Pharmacology

The emergency department (ED) is estimated to provide half of all medical care in the United States, serving as a conduit between ambulatory care and inpatient settings.¹ According to the Centers for Disease Control and Prevention, around 11 million antibiotic prescriptions were written in EDs in 2021.² A previous study conducted at a US Department of Veterans (VA) Affairs medical center found that about 40% of all antimicrobial use in the ED was inappropriate.³ The ED is a critical and high-yield space for antimicrobial stewardship efforts.⁴

Urinary tract infections (UTIs) are one of the most common reasons for ED visits.⁴ In 2018, there were about 3 million UTI discharge diagnoses reported in the US.⁵ Diagnosis and management of UTIs can vary depending on patient sex, upper or lower urinary tract involvement, and the severity of the infection.⁶ Most UTIs are uncomplicated and can be safely treated with oral antibiotics at home; however, if mismanaged, they can lead to increased morbidity and mortality.⁶

Antimicrobial prescribing in the ED is predominantly empiric with challenges such as diverse patient needs, rising antimicrobial resistance, and limited microbiologic data at the time of discharge.⁶ The lack of a standardized process for urine culture follow-up after discharge represents another major complicating factor in the outpatient management of UTIs. Studies have shown that ED pharmacists play a vital role in providing quality follow-up care by optimizing antimicrobial use, resulting in improved patient outcomes in various infectious syndromes, including UTIs.^7-13

Program Description

In June 2021, the VA Greater Los Angeles Healthcare System (VAGLAHS) piloted an ED pharmacist-led aftercare program to optimize postdischarge antimicrobial therapy management of UTIs. After a patient is discharged from the ED, the clinical pharmacist reviews urine culture results, interprets available antimicrobial susceptibility, conducts patient interviews, adjusts for patient-specific factors, and addresses potential antibiotic-associated adverse events. The ED pharmacist is then responsible for managing therapy changes in consultation with an ED health care practitioner (HCP).

Methods

This single center, retrospective chart review included veterans who were discharged with an oral antibiotic for UTI treatment from the VAGLAHS ED and evaluated by clinical pharmacists between June 1, 2021, and June 30, 2022. For patients with multiple ED visits, only the initial ED encounter was reviewed. Patients were excluded if they had a complicated UTI diagnosis requiring intravenous antibiotics or if they were admitted to the hospital. Data were generated through the Corporate Data Warehouse by VAGLAHS Pharmacy Informatics Service. Each patient was assigned a random number using the Microsoft Excel formula =RAND( ) and then sorted in chronological order to ensure randomization at baseline prior to data collection.

The primary aim of this quality improvement project was to characterize the impact of ED pharmacist-led interventions by evaluating the proportion of empiric to targeted therapy adjustments, antibiotic therapy discontinuation, and unmodified index treatment. The secondary objectives evaluated time to ED pharmacist aftercare follow-up, days of antibiotic exposure avoided, 30-day ED visits related to a urinary source, and transition of care documentation. Descriptive statistics were performed; median and IQR were calculated in Microsoft Excel.

A total of 548 ED UTI encounters were identified, including 449 patients with an index ED UTI aftercare follow-up evaluation. Of the 246 randomly screened patients, 200 veterans met inclusion criteria. The median age of included patients was 73 years and most (83.0%) were male (Table 1). One hundred thirty-two patients (66.0%) had a cystitis diagnosis, followed by complicated UTI (14.0%) and catheter-associated UTI (11.0%). The most frequently isolated uropathogen was Escherichia coli (30.5%). ß-lactams were prescribed for empiric treatment to 121 patients (60.5%), followed by 36 fluoroquinolones prescriptions (18.0%). The median treatment duration was 7 days.

The median time to ED pharmacist UTI aftercare evaluation was 2 days (Table 2). Sixty-seven cases required pharmacist intervention, which included 34 transitions to targeted therapy (17.0%) and 33 antibiotic discontinuations (16.5%). A total of 144 days of antibiotic exposure was avoided (ie, days antibiotic was prescribed minus days therapy administered). The majority of cases without modification to index therapy were due to appropriate empiric treatment selection (49.0%). Twelve (6.0%) patients had a subsequent urinary-related ED visit within 30 days due to 8 cases of persistent and/or worsening urinary symptoms (66.7%) and 2 cases of recurrent UTI (16.7%).

Discussion

Outpatient antibiotic prescribing for UTI management in the ED is challenging due to the absence of microbiologic data at time of diagnosis and lack of consistent transition of care follow-up.⁶ The VAGLAHS ED UTI aftercare program piloted a pharmacist-driven protocol for review of all urine cultures and optimization of antibiotic therapy.

Most ED UTI discharges that did not require pharmacist intervention had empiric treatment selection active against the clinical isolates. This suggests that the ED prescribing practices concur with theVAGLAHS antibiogram and treatment guidelines. Clinical pharmacists intervened in about one-third of UTI cases, which included modification or discontinuation of therapy. Further review of these cases demonstrated that about half of those with a subsequent 30-day ED visit related to a urinary source had therapy modification. Most patients with a 30-day ED visit had persistent and/or worsening urinary symptoms, prompting further exploratory workup.

Although this project did not evaluate time from urine culture results to aftercare review, the VAGLAHS ED pharmacists had a median follow-up time of 48 hours. This timeline mirrors the typical duration for urine culture results, suggesting that the pilot program allowed for real time pharmacist review and intervention. Consequently, this initiative resulted in the avoidance of 144 unnecessary days of antibiotic exposure.

While the current protocol highlights the work that ED pharmacists provide postdischarge, there are additional opportunities for pharmacist intervention. For example, one-third of these clinical encounters were completed without HCP notification, indicating an ongoing need to ensure continuity of care. Additionally, all 16 patients diagnosed with asymptomatic bacteriuria were discharged with an oral antibiotic, highlighting an opportunity to further optimize antibiotic prescribing prior to discharge. ED pharmacists continue to play an important role in mitigating inappropriate and unnecessary antibiotic use, which will reduce antibiotic-related adverse drug reactions, Clostridioides difficile infection, and antimicrobial resistance.

Limitations

Inconsistent and incomplete documentation of clinical data in the electronic health record made the characterization of patient encounters challenging. Furthermore, ED HCPs varying clinical practices may have impacted the heterogeneity of UTI diagnosis and management at VAGLAHS.

Conclusions

Implementation of an ED pharmacist-driven UTI aftercare program at VAGLAHS reduced unnecessary antimicrobial exposure, improved antibiotic management, and ensured continuity of care postdischarge. Findings from our project implicate possible future pharmacist involvement predischarge, such as targeting inappropriate asymptomatic bacteriuria treatment.^14-16 This pilot program suggested the feasibility of integrating antimicrobial stewardship practices within the ED setting in an ongoing effort to improve the quality of care for veterans.

The emergency department (ED) is estimated to provide half of all medical care in the United States, serving as a conduit between ambulatory care and inpatient settings.¹ According to the Centers for Disease Control and Prevention, around 11 million antibiotic prescriptions were written in EDs in 2021.² A previous study conducted at a US Department of Veterans (VA) Affairs medical center found that about 40% of all antimicrobial use in the ED was inappropriate.³ The ED is a critical and high-yield space for antimicrobial stewardship efforts.⁴

Urinary tract infections (UTIs) are one of the most common reasons for ED visits.⁴ In 2018, there were about 3 million UTI discharge diagnoses reported in the US.⁵ Diagnosis and management of UTIs can vary depending on patient sex, upper or lower urinary tract involvement, and the severity of the infection.⁶ Most UTIs are uncomplicated and can be safely treated with oral antibiotics at home; however, if mismanaged, they can lead to increased morbidity and mortality.⁶

Antimicrobial prescribing in the ED is predominantly empiric with challenges such as diverse patient needs, rising antimicrobial resistance, and limited microbiologic data at the time of discharge.⁶ The lack of a standardized process for urine culture follow-up after discharge represents another major complicating factor in the outpatient management of UTIs. Studies have shown that ED pharmacists play a vital role in providing quality follow-up care by optimizing antimicrobial use, resulting in improved patient outcomes in various infectious syndromes, including UTIs.^7-13

Program Description

In June 2021, the VA Greater Los Angeles Healthcare System (VAGLAHS) piloted an ED pharmacist-led aftercare program to optimize postdischarge antimicrobial therapy management of UTIs. After a patient is discharged from the ED, the clinical pharmacist reviews urine culture results, interprets available antimicrobial susceptibility, conducts patient interviews, adjusts for patient-specific factors, and addresses potential antibiotic-associated adverse events. The ED pharmacist is then responsible for managing therapy changes in consultation with an ED health care practitioner (HCP).

Methods

This single center, retrospective chart review included veterans who were discharged with an oral antibiotic for UTI treatment from the VAGLAHS ED and evaluated by clinical pharmacists between June 1, 2021, and June 30, 2022. For patients with multiple ED visits, only the initial ED encounter was reviewed. Patients were excluded if they had a complicated UTI diagnosis requiring intravenous antibiotics or if they were admitted to the hospital. Data were generated through the Corporate Data Warehouse by VAGLAHS Pharmacy Informatics Service. Each patient was assigned a random number using the Microsoft Excel formula =RAND( ) and then sorted in chronological order to ensure randomization at baseline prior to data collection.

The primary aim of this quality improvement project was to characterize the impact of ED pharmacist-led interventions by evaluating the proportion of empiric to targeted therapy adjustments, antibiotic therapy discontinuation, and unmodified index treatment. The secondary objectives evaluated time to ED pharmacist aftercare follow-up, days of antibiotic exposure avoided, 30-day ED visits related to a urinary source, and transition of care documentation. Descriptive statistics were performed; median and IQR were calculated in Microsoft Excel.

A total of 548 ED UTI encounters were identified, including 449 patients with an index ED UTI aftercare follow-up evaluation. Of the 246 randomly screened patients, 200 veterans met inclusion criteria. The median age of included patients was 73 years and most (83.0%) were male (Table 1). One hundred thirty-two patients (66.0%) had a cystitis diagnosis, followed by complicated UTI (14.0%) and catheter-associated UTI (11.0%). The most frequently isolated uropathogen was Escherichia coli (30.5%). ß-lactams were prescribed for empiric treatment to 121 patients (60.5%), followed by 36 fluoroquinolones prescriptions (18.0%). The median treatment duration was 7 days.

The median time to ED pharmacist UTI aftercare evaluation was 2 days (Table 2). Sixty-seven cases required pharmacist intervention, which included 34 transitions to targeted therapy (17.0%) and 33 antibiotic discontinuations (16.5%). A total of 144 days of antibiotic exposure was avoided (ie, days antibiotic was prescribed minus days therapy administered). The majority of cases without modification to index therapy were due to appropriate empiric treatment selection (49.0%). Twelve (6.0%) patients had a subsequent urinary-related ED visit within 30 days due to 8 cases of persistent and/or worsening urinary symptoms (66.7%) and 2 cases of recurrent UTI (16.7%).

Discussion

Outpatient antibiotic prescribing for UTI management in the ED is challenging due to the absence of microbiologic data at time of diagnosis and lack of consistent transition of care follow-up.⁶ The VAGLAHS ED UTI aftercare program piloted a pharmacist-driven protocol for review of all urine cultures and optimization of antibiotic therapy.

Most ED UTI discharges that did not require pharmacist intervention had empiric treatment selection active against the clinical isolates. This suggests that the ED prescribing practices concur with theVAGLAHS antibiogram and treatment guidelines. Clinical pharmacists intervened in about one-third of UTI cases, which included modification or discontinuation of therapy. Further review of these cases demonstrated that about half of those with a subsequent 30-day ED visit related to a urinary source had therapy modification. Most patients with a 30-day ED visit had persistent and/or worsening urinary symptoms, prompting further exploratory workup.

Although this project did not evaluate time from urine culture results to aftercare review, the VAGLAHS ED pharmacists had a median follow-up time of 48 hours. This timeline mirrors the typical duration for urine culture results, suggesting that the pilot program allowed for real time pharmacist review and intervention. Consequently, this initiative resulted in the avoidance of 144 unnecessary days of antibiotic exposure.

While the current protocol highlights the work that ED pharmacists provide postdischarge, there are additional opportunities for pharmacist intervention. For example, one-third of these clinical encounters were completed without HCP notification, indicating an ongoing need to ensure continuity of care. Additionally, all 16 patients diagnosed with asymptomatic bacteriuria were discharged with an oral antibiotic, highlighting an opportunity to further optimize antibiotic prescribing prior to discharge. ED pharmacists continue to play an important role in mitigating inappropriate and unnecessary antibiotic use, which will reduce antibiotic-related adverse drug reactions, Clostridioides difficile infection, and antimicrobial resistance.

Limitations

Inconsistent and incomplete documentation of clinical data in the electronic health record made the characterization of patient encounters challenging. Furthermore, ED HCPs varying clinical practices may have impacted the heterogeneity of UTI diagnosis and management at VAGLAHS.

Conclusions

Implementation of an ED pharmacist-driven UTI aftercare program at VAGLAHS reduced unnecessary antimicrobial exposure, improved antibiotic management, and ensured continuity of care postdischarge. Findings from our project implicate possible future pharmacist involvement predischarge, such as targeting inappropriate asymptomatic bacteriuria treatment.^14-16 This pilot program suggested the feasibility of integrating antimicrobial stewardship practices within the ED setting in an ongoing effort to improve the quality of care for veterans.

The emergency department (ED) is estimated to provide half of all medical care in the United States, serving as a conduit between ambulatory care and inpatient settings.¹ According to the Centers for Disease Control and Prevention, around 11 million antibiotic prescriptions were written in EDs in 2021.² A previous study conducted at a US Department of Veterans (VA) Affairs medical center found that about 40% of all antimicrobial use in the ED was inappropriate.³ The ED is a critical and high-yield space for antimicrobial stewardship efforts.⁴

Urinary tract infections (UTIs) are one of the most common reasons for ED visits.⁴ In 2018, there were about 3 million UTI discharge diagnoses reported in the US.⁵ Diagnosis and management of UTIs can vary depending on patient sex, upper or lower urinary tract involvement, and the severity of the infection.⁶ Most UTIs are uncomplicated and can be safely treated with oral antibiotics at home; however, if mismanaged, they can lead to increased morbidity and mortality.⁶

Antimicrobial prescribing in the ED is predominantly empiric with challenges such as diverse patient needs, rising antimicrobial resistance, and limited microbiologic data at the time of discharge.⁶ The lack of a standardized process for urine culture follow-up after discharge represents another major complicating factor in the outpatient management of UTIs. Studies have shown that ED pharmacists play a vital role in providing quality follow-up care by optimizing antimicrobial use, resulting in improved patient outcomes in various infectious syndromes, including UTIs.^7-13

Program Description

In June 2021, the VA Greater Los Angeles Healthcare System (VAGLAHS) piloted an ED pharmacist-led aftercare program to optimize postdischarge antimicrobial therapy management of UTIs. After a patient is discharged from the ED, the clinical pharmacist reviews urine culture results, interprets available antimicrobial susceptibility, conducts patient interviews, adjusts for patient-specific factors, and addresses potential antibiotic-associated adverse events. The ED pharmacist is then responsible for managing therapy changes in consultation with an ED health care practitioner (HCP).

Methods

This single center, retrospective chart review included veterans who were discharged with an oral antibiotic for UTI treatment from the VAGLAHS ED and evaluated by clinical pharmacists between June 1, 2021, and June 30, 2022. For patients with multiple ED visits, only the initial ED encounter was reviewed. Patients were excluded if they had a complicated UTI diagnosis requiring intravenous antibiotics or if they were admitted to the hospital. Data were generated through the Corporate Data Warehouse by VAGLAHS Pharmacy Informatics Service. Each patient was assigned a random number using the Microsoft Excel formula =RAND( ) and then sorted in chronological order to ensure randomization at baseline prior to data collection.

The primary aim of this quality improvement project was to characterize the impact of ED pharmacist-led interventions by evaluating the proportion of empiric to targeted therapy adjustments, antibiotic therapy discontinuation, and unmodified index treatment. The secondary objectives evaluated time to ED pharmacist aftercare follow-up, days of antibiotic exposure avoided, 30-day ED visits related to a urinary source, and transition of care documentation. Descriptive statistics were performed; median and IQR were calculated in Microsoft Excel.

A total of 548 ED UTI encounters were identified, including 449 patients with an index ED UTI aftercare follow-up evaluation. Of the 246 randomly screened patients, 200 veterans met inclusion criteria. The median age of included patients was 73 years and most (83.0%) were male (Table 1). One hundred thirty-two patients (66.0%) had a cystitis diagnosis, followed by complicated UTI (14.0%) and catheter-associated UTI (11.0%). The most frequently isolated uropathogen was Escherichia coli (30.5%). ß-lactams were prescribed for empiric treatment to 121 patients (60.5%), followed by 36 fluoroquinolones prescriptions (18.0%). The median treatment duration was 7 days.

The median time to ED pharmacist UTI aftercare evaluation was 2 days (Table 2). Sixty-seven cases required pharmacist intervention, which included 34 transitions to targeted therapy (17.0%) and 33 antibiotic discontinuations (16.5%). A total of 144 days of antibiotic exposure was avoided (ie, days antibiotic was prescribed minus days therapy administered). The majority of cases without modification to index therapy were due to appropriate empiric treatment selection (49.0%). Twelve (6.0%) patients had a subsequent urinary-related ED visit within 30 days due to 8 cases of persistent and/or worsening urinary symptoms (66.7%) and 2 cases of recurrent UTI (16.7%).

Discussion

Outpatient antibiotic prescribing for UTI management in the ED is challenging due to the absence of microbiologic data at time of diagnosis and lack of consistent transition of care follow-up.⁶ The VAGLAHS ED UTI aftercare program piloted a pharmacist-driven protocol for review of all urine cultures and optimization of antibiotic therapy.

Most ED UTI discharges that did not require pharmacist intervention had empiric treatment selection active against the clinical isolates. This suggests that the ED prescribing practices concur with theVAGLAHS antibiogram and treatment guidelines. Clinical pharmacists intervened in about one-third of UTI cases, which included modification or discontinuation of therapy. Further review of these cases demonstrated that about half of those with a subsequent 30-day ED visit related to a urinary source had therapy modification. Most patients with a 30-day ED visit had persistent and/or worsening urinary symptoms, prompting further exploratory workup.

Although this project did not evaluate time from urine culture results to aftercare review, the VAGLAHS ED pharmacists had a median follow-up time of 48 hours. This timeline mirrors the typical duration for urine culture results, suggesting that the pilot program allowed for real time pharmacist review and intervention. Consequently, this initiative resulted in the avoidance of 144 unnecessary days of antibiotic exposure.

While the current protocol highlights the work that ED pharmacists provide postdischarge, there are additional opportunities for pharmacist intervention. For example, one-third of these clinical encounters were completed without HCP notification, indicating an ongoing need to ensure continuity of care. Additionally, all 16 patients diagnosed with asymptomatic bacteriuria were discharged with an oral antibiotic, highlighting an opportunity to further optimize antibiotic prescribing prior to discharge. ED pharmacists continue to play an important role in mitigating inappropriate and unnecessary antibiotic use, which will reduce antibiotic-related adverse drug reactions, Clostridioides difficile infection, and antimicrobial resistance.

Limitations

Inconsistent and incomplete documentation of clinical data in the electronic health record made the characterization of patient encounters challenging. Furthermore, ED HCPs varying clinical practices may have impacted the heterogeneity of UTI diagnosis and management at VAGLAHS.

Conclusions

Implementation of an ED pharmacist-driven UTI aftercare program at VAGLAHS reduced unnecessary antimicrobial exposure, improved antibiotic management, and ensured continuity of care postdischarge. Findings from our project implicate possible future pharmacist involvement predischarge, such as targeting inappropriate asymptomatic bacteriuria treatment.^14-16 This pilot program suggested the feasibility of integrating antimicrobial stewardship practices within the ED setting in an ongoing effort to improve the quality of care for veterans.

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.

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.

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.