to have many benefits. First and foremost, many physicians are motivated by the VA mission to serve veterans, as this offers personal fulfillment and other intangible benefits. In addition, the VA can provide work-life balance, which is often not possible in fee-for-service settings, with patient panels and call volumes typically lower than in comparable private hospital settings. Moreover, VA physicians have outstanding teaching opportunities, as the VA is the largest supporter of medical education, with postgraduate trainees rotating through > 150 VA medical centers. Likewise, the VA offers a variety of student loan repayment programs (eg, the Specialty Education Loan Repayment Program and the EDRP). The VA offers research funding such as the Cooperative Studies Programs or program project funding, and rewards in parallel with the National Institute of Health (eg, career development awards, or merit review awards) and other grants. VA researchers have conducted many landmark studies that continue to shape the practice of gastroenterology and hepatology. From the earliest studies to demonstrate the effectiveness of screening colonoscopy, to the largest ongoing clinical trial in US history to assess the effectiveness of fecal immunochemical testing (FIT) vs screening colonoscopy.^10-12 The VA has also led the field in the study of gastroesophageal reflux disease, hepatitis C treatment, and liver cancer screening.^13-15 VA physicians also benefit from participation in the Federal Employee Retirement System, including its pension system.

These benefits apply to all medical specialties, making the VA a potentially appealing workplace for gastroenterologists. However, recent trends indicate that recruitment and retention of gastroenterologists is increasingly challenging, as the VA gastroenterology workforce grew by 5.0% in fiscal year (FY) 2020 and 1.8% in FY 2021. However, it was on track to end FY 2022 with a loss (-1.1%).⁵ It must be noted that this trend is not limited to the VA, and the National Center for Health Workforce Analysis predicts that gastroenterology will remain among the highest projected specialty shortages. Driven by increased demand for digestive health care services, more physicians nearing traditional retirement age, and substantially higher rates of burnout after the COVID-19 pandemic.¹⁶ All these factors are likely to result in an increasingly competitive market for gastroenterology, highlight the growing differences between VA and non-VA positions, and may augment the impact of differences for the individual gastroenterologist weighing employment options within and outside the VA.

Suboptimal human resources were the second most commonly cited impediment to recruiting gastroenterologists. Many section chiefs expressed frustration with the inefficient and slow administrative process of onboarding new gastroenterologists, which may take many months and not infrequently results in losing candidates to competing entities. While this issue is specific to recruitment, recurring and long-standing vacancies can increase work burdens, complicate logistics for remaining faculty, and may also negatively impact retention. One potential opportunity to improve VHA competitiveness is to streamline the administrative component of recruitment and optimize human resources support. The use of a third-party hiring company also should be considered.

Survey responses also indicated that administrative burden and insufficient support staff were significant retention challenges. Several respondents described a lack of efficient endoscopy workflow and delegation of simple administrative tasks to gastroenterologists as more likely in units without proper task distribution. Importantly, these shortcomings occur at the expense of workload-generating activities and career-enhancing opportunities.

While burnout rates among VA gastroenterologists have not been documented systematically, they likely correlate with workplace frustration and jeopardizegastroenterologist retention. Successful retention of gastroenterologists as highly trained medical professionals is more likely in workplaces that are vertically organized, efficient, and use physicians at the top of their skill level.

Conclusions

The VA offers the opportunity for a rewarding lifelong career in gastroenterology. The fulfillment of serving veterans, teaching future health care leaders, performing impactful research, and having job security is invaluable. Despite the tremendous benefits, this survey supports improving VA recruitment and retention strategies for the high-demand gastroenterology specialty. Improved salary parity is needed for workforce maintenance and recruitment, as is improved administrative and clinical support to maintain the high level of care our veterans deserve.

Veterans have a high burden of digestive diseases, and gastroenterologists are needed for the diagnosis and management of these conditions.^1-4 According to the Veterans Health Administration (VHA) Workforce Management and Consulting (WMC) office, the physician specialties with the greatest shortages are psychiatry, primary care, and gastroenterology.⁵ The VHA estimates it must hire 70 new gastroenterologists annually between fiscal years 2023 and 2027 to provide timely digestive care.⁵

Filling these positions will be increasingly difficult as competition for gastroenterologists is fierce. A recent Merritt Hawkins review states, “Gastroenterologists were the most in-demand type of provider during the 2022 review period.”⁶ In 2022, the median annual salary for US gastroenterologists was reported to be $561,375.⁷ Currently, the US Department of Veterans Affairs (VA) has an aggregate annual pay limit of $400,000 for all federal employees and cannot compete based on salary alone.

Retention of existing VA gastroenterologists also is challenging. The WMC has reported that 21.6% of VA gastroenterologists are eligible to retire, and in 2021, 8.2% left the VA to retire or seek non-VA positions.⁵ While not specific to the VA, a survey of practicing gastroenterologists conducted by the American College of Gastroenterology found a 49% burnout rate among respondents.⁸ Factors contributing to burnout at all career stages included administrative nonclinical work and a lack of clinical support staff.⁸ Burnout is also linked with higher rates of medical errors, interpersonal conflicts, and patient dissatisfaction. Burnout is more common among those with an innate strong sense of purpose and responsibility for their patients, characteristics we have observed in our VA colleagues.⁹

As members of the Section Chief Subcommittee of the VA Gastroenterology Field Advisory Board (GI FAB), we are passionate about providing outstanding gastroenterology care to US veterans, and we are alarmed at the struggles we are observing with recruiting and retaining a qualified national gastroenterology physician workforce. As such, we set out to survey the VA gastroenterology section chief community to gain insights into recruitment and retention challenges they have faced and identify potential solutions to these problems.

Methods

The GI FAB Section Chief Subcommittee developed a survey on gastroenterologist recruitment and retention using Microsoft Forms (Appendix). A link to the survey, which included 11 questions about facility location, current vacancies, and free text responses on barriers to recruitment and retention and potential solutions, was sent via email to all gastroenterology section chiefs on the National Gastroenterology and Hepatology Program Office’s email list of section chiefs on January 31, 2023. A reminder to complete the survey was sent to all section chiefs on February 8, 2023. Survey responses were aggregated and analyzed by the authors using descriptive statistics.

The VA gastroenterologist recruitment and retention survey was emailed to 131 gastroenterology section chiefs and completed by 55 respondents (42%) (Figure). Of the responding section chiefs, 36 (65%) reported gastroenterologist vacancies at their facilities. Seventeen respondents (47%) reported a single vacancy, 12 (33%) reported 2 vacancies, 4 (11%) reported 3 vacancies, and 3 (8%) reported 4 vacancies. Of the sites with reported vacancies, 32 (89%) reported a need for a general gastroenterologist, 12 (33%) reported a need for a hepatologist, 11 (31%) reported a need for an advanced endoscopist, 9 (25%) reported a need for a gastroenterologist with specialized expertise in inflammatory bowel diseases, and 1 (3%) reported a need for a gastrointestinal motility specialist.

Numerous barriers to the recruitment and retention of gastroenterologists were reported. Given the large number of respondents that reported a unique barrier (ie, being the only respondents to report the barrier), a decision was made to include only barriers to recruitment and retention that were reported by at least 2 sites (Table). While there were some common themes, the reported barriers to retention differed from those to recruitment. The most reported barriers to recruitment were 46 respondents who noted salary, 23 reported human resources-related challenges, and 12 reported location. Respondents also noted various retention barriers, including 32 respondents who reported salary barriers; 13 reported administrative burden barriers, 6 reported medical center leadership, and 4 reported burnout.

Survey respondents provided multiple recommendations on how the VA can best support the recruitment and retention of gastroenterologists. The most frequent recommendations were to increase financial compensation by increasing the current aggregate salary cap to > $400,000, increasing the use of recruitment and retention incentives, and ensuring that gastroenterology is on the national Educational Debt Reduction Program (EDRP) list, which facilitates student loan repayment. It was recommended that a third-party company assist with hiring to overcome perceived issues with human resources. Additionally, there were multiple recommendations for improving administrative and clinical support. These included mandating how many support staff should be assigned to each gastroenterologist and providing best practice recommendations for support staff so that gastroenterologists can focus on physician-level work. Recommendations also included having a dedicated gastroenterology practice manager, nurse care coordinators, a colorectal cancer screening/surveillance coordinator, sufficient medical support assistants, and quality improvement personnel tracking ongoing professional practice evaluation data. Survey respondents also highlighted specific suggestions for recruiting recent graduates. These included offering a 4-day work week, as recent graduates place a premium on work-life balance, and ensuring gastroenterologists have individual offices. One respondent commented that gastroenterology fellows seeing VA gastroenterology attendings in cramped, shared offices, contrasted with private practice gastroenterologists in large private offices, may contribute to choosing private practice over joining the VA.

Discussion

Gastroenterology is currently listed by VHA WMC as 1 of the top 3 medical specialties in the VA with the most physician shortages.⁵ Working as a physician in the VA has long been recognized to have many benefits. First and foremost, many physicians are motivated by the VA mission to serve veterans, as this offers personal fulfillment and other intangible benefits. In addition, the VA can provide work-life balance, which is often not possible in fee-for-service settings, with patient panels and call volumes typically lower than in comparable private hospital settings. Moreover, VA physicians have outstanding teaching opportunities, as the VA is the largest supporter of medical education, with postgraduate trainees rotating through > 150 VA medical centers. Likewise, the VA offers a variety of student loan repayment programs (eg, the Specialty Education Loan Repayment Program and the EDRP). The VA offers research funding such as the Cooperative Studies Programs or program project funding, and rewards in parallel with the National Institute of Health (eg, career development awards, or merit review awards) and other grants. VA researchers have conducted many landmark studies that continue to shape the practice of gastroenterology and hepatology. From the earliest studies to demonstrate the effectiveness of screening colonoscopy, to the largest ongoing clinical trial in US history to assess the effectiveness of fecal immunochemical testing (FIT) vs screening colonoscopy.^10-12 The VA has also led the field in the study of gastroesophageal reflux disease, hepatitis C treatment, and liver cancer screening.^13-15 VA physicians also benefit from participation in the Federal Employee Retirement System, including its pension system.

These benefits apply to all medical specialties, making the VA a potentially appealing workplace for gastroenterologists. However, recent trends indicate that recruitment and retention of gastroenterologists is increasingly challenging, as the VA gastroenterology workforce grew by 5.0% in fiscal year (FY) 2020 and 1.8% in FY 2021. However, it was on track to end FY 2022 with a loss (-1.1%).⁵ It must be noted that this trend is not limited to the VA, and the National Center for Health Workforce Analysis predicts that gastroenterology will remain among the highest projected specialty shortages. Driven by increased demand for digestive health care services, more physicians nearing traditional retirement age, and substantially higher rates of burnout after the COVID-19 pandemic.¹⁶ All these factors are likely to result in an increasingly competitive market for gastroenterology, highlight the growing differences between VA and non-VA positions, and may augment the impact of differences for the individual gastroenterologist weighing employment options within and outside the VA.

Suboptimal human resources were the second most commonly cited impediment to recruiting gastroenterologists. Many section chiefs expressed frustration with the inefficient and slow administrative process of onboarding new gastroenterologists, which may take many months and not infrequently results in losing candidates to competing entities. While this issue is specific to recruitment, recurring and long-standing vacancies can increase work burdens, complicate logistics for remaining faculty, and may also negatively impact retention. One potential opportunity to improve VHA competitiveness is to streamline the administrative component of recruitment and optimize human resources support. The use of a third-party hiring company also should be considered.

Survey responses also indicated that administrative burden and insufficient support staff were significant retention challenges. Several respondents described a lack of efficient endoscopy workflow and delegation of simple administrative tasks to gastroenterologists as more likely in units without proper task distribution. Importantly, these shortcomings occur at the expense of workload-generating activities and career-enhancing opportunities.

While burnout rates among VA gastroenterologists have not been documented systematically, they likely correlate with workplace frustration and jeopardizegastroenterologist retention. Successful retention of gastroenterologists as highly trained medical professionals is more likely in workplaces that are vertically organized, efficient, and use physicians at the top of their skill level.

Conclusions

The VA offers the opportunity for a rewarding lifelong career in gastroenterology. The fulfillment of serving veterans, teaching future health care leaders, performing impactful research, and having job security is invaluable. Despite the tremendous benefits, this survey supports improving VA recruitment and retention strategies for the high-demand gastroenterology specialty. Improved salary parity is needed for workforce maintenance and recruitment, as is improved administrative and clinical support to maintain the high level of care our veterans deserve.

Background

Paclitaxel was first derived from the bark of the yew tree (Taxus brevifolia). It was discovered as part of a National Cancer Institute program screen of plants and natural products with putative anticancer activity during the 1960s.^1-9 Paclitaxel works by suppressing spindle microtube dynamics, which results in the blockage of the metaphase-anaphase transitions, inhibition of mitosis, and induction of apoptosis in a broad spectrum of cancer cells. Paclitaxel also displayed additional anticancer activities, including the suppression of cell proliferation and antiangiogenic effects. However, since the growth of normal body cells may also be affected, other adverse effects (AEs) will also occur.^8-18

Two different chemotherapy drugs contain paclitaxel—paclitaxel and nab-paclitaxel—and the US Food and Drug Administration (FDA) recognizes them as separate entities.^19-21 Taxol (paclitaxel) was approved by the FDA in 1992 for treating advanced ovarian cancer.²⁰ It has since been approved for the treatment of metastatic breast cancer, AIDS-related Kaposi sarcoma (as an orphan drug), non-small cell lung cancer (NSCLC), and cervical cancers (in combination withbevacizumab) in 1994, 1997, 1999, and 2014, respectively.²¹ Since 2002, a generic version of Taxol, known as paclitaxel injectable, has been FDA-approved from different manufacturers. According to the National Cancer Institute, a combination of carboplatin and Taxol is approved to treat carcinoma of unknown primary, cervical, endometrial, NSCLC, ovarian, and thymoma cancers.¹⁹ Abraxane (nab-paclitaxel) was FDA-approved to treat metastatic breast cancer in 2005. It was later approved for first-line treatment of advanced NSCLC and late-stage pancreatic cancer in 2012 and 2013, respectively. In 2018 and 2020, both Taxol and Abraxane were approved for first-line treatment of metastatic squamous cell NSCLC in combination with carboplatin and pembrolizumab and metastatic triple-negative breast cancer in combination with pembrolizumab, respectively.^22-26 In 2019, Abraxane was approved with atezolizumab to treat metastatic triple-negative breast cancer, but this approval was withdrawn in 2021. In 2022, a generic version of Abraxane, known as paclitaxel protein-bound, was released in the United States. Furthermore, paclitaxel-containing formulations also are being studied in the treatment of other types of cancer.^19-32

One of the main limitations of paclitaxel is its low solubility in water, which complicates its drug supply. To distribute this hydrophobic anticancer drug efficiently, paclitaxel is formulated and administered to patients via polyethoxylated castor oil or albumin-bound (nab-paclitaxel). However, polyethoxylated castor oil induces complement activation and is the cause of common hypersensitivity reactions related to paclitaxel use.^2,17,33-38 Therefore, many alternatives to polyethoxylated castor oil have been researched.

Since 2000, new paclitaxel formulations have emerged using nanomedicine techniques. The difference between these formulations is the drug vehicle. Different paclitaxel-based nanotechnological vehicles have been developed and approved, such as albumin-based nanoparticles, polymeric lipidic nanoparticles, polymeric micelles, and liposomes, with many others in clinical trial phases.^3,37 Albumin-based nanoparticles have a high response rate (33%), whereas the response rate for polyethoxylated castor oil is 25% in patients with metastatic breast cancer.^33,39-52 The use of paclitaxel dimer nanoparticles also has been proposed as a method for increasing drug solubility.^33,53

Paclitaxel is metabolized by cytochrome P450 (CYP) isoenzymes 2C8 and 3A4. When administering paclitaxel with known inhibitors, inducers, or substrates of CYP2C8 or CYP3A4, caution is required.^19-22 Regulations for CYP research were not issued until 2008, so potential interactions between paclitaxel and other drugs have not been extensively evaluated in clinical trials. A study of 12 kinase inhibitors showed strong inhibition of CYP2C8 and/or CYP3A4 pathways by these inhibitors, which could alter the ratio of paclitaxel metabolites in vivo, leading to clinically relevant changes.⁵⁴ Differential metabolism has been linked to paclitaxel-induced neurotoxicity in patients with cancer.⁵⁵ Nonetheless, variants in the CYP2C8, CYP3A4, CYP3A5, and ABCB1 genes do not account for significant interindividual variability in paclitaxel pharmacokinetics.⁵⁶ In liver microsomes, losartan inhibited paclitaxel metabolism when used at concentrations > 50 µmol/L.⁵⁷ Many drug-drug interaction (DDI) studies of CYP2C8 and CYP3A4 have shown similar results for paclitaxel.^58-64

The goals of this study are to investigate prescribed drugs used with paclitaxel and determine patient outcomes through several Military Health System (MHS) databases. The investigation focused on (1) the functions of paclitaxel; (2) identifying AEs that patients experienced; (3) evaluating differences when paclitaxel is used alone vs concomitantly and between the completed vs discontinued treatment groups; (4) identifying all drugs used during paclitaxel treatment; and (5) evaluating DDIs with antidepressants (that have an FDA boxed warning and are known to have DDIs confirmed in previous publications) and other drugs.^65-67

The Walter Reed National Military Medical Center in Bethesda, Maryland, institutionalreview board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and MHS data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and the Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

METHODS

The DoD Cancer Registry Program was established in 1986 and currently contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in the CAPER record represents an ambulatory encounter at a military treatment facility (MTF). CAPER includes data from 2003 to 2024.

Each observation in the PDTS record represents a prescription filled for an MHS beneficiary at an MTF through the TRICARE mail-order program or a US retail pharmacy. Missing from this record are prescriptions filled at international civilian pharmacies and inpatient pharmacy prescriptions. The MHS Data Repository PDTS record is available from 2002 to 2024. The legacy Composite Health Care System is being replaced by GENESIS at MTFs.

Data Extraction Design

The study design involved a cross-sectional analysis. We requested data extraction for paclitaxel from 1998 to 2022. Data from the DoD Cancer Registry Program were used to identify patients who received cancer treatment. Once patients were identified, the CAPER database was searched for diagnoses to identify other health conditions, whereas the PDTS database was used to populate a list of prescription medications filled during chemotherapy treatment.

Data collected from the JPC included cancer treatment, cancer information, demographics, and physicians’ comments on AEs. Collected data from the MHS include diagnosis and filled prescription history from initiation to completion of the therapy period (or 2 years after the diagnosis date). For the analysis of the DoD Cancer Registry Program and CAPER databases, we used all collected data without excluding any. When analyzing PDTS data, we excluded patients with PDTS data but without a record of paclitaxel being filled, or medications filled outside the chemotherapy period (by evaluating the dispensed date and day of supply).

Data Extraction Analysis

The Surveillance, Epidemiology, and End Results Program Coding and Staging Manual 2016 and the International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^68,69 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the paclitaxel groups divided by the total number of patients or data variables. The subgroup percentage was calculated by using the number of patients or data available within the subgroup divided by the total number of patients in that subgroup.

In alone vs concomitant and completed vs discontinued treatment groups, a 2-tailed, 2-sample z test was used to statistical significance (P < .05) using a statistics website.⁷⁰ Concomitant was defined as paclitaxel taken with other antineoplastic agent(s) before, after, or at the same time as cancer therapy. For the retrospective data analysis, physicians’ notes with a period, comma, forward slash, semicolon, or space between medication names were interpreted as concurrent, whereas plus (+), minus/plus (-/+), or “and” between drug names that were dispensed on the same day were interpreted as combined with known common combinations: 2 drugs (DM886 paclitaxel and carboplatin and DM881-TC-1 paclitaxel and cisplatin) or 3 drugs (DM887-ACT doxorubicin, cyclophosphamide, and paclitaxel). Completed treatment was defined as paclitaxel as the last medication the patient took without recorded AEs; switching or experiencing AEs was defined as discontinued treatment.

RESULTS

The JPC provided 702 entries for 687 patients with a mean age of 56 years (range, 2 months to 88 years) who were treated with paclitaxel from March 1996 to October 2021. Fifteen patients had duplicate entries because they had multiple cancer sites or occurrences. There were 623 patients (89%) who received paclitaxel for FDA-approved indications. The most common types of cancer identified were 344 patients with breast cancer (49%), 91 patients with lung cancer (13%), 79 patients with ovarian cancer (11%), and 75 patients with endometrial cancer (11%) (Table 1). Seventy-nine patients (11%) received paclitaxel for cancers that were not for FDA-approved indications, including 19 for cancers of the fallopian tube (3%) and 17 for esophageal cancer (2%) (Table 2).

There were 477 patients (68%) aged > 50 years. A total of 304 patients (43%) had a stage III or IV cancer diagnosis and 398 (57%) had stage II or lower (combination of data for stages 0, I, and II; not applicable; and unknown) cancer diagnosis. For systemic treatment, 16 patients (2%) were treated with paclitaxel alone and 686 patients (98%) received paclitaxel concomitantly with additional chemotherapy: 59 patients (9%) in the before or after group, 410 patients (58%) had a 2-drug combination, 212 patients (30%) had a 3-drug combination, and 5 patients (1%) had a 4-drug combination. In addition, for doublet therapies, paclitaxel combined with carboplatin, trastuzumab, gemcitabine, or cisplatin had more patients (318, 58, 12, and 11, respectively) than other combinations (≤ 4 patients). For triplet therapies, paclitaxel combined withdoxorubicin plus cyclophosphamide or carboplatin plus bevacizumab had more patients (174 and 20, respectively) than other combinations, including quadruplet therapies (≤ 4 patients) (Table 3).

Patients were more likely to discontinue paclitaxel if they received concomitant treatment. None of the 16 patients receiving paclitaxel monotherapy experienced AEs, whereas 364 of 686 patients (53%) treated concomitantly discontinued (P < .001). Comparisons of 1 drug vs combination (2 to 4 drugs) and use for treating cancers that were FDA-approved indications vs off-label use were significant (P < .001), whereas comparisons of stage II or lower vs stage III and IV cancer and of those aged ≤ 50 years vs aged > 50 years were not significant (P = .50 andP = .30, respectively) (Table 4).

Among the 364 patients who had concomitant treatment and had discontinued their treatment, 332 (91%) switched treatments with no AEs documented and 32 (9%) experienced fatigue with pneumonia, mucositis, neuropathy, neurotoxicity, neutropenia, pneumonitis, allergic or hypersensitivity reaction, or an unknown AE. Patients who discontinued treatment because of unknown AEs had a physician’s note that detailed progressive disease, a significant decline in performance status, and another unknown adverse effect due to a previous sinus tract infection and infectious colitis (Table 5).

Management Analysis and Reporting Tool Database

MHS data analysts provided data on diagnoses for 639 patients among 687 submitteddiagnoses, with 294 patients completing and 345 discontinuing paclitaxel treatment. Patients in the completed treatment group had 3 to 258 unique health conditions documented, while patients in the discontinued treatment group had 4 to 181 unique health conditions documented. The MHS reported 3808 unique diagnosis conditions for the completed group and 3714 for the discontinued group (P = .02).

The mean (SD) number of diagnoses was 51 (31) for the completed and 55 (28) for the discontinued treatment groups (Figure). Among 639 patients who received paclitaxel, the top 5 diagnoses were administrative, including encounters for other administrative examinations; antineoplastic chemotherapy; administrative examination for unspecified; other specified counseling; and adjustment and management of vascular access device. The database does not differentiate between administrative and clinically significant diagnoses.

MHS data analysts provided data for 336 of 687 submitted patients who were prescribed paclitaxel; 46 patients had no PDTS data, and 305 patients had PDTS data without paclitaxel, Taxol, or Abraxane dispensed. Medications that were filled outside the chemotherapy period were removed by evaluating the dispensed date and day of supply. Among these 336 patients, 151 completed the treatment and 185 discontinued, with 14 patients experiencing documented AEs. Patients in the completed treatment group filled 9 to 56 prescriptions while patients in the discontinued treatment group filled 6 to 70 prescriptions.Patients in the discontinued group filled more prescriptions than those who completed treatment: 793 vs 591, respectively (P = .34).

The mean (SD) number of filled prescription drugs was 24 (9) for the completed and 34 (12) for the discontinued treatment group. The 5 most filled prescriptions with paclitaxel from 336 patients with PDTS data were dexamethasone (324 prescriptions with 14 recorded AEs), diphenhydramine (296 prescriptions with 12 recorded AEs), ondansetron (277 prescriptions with 11 recorded AEs), prochlorperazine (265 prescriptions with 12 recorded AEs), and sodium chloride (232 prescriptions with 11 recorded AEs).

DISCUSSION

As a retrospective review, this study is more limited in the strength of its conclusions when compared to randomized control trials. The DoD Cancer Registry Program only contains information about cancer types, stages, treatment regimens, and physicians’ notes. Therefore, noncancer drugs are based solely on the PDTS database. In most cases, physicians' notes on AEs were not detailed. There was no distinction between initial vs later lines of therapy and dosage reductions. The change in status or appearance of a new medical condition did not indicate whether paclitaxel caused the changes to develop or directly worsen a pre-existing condition. The PDTS records prescriptions filled, but that may not reflect patients taking prescriptions.

Paclitaxel

Paclitaxel has a long list of both approved and off-label uses in malignancies as a primary agent and in conjunction with other drugs. The FDA prescribing information for Taxol and Abraxane was last updated in April 2011 and September 2020, respectively.^20,21 The National Institutes of Health National Library of Medicine has the current update for paclitaxel on July 2023.^19,22 Thus, the prescribed information for paclitaxel referenced in the database may not always be up to date. The combinations of paclitaxel with bevacizumab, carboplatin, or carboplatin and pembrolizumab were not in the Taxol prescribing information. Likewise, a combination of nab-paclitaxel with atezolizumab or carboplatin and pembrolizumab is missing in the Abraxane prescribing information.^22-27

The generic name is not the same as a generic drug, which may have slight differences from the brand name product.⁷¹ The generic drug versions of Taxol and Abraxane have been approved by the FDA as paclitaxel injectable and paclitaxel-protein bound, respectively. There was a global shortage of nab-paclitaxel from October 2021 to June 2022 because of a manufacturing problem.⁷² During this shortage, data showed similar comments from physician documents that treatment switched to Taxol due to the Abraxane shortage.

Of 336 patients in the PDTS database with dispensed paclitaxel prescriptions, 276 received paclitaxel (year dispensed, 2013-2022), 27 received Abraxane (year dispensed, 2013-2022), 47 received Taxol (year dispensed, 2004-2015), 8 received both Abraxane and paclitaxel, and 6 received both Taxol and paclitaxel. Based on this information, it appears that the distinction between the drugs was not made in the PDTS until after 2015, 10 years after Abraxane received FDA approval. Abraxane was prescribed in the MHS in 2013, 8 years after FDA approval. There were a few comparison studies of Abraxane and Taxol.^73-76

Safety and effectiveness in pediatric patients have not been established for paclitaxel. According to the DoD Cancer Registry Program, the youngest patient was aged 2 months. In 2021, this patient was diagnosed with corpus uteri and treated with carboplatin and Taxol in course 1; in course 2, the patient reacted to Taxol; in course 3, Taxol was replaced with Abraxane; in courses 4 to 7, the patient was treated with carboplatin only.

Discontinued Treatment

Ten patients had prescribed Taxol that was changed due to AEs: 1 was switched to Abraxane and atezolizumab, 3 switched to Abraxane, 2 switched to docetaxel, 1 switched to doxorubicin, and 3 switched to pembrolizumab (based on physician’s comments). Of the 10 patients, 7 had Taxol reaction, 2 experienced disease progression, and 1 experienced high programmed death–ligand 1 expression (this patient with breast cancer was switched to Abraxane and atezolizumab during the accelerated FDA approval phase for atezolizumab, which was later revoked). Five patients were treated with carboplatin and Taxol for cancer of the anal canal (changed to pembrolizumab after disease progression), lung not otherwise specified (changed to carboplatin and pembrolizumab due to Taxol reaction), lower inner quadrant of the breast (changed to doxorubicin due to hypersensitivity reaction), corpus uteri (changed to Abraxane due to Taxol reaction), and ovary (changed to docetaxel due to Taxol reaction). Three patients were treated with doxorubicin, cyclophosphamide, and Taxol for breast cancer; 2 patients with breast cancer not otherwise specified switched to Abraxane due to cardiopulmonary hypersensitivity and Taxol reaction and 1 patient with cancer of the upper outer quadrant of the breast changed to docetaxel due to allergic reaction. One patient, who was treated with paclitaxel, ifosfamide, and cisplatin for metastasis of the lower lobe of the lung and kidney cancer, experienced complications due to infectious colitis (treated with ciprofloxacin) and then switched to pembrolizumab after the disease progressed. These AEs are known in paclitaxel medical literature on paclitaxel AEs.^19-24,77-81

Combining 2 or more treatments to target cancer-inducing or cell-sustaining pathways is a cornerstone of chemotherapy.^82-84 Most combinations are given on the same day, but some are not. For 3- or 4-drug combinations, doxorubicin and cyclophosphamide were given first, followed by paclitaxel with or withouttrastuzumab, carboplatin, or pembrolizumab. Only 16 patients (2%) were treated with paclitaxel alone; therefore, the completed and discontinued treatment groups are mostly concomitant treatment. As a result, the comparisons of the completed and discontinued treatment groups were almost the same for the diagnosis. The PDTS data have a better result because 2 exclusion criteria were applied before narrowing the analysis down to paclitaxel treatment specifically.

Antidepressants and Other Drugs

Drug response can vary from person to person and can lead to treatment failure related to AEs. One major factor in drug metabolism is CYP.⁸⁵ CYP2C8 is the major pathway for paclitaxel and CYP3A4 is the minor pathway. When evaluating the noncancer drugs, there were no reports of CYP2C8 inhibition or induction.Over the years, many DDI warnings have been issued for paclitaxel with different drugs in various electronic resources.

Oncologists follow guidelines to prevent DDIs, as paclitaxel is known to have severe, moderate, and minor interactions with other drugs. Among 687 patients, 261 (38%) were prescribed any of 14 antidepressants. Eight of these antidepressants (amitriptyline, citalopram, desipramine, doxepin, venlafaxine, escitalopram, nortriptyline, and trazodone) are metabolized, 3 (mirtazapine, sertraline, and fluoxetine) are metabolized and inhibited, 2 (bupropion and duloxetine) are neither metabolized nor inhibited, and 1 (paroxetine) is inhibited by CYP3A4. Duloxetine, venlafaxine, and trazodone were more commonly dispensed (84, 78, and 42 patients, respectively) than others (≤ 33 patients).

Of 32 patients with documented AEs,14 (44%) had 168 dispensed drugs in the PDTS database. Six patients (19%) were treated with doxorubicin and cyclophosphamide followed by paclitaxel for breast cancer; 6 (19%) were treated with carboplatin and paclitaxel for cancer of the lung (n = 3), corpus uteri (n = 2), and ovary (n = 1); 1 patient (3%) was treated with carboplatin and paclitaxel, then switched to carboplatin, bevacizumab, and paclitaxel, and then completed treatment with carboplatin and paclitaxel for an unspecified female genital cancer; and 1 patient (3%) was treated with cisplatin, ifosfamide, and paclitaxel for metastasis of the lower lobe lung and kidney cancer.

The 14 patients with PDTS data had 18 cancer drugs dispensed. Eleven had moderate interaction reports and 7 had no interaction reports. A total of 165 noncancer drugs were dispensed, of which 3 were antidepressants and had no interactions reported, 8 had moderate interactions reported, and 2 had minor interactions with Taxol and Abraxane, respectively (Table 6).^86-129

Of 3 patients who were dispensed bupropion, nortriptyline, or paroxetine, 1 patient with breast cancer was treated with doxorubicin andcyclophosphamide, followed by paclitaxel with bupropion, nortriptyline, pegfilgrastim,dexamethasone, and 17 other noncancer drugs that had no interaction report dispensed during paclitaxel treatment. Of 2 patients with lung cancer, 1 patient was treated with carboplatin and paclitaxel with nortriptyline, dexamethasone, and 13 additional medications, and the second patient was treated with paroxetine, cimetidine, dexamethasone, and 12 other medications. Patients were dispensed up to6 noncancer medications on the same day as paclitaxel administration to control the AEs, not including the prodrugs filled before the treatments. Paroxetine and cimetidine have weak inhibition, and dexamethasone has weak induction of CYP3A4. Therefore, while 1:1 DDIs might have little or no effect with weak inhibit/induce CYP3A4 drugs, 1:1:1 or more combinations could have a different outcome (confirmed in previous publications).^65-67

Dispensed on the same day may not mean taken at the same time. One patient experienced an AE with dispensed 50 mg losartan, carboplatin plus paclitaxel, dexamethasone, and 6 other noncancer drugs. Losartan inhibits paclitaxel, which can lead to negative AEs.^57,66,67 However, there were no blood or plasma samples taken to confirm the losartan was taken at the same time as the paclitaxel given this was not a clinical trial.

Conclusions

This retrospective study discusses the use of paclitaxel in the MHS and the potential DDIs associated with it. The study population consisted mostly of active-duty personnel, who are required to be healthy or have controlled or nonactive medical diagnoses and be physically fit. This group is mixed with dependents and retirees that are more reflective of the average US population. As a result, this patient population is healthier than the general population, with a lower prevalence of common illnesses such as diabetes and obesity. The study aimed to identify drugs used alongside paclitaxel treatment. While further research is needed to identify potential DDIs among patients who experienced AEs, in vitro testing will need to be conducted before confirming causality. The low number of AEs experienced by only 32 of 702 patients (5%), with no deaths during paclitaxel treatment, indicates that the drug is generally well tolerated. Although this study cannot conclude that concomitant use with noncancer drugs led to the discontinuation of paclitaxel, we can conclude that there seems to be no significant DDIsidentified between paclitaxel and antidepressants. This comprehensive overview provides clinicians with a complete picture of paclitaxel use for 27 years (1996-2022), enabling them to make informed decisions about paclitaxel treatment.

Acknowledgments

The Department of Research Program funds at Walter Reed National Military Medical Center supported this protocol. We sincerely appreciate the contribution of data extraction from the Joint Pathology Center teams (Francisco J. Rentas, John D. McGeeney, Beatriz A. Hallo, and Johnny P. Beason) and the MHS database personnel (Maj Ryan Costantino, Brandon E. Jenkins, and Alexander G. Rittel). We gratefully thank you for the protocol support from the Department of Research programs: CDR Martin L. Boese, CDR Wesley R. Campbell, Maj. Abhimanyu Chandel, CDR Ling Ye, Chelsea N. Powers, Yaling Zhou, Elizabeth Schafer, Micah Stretch, Diane Beaner, and Adrienne Woodard.

Background

Paclitaxel was first derived from the bark of the yew tree (Taxus brevifolia). It was discovered as part of a National Cancer Institute program screen of plants and natural products with putative anticancer activity during the 1960s.^1-9 Paclitaxel works by suppressing spindle microtube dynamics, which results in the blockage of the metaphase-anaphase transitions, inhibition of mitosis, and induction of apoptosis in a broad spectrum of cancer cells. Paclitaxel also displayed additional anticancer activities, including the suppression of cell proliferation and antiangiogenic effects. However, since the growth of normal body cells may also be affected, other adverse effects (AEs) will also occur.^8-18

Two different chemotherapy drugs contain paclitaxel—paclitaxel and nab-paclitaxel—and the US Food and Drug Administration (FDA) recognizes them as separate entities.^19-21 Taxol (paclitaxel) was approved by the FDA in 1992 for treating advanced ovarian cancer.²⁰ It has since been approved for the treatment of metastatic breast cancer, AIDS-related Kaposi sarcoma (as an orphan drug), non-small cell lung cancer (NSCLC), and cervical cancers (in combination withbevacizumab) in 1994, 1997, 1999, and 2014, respectively.²¹ Since 2002, a generic version of Taxol, known as paclitaxel injectable, has been FDA-approved from different manufacturers. According to the National Cancer Institute, a combination of carboplatin and Taxol is approved to treat carcinoma of unknown primary, cervical, endometrial, NSCLC, ovarian, and thymoma cancers.¹⁹ Abraxane (nab-paclitaxel) was FDA-approved to treat metastatic breast cancer in 2005. It was later approved for first-line treatment of advanced NSCLC and late-stage pancreatic cancer in 2012 and 2013, respectively. In 2018 and 2020, both Taxol and Abraxane were approved for first-line treatment of metastatic squamous cell NSCLC in combination with carboplatin and pembrolizumab and metastatic triple-negative breast cancer in combination with pembrolizumab, respectively.^22-26 In 2019, Abraxane was approved with atezolizumab to treat metastatic triple-negative breast cancer, but this approval was withdrawn in 2021. In 2022, a generic version of Abraxane, known as paclitaxel protein-bound, was released in the United States. Furthermore, paclitaxel-containing formulations also are being studied in the treatment of other types of cancer.^19-32

One of the main limitations of paclitaxel is its low solubility in water, which complicates its drug supply. To distribute this hydrophobic anticancer drug efficiently, paclitaxel is formulated and administered to patients via polyethoxylated castor oil or albumin-bound (nab-paclitaxel). However, polyethoxylated castor oil induces complement activation and is the cause of common hypersensitivity reactions related to paclitaxel use.^2,17,33-38 Therefore, many alternatives to polyethoxylated castor oil have been researched.

Since 2000, new paclitaxel formulations have emerged using nanomedicine techniques. The difference between these formulations is the drug vehicle. Different paclitaxel-based nanotechnological vehicles have been developed and approved, such as albumin-based nanoparticles, polymeric lipidic nanoparticles, polymeric micelles, and liposomes, with many others in clinical trial phases.^3,37 Albumin-based nanoparticles have a high response rate (33%), whereas the response rate for polyethoxylated castor oil is 25% in patients with metastatic breast cancer.^33,39-52 The use of paclitaxel dimer nanoparticles also has been proposed as a method for increasing drug solubility.^33,53

Paclitaxel is metabolized by cytochrome P450 (CYP) isoenzymes 2C8 and 3A4. When administering paclitaxel with known inhibitors, inducers, or substrates of CYP2C8 or CYP3A4, caution is required.^19-22 Regulations for CYP research were not issued until 2008, so potential interactions between paclitaxel and other drugs have not been extensively evaluated in clinical trials. A study of 12 kinase inhibitors showed strong inhibition of CYP2C8 and/or CYP3A4 pathways by these inhibitors, which could alter the ratio of paclitaxel metabolites in vivo, leading to clinically relevant changes.⁵⁴ Differential metabolism has been linked to paclitaxel-induced neurotoxicity in patients with cancer.⁵⁵ Nonetheless, variants in the CYP2C8, CYP3A4, CYP3A5, and ABCB1 genes do not account for significant interindividual variability in paclitaxel pharmacokinetics.⁵⁶ In liver microsomes, losartan inhibited paclitaxel metabolism when used at concentrations > 50 µmol/L.⁵⁷ Many drug-drug interaction (DDI) studies of CYP2C8 and CYP3A4 have shown similar results for paclitaxel.^58-64

The goals of this study are to investigate prescribed drugs used with paclitaxel and determine patient outcomes through several Military Health System (MHS) databases. The investigation focused on (1) the functions of paclitaxel; (2) identifying AEs that patients experienced; (3) evaluating differences when paclitaxel is used alone vs concomitantly and between the completed vs discontinued treatment groups; (4) identifying all drugs used during paclitaxel treatment; and (5) evaluating DDIs with antidepressants (that have an FDA boxed warning and are known to have DDIs confirmed in previous publications) and other drugs.^65-67

The Walter Reed National Military Medical Center in Bethesda, Maryland, institutionalreview board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and MHS data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and the Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

METHODS

The DoD Cancer Registry Program was established in 1986 and currently contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in the CAPER record represents an ambulatory encounter at a military treatment facility (MTF). CAPER includes data from 2003 to 2024.

Each observation in the PDTS record represents a prescription filled for an MHS beneficiary at an MTF through the TRICARE mail-order program or a US retail pharmacy. Missing from this record are prescriptions filled at international civilian pharmacies and inpatient pharmacy prescriptions. The MHS Data Repository PDTS record is available from 2002 to 2024. The legacy Composite Health Care System is being replaced by GENESIS at MTFs.

Data Extraction Design

The study design involved a cross-sectional analysis. We requested data extraction for paclitaxel from 1998 to 2022. Data from the DoD Cancer Registry Program were used to identify patients who received cancer treatment. Once patients were identified, the CAPER database was searched for diagnoses to identify other health conditions, whereas the PDTS database was used to populate a list of prescription medications filled during chemotherapy treatment.

Data collected from the JPC included cancer treatment, cancer information, demographics, and physicians’ comments on AEs. Collected data from the MHS include diagnosis and filled prescription history from initiation to completion of the therapy period (or 2 years after the diagnosis date). For the analysis of the DoD Cancer Registry Program and CAPER databases, we used all collected data without excluding any. When analyzing PDTS data, we excluded patients with PDTS data but without a record of paclitaxel being filled, or medications filled outside the chemotherapy period (by evaluating the dispensed date and day of supply).

Data Extraction Analysis

The Surveillance, Epidemiology, and End Results Program Coding and Staging Manual 2016 and the International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^68,69 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the paclitaxel groups divided by the total number of patients or data variables. The subgroup percentage was calculated by using the number of patients or data available within the subgroup divided by the total number of patients in that subgroup.

In alone vs concomitant and completed vs discontinued treatment groups, a 2-tailed, 2-sample z test was used to statistical significance (P < .05) using a statistics website.⁷⁰ Concomitant was defined as paclitaxel taken with other antineoplastic agent(s) before, after, or at the same time as cancer therapy. For the retrospective data analysis, physicians’ notes with a period, comma, forward slash, semicolon, or space between medication names were interpreted as concurrent, whereas plus (+), minus/plus (-/+), or “and” between drug names that were dispensed on the same day were interpreted as combined with known common combinations: 2 drugs (DM886 paclitaxel and carboplatin and DM881-TC-1 paclitaxel and cisplatin) or 3 drugs (DM887-ACT doxorubicin, cyclophosphamide, and paclitaxel). Completed treatment was defined as paclitaxel as the last medication the patient took without recorded AEs; switching or experiencing AEs was defined as discontinued treatment.

RESULTS

The JPC provided 702 entries for 687 patients with a mean age of 56 years (range, 2 months to 88 years) who were treated with paclitaxel from March 1996 to October 2021. Fifteen patients had duplicate entries because they had multiple cancer sites or occurrences. There were 623 patients (89%) who received paclitaxel for FDA-approved indications. The most common types of cancer identified were 344 patients with breast cancer (49%), 91 patients with lung cancer (13%), 79 patients with ovarian cancer (11%), and 75 patients with endometrial cancer (11%) (Table 1). Seventy-nine patients (11%) received paclitaxel for cancers that were not for FDA-approved indications, including 19 for cancers of the fallopian tube (3%) and 17 for esophageal cancer (2%) (Table 2).

There were 477 patients (68%) aged > 50 years. A total of 304 patients (43%) had a stage III or IV cancer diagnosis and 398 (57%) had stage II or lower (combination of data for stages 0, I, and II; not applicable; and unknown) cancer diagnosis. For systemic treatment, 16 patients (2%) were treated with paclitaxel alone and 686 patients (98%) received paclitaxel concomitantly with additional chemotherapy: 59 patients (9%) in the before or after group, 410 patients (58%) had a 2-drug combination, 212 patients (30%) had a 3-drug combination, and 5 patients (1%) had a 4-drug combination. In addition, for doublet therapies, paclitaxel combined with carboplatin, trastuzumab, gemcitabine, or cisplatin had more patients (318, 58, 12, and 11, respectively) than other combinations (≤ 4 patients). For triplet therapies, paclitaxel combined withdoxorubicin plus cyclophosphamide or carboplatin plus bevacizumab had more patients (174 and 20, respectively) than other combinations, including quadruplet therapies (≤ 4 patients) (Table 3).

Patients were more likely to discontinue paclitaxel if they received concomitant treatment. None of the 16 patients receiving paclitaxel monotherapy experienced AEs, whereas 364 of 686 patients (53%) treated concomitantly discontinued (P < .001). Comparisons of 1 drug vs combination (2 to 4 drugs) and use for treating cancers that were FDA-approved indications vs off-label use were significant (P < .001), whereas comparisons of stage II or lower vs stage III and IV cancer and of those aged ≤ 50 years vs aged > 50 years were not significant (P = .50 andP = .30, respectively) (Table 4).

Among the 364 patients who had concomitant treatment and had discontinued their treatment, 332 (91%) switched treatments with no AEs documented and 32 (9%) experienced fatigue with pneumonia, mucositis, neuropathy, neurotoxicity, neutropenia, pneumonitis, allergic or hypersensitivity reaction, or an unknown AE. Patients who discontinued treatment because of unknown AEs had a physician’s note that detailed progressive disease, a significant decline in performance status, and another unknown adverse effect due to a previous sinus tract infection and infectious colitis (Table 5).

Management Analysis and Reporting Tool Database

MHS data analysts provided data on diagnoses for 639 patients among 687 submitteddiagnoses, with 294 patients completing and 345 discontinuing paclitaxel treatment. Patients in the completed treatment group had 3 to 258 unique health conditions documented, while patients in the discontinued treatment group had 4 to 181 unique health conditions documented. The MHS reported 3808 unique diagnosis conditions for the completed group and 3714 for the discontinued group (P = .02).

The mean (SD) number of diagnoses was 51 (31) for the completed and 55 (28) for the discontinued treatment groups (Figure). Among 639 patients who received paclitaxel, the top 5 diagnoses were administrative, including encounters for other administrative examinations; antineoplastic chemotherapy; administrative examination for unspecified; other specified counseling; and adjustment and management of vascular access device. The database does not differentiate between administrative and clinically significant diagnoses.

MHS data analysts provided data for 336 of 687 submitted patients who were prescribed paclitaxel; 46 patients had no PDTS data, and 305 patients had PDTS data without paclitaxel, Taxol, or Abraxane dispensed. Medications that were filled outside the chemotherapy period were removed by evaluating the dispensed date and day of supply. Among these 336 patients, 151 completed the treatment and 185 discontinued, with 14 patients experiencing documented AEs. Patients in the completed treatment group filled 9 to 56 prescriptions while patients in the discontinued treatment group filled 6 to 70 prescriptions.Patients in the discontinued group filled more prescriptions than those who completed treatment: 793 vs 591, respectively (P = .34).

The mean (SD) number of filled prescription drugs was 24 (9) for the completed and 34 (12) for the discontinued treatment group. The 5 most filled prescriptions with paclitaxel from 336 patients with PDTS data were dexamethasone (324 prescriptions with 14 recorded AEs), diphenhydramine (296 prescriptions with 12 recorded AEs), ondansetron (277 prescriptions with 11 recorded AEs), prochlorperazine (265 prescriptions with 12 recorded AEs), and sodium chloride (232 prescriptions with 11 recorded AEs).

DISCUSSION

As a retrospective review, this study is more limited in the strength of its conclusions when compared to randomized control trials. The DoD Cancer Registry Program only contains information about cancer types, stages, treatment regimens, and physicians’ notes. Therefore, noncancer drugs are based solely on the PDTS database. In most cases, physicians' notes on AEs were not detailed. There was no distinction between initial vs later lines of therapy and dosage reductions. The change in status or appearance of a new medical condition did not indicate whether paclitaxel caused the changes to develop or directly worsen a pre-existing condition. The PDTS records prescriptions filled, but that may not reflect patients taking prescriptions.

Paclitaxel

Paclitaxel has a long list of both approved and off-label uses in malignancies as a primary agent and in conjunction with other drugs. The FDA prescribing information for Taxol and Abraxane was last updated in April 2011 and September 2020, respectively.^20,21 The National Institutes of Health National Library of Medicine has the current update for paclitaxel on July 2023.^19,22 Thus, the prescribed information for paclitaxel referenced in the database may not always be up to date. The combinations of paclitaxel with bevacizumab, carboplatin, or carboplatin and pembrolizumab were not in the Taxol prescribing information. Likewise, a combination of nab-paclitaxel with atezolizumab or carboplatin and pembrolizumab is missing in the Abraxane prescribing information.^22-27

The generic name is not the same as a generic drug, which may have slight differences from the brand name product.⁷¹ The generic drug versions of Taxol and Abraxane have been approved by the FDA as paclitaxel injectable and paclitaxel-protein bound, respectively. There was a global shortage of nab-paclitaxel from October 2021 to June 2022 because of a manufacturing problem.⁷² During this shortage, data showed similar comments from physician documents that treatment switched to Taxol due to the Abraxane shortage.

Of 336 patients in the PDTS database with dispensed paclitaxel prescriptions, 276 received paclitaxel (year dispensed, 2013-2022), 27 received Abraxane (year dispensed, 2013-2022), 47 received Taxol (year dispensed, 2004-2015), 8 received both Abraxane and paclitaxel, and 6 received both Taxol and paclitaxel. Based on this information, it appears that the distinction between the drugs was not made in the PDTS until after 2015, 10 years after Abraxane received FDA approval. Abraxane was prescribed in the MHS in 2013, 8 years after FDA approval. There were a few comparison studies of Abraxane and Taxol.^73-76

Safety and effectiveness in pediatric patients have not been established for paclitaxel. According to the DoD Cancer Registry Program, the youngest patient was aged 2 months. In 2021, this patient was diagnosed with corpus uteri and treated with carboplatin and Taxol in course 1; in course 2, the patient reacted to Taxol; in course 3, Taxol was replaced with Abraxane; in courses 4 to 7, the patient was treated with carboplatin only.

Discontinued Treatment

Ten patients had prescribed Taxol that was changed due to AEs: 1 was switched to Abraxane and atezolizumab, 3 switched to Abraxane, 2 switched to docetaxel, 1 switched to doxorubicin, and 3 switched to pembrolizumab (based on physician’s comments). Of the 10 patients, 7 had Taxol reaction, 2 experienced disease progression, and 1 experienced high programmed death–ligand 1 expression (this patient with breast cancer was switched to Abraxane and atezolizumab during the accelerated FDA approval phase for atezolizumab, which was later revoked). Five patients were treated with carboplatin and Taxol for cancer of the anal canal (changed to pembrolizumab after disease progression), lung not otherwise specified (changed to carboplatin and pembrolizumab due to Taxol reaction), lower inner quadrant of the breast (changed to doxorubicin due to hypersensitivity reaction), corpus uteri (changed to Abraxane due to Taxol reaction), and ovary (changed to docetaxel due to Taxol reaction). Three patients were treated with doxorubicin, cyclophosphamide, and Taxol for breast cancer; 2 patients with breast cancer not otherwise specified switched to Abraxane due to cardiopulmonary hypersensitivity and Taxol reaction and 1 patient with cancer of the upper outer quadrant of the breast changed to docetaxel due to allergic reaction. One patient, who was treated with paclitaxel, ifosfamide, and cisplatin for metastasis of the lower lobe of the lung and kidney cancer, experienced complications due to infectious colitis (treated with ciprofloxacin) and then switched to pembrolizumab after the disease progressed. These AEs are known in paclitaxel medical literature on paclitaxel AEs.^19-24,77-81

Combining 2 or more treatments to target cancer-inducing or cell-sustaining pathways is a cornerstone of chemotherapy.^82-84 Most combinations are given on the same day, but some are not. For 3- or 4-drug combinations, doxorubicin and cyclophosphamide were given first, followed by paclitaxel with or withouttrastuzumab, carboplatin, or pembrolizumab. Only 16 patients (2%) were treated with paclitaxel alone; therefore, the completed and discontinued treatment groups are mostly concomitant treatment. As a result, the comparisons of the completed and discontinued treatment groups were almost the same for the diagnosis. The PDTS data have a better result because 2 exclusion criteria were applied before narrowing the analysis down to paclitaxel treatment specifically.

Antidepressants and Other Drugs

Drug response can vary from person to person and can lead to treatment failure related to AEs. One major factor in drug metabolism is CYP.⁸⁵ CYP2C8 is the major pathway for paclitaxel and CYP3A4 is the minor pathway. When evaluating the noncancer drugs, there were no reports of CYP2C8 inhibition or induction.Over the years, many DDI warnings have been issued for paclitaxel with different drugs in various electronic resources.

Oncologists follow guidelines to prevent DDIs, as paclitaxel is known to have severe, moderate, and minor interactions with other drugs. Among 687 patients, 261 (38%) were prescribed any of 14 antidepressants. Eight of these antidepressants (amitriptyline, citalopram, desipramine, doxepin, venlafaxine, escitalopram, nortriptyline, and trazodone) are metabolized, 3 (mirtazapine, sertraline, and fluoxetine) are metabolized and inhibited, 2 (bupropion and duloxetine) are neither metabolized nor inhibited, and 1 (paroxetine) is inhibited by CYP3A4. Duloxetine, venlafaxine, and trazodone were more commonly dispensed (84, 78, and 42 patients, respectively) than others (≤ 33 patients).

Of 32 patients with documented AEs,14 (44%) had 168 dispensed drugs in the PDTS database. Six patients (19%) were treated with doxorubicin and cyclophosphamide followed by paclitaxel for breast cancer; 6 (19%) were treated with carboplatin and paclitaxel for cancer of the lung (n = 3), corpus uteri (n = 2), and ovary (n = 1); 1 patient (3%) was treated with carboplatin and paclitaxel, then switched to carboplatin, bevacizumab, and paclitaxel, and then completed treatment with carboplatin and paclitaxel for an unspecified female genital cancer; and 1 patient (3%) was treated with cisplatin, ifosfamide, and paclitaxel for metastasis of the lower lobe lung and kidney cancer.

The 14 patients with PDTS data had 18 cancer drugs dispensed. Eleven had moderate interaction reports and 7 had no interaction reports. A total of 165 noncancer drugs were dispensed, of which 3 were antidepressants and had no interactions reported, 8 had moderate interactions reported, and 2 had minor interactions with Taxol and Abraxane, respectively (Table 6).^86-129

Of 3 patients who were dispensed bupropion, nortriptyline, or paroxetine, 1 patient with breast cancer was treated with doxorubicin andcyclophosphamide, followed by paclitaxel with bupropion, nortriptyline, pegfilgrastim,dexamethasone, and 17 other noncancer drugs that had no interaction report dispensed during paclitaxel treatment. Of 2 patients with lung cancer, 1 patient was treated with carboplatin and paclitaxel with nortriptyline, dexamethasone, and 13 additional medications, and the second patient was treated with paroxetine, cimetidine, dexamethasone, and 12 other medications. Patients were dispensed up to6 noncancer medications on the same day as paclitaxel administration to control the AEs, not including the prodrugs filled before the treatments. Paroxetine and cimetidine have weak inhibition, and dexamethasone has weak induction of CYP3A4. Therefore, while 1:1 DDIs might have little or no effect with weak inhibit/induce CYP3A4 drugs, 1:1:1 or more combinations could have a different outcome (confirmed in previous publications).^65-67

Dispensed on the same day may not mean taken at the same time. One patient experienced an AE with dispensed 50 mg losartan, carboplatin plus paclitaxel, dexamethasone, and 6 other noncancer drugs. Losartan inhibits paclitaxel, which can lead to negative AEs.^57,66,67 However, there were no blood or plasma samples taken to confirm the losartan was taken at the same time as the paclitaxel given this was not a clinical trial.

Conclusions

This retrospective study discusses the use of paclitaxel in the MHS and the potential DDIs associated with it. The study population consisted mostly of active-duty personnel, who are required to be healthy or have controlled or nonactive medical diagnoses and be physically fit. This group is mixed with dependents and retirees that are more reflective of the average US population. As a result, this patient population is healthier than the general population, with a lower prevalence of common illnesses such as diabetes and obesity. The study aimed to identify drugs used alongside paclitaxel treatment. While further research is needed to identify potential DDIs among patients who experienced AEs, in vitro testing will need to be conducted before confirming causality. The low number of AEs experienced by only 32 of 702 patients (5%), with no deaths during paclitaxel treatment, indicates that the drug is generally well tolerated. Although this study cannot conclude that concomitant use with noncancer drugs led to the discontinuation of paclitaxel, we can conclude that there seems to be no significant DDIsidentified between paclitaxel and antidepressants. This comprehensive overview provides clinicians with a complete picture of paclitaxel use for 27 years (1996-2022), enabling them to make informed decisions about paclitaxel treatment.

Acknowledgments

The Department of Research Program funds at Walter Reed National Military Medical Center supported this protocol. We sincerely appreciate the contribution of data extraction from the Joint Pathology Center teams (Francisco J. Rentas, John D. McGeeney, Beatriz A. Hallo, and Johnny P. Beason) and the MHS database personnel (Maj Ryan Costantino, Brandon E. Jenkins, and Alexander G. Rittel). We gratefully thank you for the protocol support from the Department of Research programs: CDR Martin L. Boese, CDR Wesley R. Campbell, Maj. Abhimanyu Chandel, CDR Ling Ye, Chelsea N. Powers, Yaling Zhou, Elizabeth Schafer, Micah Stretch, Diane Beaner, and Adrienne Woodard.

Background

Paclitaxel was first derived from the bark of the yew tree (Taxus brevifolia). It was discovered as part of a National Cancer Institute program screen of plants and natural products with putative anticancer activity during the 1960s.^1-9 Paclitaxel works by suppressing spindle microtube dynamics, which results in the blockage of the metaphase-anaphase transitions, inhibition of mitosis, and induction of apoptosis in a broad spectrum of cancer cells. Paclitaxel also displayed additional anticancer activities, including the suppression of cell proliferation and antiangiogenic effects. However, since the growth of normal body cells may also be affected, other adverse effects (AEs) will also occur.^8-18

Two different chemotherapy drugs contain paclitaxel—paclitaxel and nab-paclitaxel—and the US Food and Drug Administration (FDA) recognizes them as separate entities.^19-21 Taxol (paclitaxel) was approved by the FDA in 1992 for treating advanced ovarian cancer.²⁰ It has since been approved for the treatment of metastatic breast cancer, AIDS-related Kaposi sarcoma (as an orphan drug), non-small cell lung cancer (NSCLC), and cervical cancers (in combination withbevacizumab) in 1994, 1997, 1999, and 2014, respectively.²¹ Since 2002, a generic version of Taxol, known as paclitaxel injectable, has been FDA-approved from different manufacturers. According to the National Cancer Institute, a combination of carboplatin and Taxol is approved to treat carcinoma of unknown primary, cervical, endometrial, NSCLC, ovarian, and thymoma cancers.¹⁹ Abraxane (nab-paclitaxel) was FDA-approved to treat metastatic breast cancer in 2005. It was later approved for first-line treatment of advanced NSCLC and late-stage pancreatic cancer in 2012 and 2013, respectively. In 2018 and 2020, both Taxol and Abraxane were approved for first-line treatment of metastatic squamous cell NSCLC in combination with carboplatin and pembrolizumab and metastatic triple-negative breast cancer in combination with pembrolizumab, respectively.^22-26 In 2019, Abraxane was approved with atezolizumab to treat metastatic triple-negative breast cancer, but this approval was withdrawn in 2021. In 2022, a generic version of Abraxane, known as paclitaxel protein-bound, was released in the United States. Furthermore, paclitaxel-containing formulations also are being studied in the treatment of other types of cancer.^19-32

One of the main limitations of paclitaxel is its low solubility in water, which complicates its drug supply. To distribute this hydrophobic anticancer drug efficiently, paclitaxel is formulated and administered to patients via polyethoxylated castor oil or albumin-bound (nab-paclitaxel). However, polyethoxylated castor oil induces complement activation and is the cause of common hypersensitivity reactions related to paclitaxel use.^2,17,33-38 Therefore, many alternatives to polyethoxylated castor oil have been researched.

Since 2000, new paclitaxel formulations have emerged using nanomedicine techniques. The difference between these formulations is the drug vehicle. Different paclitaxel-based nanotechnological vehicles have been developed and approved, such as albumin-based nanoparticles, polymeric lipidic nanoparticles, polymeric micelles, and liposomes, with many others in clinical trial phases.^3,37 Albumin-based nanoparticles have a high response rate (33%), whereas the response rate for polyethoxylated castor oil is 25% in patients with metastatic breast cancer.^33,39-52 The use of paclitaxel dimer nanoparticles also has been proposed as a method for increasing drug solubility.^33,53

Paclitaxel is metabolized by cytochrome P450 (CYP) isoenzymes 2C8 and 3A4. When administering paclitaxel with known inhibitors, inducers, or substrates of CYP2C8 or CYP3A4, caution is required.^19-22 Regulations for CYP research were not issued until 2008, so potential interactions between paclitaxel and other drugs have not been extensively evaluated in clinical trials. A study of 12 kinase inhibitors showed strong inhibition of CYP2C8 and/or CYP3A4 pathways by these inhibitors, which could alter the ratio of paclitaxel metabolites in vivo, leading to clinically relevant changes.⁵⁴ Differential metabolism has been linked to paclitaxel-induced neurotoxicity in patients with cancer.⁵⁵ Nonetheless, variants in the CYP2C8, CYP3A4, CYP3A5, and ABCB1 genes do not account for significant interindividual variability in paclitaxel pharmacokinetics.⁵⁶ In liver microsomes, losartan inhibited paclitaxel metabolism when used at concentrations > 50 µmol/L.⁵⁷ Many drug-drug interaction (DDI) studies of CYP2C8 and CYP3A4 have shown similar results for paclitaxel.^58-64

The goals of this study are to investigate prescribed drugs used with paclitaxel and determine patient outcomes through several Military Health System (MHS) databases. The investigation focused on (1) the functions of paclitaxel; (2) identifying AEs that patients experienced; (3) evaluating differences when paclitaxel is used alone vs concomitantly and between the completed vs discontinued treatment groups; (4) identifying all drugs used during paclitaxel treatment; and (5) evaluating DDIs with antidepressants (that have an FDA boxed warning and are known to have DDIs confirmed in previous publications) and other drugs.^65-67

The Walter Reed National Military Medical Center in Bethesda, Maryland, institutionalreview board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and MHS data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and the Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

METHODS

The DoD Cancer Registry Program was established in 1986 and currently contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in the CAPER record represents an ambulatory encounter at a military treatment facility (MTF). CAPER includes data from 2003 to 2024.

Each observation in the PDTS record represents a prescription filled for an MHS beneficiary at an MTF through the TRICARE mail-order program or a US retail pharmacy. Missing from this record are prescriptions filled at international civilian pharmacies and inpatient pharmacy prescriptions. The MHS Data Repository PDTS record is available from 2002 to 2024. The legacy Composite Health Care System is being replaced by GENESIS at MTFs.

Data Extraction Design

The study design involved a cross-sectional analysis. We requested data extraction for paclitaxel from 1998 to 2022. Data from the DoD Cancer Registry Program were used to identify patients who received cancer treatment. Once patients were identified, the CAPER database was searched for diagnoses to identify other health conditions, whereas the PDTS database was used to populate a list of prescription medications filled during chemotherapy treatment.

Data collected from the JPC included cancer treatment, cancer information, demographics, and physicians’ comments on AEs. Collected data from the MHS include diagnosis and filled prescription history from initiation to completion of the therapy period (or 2 years after the diagnosis date). For the analysis of the DoD Cancer Registry Program and CAPER databases, we used all collected data without excluding any. When analyzing PDTS data, we excluded patients with PDTS data but without a record of paclitaxel being filled, or medications filled outside the chemotherapy period (by evaluating the dispensed date and day of supply).

Data Extraction Analysis

The Surveillance, Epidemiology, and End Results Program Coding and Staging Manual 2016 and the International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^68,69 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the paclitaxel groups divided by the total number of patients or data variables. The subgroup percentage was calculated by using the number of patients or data available within the subgroup divided by the total number of patients in that subgroup.

In alone vs concomitant and completed vs discontinued treatment groups, a 2-tailed, 2-sample z test was used to statistical significance (P < .05) using a statistics website.⁷⁰ Concomitant was defined as paclitaxel taken with other antineoplastic agent(s) before, after, or at the same time as cancer therapy. For the retrospective data analysis, physicians’ notes with a period, comma, forward slash, semicolon, or space between medication names were interpreted as concurrent, whereas plus (+), minus/plus (-/+), or “and” between drug names that were dispensed on the same day were interpreted as combined with known common combinations: 2 drugs (DM886 paclitaxel and carboplatin and DM881-TC-1 paclitaxel and cisplatin) or 3 drugs (DM887-ACT doxorubicin, cyclophosphamide, and paclitaxel). Completed treatment was defined as paclitaxel as the last medication the patient took without recorded AEs; switching or experiencing AEs was defined as discontinued treatment.

RESULTS

The JPC provided 702 entries for 687 patients with a mean age of 56 years (range, 2 months to 88 years) who were treated with paclitaxel from March 1996 to October 2021. Fifteen patients had duplicate entries because they had multiple cancer sites or occurrences. There were 623 patients (89%) who received paclitaxel for FDA-approved indications. The most common types of cancer identified were 344 patients with breast cancer (49%), 91 patients with lung cancer (13%), 79 patients with ovarian cancer (11%), and 75 patients with endometrial cancer (11%) (Table 1). Seventy-nine patients (11%) received paclitaxel for cancers that were not for FDA-approved indications, including 19 for cancers of the fallopian tube (3%) and 17 for esophageal cancer (2%) (Table 2).

There were 477 patients (68%) aged > 50 years. A total of 304 patients (43%) had a stage III or IV cancer diagnosis and 398 (57%) had stage II or lower (combination of data for stages 0, I, and II; not applicable; and unknown) cancer diagnosis. For systemic treatment, 16 patients (2%) were treated with paclitaxel alone and 686 patients (98%) received paclitaxel concomitantly with additional chemotherapy: 59 patients (9%) in the before or after group, 410 patients (58%) had a 2-drug combination, 212 patients (30%) had a 3-drug combination, and 5 patients (1%) had a 4-drug combination. In addition, for doublet therapies, paclitaxel combined with carboplatin, trastuzumab, gemcitabine, or cisplatin had more patients (318, 58, 12, and 11, respectively) than other combinations (≤ 4 patients). For triplet therapies, paclitaxel combined withdoxorubicin plus cyclophosphamide or carboplatin plus bevacizumab had more patients (174 and 20, respectively) than other combinations, including quadruplet therapies (≤ 4 patients) (Table 3).

Patients were more likely to discontinue paclitaxel if they received concomitant treatment. None of the 16 patients receiving paclitaxel monotherapy experienced AEs, whereas 364 of 686 patients (53%) treated concomitantly discontinued (P < .001). Comparisons of 1 drug vs combination (2 to 4 drugs) and use for treating cancers that were FDA-approved indications vs off-label use were significant (P < .001), whereas comparisons of stage II or lower vs stage III and IV cancer and of those aged ≤ 50 years vs aged > 50 years were not significant (P = .50 andP = .30, respectively) (Table 4).

Among the 364 patients who had concomitant treatment and had discontinued their treatment, 332 (91%) switched treatments with no AEs documented and 32 (9%) experienced fatigue with pneumonia, mucositis, neuropathy, neurotoxicity, neutropenia, pneumonitis, allergic or hypersensitivity reaction, or an unknown AE. Patients who discontinued treatment because of unknown AEs had a physician’s note that detailed progressive disease, a significant decline in performance status, and another unknown adverse effect due to a previous sinus tract infection and infectious colitis (Table 5).

Management Analysis and Reporting Tool Database

MHS data analysts provided data on diagnoses for 639 patients among 687 submitteddiagnoses, with 294 patients completing and 345 discontinuing paclitaxel treatment. Patients in the completed treatment group had 3 to 258 unique health conditions documented, while patients in the discontinued treatment group had 4 to 181 unique health conditions documented. The MHS reported 3808 unique diagnosis conditions for the completed group and 3714 for the discontinued group (P = .02).

The mean (SD) number of diagnoses was 51 (31) for the completed and 55 (28) for the discontinued treatment groups (Figure). Among 639 patients who received paclitaxel, the top 5 diagnoses were administrative, including encounters for other administrative examinations; antineoplastic chemotherapy; administrative examination for unspecified; other specified counseling; and adjustment and management of vascular access device. The database does not differentiate between administrative and clinically significant diagnoses.

MHS data analysts provided data for 336 of 687 submitted patients who were prescribed paclitaxel; 46 patients had no PDTS data, and 305 patients had PDTS data without paclitaxel, Taxol, or Abraxane dispensed. Medications that were filled outside the chemotherapy period were removed by evaluating the dispensed date and day of supply. Among these 336 patients, 151 completed the treatment and 185 discontinued, with 14 patients experiencing documented AEs. Patients in the completed treatment group filled 9 to 56 prescriptions while patients in the discontinued treatment group filled 6 to 70 prescriptions.Patients in the discontinued group filled more prescriptions than those who completed treatment: 793 vs 591, respectively (P = .34).

The mean (SD) number of filled prescription drugs was 24 (9) for the completed and 34 (12) for the discontinued treatment group. The 5 most filled prescriptions with paclitaxel from 336 patients with PDTS data were dexamethasone (324 prescriptions with 14 recorded AEs), diphenhydramine (296 prescriptions with 12 recorded AEs), ondansetron (277 prescriptions with 11 recorded AEs), prochlorperazine (265 prescriptions with 12 recorded AEs), and sodium chloride (232 prescriptions with 11 recorded AEs).

DISCUSSION

As a retrospective review, this study is more limited in the strength of its conclusions when compared to randomized control trials. The DoD Cancer Registry Program only contains information about cancer types, stages, treatment regimens, and physicians’ notes. Therefore, noncancer drugs are based solely on the PDTS database. In most cases, physicians' notes on AEs were not detailed. There was no distinction between initial vs later lines of therapy and dosage reductions. The change in status or appearance of a new medical condition did not indicate whether paclitaxel caused the changes to develop or directly worsen a pre-existing condition. The PDTS records prescriptions filled, but that may not reflect patients taking prescriptions.

Paclitaxel

Paclitaxel has a long list of both approved and off-label uses in malignancies as a primary agent and in conjunction with other drugs. The FDA prescribing information for Taxol and Abraxane was last updated in April 2011 and September 2020, respectively.^20,21 The National Institutes of Health National Library of Medicine has the current update for paclitaxel on July 2023.^19,22 Thus, the prescribed information for paclitaxel referenced in the database may not always be up to date. The combinations of paclitaxel with bevacizumab, carboplatin, or carboplatin and pembrolizumab were not in the Taxol prescribing information. Likewise, a combination of nab-paclitaxel with atezolizumab or carboplatin and pembrolizumab is missing in the Abraxane prescribing information.^22-27

The generic name is not the same as a generic drug, which may have slight differences from the brand name product.⁷¹ The generic drug versions of Taxol and Abraxane have been approved by the FDA as paclitaxel injectable and paclitaxel-protein bound, respectively. There was a global shortage of nab-paclitaxel from October 2021 to June 2022 because of a manufacturing problem.⁷² During this shortage, data showed similar comments from physician documents that treatment switched to Taxol due to the Abraxane shortage.

Of 336 patients in the PDTS database with dispensed paclitaxel prescriptions, 276 received paclitaxel (year dispensed, 2013-2022), 27 received Abraxane (year dispensed, 2013-2022), 47 received Taxol (year dispensed, 2004-2015), 8 received both Abraxane and paclitaxel, and 6 received both Taxol and paclitaxel. Based on this information, it appears that the distinction between the drugs was not made in the PDTS until after 2015, 10 years after Abraxane received FDA approval. Abraxane was prescribed in the MHS in 2013, 8 years after FDA approval. There were a few comparison studies of Abraxane and Taxol.^73-76

Safety and effectiveness in pediatric patients have not been established for paclitaxel. According to the DoD Cancer Registry Program, the youngest patient was aged 2 months. In 2021, this patient was diagnosed with corpus uteri and treated with carboplatin and Taxol in course 1; in course 2, the patient reacted to Taxol; in course 3, Taxol was replaced with Abraxane; in courses 4 to 7, the patient was treated with carboplatin only.

Discontinued Treatment

Ten patients had prescribed Taxol that was changed due to AEs: 1 was switched to Abraxane and atezolizumab, 3 switched to Abraxane, 2 switched to docetaxel, 1 switched to doxorubicin, and 3 switched to pembrolizumab (based on physician’s comments). Of the 10 patients, 7 had Taxol reaction, 2 experienced disease progression, and 1 experienced high programmed death–ligand 1 expression (this patient with breast cancer was switched to Abraxane and atezolizumab during the accelerated FDA approval phase for atezolizumab, which was later revoked). Five patients were treated with carboplatin and Taxol for cancer of the anal canal (changed to pembrolizumab after disease progression), lung not otherwise specified (changed to carboplatin and pembrolizumab due to Taxol reaction), lower inner quadrant of the breast (changed to doxorubicin due to hypersensitivity reaction), corpus uteri (changed to Abraxane due to Taxol reaction), and ovary (changed to docetaxel due to Taxol reaction). Three patients were treated with doxorubicin, cyclophosphamide, and Taxol for breast cancer; 2 patients with breast cancer not otherwise specified switched to Abraxane due to cardiopulmonary hypersensitivity and Taxol reaction and 1 patient with cancer of the upper outer quadrant of the breast changed to docetaxel due to allergic reaction. One patient, who was treated with paclitaxel, ifosfamide, and cisplatin for metastasis of the lower lobe of the lung and kidney cancer, experienced complications due to infectious colitis (treated with ciprofloxacin) and then switched to pembrolizumab after the disease progressed. These AEs are known in paclitaxel medical literature on paclitaxel AEs.^19-24,77-81

Combining 2 or more treatments to target cancer-inducing or cell-sustaining pathways is a cornerstone of chemotherapy.^82-84 Most combinations are given on the same day, but some are not. For 3- or 4-drug combinations, doxorubicin and cyclophosphamide were given first, followed by paclitaxel with or withouttrastuzumab, carboplatin, or pembrolizumab. Only 16 patients (2%) were treated with paclitaxel alone; therefore, the completed and discontinued treatment groups are mostly concomitant treatment. As a result, the comparisons of the completed and discontinued treatment groups were almost the same for the diagnosis. The PDTS data have a better result because 2 exclusion criteria were applied before narrowing the analysis down to paclitaxel treatment specifically.

Antidepressants and Other Drugs

Drug response can vary from person to person and can lead to treatment failure related to AEs. One major factor in drug metabolism is CYP.⁸⁵ CYP2C8 is the major pathway for paclitaxel and CYP3A4 is the minor pathway. When evaluating the noncancer drugs, there were no reports of CYP2C8 inhibition or induction.Over the years, many DDI warnings have been issued for paclitaxel with different drugs in various electronic resources.

Oncologists follow guidelines to prevent DDIs, as paclitaxel is known to have severe, moderate, and minor interactions with other drugs. Among 687 patients, 261 (38%) were prescribed any of 14 antidepressants. Eight of these antidepressants (amitriptyline, citalopram, desipramine, doxepin, venlafaxine, escitalopram, nortriptyline, and trazodone) are metabolized, 3 (mirtazapine, sertraline, and fluoxetine) are metabolized and inhibited, 2 (bupropion and duloxetine) are neither metabolized nor inhibited, and 1 (paroxetine) is inhibited by CYP3A4. Duloxetine, venlafaxine, and trazodone were more commonly dispensed (84, 78, and 42 patients, respectively) than others (≤ 33 patients).

Of 32 patients with documented AEs,14 (44%) had 168 dispensed drugs in the PDTS database. Six patients (19%) were treated with doxorubicin and cyclophosphamide followed by paclitaxel for breast cancer; 6 (19%) were treated with carboplatin and paclitaxel for cancer of the lung (n = 3), corpus uteri (n = 2), and ovary (n = 1); 1 patient (3%) was treated with carboplatin and paclitaxel, then switched to carboplatin, bevacizumab, and paclitaxel, and then completed treatment with carboplatin and paclitaxel for an unspecified female genital cancer; and 1 patient (3%) was treated with cisplatin, ifosfamide, and paclitaxel for metastasis of the lower lobe lung and kidney cancer.

The 14 patients with PDTS data had 18 cancer drugs dispensed. Eleven had moderate interaction reports and 7 had no interaction reports. A total of 165 noncancer drugs were dispensed, of which 3 were antidepressants and had no interactions reported, 8 had moderate interactions reported, and 2 had minor interactions with Taxol and Abraxane, respectively (Table 6).^86-129

Of 3 patients who were dispensed bupropion, nortriptyline, or paroxetine, 1 patient with breast cancer was treated with doxorubicin andcyclophosphamide, followed by paclitaxel with bupropion, nortriptyline, pegfilgrastim,dexamethasone, and 17 other noncancer drugs that had no interaction report dispensed during paclitaxel treatment. Of 2 patients with lung cancer, 1 patient was treated with carboplatin and paclitaxel with nortriptyline, dexamethasone, and 13 additional medications, and the second patient was treated with paroxetine, cimetidine, dexamethasone, and 12 other medications. Patients were dispensed up to6 noncancer medications on the same day as paclitaxel administration to control the AEs, not including the prodrugs filled before the treatments. Paroxetine and cimetidine have weak inhibition, and dexamethasone has weak induction of CYP3A4. Therefore, while 1:1 DDIs might have little or no effect with weak inhibit/induce CYP3A4 drugs, 1:1:1 or more combinations could have a different outcome (confirmed in previous publications).^65-67

Dispensed on the same day may not mean taken at the same time. One patient experienced an AE with dispensed 50 mg losartan, carboplatin plus paclitaxel, dexamethasone, and 6 other noncancer drugs. Losartan inhibits paclitaxel, which can lead to negative AEs.^57,66,67 However, there were no blood or plasma samples taken to confirm the losartan was taken at the same time as the paclitaxel given this was not a clinical trial.

Conclusions

This retrospective study discusses the use of paclitaxel in the MHS and the potential DDIs associated with it. The study population consisted mostly of active-duty personnel, who are required to be healthy or have controlled or nonactive medical diagnoses and be physically fit. This group is mixed with dependents and retirees that are more reflective of the average US population. As a result, this patient population is healthier than the general population, with a lower prevalence of common illnesses such as diabetes and obesity. The study aimed to identify drugs used alongside paclitaxel treatment. While further research is needed to identify potential DDIs among patients who experienced AEs, in vitro testing will need to be conducted before confirming causality. The low number of AEs experienced by only 32 of 702 patients (5%), with no deaths during paclitaxel treatment, indicates that the drug is generally well tolerated. Although this study cannot conclude that concomitant use with noncancer drugs led to the discontinuation of paclitaxel, we can conclude that there seems to be no significant DDIsidentified between paclitaxel and antidepressants. This comprehensive overview provides clinicians with a complete picture of paclitaxel use for 27 years (1996-2022), enabling them to make informed decisions about paclitaxel treatment.

Acknowledgments

The Department of Research Program funds at Walter Reed National Military Medical Center supported this protocol. We sincerely appreciate the contribution of data extraction from the Joint Pathology Center teams (Francisco J. Rentas, John D. McGeeney, Beatriz A. Hallo, and Johnny P. Beason) and the MHS database personnel (Maj Ryan Costantino, Brandon E. Jenkins, and Alexander G. Rittel). We gratefully thank you for the protocol support from the Department of Research programs: CDR Martin L. Boese, CDR Wesley R. Campbell, Maj. Abhimanyu Chandel, CDR Ling Ye, Chelsea N. Powers, Yaling Zhou, Elizabeth Schafer, Micah Stretch, Diane Beaner, and Adrienne Woodard.

Annual lung cancer screening (LCS) with low-dose computed tomography (LDCT) of the chest has been shown to reduce mortality rates for individuals at risk for lung cancer.¹ Despite the benefits, < 5% of those who were eligible for LCS in the United States were screened in 2022.² Implementation of a LCS program in rural communities is especially challenging because they are sparsely populated, medically underserved, and located far from urban centers.^2-7 It is estimated that 1 in 5 people live in rural areas. Rates of tobacco smoking and cancer are higher in rural communities when compared with urban communities.^8,9 The scarcity of physicians in rural areas who are familiar with LCS may further impede individuals who are at risk from accessing this life saving service.^5,6 As a result, these individuals may not regularly undergo LCS as recommended.⁹

Telehealth, or the remote delivery of health care services via telecommunications, is an emerging approach for addressing unmet medical needs in rural communities and is being utilized widely by the US Department of Veterans Affairs (VA).^4,10-15 The Veterans Integrated Service Network 12 (Great Lakes Network) has established the Clinical Resource Hub (CRH), a telehealth network comprising of licensed independent physicians, nurse practitioners, registered nurses, and ancillary staff. The CRH offers regular, remote health care services to several community-based outpatient clinics (CBOC) primary care clinics located in rural northern Wisconsin and the Upper Peninsula of Michigan.^10,14

The utility of telehealth in promoting LCS among at-risk veterans living in rural communities has not been firmly established.^4-6 To address this issue, we conducted a proof-of-principle quality improvement project to determine whether a telehealth intervention would increase referrals among at-risk veterans who reside in rural northern Wisconsin and the Upper Peninsula of Michigan who are self-enrolled in a CBOC smoking cessation program in Green Bay, Wisconsin. The CBOC provides primary health care to veterans residing in rural northern Wisconsin and the Upper Peninsula of Michigan as defined by US Department of Agriculture rural-urban commuting area codes.¹⁶ The intervention aimed to refer these individuals to the closest available and centralized LCS program, which is located at the Clement J. Zablocki VA Medical Center (CJZVAMC) in Milwaukee, Wisconsin.

METHODS

We reviewed electronic health records (EHR) of LCS-eligible veterans treated by 2 authors (SH and TB) who were self-enrolled in the smoking cessation program at the Green Bay CBOC between October 1, 2020, and September 30, 2021. The program provides comprehensive evidence-based tobacco use treatment, online self-help resources, behavioral counseling, and medicines for smoking cessation.¹⁷ Veterans aged 50 to 80 years with a smoking history of ≥ 20 pack-years, who currently smoke cigarettes or quit within the past 15 years, were considered at risk for lung cancer and eligible for LCS. After confirming eligibility, pertinent demographic data were abstracted from each EHR.

Telehealth Intervention

The CJZVAMC centralized LCS program manages all delivery processes and has been previously shown to increase uptake of LCS and improve patient outcomes among veterans as compared to a decentralized approach.^18,19 In the centralized approach, eligible veterans were referred by a CBOC primary care practitioner (PCP) to a designated centralized LCS program. The centralized LCS program provides further evaluation and disposition, which includes structured and shared decision making, ordering LDCT of the chest, reporting LDCT results to the patient and PCP, devising a goal-directed care plan, and managing follow-up LDCTs as indicated (Figure 1).^18,19

This intervention was initiated before other measures aimed to increase the LCS enrollment for at-risk rural veterans at the CBOC, (eg, mailing LCS education fact sheet to veterans).²⁰ After reviewing prospective veterans’ EHRs, 1 author (TB) contacted LCS-eligible veterans by telephone and left a voicemail if contact could not be established. A second telephone call was placed within 2 months of the initial call if no call back was documented in the EHR. When verbal contact was established, the goals of the centralized LCS program were described and the veteran was invited to participate.²¹

Veterans were seen at CBOCs affiliated with CJZVAMC. The CJZVAMC LCS coordinator was notified whenever a veteran agreed to enroll into LCS and then ordered LDCT, which was performed and read at CJZVAMC. Once LDCT has been ordered, 1 author (TB) reviewed the veteran’s EHR for LDCT completion over the next 4 months.Upon conclusion of the intervention period, the number of veterans referred for LDCT and the number of LDCTs performed were recorded. Each LDCT was reviewed and coded by medical imaging clinicians according to Lung CT Screening Reporting and Data System (Lung-RADS) version 1.1 and coded as 0, 1, 2, 3, or 4 based on the nodule with the highest degree of suspicion.²² The LDCT and reports were also reviewed by pulmonary physicians at the CJZVAMC Lung Nodule Clinic with recommendations issued and reported to the PCP treating the veteran, such as annual follow-up with LDCT or referral to specialty care for further evaluation as indicated.

Of 117 veterans enrolled in the smoking cessation program at the CBOC during the intervention period, 74 (63%) were eligible to undergo LCS, and 68 (58%) were contacted by telephone (Figure 2). Eligible patients were primarily White male veterans; their mean (SD) age was 65.0 years (7.6). Participation in LCS was discussed with 41 (60%) veterans either during the initial or second telephone call of which 29 (71%) agreed to enroll and 12 (29%) declined. Veterans did not provide reasons for declining participation at the time of the telephone call.

Among the 74 eligible veterans who attended the smoking cessation program, only 3 had LDCT performed before initiation of this project (4%). At the conclusion of the telehealth intervention period, 19 veterans had LDCT performed (26%). Ten LDCTs were coded Lung-RADS 1, 7 Lung-RADS 2, 1 Lung-RADS 3, and 1 Lung-RADS 4B. In each case, annual follow-up LDCT or referral to a LCS clinician was pursued as indicated.²²

DISCUSSION

This proof-of-principle quality improvement project found that a high percentage (66%) of individuals in rural communities who were contacted via telehealth agreed to participate in a regional LCS program. The program reviewed LDCT results, ordered follow-up LDCTs, and recommended further evaluations.^18,19 Whether this centralized LCS process could also promote adherence with subsequent annual LDCT and/or scheduled clinic appointments with designated clinicians, if abnormal imaging findings are detected, remains unclear.

It has been well established LDCT LCS reduces lung cancer-specific and overall mortality rates among eligible current and former smokers.^1,9,23 The 5-year relative survival rate of veterans diagnosed with localized non-small cell lung cancer is 63%; that number drops to 7% in those with advanced disease attesting to the utility of LCS in detecting early stage lung cancer.² Despite these favorable observations, however, screening rates with free LDCT remains low in rural communities.^3-7

This proof-of-principle quality improvement project found that telehealth intervention may increase referrals of at-risk veterans who reside in rural communities to the closest centralized LCS program located at aregional VAMC. This program is responsible for reviewing the results of the initial LDCT, ordering follow-up LDCT, and recommending further evaluation as indicated.^18,19 Whether this centralized LCS process would promote adherence with subsequent annual LDCT and/or scheduled clinic appointments with designated clinicians if abnormal imaging findings are detected is yet to be determined.

We found that among 74 LCS-eligible rural veterans attending a CBOC-based smoking cessation program, only 3 (4%) underwent LDCT screening before this telehealth intervention was launched. This low LCS rate among veterans attempting to quit smoking may have been related, in part, to a lack of awareness of this intervention and/or barriers to LCS access.^7,10,21,24 Deploying a telehealth intervention targeting LCS could address this life threatening and unmet medical need in rural communities.²⁵ The results of this proof-of-principle quality improvement project support this contention with the reported increased referrals to and completion of initial LDCT within 4 months of the telehealth encounter.

This was a small, single site project composed of predominantly White male rural veterans participating in a smoking cessation program associated with a VA facility.^26,27 It is not clear whether similar outcomes would be observed in at-risk veterans who do not participate in a smoking cessation program or in more diverse communities. We were unable to contact 40% of LCS-eligible rural veterans by telephone. Twelve veterans reached by telephone declined to participate in LCS without providing a reason, and only 19 of 68 eligible veterans (28%) underwent LDCT screening during the 4-month telehealth intervention. The reasons underlying this overall low accrual rate and whether rural veterans prefer other means of personal communication regarding LCS were not determined. Lastly, generalizability of our initial observations to other veterans living in rural communities is limited because the project was conducted only in rural northern Wisconsin and the Upper Peninsula of Michigan.

Conclusions

At-risk rural veterans may be willing to participate in a centralized LCS program at a regional VA medical facility when contacted and coordinated using telehealth modalities. These findings offer support for future prospective, multisite, VA telehealth-based studies to be conducted in rural areas. The results of this project also suggest that telehealth intervention could increase referrals of at-risk rural veterans to the closest centralized LCS program located at a regional VA medical facility.

Annual lung cancer screening (LCS) with low-dose computed tomography (LDCT) of the chest has been shown to reduce mortality rates for individuals at risk for lung cancer.¹ Despite the benefits, < 5% of those who were eligible for LCS in the United States were screened in 2022.² Implementation of a LCS program in rural communities is especially challenging because they are sparsely populated, medically underserved, and located far from urban centers.^2-7 It is estimated that 1 in 5 people live in rural areas. Rates of tobacco smoking and cancer are higher in rural communities when compared with urban communities.^8,9 The scarcity of physicians in rural areas who are familiar with LCS may further impede individuals who are at risk from accessing this life saving service.^5,6 As a result, these individuals may not regularly undergo LCS as recommended.⁹

Telehealth, or the remote delivery of health care services via telecommunications, is an emerging approach for addressing unmet medical needs in rural communities and is being utilized widely by the US Department of Veterans Affairs (VA).^4,10-15 The Veterans Integrated Service Network 12 (Great Lakes Network) has established the Clinical Resource Hub (CRH), a telehealth network comprising of licensed independent physicians, nurse practitioners, registered nurses, and ancillary staff. The CRH offers regular, remote health care services to several community-based outpatient clinics (CBOC) primary care clinics located in rural northern Wisconsin and the Upper Peninsula of Michigan.^10,14

The utility of telehealth in promoting LCS among at-risk veterans living in rural communities has not been firmly established.^4-6 To address this issue, we conducted a proof-of-principle quality improvement project to determine whether a telehealth intervention would increase referrals among at-risk veterans who reside in rural northern Wisconsin and the Upper Peninsula of Michigan who are self-enrolled in a CBOC smoking cessation program in Green Bay, Wisconsin. The CBOC provides primary health care to veterans residing in rural northern Wisconsin and the Upper Peninsula of Michigan as defined by US Department of Agriculture rural-urban commuting area codes.¹⁶ The intervention aimed to refer these individuals to the closest available and centralized LCS program, which is located at the Clement J. Zablocki VA Medical Center (CJZVAMC) in Milwaukee, Wisconsin.

METHODS

We reviewed electronic health records (EHR) of LCS-eligible veterans treated by 2 authors (SH and TB) who were self-enrolled in the smoking cessation program at the Green Bay CBOC between October 1, 2020, and September 30, 2021. The program provides comprehensive evidence-based tobacco use treatment, online self-help resources, behavioral counseling, and medicines for smoking cessation.¹⁷ Veterans aged 50 to 80 years with a smoking history of ≥ 20 pack-years, who currently smoke cigarettes or quit within the past 15 years, were considered at risk for lung cancer and eligible for LCS. After confirming eligibility, pertinent demographic data were abstracted from each EHR.

Telehealth Intervention

The CJZVAMC centralized LCS program manages all delivery processes and has been previously shown to increase uptake of LCS and improve patient outcomes among veterans as compared to a decentralized approach.^18,19 In the centralized approach, eligible veterans were referred by a CBOC primary care practitioner (PCP) to a designated centralized LCS program. The centralized LCS program provides further evaluation and disposition, which includes structured and shared decision making, ordering LDCT of the chest, reporting LDCT results to the patient and PCP, devising a goal-directed care plan, and managing follow-up LDCTs as indicated (Figure 1).^18,19

This intervention was initiated before other measures aimed to increase the LCS enrollment for at-risk rural veterans at the CBOC, (eg, mailing LCS education fact sheet to veterans).²⁰ After reviewing prospective veterans’ EHRs, 1 author (TB) contacted LCS-eligible veterans by telephone and left a voicemail if contact could not be established. A second telephone call was placed within 2 months of the initial call if no call back was documented in the EHR. When verbal contact was established, the goals of the centralized LCS program were described and the veteran was invited to participate.²¹

Veterans were seen at CBOCs affiliated with CJZVAMC. The CJZVAMC LCS coordinator was notified whenever a veteran agreed to enroll into LCS and then ordered LDCT, which was performed and read at CJZVAMC. Once LDCT has been ordered, 1 author (TB) reviewed the veteran’s EHR for LDCT completion over the next 4 months.Upon conclusion of the intervention period, the number of veterans referred for LDCT and the number of LDCTs performed were recorded. Each LDCT was reviewed and coded by medical imaging clinicians according to Lung CT Screening Reporting and Data System (Lung-RADS) version 1.1 and coded as 0, 1, 2, 3, or 4 based on the nodule with the highest degree of suspicion.²² The LDCT and reports were also reviewed by pulmonary physicians at the CJZVAMC Lung Nodule Clinic with recommendations issued and reported to the PCP treating the veteran, such as annual follow-up with LDCT or referral to specialty care for further evaluation as indicated.

Of 117 veterans enrolled in the smoking cessation program at the CBOC during the intervention period, 74 (63%) were eligible to undergo LCS, and 68 (58%) were contacted by telephone (Figure 2). Eligible patients were primarily White male veterans; their mean (SD) age was 65.0 years (7.6). Participation in LCS was discussed with 41 (60%) veterans either during the initial or second telephone call of which 29 (71%) agreed to enroll and 12 (29%) declined. Veterans did not provide reasons for declining participation at the time of the telephone call.

Among the 74 eligible veterans who attended the smoking cessation program, only 3 had LDCT performed before initiation of this project (4%). At the conclusion of the telehealth intervention period, 19 veterans had LDCT performed (26%). Ten LDCTs were coded Lung-RADS 1, 7 Lung-RADS 2, 1 Lung-RADS 3, and 1 Lung-RADS 4B. In each case, annual follow-up LDCT or referral to a LCS clinician was pursued as indicated.²²

DISCUSSION

This proof-of-principle quality improvement project found that a high percentage (66%) of individuals in rural communities who were contacted via telehealth agreed to participate in a regional LCS program. The program reviewed LDCT results, ordered follow-up LDCTs, and recommended further evaluations.^18,19 Whether this centralized LCS process could also promote adherence with subsequent annual LDCT and/or scheduled clinic appointments with designated clinicians, if abnormal imaging findings are detected, remains unclear.

It has been well established LDCT LCS reduces lung cancer-specific and overall mortality rates among eligible current and former smokers.^1,9,23 The 5-year relative survival rate of veterans diagnosed with localized non-small cell lung cancer is 63%; that number drops to 7% in those with advanced disease attesting to the utility of LCS in detecting early stage lung cancer.² Despite these favorable observations, however, screening rates with free LDCT remains low in rural communities.^3-7

This proof-of-principle quality improvement project found that telehealth intervention may increase referrals of at-risk veterans who reside in rural communities to the closest centralized LCS program located at aregional VAMC. This program is responsible for reviewing the results of the initial LDCT, ordering follow-up LDCT, and recommending further evaluation as indicated.^18,19 Whether this centralized LCS process would promote adherence with subsequent annual LDCT and/or scheduled clinic appointments with designated clinicians if abnormal imaging findings are detected is yet to be determined.

We found that among 74 LCS-eligible rural veterans attending a CBOC-based smoking cessation program, only 3 (4%) underwent LDCT screening before this telehealth intervention was launched. This low LCS rate among veterans attempting to quit smoking may have been related, in part, to a lack of awareness of this intervention and/or barriers to LCS access.^7,10,21,24 Deploying a telehealth intervention targeting LCS could address this life threatening and unmet medical need in rural communities.²⁵ The results of this proof-of-principle quality improvement project support this contention with the reported increased referrals to and completion of initial LDCT within 4 months of the telehealth encounter.

This was a small, single site project composed of predominantly White male rural veterans participating in a smoking cessation program associated with a VA facility.^26,27 It is not clear whether similar outcomes would be observed in at-risk veterans who do not participate in a smoking cessation program or in more diverse communities. We were unable to contact 40% of LCS-eligible rural veterans by telephone. Twelve veterans reached by telephone declined to participate in LCS without providing a reason, and only 19 of 68 eligible veterans (28%) underwent LDCT screening during the 4-month telehealth intervention. The reasons underlying this overall low accrual rate and whether rural veterans prefer other means of personal communication regarding LCS were not determined. Lastly, generalizability of our initial observations to other veterans living in rural communities is limited because the project was conducted only in rural northern Wisconsin and the Upper Peninsula of Michigan.

Conclusions

At-risk rural veterans may be willing to participate in a centralized LCS program at a regional VA medical facility when contacted and coordinated using telehealth modalities. These findings offer support for future prospective, multisite, VA telehealth-based studies to be conducted in rural areas. The results of this project also suggest that telehealth intervention could increase referrals of at-risk rural veterans to the closest centralized LCS program located at a regional VA medical facility.

Annual lung cancer screening (LCS) with low-dose computed tomography (LDCT) of the chest has been shown to reduce mortality rates for individuals at risk for lung cancer.¹ Despite the benefits, < 5% of those who were eligible for LCS in the United States were screened in 2022.² Implementation of a LCS program in rural communities is especially challenging because they are sparsely populated, medically underserved, and located far from urban centers.^2-7 It is estimated that 1 in 5 people live in rural areas. Rates of tobacco smoking and cancer are higher in rural communities when compared with urban communities.^8,9 The scarcity of physicians in rural areas who are familiar with LCS may further impede individuals who are at risk from accessing this life saving service.^5,6 As a result, these individuals may not regularly undergo LCS as recommended.⁹

Telehealth, or the remote delivery of health care services via telecommunications, is an emerging approach for addressing unmet medical needs in rural communities and is being utilized widely by the US Department of Veterans Affairs (VA).^4,10-15 The Veterans Integrated Service Network 12 (Great Lakes Network) has established the Clinical Resource Hub (CRH), a telehealth network comprising of licensed independent physicians, nurse practitioners, registered nurses, and ancillary staff. The CRH offers regular, remote health care services to several community-based outpatient clinics (CBOC) primary care clinics located in rural northern Wisconsin and the Upper Peninsula of Michigan.^10,14

The utility of telehealth in promoting LCS among at-risk veterans living in rural communities has not been firmly established.^4-6 To address this issue, we conducted a proof-of-principle quality improvement project to determine whether a telehealth intervention would increase referrals among at-risk veterans who reside in rural northern Wisconsin and the Upper Peninsula of Michigan who are self-enrolled in a CBOC smoking cessation program in Green Bay, Wisconsin. The CBOC provides primary health care to veterans residing in rural northern Wisconsin and the Upper Peninsula of Michigan as defined by US Department of Agriculture rural-urban commuting area codes.¹⁶ The intervention aimed to refer these individuals to the closest available and centralized LCS program, which is located at the Clement J. Zablocki VA Medical Center (CJZVAMC) in Milwaukee, Wisconsin.

METHODS

We reviewed electronic health records (EHR) of LCS-eligible veterans treated by 2 authors (SH and TB) who were self-enrolled in the smoking cessation program at the Green Bay CBOC between October 1, 2020, and September 30, 2021. The program provides comprehensive evidence-based tobacco use treatment, online self-help resources, behavioral counseling, and medicines for smoking cessation.¹⁷ Veterans aged 50 to 80 years with a smoking history of ≥ 20 pack-years, who currently smoke cigarettes or quit within the past 15 years, were considered at risk for lung cancer and eligible for LCS. After confirming eligibility, pertinent demographic data were abstracted from each EHR.

Telehealth Intervention

The CJZVAMC centralized LCS program manages all delivery processes and has been previously shown to increase uptake of LCS and improve patient outcomes among veterans as compared to a decentralized approach.^18,19 In the centralized approach, eligible veterans were referred by a CBOC primary care practitioner (PCP) to a designated centralized LCS program. The centralized LCS program provides further evaluation and disposition, which includes structured and shared decision making, ordering LDCT of the chest, reporting LDCT results to the patient and PCP, devising a goal-directed care plan, and managing follow-up LDCTs as indicated (Figure 1).^18,19

This intervention was initiated before other measures aimed to increase the LCS enrollment for at-risk rural veterans at the CBOC, (eg, mailing LCS education fact sheet to veterans).²⁰ After reviewing prospective veterans’ EHRs, 1 author (TB) contacted LCS-eligible veterans by telephone and left a voicemail if contact could not be established. A second telephone call was placed within 2 months of the initial call if no call back was documented in the EHR. When verbal contact was established, the goals of the centralized LCS program were described and the veteran was invited to participate.²¹

Veterans were seen at CBOCs affiliated with CJZVAMC. The CJZVAMC LCS coordinator was notified whenever a veteran agreed to enroll into LCS and then ordered LDCT, which was performed and read at CJZVAMC. Once LDCT has been ordered, 1 author (TB) reviewed the veteran’s EHR for LDCT completion over the next 4 months.Upon conclusion of the intervention period, the number of veterans referred for LDCT and the number of LDCTs performed were recorded. Each LDCT was reviewed and coded by medical imaging clinicians according to Lung CT Screening Reporting and Data System (Lung-RADS) version 1.1 and coded as 0, 1, 2, 3, or 4 based on the nodule with the highest degree of suspicion.²² The LDCT and reports were also reviewed by pulmonary physicians at the CJZVAMC Lung Nodule Clinic with recommendations issued and reported to the PCP treating the veteran, such as annual follow-up with LDCT or referral to specialty care for further evaluation as indicated.

Of 117 veterans enrolled in the smoking cessation program at the CBOC during the intervention period, 74 (63%) were eligible to undergo LCS, and 68 (58%) were contacted by telephone (Figure 2). Eligible patients were primarily White male veterans; their mean (SD) age was 65.0 years (7.6). Participation in LCS was discussed with 41 (60%) veterans either during the initial or second telephone call of which 29 (71%) agreed to enroll and 12 (29%) declined. Veterans did not provide reasons for declining participation at the time of the telephone call.

Among the 74 eligible veterans who attended the smoking cessation program, only 3 had LDCT performed before initiation of this project (4%). At the conclusion of the telehealth intervention period, 19 veterans had LDCT performed (26%). Ten LDCTs were coded Lung-RADS 1, 7 Lung-RADS 2, 1 Lung-RADS 3, and 1 Lung-RADS 4B. In each case, annual follow-up LDCT or referral to a LCS clinician was pursued as indicated.²²

DISCUSSION

This proof-of-principle quality improvement project found that a high percentage (66%) of individuals in rural communities who were contacted via telehealth agreed to participate in a regional LCS program. The program reviewed LDCT results, ordered follow-up LDCTs, and recommended further evaluations.^18,19 Whether this centralized LCS process could also promote adherence with subsequent annual LDCT and/or scheduled clinic appointments with designated clinicians, if abnormal imaging findings are detected, remains unclear.

It has been well established LDCT LCS reduces lung cancer-specific and overall mortality rates among eligible current and former smokers.^1,9,23 The 5-year relative survival rate of veterans diagnosed with localized non-small cell lung cancer is 63%; that number drops to 7% in those with advanced disease attesting to the utility of LCS in detecting early stage lung cancer.² Despite these favorable observations, however, screening rates with free LDCT remains low in rural communities.^3-7

This proof-of-principle quality improvement project found that telehealth intervention may increase referrals of at-risk veterans who reside in rural communities to the closest centralized LCS program located at aregional VAMC. This program is responsible for reviewing the results of the initial LDCT, ordering follow-up LDCT, and recommending further evaluation as indicated.^18,19 Whether this centralized LCS process would promote adherence with subsequent annual LDCT and/or scheduled clinic appointments with designated clinicians if abnormal imaging findings are detected is yet to be determined.

We found that among 74 LCS-eligible rural veterans attending a CBOC-based smoking cessation program, only 3 (4%) underwent LDCT screening before this telehealth intervention was launched. This low LCS rate among veterans attempting to quit smoking may have been related, in part, to a lack of awareness of this intervention and/or barriers to LCS access.^7,10,21,24 Deploying a telehealth intervention targeting LCS could address this life threatening and unmet medical need in rural communities.²⁵ The results of this proof-of-principle quality improvement project support this contention with the reported increased referrals to and completion of initial LDCT within 4 months of the telehealth encounter.

This was a small, single site project composed of predominantly White male rural veterans participating in a smoking cessation program associated with a VA facility.^26,27 It is not clear whether similar outcomes would be observed in at-risk veterans who do not participate in a smoking cessation program or in more diverse communities. We were unable to contact 40% of LCS-eligible rural veterans by telephone. Twelve veterans reached by telephone declined to participate in LCS without providing a reason, and only 19 of 68 eligible veterans (28%) underwent LDCT screening during the 4-month telehealth intervention. The reasons underlying this overall low accrual rate and whether rural veterans prefer other means of personal communication regarding LCS were not determined. Lastly, generalizability of our initial observations to other veterans living in rural communities is limited because the project was conducted only in rural northern Wisconsin and the Upper Peninsula of Michigan.

Conclusions

At-risk rural veterans may be willing to participate in a centralized LCS program at a regional VA medical facility when contacted and coordinated using telehealth modalities. These findings offer support for future prospective, multisite, VA telehealth-based studies to be conducted in rural areas. The results of this project also suggest that telehealth intervention could increase referrals of at-risk rural veterans to the closest centralized LCS program located at a regional VA medical facility.

The value of a hematology/oncology clinical pharmacy practitioner (CPP) has been validated in several studies documenting their positive impact on patient outcomes, supportive care management, laboratory monitoring, medication error identification, and drug expenditure.^1-6 With> 200 oncology-related US Food and Drug Administration approval notifications published from 2020 to 2023, it is no surprise that national trends in oncology drug clinic expenditures increased from $39.9 billion in 2020 to $44.1 billion in 2021.^7,8 With the rapidly changing treatment landscape, new drug approvals, and risk of polypharmacy, oral anticancer agents carry a high risk for medication errors.⁴ Additional challenges include complex dosing regimens and instructions, adherence issues, drug interactions, adjustments for organ dysfunction, and extensive adverse effect (AE) profiles.

Because of the niche and complexity of oral anticancer agents, trained CPPs havehematology/oncology education and expertise that pharmacists without specialized training lack. A survey of 243 nonspecialized community pharmacists that assessed their knowledge of oral anticancer therapies revealed that only about half of the knowledge questions were answered correctly, illustrating an education gap among these pharmacists.⁹ The Hematology/Oncology Pharmacist Association's suggests that best practices for managing oral oncology therapy should include comprehensive medication review by an oncology-trained pharmacist for each prescription.¹⁰

The US Department of Veterans Affairs (VA) community care network, which was established by the MISSION Act, allows covered access for eligible veterans in the local community outside of the VA network. Unfortunately, this dual-system use of health care could increase the risk of poorly coordinated care and has been associated with the risk of inappropriate prescribing.^11,12 It is unclear how many private practices enrolled in the community care program have access to oncology-trained pharmacists. Specialized pharmaceutical reviews of oral anticancer medication prescriptions from these practices are vital for veteran care. This study evaluates the clinical and financial interventions of hematology/oncology CPPs review of specialty hematology/oncology prescriptions from community care health care practitioners (HCPs) at the Veterans Affairs North Texas Health Care System (VANTHCS) in Dallas.

METHODS

This study is a retrospective review of Computerized Patient Record System (CPRS) records of patients at VANTHCS from January 1, 2015, to June 30, 2023. Patients included were aged ≥ 18 years, enrolled in the VA community care program, received a specialty hematology/oncology medication that was dispensed through VA pharmacies or VA-contracted pharmacies, and had an hematology/oncology CPP medication review documented in CPRS. The primary aim of this study was to assess the number and types of clinical interventions performed. A clinical intervention was defined as a documented communication attempt with a community care HCP or direct communication with a patient to address a specific medication-related issue noted during CPP review.

Review of specialty hematology/oncology medications by a hematology/oncology CPP included evaluation of therapy indication, such as whether the prescription meets clinical guidelines, VA criteria for use, or other clinical literature as judged appropriate by the CPP. In some cases, the CPP requested that the community care HCP prescribe a more cost-effective or formulary-preferred agent. Each prescription was reviewed for dosage and formulation appropriateness, drug interactions with available medication lists, baseline laboratory test completion, and recommended supportive care medicines. At times, patient counseling is completed as part of the clinical review. When necessary, CPPs could discuss patient cases with a VA-employed oncologist for further oversight regarding appropriateness and safety. Secondary outcomes included the number of interventions accepted or denied by the prescriber provider and cost savings.

Data collected included the type of malignancy, hematology/oncology specialty medication requested, number and type of interventions sent to the community care prescriber, number of interventions accepted or denied by the community care prescriber, and whether the CPP conducted patient counseling or dispensed or denied the product. Cost savings were calculated for medications that were denied or changed to a formulary preferred or cost-effective agent using pricing data from the National Acquisition Center Contract Catalog or Federal Supply Schedule Service as of April 2024.

A total of 221 hematology/oncology prescriptions met inclusion criteria. Among patients receiving these prescriptions, the median age was 70 years and 91% were male. The most common malignancies included 31 instances of multiple myeloma (14%), 26 for chronic lymphocytic leukemia (12%), 24 for prostate cancer (11%), 23 for glioblastoma/brain cancer (10%), 18 for renal cell carcinoma (8%), 17 for colorectal cancer (8%), and 15 for acute myeloid leukemia (7%). Clinical interventions by the hematology/oncology CPP were completed for 82 (37%) of the 221 prescriptions. One clinical intervention was communicated directly to the patient, and attempts were made to communicate with the community care HCP for the remaining 81 prescriptions. The CPP documented 97 clinical interventions for the 82 prescriptions (Table 1). The most commonly documented clinical interventions included: 25 for managing/preventing a drug interaction (26%), 24 for dose adjustment request (25%), 13 for prescription denial (13%), and 11 for requesting the use of a preferred or more cost-effective product (11%). Of note, 16 patients (7%) received counseling from the hematology/oncology CPP. Ten patients (5%) received counseling alone with no other intervention and did not meet the definition of a clinical intervention.

The most frequent prescriptions requiring intervention included 8 for enzalutamide, 7 for venetoclax, 6 for ibrutinib, and 5 each for lenalidomide, cabozantinib, and temozolomide. Among the 97 interventions, 68 were approved (70%), 15 received no response (16%), and 14 were denied by the community care HCP (14%). Despite obtaining no response or intervention denial from the community care HCP, hematology/oncology CPPs could approve these prescriptions if clinically appropriate, and their reasoning was documented. Table 2 further describes the types of interventions that were denied or obtained no response by the community care practitioner. Among the prescriptions denied by the hematology/oncology CPP, 11 were rejected for off-label indications and/or did not have support through primary literature, national guidelines, or VA criteria for use. Only 2 prescriptions were denied for safety concerns.

These documented clinical interventions had financial implications. For drugs with available cost data, requesting the use of a preferred/cost-effective product led to estimated savings of at least $263,536 over the study period with some ongoing cost savings. Prescription denials led to further estimated savings of $186,275 per month, although this is limited by the lack of known costs of alternative therapies the community care physicians chose.

DISCUSSION

More than one-third of prescriptions required clinical interventions, and 70% of these interventions were accepted by the community care prescriber, demonstrating the CPP’s essential role. Results indicate that most CPP clinical interventions involved clarifying and correcting doses, managing pertinent drug interactions, and ensuring appropriate use of medications according to clinical and national VA guidelines. Other studies have examined the impact of CPPs on patient care and cancer treatment.^5,6 The randomized, multicenter AMBORA trial found that clinical pharmacist support reduced severe AEs and medication errors related to oral anticancer agents.⁵ The per-patient mean number of medication errors found by pharmacist review was 1.7 (range, 0 to 9), with most medication errors noted at the prescribing stage.⁵ Suzuki and colleagues analyzed data from 35,062 chemotherapy regimens and found that 53.1% of the chemotherapy prescriptions were modified because of pharmacist interventions.⁶ The most common reason for prescription modifications was prescription error.

Most of the clinical interventions in this study were accepted by community HCPs, indicating that these prescribers are receptive to hematology/oncology CPP input. Among those with no response, most were in relation to recommendations regarding drug interactions. In most of these cases, the drug interaction was not clinically concerning enough to require a response before the CPP approved the prescription. Therefore, it is unknown whether the outside HCP implemented the clinical recommendations. The most common types of clinical interventions the community care HCP declined were dose adjustment requests or requests to switch to a more cost-effective/formulary-preferred agent. In these cases, the prescriber’s preference was documented and, if clinically appropriate, approved by the CPP.

Although the financial implications of CPP clinical interventions were only marginally evaluated in this review, results suggest that cost savings by requests to switch to a cost-effective/formulary preferred agent or prescription denials are substantial. Because of changes in prescription costs over time, it is possible that savings from CPP intervention were greater than calculations using current Federal Supply Schedule Service pricing. The total impact of CPP prescription interventions on reducing or preventing hospitalizations or AEs is not known from this review, but other data suggest that cost savings may benefit the system.^13,14

Limitations

This study's retrospective design is a limitation because practice patterns at the VANTHCS involving multiple hematology/oncology CPPs review of community care prescriptions might have evolved over time. The total financial implications of CPP interventions cannot fully be elucidated. The cost of alternative therapies used for patients who received a prescription denial is not factored into this review.

Conclusions

VANTHCS CPPs played an essential role in reviewing anticancer medication prescriptions from community care prescribers. In this study, CPP clinical interventions were completed for more than one-third of the prescriptions and the community-based HCP approved most of these interventions. These changes also resulted in financial benefits.

These findings add to the body of literature emphasizing the need for hematology/oncology-trained CPPs to review anticancer prescriptions and treatment plans. Our review could be used to justify CPP involvement in community care specialty medication review at VA facilities that do not currently have CPP involvement.

The value of a hematology/oncology clinical pharmacy practitioner (CPP) has been validated in several studies documenting their positive impact on patient outcomes, supportive care management, laboratory monitoring, medication error identification, and drug expenditure.^1-6 With> 200 oncology-related US Food and Drug Administration approval notifications published from 2020 to 2023, it is no surprise that national trends in oncology drug clinic expenditures increased from $39.9 billion in 2020 to $44.1 billion in 2021.^7,8 With the rapidly changing treatment landscape, new drug approvals, and risk of polypharmacy, oral anticancer agents carry a high risk for medication errors.⁴ Additional challenges include complex dosing regimens and instructions, adherence issues, drug interactions, adjustments for organ dysfunction, and extensive adverse effect (AE) profiles.

Because of the niche and complexity of oral anticancer agents, trained CPPs havehematology/oncology education and expertise that pharmacists without specialized training lack. A survey of 243 nonspecialized community pharmacists that assessed their knowledge of oral anticancer therapies revealed that only about half of the knowledge questions were answered correctly, illustrating an education gap among these pharmacists.⁹ The Hematology/Oncology Pharmacist Association's suggests that best practices for managing oral oncology therapy should include comprehensive medication review by an oncology-trained pharmacist for each prescription.¹⁰

The US Department of Veterans Affairs (VA) community care network, which was established by the MISSION Act, allows covered access for eligible veterans in the local community outside of the VA network. Unfortunately, this dual-system use of health care could increase the risk of poorly coordinated care and has been associated with the risk of inappropriate prescribing.^11,12 It is unclear how many private practices enrolled in the community care program have access to oncology-trained pharmacists. Specialized pharmaceutical reviews of oral anticancer medication prescriptions from these practices are vital for veteran care. This study evaluates the clinical and financial interventions of hematology/oncology CPPs review of specialty hematology/oncology prescriptions from community care health care practitioners (HCPs) at the Veterans Affairs North Texas Health Care System (VANTHCS) in Dallas.

METHODS

This study is a retrospective review of Computerized Patient Record System (CPRS) records of patients at VANTHCS from January 1, 2015, to June 30, 2023. Patients included were aged ≥ 18 years, enrolled in the VA community care program, received a specialty hematology/oncology medication that was dispensed through VA pharmacies or VA-contracted pharmacies, and had an hematology/oncology CPP medication review documented in CPRS. The primary aim of this study was to assess the number and types of clinical interventions performed. A clinical intervention was defined as a documented communication attempt with a community care HCP or direct communication with a patient to address a specific medication-related issue noted during CPP review.

Review of specialty hematology/oncology medications by a hematology/oncology CPP included evaluation of therapy indication, such as whether the prescription meets clinical guidelines, VA criteria for use, or other clinical literature as judged appropriate by the CPP. In some cases, the CPP requested that the community care HCP prescribe a more cost-effective or formulary-preferred agent. Each prescription was reviewed for dosage and formulation appropriateness, drug interactions with available medication lists, baseline laboratory test completion, and recommended supportive care medicines. At times, patient counseling is completed as part of the clinical review. When necessary, CPPs could discuss patient cases with a VA-employed oncologist for further oversight regarding appropriateness and safety. Secondary outcomes included the number of interventions accepted or denied by the prescriber provider and cost savings.

Data collected included the type of malignancy, hematology/oncology specialty medication requested, number and type of interventions sent to the community care prescriber, number of interventions accepted or denied by the community care prescriber, and whether the CPP conducted patient counseling or dispensed or denied the product. Cost savings were calculated for medications that were denied or changed to a formulary preferred or cost-effective agent using pricing data from the National Acquisition Center Contract Catalog or Federal Supply Schedule Service as of April 2024.

A total of 221 hematology/oncology prescriptions met inclusion criteria. Among patients receiving these prescriptions, the median age was 70 years and 91% were male. The most common malignancies included 31 instances of multiple myeloma (14%), 26 for chronic lymphocytic leukemia (12%), 24 for prostate cancer (11%), 23 for glioblastoma/brain cancer (10%), 18 for renal cell carcinoma (8%), 17 for colorectal cancer (8%), and 15 for acute myeloid leukemia (7%). Clinical interventions by the hematology/oncology CPP were completed for 82 (37%) of the 221 prescriptions. One clinical intervention was communicated directly to the patient, and attempts were made to communicate with the community care HCP for the remaining 81 prescriptions. The CPP documented 97 clinical interventions for the 82 prescriptions (Table 1). The most commonly documented clinical interventions included: 25 for managing/preventing a drug interaction (26%), 24 for dose adjustment request (25%), 13 for prescription denial (13%), and 11 for requesting the use of a preferred or more cost-effective product (11%). Of note, 16 patients (7%) received counseling from the hematology/oncology CPP. Ten patients (5%) received counseling alone with no other intervention and did not meet the definition of a clinical intervention.

The most frequent prescriptions requiring intervention included 8 for enzalutamide, 7 for venetoclax, 6 for ibrutinib, and 5 each for lenalidomide, cabozantinib, and temozolomide. Among the 97 interventions, 68 were approved (70%), 15 received no response (16%), and 14 were denied by the community care HCP (14%). Despite obtaining no response or intervention denial from the community care HCP, hematology/oncology CPPs could approve these prescriptions if clinically appropriate, and their reasoning was documented. Table 2 further describes the types of interventions that were denied or obtained no response by the community care practitioner. Among the prescriptions denied by the hematology/oncology CPP, 11 were rejected for off-label indications and/or did not have support through primary literature, national guidelines, or VA criteria for use. Only 2 prescriptions were denied for safety concerns.

These documented clinical interventions had financial implications. For drugs with available cost data, requesting the use of a preferred/cost-effective product led to estimated savings of at least $263,536 over the study period with some ongoing cost savings. Prescription denials led to further estimated savings of $186,275 per month, although this is limited by the lack of known costs of alternative therapies the community care physicians chose.

DISCUSSION

More than one-third of prescriptions required clinical interventions, and 70% of these interventions were accepted by the community care prescriber, demonstrating the CPP’s essential role. Results indicate that most CPP clinical interventions involved clarifying and correcting doses, managing pertinent drug interactions, and ensuring appropriate use of medications according to clinical and national VA guidelines. Other studies have examined the impact of CPPs on patient care and cancer treatment.^5,6 The randomized, multicenter AMBORA trial found that clinical pharmacist support reduced severe AEs and medication errors related to oral anticancer agents.⁵ The per-patient mean number of medication errors found by pharmacist review was 1.7 (range, 0 to 9), with most medication errors noted at the prescribing stage.⁵ Suzuki and colleagues analyzed data from 35,062 chemotherapy regimens and found that 53.1% of the chemotherapy prescriptions were modified because of pharmacist interventions.⁶ The most common reason for prescription modifications was prescription error.

Most of the clinical interventions in this study were accepted by community HCPs, indicating that these prescribers are receptive to hematology/oncology CPP input. Among those with no response, most were in relation to recommendations regarding drug interactions. In most of these cases, the drug interaction was not clinically concerning enough to require a response before the CPP approved the prescription. Therefore, it is unknown whether the outside HCP implemented the clinical recommendations. The most common types of clinical interventions the community care HCP declined were dose adjustment requests or requests to switch to a more cost-effective/formulary-preferred agent. In these cases, the prescriber’s preference was documented and, if clinically appropriate, approved by the CPP.

Although the financial implications of CPP clinical interventions were only marginally evaluated in this review, results suggest that cost savings by requests to switch to a cost-effective/formulary preferred agent or prescription denials are substantial. Because of changes in prescription costs over time, it is possible that savings from CPP intervention were greater than calculations using current Federal Supply Schedule Service pricing. The total impact of CPP prescription interventions on reducing or preventing hospitalizations or AEs is not known from this review, but other data suggest that cost savings may benefit the system.^13,14

Limitations

This study's retrospective design is a limitation because practice patterns at the VANTHCS involving multiple hematology/oncology CPPs review of community care prescriptions might have evolved over time. The total financial implications of CPP interventions cannot fully be elucidated. The cost of alternative therapies used for patients who received a prescription denial is not factored into this review.

Conclusions

VANTHCS CPPs played an essential role in reviewing anticancer medication prescriptions from community care prescribers. In this study, CPP clinical interventions were completed for more than one-third of the prescriptions and the community-based HCP approved most of these interventions. These changes also resulted in financial benefits.

These findings add to the body of literature emphasizing the need for hematology/oncology-trained CPPs to review anticancer prescriptions and treatment plans. Our review could be used to justify CPP involvement in community care specialty medication review at VA facilities that do not currently have CPP involvement.

The value of a hematology/oncology clinical pharmacy practitioner (CPP) has been validated in several studies documenting their positive impact on patient outcomes, supportive care management, laboratory monitoring, medication error identification, and drug expenditure.^1-6 With> 200 oncology-related US Food and Drug Administration approval notifications published from 2020 to 2023, it is no surprise that national trends in oncology drug clinic expenditures increased from $39.9 billion in 2020 to $44.1 billion in 2021.^7,8 With the rapidly changing treatment landscape, new drug approvals, and risk of polypharmacy, oral anticancer agents carry a high risk for medication errors.⁴ Additional challenges include complex dosing regimens and instructions, adherence issues, drug interactions, adjustments for organ dysfunction, and extensive adverse effect (AE) profiles.

Because of the niche and complexity of oral anticancer agents, trained CPPs havehematology/oncology education and expertise that pharmacists without specialized training lack. A survey of 243 nonspecialized community pharmacists that assessed their knowledge of oral anticancer therapies revealed that only about half of the knowledge questions were answered correctly, illustrating an education gap among these pharmacists.⁹ The Hematology/Oncology Pharmacist Association's suggests that best practices for managing oral oncology therapy should include comprehensive medication review by an oncology-trained pharmacist for each prescription.¹⁰

The US Department of Veterans Affairs (VA) community care network, which was established by the MISSION Act, allows covered access for eligible veterans in the local community outside of the VA network. Unfortunately, this dual-system use of health care could increase the risk of poorly coordinated care and has been associated with the risk of inappropriate prescribing.^11,12 It is unclear how many private practices enrolled in the community care program have access to oncology-trained pharmacists. Specialized pharmaceutical reviews of oral anticancer medication prescriptions from these practices are vital for veteran care. This study evaluates the clinical and financial interventions of hematology/oncology CPPs review of specialty hematology/oncology prescriptions from community care health care practitioners (HCPs) at the Veterans Affairs North Texas Health Care System (VANTHCS) in Dallas.

METHODS

This study is a retrospective review of Computerized Patient Record System (CPRS) records of patients at VANTHCS from January 1, 2015, to June 30, 2023. Patients included were aged ≥ 18 years, enrolled in the VA community care program, received a specialty hematology/oncology medication that was dispensed through VA pharmacies or VA-contracted pharmacies, and had an hematology/oncology CPP medication review documented in CPRS. The primary aim of this study was to assess the number and types of clinical interventions performed. A clinical intervention was defined as a documented communication attempt with a community care HCP or direct communication with a patient to address a specific medication-related issue noted during CPP review.

Review of specialty hematology/oncology medications by a hematology/oncology CPP included evaluation of therapy indication, such as whether the prescription meets clinical guidelines, VA criteria for use, or other clinical literature as judged appropriate by the CPP. In some cases, the CPP requested that the community care HCP prescribe a more cost-effective or formulary-preferred agent. Each prescription was reviewed for dosage and formulation appropriateness, drug interactions with available medication lists, baseline laboratory test completion, and recommended supportive care medicines. At times, patient counseling is completed as part of the clinical review. When necessary, CPPs could discuss patient cases with a VA-employed oncologist for further oversight regarding appropriateness and safety. Secondary outcomes included the number of interventions accepted or denied by the prescriber provider and cost savings.

Data collected included the type of malignancy, hematology/oncology specialty medication requested, number and type of interventions sent to the community care prescriber, number of interventions accepted or denied by the community care prescriber, and whether the CPP conducted patient counseling or dispensed or denied the product. Cost savings were calculated for medications that were denied or changed to a formulary preferred or cost-effective agent using pricing data from the National Acquisition Center Contract Catalog or Federal Supply Schedule Service as of April 2024.

A total of 221 hematology/oncology prescriptions met inclusion criteria. Among patients receiving these prescriptions, the median age was 70 years and 91% were male. The most common malignancies included 31 instances of multiple myeloma (14%), 26 for chronic lymphocytic leukemia (12%), 24 for prostate cancer (11%), 23 for glioblastoma/brain cancer (10%), 18 for renal cell carcinoma (8%), 17 for colorectal cancer (8%), and 15 for acute myeloid leukemia (7%). Clinical interventions by the hematology/oncology CPP were completed for 82 (37%) of the 221 prescriptions. One clinical intervention was communicated directly to the patient, and attempts were made to communicate with the community care HCP for the remaining 81 prescriptions. The CPP documented 97 clinical interventions for the 82 prescriptions (Table 1). The most commonly documented clinical interventions included: 25 for managing/preventing a drug interaction (26%), 24 for dose adjustment request (25%), 13 for prescription denial (13%), and 11 for requesting the use of a preferred or more cost-effective product (11%). Of note, 16 patients (7%) received counseling from the hematology/oncology CPP. Ten patients (5%) received counseling alone with no other intervention and did not meet the definition of a clinical intervention.

The most frequent prescriptions requiring intervention included 8 for enzalutamide, 7 for venetoclax, 6 for ibrutinib, and 5 each for lenalidomide, cabozantinib, and temozolomide. Among the 97 interventions, 68 were approved (70%), 15 received no response (16%), and 14 were denied by the community care HCP (14%). Despite obtaining no response or intervention denial from the community care HCP, hematology/oncology CPPs could approve these prescriptions if clinically appropriate, and their reasoning was documented. Table 2 further describes the types of interventions that were denied or obtained no response by the community care practitioner. Among the prescriptions denied by the hematology/oncology CPP, 11 were rejected for off-label indications and/or did not have support through primary literature, national guidelines, or VA criteria for use. Only 2 prescriptions were denied for safety concerns.

These documented clinical interventions had financial implications. For drugs with available cost data, requesting the use of a preferred/cost-effective product led to estimated savings of at least $263,536 over the study period with some ongoing cost savings. Prescription denials led to further estimated savings of $186,275 per month, although this is limited by the lack of known costs of alternative therapies the community care physicians chose.

DISCUSSION

More than one-third of prescriptions required clinical interventions, and 70% of these interventions were accepted by the community care prescriber, demonstrating the CPP’s essential role. Results indicate that most CPP clinical interventions involved clarifying and correcting doses, managing pertinent drug interactions, and ensuring appropriate use of medications according to clinical and national VA guidelines. Other studies have examined the impact of CPPs on patient care and cancer treatment.^5,6 The randomized, multicenter AMBORA trial found that clinical pharmacist support reduced severe AEs and medication errors related to oral anticancer agents.⁵ The per-patient mean number of medication errors found by pharmacist review was 1.7 (range, 0 to 9), with most medication errors noted at the prescribing stage.⁵ Suzuki and colleagues analyzed data from 35,062 chemotherapy regimens and found that 53.1% of the chemotherapy prescriptions were modified because of pharmacist interventions.⁶ The most common reason for prescription modifications was prescription error.

Most of the clinical interventions in this study were accepted by community HCPs, indicating that these prescribers are receptive to hematology/oncology CPP input. Among those with no response, most were in relation to recommendations regarding drug interactions. In most of these cases, the drug interaction was not clinically concerning enough to require a response before the CPP approved the prescription. Therefore, it is unknown whether the outside HCP implemented the clinical recommendations. The most common types of clinical interventions the community care HCP declined were dose adjustment requests or requests to switch to a more cost-effective/formulary-preferred agent. In these cases, the prescriber’s preference was documented and, if clinically appropriate, approved by the CPP.

Although the financial implications of CPP clinical interventions were only marginally evaluated in this review, results suggest that cost savings by requests to switch to a cost-effective/formulary preferred agent or prescription denials are substantial. Because of changes in prescription costs over time, it is possible that savings from CPP intervention were greater than calculations using current Federal Supply Schedule Service pricing. The total impact of CPP prescription interventions on reducing or preventing hospitalizations or AEs is not known from this review, but other data suggest that cost savings may benefit the system.^13,14

Limitations

This study's retrospective design is a limitation because practice patterns at the VANTHCS involving multiple hematology/oncology CPPs review of community care prescriptions might have evolved over time. The total financial implications of CPP interventions cannot fully be elucidated. The cost of alternative therapies used for patients who received a prescription denial is not factored into this review.

Conclusions

VANTHCS CPPs played an essential role in reviewing anticancer medication prescriptions from community care prescribers. In this study, CPP clinical interventions were completed for more than one-third of the prescriptions and the community-based HCP approved most of these interventions. These changes also resulted in financial benefits.

These findings add to the body of literature emphasizing the need for hematology/oncology-trained CPPs to review anticancer prescriptions and treatment plans. Our review could be used to justify CPP involvement in community care specialty medication review at VA facilities that do not currently have CPP involvement.

Respiratory diseases have varied clinical presentations and are classified as restrictive, obstructive, mixed, or normal. Restrictive lung diseases have reduced lung volumes, either due to an alteration in lung parenchyma or a disease of the pleura, chest wall, or neuromuscular apparatus. If caused by parenchymal lung disease, restrictive lung disorders are accompanied by reduced gas transfer, which may be portrayed clinically by desaturation after exercise. Based on anatomical structures, the causes of lung volume reduction may be intrinsic or extrinsic. Intrinsic causes correspond to diseases of the lung parenchyma, such as idiopathic fibrotic diseases, connective-tissue diseases, drug-induced lung diseases, and other primary diseases of the lungs. Extrinsic causes refer to disorders outside the lungs or extra-pulmonary diseases such as neuromuscular and nonmuscular diseases of the chest wall.¹ For example, obesity and myasthenia gravis can cause restrictive lung diseases, one through mechanical interference of lung expansion and the other through neuromuscular impedance of thoracic cage expansion. All these diseases eventually result in lung restriction, impaired lung function, and respiratory failure. This heterogenicity of disease makes establishing a single severity criterion difficult.

Laboratory testing, imaging studies, and examinations are important for determining the pulmonary disease and its course and progression. The pulmonary function test (PFT), which consists of multiple procedures that are performed depending on the information needed, has been an essential tool in practice for the pulmonologist. The PFT includes spirometry, lung volume measurement, respiratory muscle strength, diffusion capacity, and a broncho-provocation test. Each test has a particular role in assisting the diagnosis and/or follow-up of the patient. Spirometry is frequently used due to its range of dynamic physiological parameters, ease of use, and accessibility. It is used for the diagnosis of pulmonary symptoms, in the assessment of disability, and preoperatory evaluation, including lung resection surgery, assisting in the diagnosis, monitoring, and therapy response of pulmonary diseases.

A systematic approach to PFT interpretation is recommended by several societies, such as the American Thoracic Society (ATS) and the European Respiratory Society (ERS).² The pulmonary function test results must be reproducible and meet established standards to ensure reliable and consistent clinical outcomes. A restrictive respiratory disease is defined by a decrease in total lung capacity (TLC) (< 5% of predicted value) and a normal forced expiratory volume in 1 second (FEV₁)/forced vital capacity (FVC) ratio.² Although other findings—such as a decrease in vital capacity—should prompt an investigation into whether the patient has a possible restrictive respiratory disease, the sole presence of this parameter is not definitive or diagnostic of a restrictive impairment.^2-4 The assessment of severity is typically determined by TLC. Unfortunately, the severity of a restrictive respiratory disease and the degree of patient discomfort do not always correlate when utilizing just TLC. Pulmonary sarcoidosis, for example, is a granulomatous lung disease with a restrictive PFT pattern and a disease burden that may vary over time. Having a more consistent method of grading the severity of the restrictive lung disease may help guide treatment. The modified Medical Research Council (mMRC) scale, a 5-point dyspnea scale, is widely used in assessing the severity of dyspnea in various respiratory conditions, including chronic obstructive pulmonary disease (COPD), where its scores have been associated with patient mortality.^1,5 The goal of this study was to document the associations between objective parameters obtained through PFT and other variables, with an established measurement of dyspnea to assess the severity grade of restrictive lung diseases.

Methods

This retrospective record review at the Veterans Affairs Caribbean Healthcare System (VACHS) in San Juan, Puerto Rico, wasconducted using the Veterans Health Information Systems and Technology Architecture to identify patients with a PFT, including spirometry, that indicated a restrictive ventilator pattern based on the current ATS/ERS Task Force on Lung Function Testing.² Patients were included if they were aged ≥ 21 years, PFT with TLC ≤ 80% predicted, mMRC score documented on PFT, and documented diffusing capacity of the lung for carbon monoxide (DLCO). Patients were excluded if their FEV₁/vital capacity (VC) was < 70% predicted using the largest VC, or no mMRC score was available. All patients meeting the inclusion criteria were considered regardless of comorbidities.

The PFT results of all adult patients, including those performed between June 1, 2013, and January 6, 2016, were submitted to spirometry, and lung volume measurements were analyzed. Sociodemographic information was collected, including sex, ethnicity, age, height, weight, and basal metabolic index. Other data found in PFTs, such as smoking status, smoking in packs/year, mMRC score, predicted TLC value, imaging present (chest X-ray, computed tomography), and hospitalizations and exacerbations within 1 year were collected. In addition, we examined the predicted values for FEV₁, DLCO, and DLCO/VA (calculated using the Ayer equation), FVC (calculated using the Knudson equation), expiratory reserve volume, inspiratory VC, and slow VC. PaO₂, PaCO₂, and Alveolar-arterial gradients also were collected.^6-9 Information about heart failure status was gathered through medical evaluation of notes and cardiac studies. All categorical variables were correlated with Spearman analysis and quantitative variables with average percentages. P values were calculated with analysis of variance.

Of 6461 VACHS patient records reviewed, 415 met the inclusion criteria. Patients were divided according to their mMRC score: 65 had mMRC score of 0, 87 had an mMRC score of 1, 2 had an mMRC score of 2, 146 had an mMRC of 3, and 115 had an mMRC score of 4. The population was primarily male (98.6%) and of Hispanic ethnicity (96.4%), with a mean age of 72 years (Table 1). Most patients (n = 269, 64.0%) were prior smokers, while 135 patients (32.5%) had never smoked, and 11 (2.7%) were current smokers. At baseline, 169 patients (41.4%) had interstitial lung disease, 39 (9.6%) had chest wall disorders, 29 (7.1%) had occupational exposure, 25 (6.1%) had pneumonitis, and 14 (3.4%) had neuromuscular disorders.

There was a statistically significant relationship between mMRC score and hospitalization and FEV₁ but not TLC (Table 2). As mMRC increased, so did hospitalizations: a total of 168 patients (40.5%) were hospitalized; 24 patients (36.9%) had an mMRC score of 0, 30 patients (34.0%) had an mMRC score of 1, 2 patients (100%) had an mMRC score of 2, 54 patients (37.0%) had an mMRC score of 3, and 58 patients (50.0%) had an mMRC score of 4 (P = .04). Mean (SD) TLC values increased as mMRC scores increased. Mean (SD) TLC was 70.5% (33.0) for the entire population; 68.8% (7.2) for patients with an mMRC score of 0, 70.8% (5.8) for patients with an mMRC score of 1, 75.0% (1.4) for patients with an mMRC score of 2, 70.1% (7.2) for patients with an mMRC score of 3, and 71.5% (62.1) for patients with an mMRC score of 4 (P = .10) (Figure 1). There was an associated decrease in mean (SD) FEV₁ with mMRC. Mean (SD) FEV₁ was 76.2% (18.9) for the entire population; 81.7% (19.3) for patients with an mMRC score of 0, 80.9% (18) for patients with an mMRC score of 1, 93.5% (34.6) for patients with an mMRC score of 2, 76.2% (17.1) for patients with an mMRC score of 3, and 69.2% (19.4) for patients with an mMRC score of 4; (P < .001) (Figure 2).

The correlation between mMRC and FEV₁ (r = 0.25, P < .001) was stronger than the correlation between mMRC and TLC (r = 0.15, P < .001). The correlations for DLCO (P < .001), DLCO/VA (P < .001), hemoglobin (P < .02), and PaO₂ (P < .001) were all statistically significant (P < .005), but with no strong identifiable trend.

Discussion

The patient population of this study was primarily older males of Hispanic ethnicity with a history of smoking. There was no association between body mass index or smoking status with worsening dyspnea as measured with mMRC scores. We observed no significant correlation between mMRC scores and various factors such as comorbidities including heart conditions, and epidemiological factors like the etiology of lung disease, including both intrinsic and extrinsic causes. This lack of association was anticipated, as restrictive lung diseases in our study predominantly arose from intrinsic pulmonary etiologies, such as interstitial lung disease. A difference between more hospitalizations and worsening dyspnea was identified. There was a slightly higher correlation between FEV₁ and mMRC scores when compared with TLC and mMRC scores concerning worsening dyspnea, which could indicate that the use of FEV₁ should be preferred over previous recommendations to use TLC.¹⁰ Other guidelines have utilized exercise capacity via the 6-minute walk test as a marker of severity with spirometry values and found that DLCO was correlated with severity.¹¹

The latest ERS/ATS guidelines recommend z scores for grading the severity of obstructive lung diseases but do not recommend them for the diagnosis of restrictive lung diseases.¹² A z score encompasses diverse variables (eg, age, sex, and ethnicity) to provide more uniform and consistent results. Other studies have been done to relate z scores to other spirometry variables with restrictive lung disease. One such study indicates the potential benefit of using FVC alone to grade restrictive lung diseases.¹³ There continues to be great diversity in the interpretation of pulmonary function tests, and we believe the information gathered can provide valuable insight for managing patients with restrictive lung diseases.

Limitations

Only 2 patients reported an mMRC score of 2 in our study. This may have affected statistical outcomes. It also may reveal possible deficits in the efficacy of patient education on the mMRC scale. This study was also limited by its small sample size, single center location, and the distribution of patients that reported an mMRC favored either low or high values. The patients in this study, who were all veterans, may not be representative of other patient populations.

Conclusions

There continue to be few factors associated with the physiological severity of the defective oxygen delivery and reported dyspnea of a patient with restrictive lung disease that allows for an accurate, repeatable grading of severity. Using FEV₁ instead of TLC to determine the severity of a restrictive lung disease should be reconsidered. We could not find any other strong correlation among other factors studied. Further research should be conducted to continue looking for variables that more accurately depict patient dyspnea in restrictive lung disease.

Acknowledgments

This study is based upon work supported by the Veterans Affairs Caribbean Healthcare System in San Juan, Puerto Rico, and is the result of work supported by Pulmonary & Critical Care Medicine service, with resources and the use of its facilities.

Respiratory diseases have varied clinical presentations and are classified as restrictive, obstructive, mixed, or normal. Restrictive lung diseases have reduced lung volumes, either due to an alteration in lung parenchyma or a disease of the pleura, chest wall, or neuromuscular apparatus. If caused by parenchymal lung disease, restrictive lung disorders are accompanied by reduced gas transfer, which may be portrayed clinically by desaturation after exercise. Based on anatomical structures, the causes of lung volume reduction may be intrinsic or extrinsic. Intrinsic causes correspond to diseases of the lung parenchyma, such as idiopathic fibrotic diseases, connective-tissue diseases, drug-induced lung diseases, and other primary diseases of the lungs. Extrinsic causes refer to disorders outside the lungs or extra-pulmonary diseases such as neuromuscular and nonmuscular diseases of the chest wall.¹ For example, obesity and myasthenia gravis can cause restrictive lung diseases, one through mechanical interference of lung expansion and the other through neuromuscular impedance of thoracic cage expansion. All these diseases eventually result in lung restriction, impaired lung function, and respiratory failure. This heterogenicity of disease makes establishing a single severity criterion difficult.

Laboratory testing, imaging studies, and examinations are important for determining the pulmonary disease and its course and progression. The pulmonary function test (PFT), which consists of multiple procedures that are performed depending on the information needed, has been an essential tool in practice for the pulmonologist. The PFT includes spirometry, lung volume measurement, respiratory muscle strength, diffusion capacity, and a broncho-provocation test. Each test has a particular role in assisting the diagnosis and/or follow-up of the patient. Spirometry is frequently used due to its range of dynamic physiological parameters, ease of use, and accessibility. It is used for the diagnosis of pulmonary symptoms, in the assessment of disability, and preoperatory evaluation, including lung resection surgery, assisting in the diagnosis, monitoring, and therapy response of pulmonary diseases.

A systematic approach to PFT interpretation is recommended by several societies, such as the American Thoracic Society (ATS) and the European Respiratory Society (ERS).² The pulmonary function test results must be reproducible and meet established standards to ensure reliable and consistent clinical outcomes. A restrictive respiratory disease is defined by a decrease in total lung capacity (TLC) (< 5% of predicted value) and a normal forced expiratory volume in 1 second (FEV₁)/forced vital capacity (FVC) ratio.² Although other findings—such as a decrease in vital capacity—should prompt an investigation into whether the patient has a possible restrictive respiratory disease, the sole presence of this parameter is not definitive or diagnostic of a restrictive impairment.^2-4 The assessment of severity is typically determined by TLC. Unfortunately, the severity of a restrictive respiratory disease and the degree of patient discomfort do not always correlate when utilizing just TLC. Pulmonary sarcoidosis, for example, is a granulomatous lung disease with a restrictive PFT pattern and a disease burden that may vary over time. Having a more consistent method of grading the severity of the restrictive lung disease may help guide treatment. The modified Medical Research Council (mMRC) scale, a 5-point dyspnea scale, is widely used in assessing the severity of dyspnea in various respiratory conditions, including chronic obstructive pulmonary disease (COPD), where its scores have been associated with patient mortality.^1,5 The goal of this study was to document the associations between objective parameters obtained through PFT and other variables, with an established measurement of dyspnea to assess the severity grade of restrictive lung diseases.

Methods

This retrospective record review at the Veterans Affairs Caribbean Healthcare System (VACHS) in San Juan, Puerto Rico, wasconducted using the Veterans Health Information Systems and Technology Architecture to identify patients with a PFT, including spirometry, that indicated a restrictive ventilator pattern based on the current ATS/ERS Task Force on Lung Function Testing.² Patients were included if they were aged ≥ 21 years, PFT with TLC ≤ 80% predicted, mMRC score documented on PFT, and documented diffusing capacity of the lung for carbon monoxide (DLCO). Patients were excluded if their FEV₁/vital capacity (VC) was < 70% predicted using the largest VC, or no mMRC score was available. All patients meeting the inclusion criteria were considered regardless of comorbidities.

The PFT results of all adult patients, including those performed between June 1, 2013, and January 6, 2016, were submitted to spirometry, and lung volume measurements were analyzed. Sociodemographic information was collected, including sex, ethnicity, age, height, weight, and basal metabolic index. Other data found in PFTs, such as smoking status, smoking in packs/year, mMRC score, predicted TLC value, imaging present (chest X-ray, computed tomography), and hospitalizations and exacerbations within 1 year were collected. In addition, we examined the predicted values for FEV₁, DLCO, and DLCO/VA (calculated using the Ayer equation), FVC (calculated using the Knudson equation), expiratory reserve volume, inspiratory VC, and slow VC. PaO₂, PaCO₂, and Alveolar-arterial gradients also were collected.^6-9 Information about heart failure status was gathered through medical evaluation of notes and cardiac studies. All categorical variables were correlated with Spearman analysis and quantitative variables with average percentages. P values were calculated with analysis of variance.

Of 6461 VACHS patient records reviewed, 415 met the inclusion criteria. Patients were divided according to their mMRC score: 65 had mMRC score of 0, 87 had an mMRC score of 1, 2 had an mMRC score of 2, 146 had an mMRC of 3, and 115 had an mMRC score of 4. The population was primarily male (98.6%) and of Hispanic ethnicity (96.4%), with a mean age of 72 years (Table 1). Most patients (n = 269, 64.0%) were prior smokers, while 135 patients (32.5%) had never smoked, and 11 (2.7%) were current smokers. At baseline, 169 patients (41.4%) had interstitial lung disease, 39 (9.6%) had chest wall disorders, 29 (7.1%) had occupational exposure, 25 (6.1%) had pneumonitis, and 14 (3.4%) had neuromuscular disorders.

There was a statistically significant relationship between mMRC score and hospitalization and FEV₁ but not TLC (Table 2). As mMRC increased, so did hospitalizations: a total of 168 patients (40.5%) were hospitalized; 24 patients (36.9%) had an mMRC score of 0, 30 patients (34.0%) had an mMRC score of 1, 2 patients (100%) had an mMRC score of 2, 54 patients (37.0%) had an mMRC score of 3, and 58 patients (50.0%) had an mMRC score of 4 (P = .04). Mean (SD) TLC values increased as mMRC scores increased. Mean (SD) TLC was 70.5% (33.0) for the entire population; 68.8% (7.2) for patients with an mMRC score of 0, 70.8% (5.8) for patients with an mMRC score of 1, 75.0% (1.4) for patients with an mMRC score of 2, 70.1% (7.2) for patients with an mMRC score of 3, and 71.5% (62.1) for patients with an mMRC score of 4 (P = .10) (Figure 1). There was an associated decrease in mean (SD) FEV₁ with mMRC. Mean (SD) FEV₁ was 76.2% (18.9) for the entire population; 81.7% (19.3) for patients with an mMRC score of 0, 80.9% (18) for patients with an mMRC score of 1, 93.5% (34.6) for patients with an mMRC score of 2, 76.2% (17.1) for patients with an mMRC score of 3, and 69.2% (19.4) for patients with an mMRC score of 4; (P < .001) (Figure 2).

The correlation between mMRC and FEV₁ (r = 0.25, P < .001) was stronger than the correlation between mMRC and TLC (r = 0.15, P < .001). The correlations for DLCO (P < .001), DLCO/VA (P < .001), hemoglobin (P < .02), and PaO₂ (P < .001) were all statistically significant (P < .005), but with no strong identifiable trend.

Discussion

The patient population of this study was primarily older males of Hispanic ethnicity with a history of smoking. There was no association between body mass index or smoking status with worsening dyspnea as measured with mMRC scores. We observed no significant correlation between mMRC scores and various factors such as comorbidities including heart conditions, and epidemiological factors like the etiology of lung disease, including both intrinsic and extrinsic causes. This lack of association was anticipated, as restrictive lung diseases in our study predominantly arose from intrinsic pulmonary etiologies, such as interstitial lung disease. A difference between more hospitalizations and worsening dyspnea was identified. There was a slightly higher correlation between FEV₁ and mMRC scores when compared with TLC and mMRC scores concerning worsening dyspnea, which could indicate that the use of FEV₁ should be preferred over previous recommendations to use TLC.¹⁰ Other guidelines have utilized exercise capacity via the 6-minute walk test as a marker of severity with spirometry values and found that DLCO was correlated with severity.¹¹

The latest ERS/ATS guidelines recommend z scores for grading the severity of obstructive lung diseases but do not recommend them for the diagnosis of restrictive lung diseases.¹² A z score encompasses diverse variables (eg, age, sex, and ethnicity) to provide more uniform and consistent results. Other studies have been done to relate z scores to other spirometry variables with restrictive lung disease. One such study indicates the potential benefit of using FVC alone to grade restrictive lung diseases.¹³ There continues to be great diversity in the interpretation of pulmonary function tests, and we believe the information gathered can provide valuable insight for managing patients with restrictive lung diseases.

Limitations

Only 2 patients reported an mMRC score of 2 in our study. This may have affected statistical outcomes. It also may reveal possible deficits in the efficacy of patient education on the mMRC scale. This study was also limited by its small sample size, single center location, and the distribution of patients that reported an mMRC favored either low or high values. The patients in this study, who were all veterans, may not be representative of other patient populations.

Conclusions

There continue to be few factors associated with the physiological severity of the defective oxygen delivery and reported dyspnea of a patient with restrictive lung disease that allows for an accurate, repeatable grading of severity. Using FEV₁ instead of TLC to determine the severity of a restrictive lung disease should be reconsidered. We could not find any other strong correlation among other factors studied. Further research should be conducted to continue looking for variables that more accurately depict patient dyspnea in restrictive lung disease.

Acknowledgments

This study is based upon work supported by the Veterans Affairs Caribbean Healthcare System in San Juan, Puerto Rico, and is the result of work supported by Pulmonary & Critical Care Medicine service, with resources and the use of its facilities.

Respiratory diseases have varied clinical presentations and are classified as restrictive, obstructive, mixed, or normal. Restrictive lung diseases have reduced lung volumes, either due to an alteration in lung parenchyma or a disease of the pleura, chest wall, or neuromuscular apparatus. If caused by parenchymal lung disease, restrictive lung disorders are accompanied by reduced gas transfer, which may be portrayed clinically by desaturation after exercise. Based on anatomical structures, the causes of lung volume reduction may be intrinsic or extrinsic. Intrinsic causes correspond to diseases of the lung parenchyma, such as idiopathic fibrotic diseases, connective-tissue diseases, drug-induced lung diseases, and other primary diseases of the lungs. Extrinsic causes refer to disorders outside the lungs or extra-pulmonary diseases such as neuromuscular and nonmuscular diseases of the chest wall.¹ For example, obesity and myasthenia gravis can cause restrictive lung diseases, one through mechanical interference of lung expansion and the other through neuromuscular impedance of thoracic cage expansion. All these diseases eventually result in lung restriction, impaired lung function, and respiratory failure. This heterogenicity of disease makes establishing a single severity criterion difficult.

Laboratory testing, imaging studies, and examinations are important for determining the pulmonary disease and its course and progression. The pulmonary function test (PFT), which consists of multiple procedures that are performed depending on the information needed, has been an essential tool in practice for the pulmonologist. The PFT includes spirometry, lung volume measurement, respiratory muscle strength, diffusion capacity, and a broncho-provocation test. Each test has a particular role in assisting the diagnosis and/or follow-up of the patient. Spirometry is frequently used due to its range of dynamic physiological parameters, ease of use, and accessibility. It is used for the diagnosis of pulmonary symptoms, in the assessment of disability, and preoperatory evaluation, including lung resection surgery, assisting in the diagnosis, monitoring, and therapy response of pulmonary diseases.

A systematic approach to PFT interpretation is recommended by several societies, such as the American Thoracic Society (ATS) and the European Respiratory Society (ERS).² The pulmonary function test results must be reproducible and meet established standards to ensure reliable and consistent clinical outcomes. A restrictive respiratory disease is defined by a decrease in total lung capacity (TLC) (< 5% of predicted value) and a normal forced expiratory volume in 1 second (FEV₁)/forced vital capacity (FVC) ratio.² Although other findings—such as a decrease in vital capacity—should prompt an investigation into whether the patient has a possible restrictive respiratory disease, the sole presence of this parameter is not definitive or diagnostic of a restrictive impairment.^2-4 The assessment of severity is typically determined by TLC. Unfortunately, the severity of a restrictive respiratory disease and the degree of patient discomfort do not always correlate when utilizing just TLC. Pulmonary sarcoidosis, for example, is a granulomatous lung disease with a restrictive PFT pattern and a disease burden that may vary over time. Having a more consistent method of grading the severity of the restrictive lung disease may help guide treatment. The modified Medical Research Council (mMRC) scale, a 5-point dyspnea scale, is widely used in assessing the severity of dyspnea in various respiratory conditions, including chronic obstructive pulmonary disease (COPD), where its scores have been associated with patient mortality.^1,5 The goal of this study was to document the associations between objective parameters obtained through PFT and other variables, with an established measurement of dyspnea to assess the severity grade of restrictive lung diseases.

Methods

This retrospective record review at the Veterans Affairs Caribbean Healthcare System (VACHS) in San Juan, Puerto Rico, wasconducted using the Veterans Health Information Systems and Technology Architecture to identify patients with a PFT, including spirometry, that indicated a restrictive ventilator pattern based on the current ATS/ERS Task Force on Lung Function Testing.² Patients were included if they were aged ≥ 21 years, PFT with TLC ≤ 80% predicted, mMRC score documented on PFT, and documented diffusing capacity of the lung for carbon monoxide (DLCO). Patients were excluded if their FEV₁/vital capacity (VC) was < 70% predicted using the largest VC, or no mMRC score was available. All patients meeting the inclusion criteria were considered regardless of comorbidities.

The PFT results of all adult patients, including those performed between June 1, 2013, and January 6, 2016, were submitted to spirometry, and lung volume measurements were analyzed. Sociodemographic information was collected, including sex, ethnicity, age, height, weight, and basal metabolic index. Other data found in PFTs, such as smoking status, smoking in packs/year, mMRC score, predicted TLC value, imaging present (chest X-ray, computed tomography), and hospitalizations and exacerbations within 1 year were collected. In addition, we examined the predicted values for FEV₁, DLCO, and DLCO/VA (calculated using the Ayer equation), FVC (calculated using the Knudson equation), expiratory reserve volume, inspiratory VC, and slow VC. PaO₂, PaCO₂, and Alveolar-arterial gradients also were collected.^6-9 Information about heart failure status was gathered through medical evaluation of notes and cardiac studies. All categorical variables were correlated with Spearman analysis and quantitative variables with average percentages. P values were calculated with analysis of variance.

Of 6461 VACHS patient records reviewed, 415 met the inclusion criteria. Patients were divided according to their mMRC score: 65 had mMRC score of 0, 87 had an mMRC score of 1, 2 had an mMRC score of 2, 146 had an mMRC of 3, and 115 had an mMRC score of 4. The population was primarily male (98.6%) and of Hispanic ethnicity (96.4%), with a mean age of 72 years (Table 1). Most patients (n = 269, 64.0%) were prior smokers, while 135 patients (32.5%) had never smoked, and 11 (2.7%) were current smokers. At baseline, 169 patients (41.4%) had interstitial lung disease, 39 (9.6%) had chest wall disorders, 29 (7.1%) had occupational exposure, 25 (6.1%) had pneumonitis, and 14 (3.4%) had neuromuscular disorders.

There was a statistically significant relationship between mMRC score and hospitalization and FEV₁ but not TLC (Table 2). As mMRC increased, so did hospitalizations: a total of 168 patients (40.5%) were hospitalized; 24 patients (36.9%) had an mMRC score of 0, 30 patients (34.0%) had an mMRC score of 1, 2 patients (100%) had an mMRC score of 2, 54 patients (37.0%) had an mMRC score of 3, and 58 patients (50.0%) had an mMRC score of 4 (P = .04). Mean (SD) TLC values increased as mMRC scores increased. Mean (SD) TLC was 70.5% (33.0) for the entire population; 68.8% (7.2) for patients with an mMRC score of 0, 70.8% (5.8) for patients with an mMRC score of 1, 75.0% (1.4) for patients with an mMRC score of 2, 70.1% (7.2) for patients with an mMRC score of 3, and 71.5% (62.1) for patients with an mMRC score of 4 (P = .10) (Figure 1). There was an associated decrease in mean (SD) FEV₁ with mMRC. Mean (SD) FEV₁ was 76.2% (18.9) for the entire population; 81.7% (19.3) for patients with an mMRC score of 0, 80.9% (18) for patients with an mMRC score of 1, 93.5% (34.6) for patients with an mMRC score of 2, 76.2% (17.1) for patients with an mMRC score of 3, and 69.2% (19.4) for patients with an mMRC score of 4; (P < .001) (Figure 2).

The correlation between mMRC and FEV₁ (r = 0.25, P < .001) was stronger than the correlation between mMRC and TLC (r = 0.15, P < .001). The correlations for DLCO (P < .001), DLCO/VA (P < .001), hemoglobin (P < .02), and PaO₂ (P < .001) were all statistically significant (P < .005), but with no strong identifiable trend.

Discussion

The patient population of this study was primarily older males of Hispanic ethnicity with a history of smoking. There was no association between body mass index or smoking status with worsening dyspnea as measured with mMRC scores. We observed no significant correlation between mMRC scores and various factors such as comorbidities including heart conditions, and epidemiological factors like the etiology of lung disease, including both intrinsic and extrinsic causes. This lack of association was anticipated, as restrictive lung diseases in our study predominantly arose from intrinsic pulmonary etiologies, such as interstitial lung disease. A difference between more hospitalizations and worsening dyspnea was identified. There was a slightly higher correlation between FEV₁ and mMRC scores when compared with TLC and mMRC scores concerning worsening dyspnea, which could indicate that the use of FEV₁ should be preferred over previous recommendations to use TLC.¹⁰ Other guidelines have utilized exercise capacity via the 6-minute walk test as a marker of severity with spirometry values and found that DLCO was correlated with severity.¹¹

The latest ERS/ATS guidelines recommend z scores for grading the severity of obstructive lung diseases but do not recommend them for the diagnosis of restrictive lung diseases.¹² A z score encompasses diverse variables (eg, age, sex, and ethnicity) to provide more uniform and consistent results. Other studies have been done to relate z scores to other spirometry variables with restrictive lung disease. One such study indicates the potential benefit of using FVC alone to grade restrictive lung diseases.¹³ There continues to be great diversity in the interpretation of pulmonary function tests, and we believe the information gathered can provide valuable insight for managing patients with restrictive lung diseases.

Limitations

Only 2 patients reported an mMRC score of 2 in our study. This may have affected statistical outcomes. It also may reveal possible deficits in the efficacy of patient education on the mMRC scale. This study was also limited by its small sample size, single center location, and the distribution of patients that reported an mMRC favored either low or high values. The patients in this study, who were all veterans, may not be representative of other patient populations.

Conclusions

There continue to be few factors associated with the physiological severity of the defective oxygen delivery and reported dyspnea of a patient with restrictive lung disease that allows for an accurate, repeatable grading of severity. Using FEV₁ instead of TLC to determine the severity of a restrictive lung disease should be reconsidered. We could not find any other strong correlation among other factors studied. Further research should be conducted to continue looking for variables that more accurately depict patient dyspnea in restrictive lung disease.

Acknowledgments

This study is based upon work supported by the Veterans Affairs Caribbean Healthcare System in San Juan, Puerto Rico, and is the result of work supported by Pulmonary & Critical Care Medicine service, with resources and the use of its facilities.

The Centers for Disease Control and Prevention (CDC) classifies individuals with a body mass index (BMI) of 25 to 29.9as overweight and those with a BMI > 30 as obese (obesity classes: I, BMI 30 to 34.9; II, BMI 35 to 39.9; and III, BMI ≥ 40).¹ In 2011, the CDC estimated that 27.4% of adults in the United States were obese; less than a decade later, that number increased to 31.9%.¹ In that same period, the percentage of adults in Indiana classified as obese increased from 30.8% to 36.8%.¹ About 1 in 14 individuals in the US have class III obesity and 86% of veterans are either overweight or obese.²

High medical expenses can likely be attributed to the long-term health consequences of obesity. Compared to those with a healthy weight, individuals who are overweight or obese are at an increased risk for high blood pressure, high low-density lipoprotein cholesterol levels, low high-density lipoprotein cholesterol levels, high triglyceride levels, type 2 diabetes mellitus (T2DM), coronary heart disease, stroke, gallbladder disease, osteoarthritis, sleep apnea, cancer, mental health disorders, body pain, low quality of life, and death.³ Many of these conditions lead to increased health care needs, medication needs, hospitalizations, and overall health care system use.

Guidelines for the prevention and treatment of obesity have been produced by the American Heart Association, American College of Cardiology, and The Obesity Society; the Endocrine Society; the American Diabetes Association; and the US Departments of Veterans Affairs (VA) and Defense. Each follows a general algorithm to manage and prevent adverse effects (AEs) related to obesity. General practice is to assess a patient for elevated BMI (> 25), implement intense lifestyle modifications including calorie restriction and exercise, reassess for a maintained 5% to 10% weight loss for cardiovascular benefits, and potentially assess for pharmacological or surgical intervention to assist in weight loss.^2,4-6

While some weight loss medications (eg, phentermine/topiramate, naltrexone/bupropion, orlistat, and lorcaserin) tend to have unfavorable AEs or mixed efficacy, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have provided new options.^7-10 Lorcaserin, for example, was removed from the market in 2020 due to its association with cancer risks.¹¹ The GLP-1RAs liraglutide and semaglutide received US Food and Drug Administration (FDA) approval for weight loss in 2014 and 2021, respectively.^12,13 GLP-1RAs have shown the greatest efficacy and benefits in reducing hemoglobin A_1c (HbA_1c); they are the preferred agents for patients who qualify for pharmacologic intervention for weight loss, especially those with T2DM. However, these studies have not evaluated the long-term outcomes of using these medications for weight loss and may not reflect the veteran population.^14,15

At Veteran Health Indiana (VHI), clinicians may use several weight loss medications for patients to achieve 5% to 10% weight loss. The medications most often used include liraglutide, phentermine/topiramate, naltrexone/bupropion, orlistat, and phentermine alone. However, more research is needed to determine which weight loss medication is the most beneficial for veterans, particularly following FDA approval of GLP-1RAs. At VHI, phentermine/topiramate is the preferred first-line agent unless patients have contraindications for use, in which case naltrexone/bupropion is recommended. These are considered first-line due to their ease of use in pill form, lower cost, and comparable weight loss to the GLP-1 medication class.² However, for patients with prediabetes, T2DM, BMI > 40, or BMI > 35 with specific comorbid conditions, liraglutide is preferred because of its beneficial effects for both weight loss and blood glucose control.²

This study aimed to expand on the 2021 Hood and colleagues study that examined total weight loss and weight loss as a percentage of baseline weight in patients with obesity at 3, 6, 12, and > 12 months of pharmacologic therapy by extending the time frame to 48 months.¹⁶ This study excluded semaglutide because few patients were prescribed the medication for weight loss during the study.

METHODS

We conducted a single-center, retrospective chart review of patients prescribed weight loss medications at VHI. A patient list was generated based on prescription fills from June 1, 2017, to July 31, 2021. Data were obtained from the Computerized Patient Record System; patients were not contacted. This study was approved by the Indiana University Health Institutional Review Board and VHI Research and Development Committee.

At the time of this study, liraglutide, phentermine/topiramate, naltrexone/bupropion, orlistat, and phentermine alone were available at VHI for patients who met the clinical criteria for use. All patients must have been enrolled in dietary and lifestyle management programs, including the VA MOVE! program, to be approved for these medications. After the MOVE! orientation, patients could participate in group or individual 12-week programs that included weigh-ins, goal-setting strategies, meal planning, and habit modification support. If patients could not meet in person, phone and other telehealth opportunities were available.

Patients were included in the study if they were aged ≥ 18 years, received a prescription for any of the 5 available medications for weight loss during the enrollment period, and were on the medication for ≥ 6 consecutive months. Patients were excluded if they received a prescription, were treated outside the VA system, or were pregnant. The primary indication for the included medication was not weight loss; the primary indication for the GLP-1RA was T2DM, or the weight loss was attributed to another disease. Adherence was not a measured outcome of this study; if patients were filling the medication, it was assumed they were taking it. Data were collected for each instance of medication use; as a result, a few patients were included more than once. Data collection for a failed medication ended when failure was documented. New data points began when new medication was prescribed; all data were per medication, not per patient. This allowed us to account for medication failure and provide accurate weight loss results based on medication choice within VHI.

Primary outcomes included total weight loss and weight loss as a percentage ofbaseline weight during the study period at 3, 6, 12, 24, 36, and 48 months of therapy. Secondary outcomes included the percentage of patients who lost 5% to 10% of their body weight from baseline; the percentage of patients who maintained ≥ 5% weight loss from baseline to 12, 24, 36, and 48 months if maintained on medication for that duration; duration of medication treatment in weeks; medication discontinuation rate; reason for medication discontinuation; enrollment in the MOVE! clinic and the time enrolled; percentage of patients with a BMI of 18 to 24.9 at the end of the study; and change in HbA_1c at 3, 6, 12, 24, 36, and 48 months.

Demographic data included race, age, sex, baseline weight, height, baseline BMI, and comorbid conditions (collected based on the most recent primary care clinical note before initiating medication). Medication data collected included medications used to manage comorbidities. Data related to weight management medication included prescribing clinic, maintenance dose of medication, duration of medication during the study period, the reason for medication discontinuation, or bariatric surgery intervention if applicable.

Basic descriptive statistics were used to characterize study participants. For continuous data, analysis of variance tests were used; if those results were not normal, then nonparametric tests were used, followed by pairwise tests between medication groups if the overall test was significant using the Fisher significant differences test. For nominal data, χ² or Fisher exact tests were used. For comparisons of primary and secondary outcomes, if the analyses needed to include adjustment for confounding variables, analysis of covariance was used for continuous data. A 2-sided 5% significance level was used for all tests.

RESULTS

A total of 228 instances of medication use were identified based on prescription fills; 123 did not meet inclusion criteria (117 for < 6 consecutive months of medication use) (Figure). The study included 105 participants with a mean age of 56 years; 80 were male (76.2%), and 85 identified as White race (81.0%). Mean (SD) weight was 130.1 kg (26.8) and BMI was 41.6 (7.2). The most common comorbid disease states among patients included hypertension, dyslipidemia, obstructive sleep apnea, and T2DM (Table 1). The baseline characteristics were comparable to those of Hood and colleagues.¹⁶

Most patients at VHI started on liraglutide (63%) or phentermine/topiramate (28%). For primary and secondary outcomes, statistics were calculated to determine whether the results were statistically significant for comparing the liraglutide and phentermine/topiramate subgroups. Sample sizes were too small for statistical analysis for bupropion/naltrexone, phentermine, and orlistat.

Primary Outcomes

The mean (SD) weight of participants dropped 8.1% from 130.1 kg to 119.5 kg over the patient-specific duration of weight management medication therapy for an absolute difference of 10.6 kg (9.7). Duration of individual medication use varied from 6 to 48 months. Weight loss was recorded at 6, 12, 24, 36, and 48 months of weight management therapy. Patient weight was not recorded after the medication was discontinued.

When classified by medication choice, the mean change in weight over the duration of the study was −23.9 kg for 2 patients using orlistat, −10.2 kg for 46 patients using liraglutide, −11.0 kg for 25 patients using phentermine/topiramate, -7.4 kg for 1 patient using phentermine, and -13.0 kg for 4 patients using naltrexone/bupropion. Patients without a weight documented at the end of their therapy or at the conclusion of the data collection period were not included in the total weight loss at the end of therapy. There were 78 documented instances of weight loss at the end of therapy (Table 2).

Body weight loss percentage was recorded at 6, 12, 24, 36, and 48 months of weight management therapy. The mean (SD) body weight loss percentage over the duration of the study was 9.2% (11.2). When classified by medication choice, the mean percentage of body weight loss was 16.8% for 2 patients using orlistat, 9.4% for 46 patients using liraglutide, 8.2% for 25 patients using phentermine/topiramate, 6.0% for 1 patient using phentermine alone, and 10.6% for 4 patients using naltrexone/bupropion (Table 3).

Secondary Outcomes

While none of the secondary outcomes were statistically significant, the results of this study suggest that both medications may contribute to weight loss in many patients included in this study. Almost two-thirds of the included patients analyzed lost ≥ 5% of weight from baseline while taking weight management medication. Sixty-six patients (63%) lost ≥ 5% of body weight at any time during the data collection period. When stratified by liraglutide and phentermine/topiramate, 41 patients (63%) taking liraglutide and 20 patients (67%) taking phentermine/topiramate lost ≥ 5% of weight from baseline. Of the 66 patients who lost ≥ 5% of body weight from baseline, 36 (55%) lost ≥ 10% of body weight from baseline at any time during the data collection period.

The mean (SD) duration for weight management medication use was 23 months (14.9). Phentermine/topiramate was tolerated longer than liraglutide: 22.7 months vs 21.7 months, respectively (Table 4).

The average overall documented medication discontinuation rate was 35.2%. Reasons for discontinuation included 21 patient-elected discontinuations, 8 patients no longer met criteria for use, 4 medications were no longer indicated, and 4 patients experienced AEs. It is unknown whether weight management medication was discontinued or not in 18 patients (17.2%).

This study evaluated the use and outcomes of weight loss medications over a longer period (up to 48 months) than what was previously studied among patients at VHI (12 months). The study aimed to better understand the long-term effect of weight loss medications, determine which medication had better long-term outcomes, and examine the reasons for medication discontinuation.

The results of this study displayed some similarities and differences compared with the Hood and colleagues study.¹⁶ Both yielded similar results for 5% of body weight loss and 10% of body weight loss. The largest difference was mean weight loss over the study period. In this study, patients lost a mean 10.6 kg over the course of weight loss medication use compared to 15.8 kg found by Hood and colleagues.¹⁶ A reason patients in the current study lost less weight overall could be the difference in time frames. The current study encompassed the COVID-19 pandemic, meaning fewer overall in-person patient appointments, which led to patients being lost to follow-up, missing weigh-ins during the time period, and gaps in care. For some patients, the pandemic possibly contributed to depression, missed medication doses, and a more sedentary lifestyle, leading to more weight gain.¹⁷ Telemedicine services at VHI expanded during the pandemic in an attempt to increase patient monitoring and counseling. It is unclear whether this expansion was enough to replace the in-person contact necessary to promote a healthy lifestyle.

VA pharmacists now care for patients through telehealth and are more involved in weight loss management. Since the conclusion of the Hood and colleagues study and start of this research, 2 pharmacists at VHI have been assigned to follow patients for obesity management to help with adherence to medication and lifestyle changes, management of AEs, dispense logistics, interventions for medications that may cause weight gain, and case management of glycemic control and weight loss with GLP-1RAs. Care management by pharmacists at VHI helps improve the logistics of titratable orders and save money by improving the use of high-cost items like GLP-1RAs. VA clinical pharmacy practitioners already monitor GLP-1RAs for patients with T2DM, so they are prepared to educate and assist patients with these medications.

It is important to continue developing a standardized process for weight loss medication management across the VA to improve the quality of patient care and optimize prescription outcomes. VA facilities differ in how weight loss management care is delivered and the level at which pharmacists are involved. Given the high rate of obesity among patients at the VA, the advent of new prescription options for weight loss, and the high cost associated with these medications, there has been increased attention to obesity care. Some Veterans Integrated Service Networks are forming a weight management community of practice groups to create standard operating procedures and algorithms to standardize care. Developing consistent processes is necessary to improve weight loss and patient care for veterans regardless where they receive treatment.

Limitations

The data used in this study were dependent on clinician documentation. Because of a lack of documentation in many instances, it was difficult to determine the full efficacy of the medications studied due to missing weight recordings. The lack of documentation made it difficult to determine whether patients were enrolled and active in the MOVE! program. It is required that patients enroll in MOVE! to obtain medications, but many did not have any follow-up MOVE! visits after initially obtaining their weight loss medication.

In this study, differences in the outcomes of patients with and without T2DM were not compared. It is the VA standard of care to prefer liraglutide over phentermine/topiramate in patients with T2DM or prediabetes.² This makes it difficult to assess whether phentermine/topiramate or liraglutide is more effective for weight loss in patients with T2DM. Weight gain after the discontinuation of weight loss medications was not assessed. Collecting this data may help determine whether a certain weight loss medication is less likely to cause rebound weight gain when discontinued.

Other limitations to this study consisted of excluding patients who discontinued therapy within 6 months, small sample sizes on some medications, and lack of data on adherence. Adherence was based on medication refills, which means that if a patient refilled the medication, it was assumed they were taking it. This is not always the case, and while accurate data on adherence is difficult to gather, it can impact how results may be interpreted. These additional limitations make it difficult to accurately determine the efficacy of the medications in this study.

CONCLUSIONS

This study found similar outcomes to what has been observed in larger clinical trials regarding weight loss medications. Nevertheless, there was a lack of accurate clinical documentation for most patients, which limits the conclusions. This lack of documentation potentially led to inaccurate results. It revealed that many patients at VHI did not uniformly receive consistent follow-up after starting a weight loss medication during the study period. With more standardized processes implemented at VA facilities, increased pharmacist involvement in weight loss medication management, and increased use of established telehealth services, patients could have the opportunity for closer follow-up that may lead to better weight loss outcomes. With these changes, there is more reason for additional studies to be conducted to assess follow-up, medication management, and weight loss overall.

The Centers for Disease Control and Prevention (CDC) classifies individuals with a body mass index (BMI) of 25 to 29.9as overweight and those with a BMI > 30 as obese (obesity classes: I, BMI 30 to 34.9; II, BMI 35 to 39.9; and III, BMI ≥ 40).¹ In 2011, the CDC estimated that 27.4% of adults in the United States were obese; less than a decade later, that number increased to 31.9%.¹ In that same period, the percentage of adults in Indiana classified as obese increased from 30.8% to 36.8%.¹ About 1 in 14 individuals in the US have class III obesity and 86% of veterans are either overweight or obese.²

High medical expenses can likely be attributed to the long-term health consequences of obesity. Compared to those with a healthy weight, individuals who are overweight or obese are at an increased risk for high blood pressure, high low-density lipoprotein cholesterol levels, low high-density lipoprotein cholesterol levels, high triglyceride levels, type 2 diabetes mellitus (T2DM), coronary heart disease, stroke, gallbladder disease, osteoarthritis, sleep apnea, cancer, mental health disorders, body pain, low quality of life, and death.³ Many of these conditions lead to increased health care needs, medication needs, hospitalizations, and overall health care system use.

Guidelines for the prevention and treatment of obesity have been produced by the American Heart Association, American College of Cardiology, and The Obesity Society; the Endocrine Society; the American Diabetes Association; and the US Departments of Veterans Affairs (VA) and Defense. Each follows a general algorithm to manage and prevent adverse effects (AEs) related to obesity. General practice is to assess a patient for elevated BMI (> 25), implement intense lifestyle modifications including calorie restriction and exercise, reassess for a maintained 5% to 10% weight loss for cardiovascular benefits, and potentially assess for pharmacological or surgical intervention to assist in weight loss.^2,4-6

While some weight loss medications (eg, phentermine/topiramate, naltrexone/bupropion, orlistat, and lorcaserin) tend to have unfavorable AEs or mixed efficacy, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have provided new options.^7-10 Lorcaserin, for example, was removed from the market in 2020 due to its association with cancer risks.¹¹ The GLP-1RAs liraglutide and semaglutide received US Food and Drug Administration (FDA) approval for weight loss in 2014 and 2021, respectively.^12,13 GLP-1RAs have shown the greatest efficacy and benefits in reducing hemoglobin A_1c (HbA_1c); they are the preferred agents for patients who qualify for pharmacologic intervention for weight loss, especially those with T2DM. However, these studies have not evaluated the long-term outcomes of using these medications for weight loss and may not reflect the veteran population.^14,15

At Veteran Health Indiana (VHI), clinicians may use several weight loss medications for patients to achieve 5% to 10% weight loss. The medications most often used include liraglutide, phentermine/topiramate, naltrexone/bupropion, orlistat, and phentermine alone. However, more research is needed to determine which weight loss medication is the most beneficial for veterans, particularly following FDA approval of GLP-1RAs. At VHI, phentermine/topiramate is the preferred first-line agent unless patients have contraindications for use, in which case naltrexone/bupropion is recommended. These are considered first-line due to their ease of use in pill form, lower cost, and comparable weight loss to the GLP-1 medication class.² However, for patients with prediabetes, T2DM, BMI > 40, or BMI > 35 with specific comorbid conditions, liraglutide is preferred because of its beneficial effects for both weight loss and blood glucose control.²

This study aimed to expand on the 2021 Hood and colleagues study that examined total weight loss and weight loss as a percentage of baseline weight in patients with obesity at 3, 6, 12, and > 12 months of pharmacologic therapy by extending the time frame to 48 months.¹⁶ This study excluded semaglutide because few patients were prescribed the medication for weight loss during the study.

METHODS

We conducted a single-center, retrospective chart review of patients prescribed weight loss medications at VHI. A patient list was generated based on prescription fills from June 1, 2017, to July 31, 2021. Data were obtained from the Computerized Patient Record System; patients were not contacted. This study was approved by the Indiana University Health Institutional Review Board and VHI Research and Development Committee.

At the time of this study, liraglutide, phentermine/topiramate, naltrexone/bupropion, orlistat, and phentermine alone were available at VHI for patients who met the clinical criteria for use. All patients must have been enrolled in dietary and lifestyle management programs, including the VA MOVE! program, to be approved for these medications. After the MOVE! orientation, patients could participate in group or individual 12-week programs that included weigh-ins, goal-setting strategies, meal planning, and habit modification support. If patients could not meet in person, phone and other telehealth opportunities were available.

Patients were included in the study if they were aged ≥ 18 years, received a prescription for any of the 5 available medications for weight loss during the enrollment period, and were on the medication for ≥ 6 consecutive months. Patients were excluded if they received a prescription, were treated outside the VA system, or were pregnant. The primary indication for the included medication was not weight loss; the primary indication for the GLP-1RA was T2DM, or the weight loss was attributed to another disease. Adherence was not a measured outcome of this study; if patients were filling the medication, it was assumed they were taking it. Data were collected for each instance of medication use; as a result, a few patients were included more than once. Data collection for a failed medication ended when failure was documented. New data points began when new medication was prescribed; all data were per medication, not per patient. This allowed us to account for medication failure and provide accurate weight loss results based on medication choice within VHI.

Primary outcomes included total weight loss and weight loss as a percentage ofbaseline weight during the study period at 3, 6, 12, 24, 36, and 48 months of therapy. Secondary outcomes included the percentage of patients who lost 5% to 10% of their body weight from baseline; the percentage of patients who maintained ≥ 5% weight loss from baseline to 12, 24, 36, and 48 months if maintained on medication for that duration; duration of medication treatment in weeks; medication discontinuation rate; reason for medication discontinuation; enrollment in the MOVE! clinic and the time enrolled; percentage of patients with a BMI of 18 to 24.9 at the end of the study; and change in HbA_1c at 3, 6, 12, 24, 36, and 48 months.

Demographic data included race, age, sex, baseline weight, height, baseline BMI, and comorbid conditions (collected based on the most recent primary care clinical note before initiating medication). Medication data collected included medications used to manage comorbidities. Data related to weight management medication included prescribing clinic, maintenance dose of medication, duration of medication during the study period, the reason for medication discontinuation, or bariatric surgery intervention if applicable.

Basic descriptive statistics were used to characterize study participants. For continuous data, analysis of variance tests were used; if those results were not normal, then nonparametric tests were used, followed by pairwise tests between medication groups if the overall test was significant using the Fisher significant differences test. For nominal data, χ² or Fisher exact tests were used. For comparisons of primary and secondary outcomes, if the analyses needed to include adjustment for confounding variables, analysis of covariance was used for continuous data. A 2-sided 5% significance level was used for all tests.

RESULTS

A total of 228 instances of medication use were identified based on prescription fills; 123 did not meet inclusion criteria (117 for < 6 consecutive months of medication use) (Figure). The study included 105 participants with a mean age of 56 years; 80 were male (76.2%), and 85 identified as White race (81.0%). Mean (SD) weight was 130.1 kg (26.8) and BMI was 41.6 (7.2). The most common comorbid disease states among patients included hypertension, dyslipidemia, obstructive sleep apnea, and T2DM (Table 1). The baseline characteristics were comparable to those of Hood and colleagues.¹⁶

Most patients at VHI started on liraglutide (63%) or phentermine/topiramate (28%). For primary and secondary outcomes, statistics were calculated to determine whether the results were statistically significant for comparing the liraglutide and phentermine/topiramate subgroups. Sample sizes were too small for statistical analysis for bupropion/naltrexone, phentermine, and orlistat.

Primary Outcomes

The mean (SD) weight of participants dropped 8.1% from 130.1 kg to 119.5 kg over the patient-specific duration of weight management medication therapy for an absolute difference of 10.6 kg (9.7). Duration of individual medication use varied from 6 to 48 months. Weight loss was recorded at 6, 12, 24, 36, and 48 months of weight management therapy. Patient weight was not recorded after the medication was discontinued.

When classified by medication choice, the mean change in weight over the duration of the study was −23.9 kg for 2 patients using orlistat, −10.2 kg for 46 patients using liraglutide, −11.0 kg for 25 patients using phentermine/topiramate, -7.4 kg for 1 patient using phentermine, and -13.0 kg for 4 patients using naltrexone/bupropion. Patients without a weight documented at the end of their therapy or at the conclusion of the data collection period were not included in the total weight loss at the end of therapy. There were 78 documented instances of weight loss at the end of therapy (Table 2).

Body weight loss percentage was recorded at 6, 12, 24, 36, and 48 months of weight management therapy. The mean (SD) body weight loss percentage over the duration of the study was 9.2% (11.2). When classified by medication choice, the mean percentage of body weight loss was 16.8% for 2 patients using orlistat, 9.4% for 46 patients using liraglutide, 8.2% for 25 patients using phentermine/topiramate, 6.0% for 1 patient using phentermine alone, and 10.6% for 4 patients using naltrexone/bupropion (Table 3).

Secondary Outcomes

While none of the secondary outcomes were statistically significant, the results of this study suggest that both medications may contribute to weight loss in many patients included in this study. Almost two-thirds of the included patients analyzed lost ≥ 5% of weight from baseline while taking weight management medication. Sixty-six patients (63%) lost ≥ 5% of body weight at any time during the data collection period. When stratified by liraglutide and phentermine/topiramate, 41 patients (63%) taking liraglutide and 20 patients (67%) taking phentermine/topiramate lost ≥ 5% of weight from baseline. Of the 66 patients who lost ≥ 5% of body weight from baseline, 36 (55%) lost ≥ 10% of body weight from baseline at any time during the data collection period.

The mean (SD) duration for weight management medication use was 23 months (14.9). Phentermine/topiramate was tolerated longer than liraglutide: 22.7 months vs 21.7 months, respectively (Table 4).

The average overall documented medication discontinuation rate was 35.2%. Reasons for discontinuation included 21 patient-elected discontinuations, 8 patients no longer met criteria for use, 4 medications were no longer indicated, and 4 patients experienced AEs. It is unknown whether weight management medication was discontinued or not in 18 patients (17.2%).

This study evaluated the use and outcomes of weight loss medications over a longer period (up to 48 months) than what was previously studied among patients at VHI (12 months). The study aimed to better understand the long-term effect of weight loss medications, determine which medication had better long-term outcomes, and examine the reasons for medication discontinuation.

The results of this study displayed some similarities and differences compared with the Hood and colleagues study.¹⁶ Both yielded similar results for 5% of body weight loss and 10% of body weight loss. The largest difference was mean weight loss over the study period. In this study, patients lost a mean 10.6 kg over the course of weight loss medication use compared to 15.8 kg found by Hood and colleagues.¹⁶ A reason patients in the current study lost less weight overall could be the difference in time frames. The current study encompassed the COVID-19 pandemic, meaning fewer overall in-person patient appointments, which led to patients being lost to follow-up, missing weigh-ins during the time period, and gaps in care. For some patients, the pandemic possibly contributed to depression, missed medication doses, and a more sedentary lifestyle, leading to more weight gain.¹⁷ Telemedicine services at VHI expanded during the pandemic in an attempt to increase patient monitoring and counseling. It is unclear whether this expansion was enough to replace the in-person contact necessary to promote a healthy lifestyle.

VA pharmacists now care for patients through telehealth and are more involved in weight loss management. Since the conclusion of the Hood and colleagues study and start of this research, 2 pharmacists at VHI have been assigned to follow patients for obesity management to help with adherence to medication and lifestyle changes, management of AEs, dispense logistics, interventions for medications that may cause weight gain, and case management of glycemic control and weight loss with GLP-1RAs. Care management by pharmacists at VHI helps improve the logistics of titratable orders and save money by improving the use of high-cost items like GLP-1RAs. VA clinical pharmacy practitioners already monitor GLP-1RAs for patients with T2DM, so they are prepared to educate and assist patients with these medications.

It is important to continue developing a standardized process for weight loss medication management across the VA to improve the quality of patient care and optimize prescription outcomes. VA facilities differ in how weight loss management care is delivered and the level at which pharmacists are involved. Given the high rate of obesity among patients at the VA, the advent of new prescription options for weight loss, and the high cost associated with these medications, there has been increased attention to obesity care. Some Veterans Integrated Service Networks are forming a weight management community of practice groups to create standard operating procedures and algorithms to standardize care. Developing consistent processes is necessary to improve weight loss and patient care for veterans regardless where they receive treatment.

Limitations

The data used in this study were dependent on clinician documentation. Because of a lack of documentation in many instances, it was difficult to determine the full efficacy of the medications studied due to missing weight recordings. The lack of documentation made it difficult to determine whether patients were enrolled and active in the MOVE! program. It is required that patients enroll in MOVE! to obtain medications, but many did not have any follow-up MOVE! visits after initially obtaining their weight loss medication.

In this study, differences in the outcomes of patients with and without T2DM were not compared. It is the VA standard of care to prefer liraglutide over phentermine/topiramate in patients with T2DM or prediabetes.² This makes it difficult to assess whether phentermine/topiramate or liraglutide is more effective for weight loss in patients with T2DM. Weight gain after the discontinuation of weight loss medications was not assessed. Collecting this data may help determine whether a certain weight loss medication is less likely to cause rebound weight gain when discontinued.

Other limitations to this study consisted of excluding patients who discontinued therapy within 6 months, small sample sizes on some medications, and lack of data on adherence. Adherence was based on medication refills, which means that if a patient refilled the medication, it was assumed they were taking it. This is not always the case, and while accurate data on adherence is difficult to gather, it can impact how results may be interpreted. These additional limitations make it difficult to accurately determine the efficacy of the medications in this study.

CONCLUSIONS

This study found similar outcomes to what has been observed in larger clinical trials regarding weight loss medications. Nevertheless, there was a lack of accurate clinical documentation for most patients, which limits the conclusions. This lack of documentation potentially led to inaccurate results. It revealed that many patients at VHI did not uniformly receive consistent follow-up after starting a weight loss medication during the study period. With more standardized processes implemented at VA facilities, increased pharmacist involvement in weight loss medication management, and increased use of established telehealth services, patients could have the opportunity for closer follow-up that may lead to better weight loss outcomes. With these changes, there is more reason for additional studies to be conducted to assess follow-up, medication management, and weight loss overall.

The Centers for Disease Control and Prevention (CDC) classifies individuals with a body mass index (BMI) of 25 to 29.9as overweight and those with a BMI > 30 as obese (obesity classes: I, BMI 30 to 34.9; II, BMI 35 to 39.9; and III, BMI ≥ 40).¹ In 2011, the CDC estimated that 27.4% of adults in the United States were obese; less than a decade later, that number increased to 31.9%.¹ In that same period, the percentage of adults in Indiana classified as obese increased from 30.8% to 36.8%.¹ About 1 in 14 individuals in the US have class III obesity and 86% of veterans are either overweight or obese.²

High medical expenses can likely be attributed to the long-term health consequences of obesity. Compared to those with a healthy weight, individuals who are overweight or obese are at an increased risk for high blood pressure, high low-density lipoprotein cholesterol levels, low high-density lipoprotein cholesterol levels, high triglyceride levels, type 2 diabetes mellitus (T2DM), coronary heart disease, stroke, gallbladder disease, osteoarthritis, sleep apnea, cancer, mental health disorders, body pain, low quality of life, and death.³ Many of these conditions lead to increased health care needs, medication needs, hospitalizations, and overall health care system use.

Guidelines for the prevention and treatment of obesity have been produced by the American Heart Association, American College of Cardiology, and The Obesity Society; the Endocrine Society; the American Diabetes Association; and the US Departments of Veterans Affairs (VA) and Defense. Each follows a general algorithm to manage and prevent adverse effects (AEs) related to obesity. General practice is to assess a patient for elevated BMI (> 25), implement intense lifestyle modifications including calorie restriction and exercise, reassess for a maintained 5% to 10% weight loss for cardiovascular benefits, and potentially assess for pharmacological or surgical intervention to assist in weight loss.^2,4-6

While some weight loss medications (eg, phentermine/topiramate, naltrexone/bupropion, orlistat, and lorcaserin) tend to have unfavorable AEs or mixed efficacy, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have provided new options.^7-10 Lorcaserin, for example, was removed from the market in 2020 due to its association with cancer risks.¹¹ The GLP-1RAs liraglutide and semaglutide received US Food and Drug Administration (FDA) approval for weight loss in 2014 and 2021, respectively.^12,13 GLP-1RAs have shown the greatest efficacy and benefits in reducing hemoglobin A_1c (HbA_1c); they are the preferred agents for patients who qualify for pharmacologic intervention for weight loss, especially those with T2DM. However, these studies have not evaluated the long-term outcomes of using these medications for weight loss and may not reflect the veteran population.^14,15

At Veteran Health Indiana (VHI), clinicians may use several weight loss medications for patients to achieve 5% to 10% weight loss. The medications most often used include liraglutide, phentermine/topiramate, naltrexone/bupropion, orlistat, and phentermine alone. However, more research is needed to determine which weight loss medication is the most beneficial for veterans, particularly following FDA approval of GLP-1RAs. At VHI, phentermine/topiramate is the preferred first-line agent unless patients have contraindications for use, in which case naltrexone/bupropion is recommended. These are considered first-line due to their ease of use in pill form, lower cost, and comparable weight loss to the GLP-1 medication class.² However, for patients with prediabetes, T2DM, BMI > 40, or BMI > 35 with specific comorbid conditions, liraglutide is preferred because of its beneficial effects for both weight loss and blood glucose control.²

This study aimed to expand on the 2021 Hood and colleagues study that examined total weight loss and weight loss as a percentage of baseline weight in patients with obesity at 3, 6, 12, and > 12 months of pharmacologic therapy by extending the time frame to 48 months.¹⁶ This study excluded semaglutide because few patients were prescribed the medication for weight loss during the study.

METHODS

We conducted a single-center, retrospective chart review of patients prescribed weight loss medications at VHI. A patient list was generated based on prescription fills from June 1, 2017, to July 31, 2021. Data were obtained from the Computerized Patient Record System; patients were not contacted. This study was approved by the Indiana University Health Institutional Review Board and VHI Research and Development Committee.

At the time of this study, liraglutide, phentermine/topiramate, naltrexone/bupropion, orlistat, and phentermine alone were available at VHI for patients who met the clinical criteria for use. All patients must have been enrolled in dietary and lifestyle management programs, including the VA MOVE! program, to be approved for these medications. After the MOVE! orientation, patients could participate in group or individual 12-week programs that included weigh-ins, goal-setting strategies, meal planning, and habit modification support. If patients could not meet in person, phone and other telehealth opportunities were available.

Patients were included in the study if they were aged ≥ 18 years, received a prescription for any of the 5 available medications for weight loss during the enrollment period, and were on the medication for ≥ 6 consecutive months. Patients were excluded if they received a prescription, were treated outside the VA system, or were pregnant. The primary indication for the included medication was not weight loss; the primary indication for the GLP-1RA was T2DM, or the weight loss was attributed to another disease. Adherence was not a measured outcome of this study; if patients were filling the medication, it was assumed they were taking it. Data were collected for each instance of medication use; as a result, a few patients were included more than once. Data collection for a failed medication ended when failure was documented. New data points began when new medication was prescribed; all data were per medication, not per patient. This allowed us to account for medication failure and provide accurate weight loss results based on medication choice within VHI.

Primary outcomes included total weight loss and weight loss as a percentage ofbaseline weight during the study period at 3, 6, 12, 24, 36, and 48 months of therapy. Secondary outcomes included the percentage of patients who lost 5% to 10% of their body weight from baseline; the percentage of patients who maintained ≥ 5% weight loss from baseline to 12, 24, 36, and 48 months if maintained on medication for that duration; duration of medication treatment in weeks; medication discontinuation rate; reason for medication discontinuation; enrollment in the MOVE! clinic and the time enrolled; percentage of patients with a BMI of 18 to 24.9 at the end of the study; and change in HbA_1c at 3, 6, 12, 24, 36, and 48 months.

Demographic data included race, age, sex, baseline weight, height, baseline BMI, and comorbid conditions (collected based on the most recent primary care clinical note before initiating medication). Medication data collected included medications used to manage comorbidities. Data related to weight management medication included prescribing clinic, maintenance dose of medication, duration of medication during the study period, the reason for medication discontinuation, or bariatric surgery intervention if applicable.

Basic descriptive statistics were used to characterize study participants. For continuous data, analysis of variance tests were used; if those results were not normal, then nonparametric tests were used, followed by pairwise tests between medication groups if the overall test was significant using the Fisher significant differences test. For nominal data, χ² or Fisher exact tests were used. For comparisons of primary and secondary outcomes, if the analyses needed to include adjustment for confounding variables, analysis of covariance was used for continuous data. A 2-sided 5% significance level was used for all tests.

RESULTS

A total of 228 instances of medication use were identified based on prescription fills; 123 did not meet inclusion criteria (117 for < 6 consecutive months of medication use) (Figure). The study included 105 participants with a mean age of 56 years; 80 were male (76.2%), and 85 identified as White race (81.0%). Mean (SD) weight was 130.1 kg (26.8) and BMI was 41.6 (7.2). The most common comorbid disease states among patients included hypertension, dyslipidemia, obstructive sleep apnea, and T2DM (Table 1). The baseline characteristics were comparable to those of Hood and colleagues.¹⁶

Most patients at VHI started on liraglutide (63%) or phentermine/topiramate (28%). For primary and secondary outcomes, statistics were calculated to determine whether the results were statistically significant for comparing the liraglutide and phentermine/topiramate subgroups. Sample sizes were too small for statistical analysis for bupropion/naltrexone, phentermine, and orlistat.

Primary Outcomes

The mean (SD) weight of participants dropped 8.1% from 130.1 kg to 119.5 kg over the patient-specific duration of weight management medication therapy for an absolute difference of 10.6 kg (9.7). Duration of individual medication use varied from 6 to 48 months. Weight loss was recorded at 6, 12, 24, 36, and 48 months of weight management therapy. Patient weight was not recorded after the medication was discontinued.

When classified by medication choice, the mean change in weight over the duration of the study was −23.9 kg for 2 patients using orlistat, −10.2 kg for 46 patients using liraglutide, −11.0 kg for 25 patients using phentermine/topiramate, -7.4 kg for 1 patient using phentermine, and -13.0 kg for 4 patients using naltrexone/bupropion. Patients without a weight documented at the end of their therapy or at the conclusion of the data collection period were not included in the total weight loss at the end of therapy. There were 78 documented instances of weight loss at the end of therapy (Table 2).

Body weight loss percentage was recorded at 6, 12, 24, 36, and 48 months of weight management therapy. The mean (SD) body weight loss percentage over the duration of the study was 9.2% (11.2). When classified by medication choice, the mean percentage of body weight loss was 16.8% for 2 patients using orlistat, 9.4% for 46 patients using liraglutide, 8.2% for 25 patients using phentermine/topiramate, 6.0% for 1 patient using phentermine alone, and 10.6% for 4 patients using naltrexone/bupropion (Table 3).

Secondary Outcomes

While none of the secondary outcomes were statistically significant, the results of this study suggest that both medications may contribute to weight loss in many patients included in this study. Almost two-thirds of the included patients analyzed lost ≥ 5% of weight from baseline while taking weight management medication. Sixty-six patients (63%) lost ≥ 5% of body weight at any time during the data collection period. When stratified by liraglutide and phentermine/topiramate, 41 patients (63%) taking liraglutide and 20 patients (67%) taking phentermine/topiramate lost ≥ 5% of weight from baseline. Of the 66 patients who lost ≥ 5% of body weight from baseline, 36 (55%) lost ≥ 10% of body weight from baseline at any time during the data collection period.

The mean (SD) duration for weight management medication use was 23 months (14.9). Phentermine/topiramate was tolerated longer than liraglutide: 22.7 months vs 21.7 months, respectively (Table 4).

The average overall documented medication discontinuation rate was 35.2%. Reasons for discontinuation included 21 patient-elected discontinuations, 8 patients no longer met criteria for use, 4 medications were no longer indicated, and 4 patients experienced AEs. It is unknown whether weight management medication was discontinued or not in 18 patients (17.2%).

This study evaluated the use and outcomes of weight loss medications over a longer period (up to 48 months) than what was previously studied among patients at VHI (12 months). The study aimed to better understand the long-term effect of weight loss medications, determine which medication had better long-term outcomes, and examine the reasons for medication discontinuation.

The results of this study displayed some similarities and differences compared with the Hood and colleagues study.¹⁶ Both yielded similar results for 5% of body weight loss and 10% of body weight loss. The largest difference was mean weight loss over the study period. In this study, patients lost a mean 10.6 kg over the course of weight loss medication use compared to 15.8 kg found by Hood and colleagues.¹⁶ A reason patients in the current study lost less weight overall could be the difference in time frames. The current study encompassed the COVID-19 pandemic, meaning fewer overall in-person patient appointments, which led to patients being lost to follow-up, missing weigh-ins during the time period, and gaps in care. For some patients, the pandemic possibly contributed to depression, missed medication doses, and a more sedentary lifestyle, leading to more weight gain.¹⁷ Telemedicine services at VHI expanded during the pandemic in an attempt to increase patient monitoring and counseling. It is unclear whether this expansion was enough to replace the in-person contact necessary to promote a healthy lifestyle.

VA pharmacists now care for patients through telehealth and are more involved in weight loss management. Since the conclusion of the Hood and colleagues study and start of this research, 2 pharmacists at VHI have been assigned to follow patients for obesity management to help with adherence to medication and lifestyle changes, management of AEs, dispense logistics, interventions for medications that may cause weight gain, and case management of glycemic control and weight loss with GLP-1RAs. Care management by pharmacists at VHI helps improve the logistics of titratable orders and save money by improving the use of high-cost items like GLP-1RAs. VA clinical pharmacy practitioners already monitor GLP-1RAs for patients with T2DM, so they are prepared to educate and assist patients with these medications.

It is important to continue developing a standardized process for weight loss medication management across the VA to improve the quality of patient care and optimize prescription outcomes. VA facilities differ in how weight loss management care is delivered and the level at which pharmacists are involved. Given the high rate of obesity among patients at the VA, the advent of new prescription options for weight loss, and the high cost associated with these medications, there has been increased attention to obesity care. Some Veterans Integrated Service Networks are forming a weight management community of practice groups to create standard operating procedures and algorithms to standardize care. Developing consistent processes is necessary to improve weight loss and patient care for veterans regardless where they receive treatment.

Limitations

The data used in this study were dependent on clinician documentation. Because of a lack of documentation in many instances, it was difficult to determine the full efficacy of the medications studied due to missing weight recordings. The lack of documentation made it difficult to determine whether patients were enrolled and active in the MOVE! program. It is required that patients enroll in MOVE! to obtain medications, but many did not have any follow-up MOVE! visits after initially obtaining their weight loss medication.

In this study, differences in the outcomes of patients with and without T2DM were not compared. It is the VA standard of care to prefer liraglutide over phentermine/topiramate in patients with T2DM or prediabetes.² This makes it difficult to assess whether phentermine/topiramate or liraglutide is more effective for weight loss in patients with T2DM. Weight gain after the discontinuation of weight loss medications was not assessed. Collecting this data may help determine whether a certain weight loss medication is less likely to cause rebound weight gain when discontinued.

Other limitations to this study consisted of excluding patients who discontinued therapy within 6 months, small sample sizes on some medications, and lack of data on adherence. Adherence was based on medication refills, which means that if a patient refilled the medication, it was assumed they were taking it. This is not always the case, and while accurate data on adherence is difficult to gather, it can impact how results may be interpreted. These additional limitations make it difficult to accurately determine the efficacy of the medications in this study.

CONCLUSIONS

This study found similar outcomes to what has been observed in larger clinical trials regarding weight loss medications. Nevertheless, there was a lack of accurate clinical documentation for most patients, which limits the conclusions. This lack of documentation potentially led to inaccurate results. It revealed that many patients at VHI did not uniformly receive consistent follow-up after starting a weight loss medication during the study period. With more standardized processes implemented at VA facilities, increased pharmacist involvement in weight loss medication management, and increased use of established telehealth services, patients could have the opportunity for closer follow-up that may lead to better weight loss outcomes. With these changes, there is more reason for additional studies to be conducted to assess follow-up, medication management, and weight loss overall.