Publications

INTRODUCTION: Concurrent use of prescription opioids with gabapentinoids may pose risks of serious drug interactions. Yet, little is known about the trends in and patient characteristics associated with concurrent opioid-gabapentinoid use among older Medicare opioid users with chronic noncancer pain.

METHODS: A cross-sectional study was conducted among Medicare older beneficiaries (aged ≥65 years) with chronic noncancer pain who filled ≥1 opioid prescription within 3 months after a randomly selected chronic noncancer pain diagnosis (index date) in a calendar year between 2011 and 2018. Patient characteristics were measured in the 6-month baseline before the index date, and concurrent opioid-gabapentinoid use for ≥1 day was measured in the 3-month follow-up after the index date. Multivariable modified Poisson regression hwas used to assess the trends and characteristics of concurrent opioid-gabapentinoid use. Analyses were conducted from January to June 2021.

RESULTS: Among 464,721 eligible older beneficiaries with chronic noncancer pain and prescription opioids, the prevalence of concurrent opioid-gabapentinoid use increased from 17.0% in 2011 to 23.5% in 2018 (adjusted prevalence ratio=1.48, 95% CI=1.45, 1.53). Concurrent users versus opioid-only users tended to be non-Black, low-income subsidy recipients, and Southern residents. The clinical factors associated with concurrent opioid-gabapentinoid use included having a diagnosis of neuropathic pain, polypharmacy, and risk factors for opioid-related adverse events.

CONCLUSIONS: Concurrent opioid-gabapentinoid use among older Medicare beneficiaries with chronic noncancer pain and prescription opioids has increased significantly between 2011 and 2018. Future studies are warranted to investigate the impact of concurrent use on outcomes in older patients. Interventions that reduce inappropriate concurrent use may target older patients with identified characteristics.

BACKGROUND: Low-income subsidy/dual eligibility (LIS/DE) status and disability status may be associated with high-risk medication (HRM) use but are not usually accounted for in medication-use quality measures.

OBJECTIVE: To examine the association of: 1) LIS/DE status and HRM use; and 2) disability status and HRM use, while controlling for both health plan level effects and patient characteristics for Medicare beneficiaries enrolled in Medicare Advantage Prescription Drug Plans (MA-PD) and stand-alone Prescription Drug Plans (PDP).

METHODS: This retrospective cross-sectional study used 2013 Medicare data to determine if LIS/DE status and disability status were independently associated with HRM use (using the Pharmacy Quality Alliance HRM measure) in MA-PDs and PDPs. Multivariable generalized linear mixed models assessed the association of LIS/DE and HRM use, and disability and HRM use, after adjusting for health plan effect and patient-level confounders for MA-PD and PDP beneficiaries.

RESULTS: Of 520,019 MA-PD beneficiaries, 88,693 (17.1%) were LIS/DE and 48,997 (9.4%) were disabled. Of 881,264 PDP beneficiaries, 213,096 (24.2%) were LIS/DE, and 83,593 (9.5%) were disabled. LIS/DE beneficiaries had a higher percent of HRM users compared to non-LIS/DE MA-PD (13.3% vs. 9.7%, p < 0.001) and PDP (17.1% vs. 13.2%, p < 0.001) beneficiaries. Disabled beneficiaries had a higher percent of HRM users compared to non-disabled MA-PD (17.0% vs. 9.6%, p < 0.001) and PDP (22.9% vs. 13.2%, p < 0.001) beneficiaries. Multivariable analyses showed LIS/DE (adjusted odds ratio [AOR] = 1.07; 95% CI = 1.04, 1.10) and disability (AOR = 1.38; 95% CI = 1.34, 1.42) were associated with HRM use among MA-PD and PDP beneficiaries (LIS/DE AOR = 1.14; 95% CI = 1.12, 1.16; disability AOR = 1.37; 95% CI = 1.34, 1.40).

CONCLUSIONS: The association of LIS/DE and disability with higher HRM use in both MA-PD and PDP beneficiaries, when controlling for health plan effects and patient characteristics, suggests these factors should be considered when comparing health plan performance on HRM measures.

Limited real-world evidence exists for effectiveness and safety of extended oral anticoagulation beyond 6 months of initial treatment in prevention of recurrent venous thromboembolism (VTE) and adverse major bleeding events among patients with VTE. Using MarketScan Commercial and Medicare Supplemental databases (2013-2019), we conducted a retrospective cohort study to compare the risk of recurrent VTE and major bleeding events during extended treatment among patients with VTE who completed the 6-month initial treatment and received extended oral anticoagulant treatment with apixaban, warfarin, or no extended treatment. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using Cox proportional hazards modeling with inverse probability treatment weighting. We identified 14,818 patients with extended treatment of apixaban (n = 4,338), warfarin (n = 5,298), or no extended treatment (n = 5,182). Compared with no extended treatment, apixaban use was associated with decreased risk of recurrent VTE (HR: 0.10, 95% CI: 0.04-0.26) without increased risk of major bleeding events (HR: 1.06, 95% CI: 0.52-2.17); warfarin use was associated with decreased risk of recurrent VTE (HR: 0.23, 95% CI: 0.12-0.44) but with increased risk of major bleeding events (HR: 2.64, 95% CI: 1.51-4.59). Compared with warfarin, apixaban use was associated with decreased risk of major bleeding events (HR: 0.42, 95% CI: 0.22-0.80) but no difference in risk of recurrent VTE (HR: 0.46, 95% CI: 0.15-1.36). In a real-world clinical setting, extended anticoagulation with apixaban or warfarin was associated with decreased risk of recurrent VTE compared with no extended treatment, and apixaban had a better safety profile with fewer major bleeding events compared with warfarin among commercially insured patients with VTE.

BACKGROUND AND AIMS: One-third of opioid (OPI) overdose deaths involve concurrent benzodiazepine (BZD) use. Little is known about concurrent opioid and benzodiazepine use (OPI-BZD) most associated with overdose risk. We aimed to examine associations between OPI-BZD dose and duration trajectories, and subsequent OPI or BZD overdose in US Medicare.

DESIGN: Retrospective cohort study.

SETTING: US Medicare.

PARTICIPANTS: Using a 5% national Medicare data sample (2013-16) of fee-for-service beneficiaries without cancer initiating OPI prescriptions, we identified 37 879 beneficiaries (age ≥ 65 = 59.3%, female = 71.9%, white = 87.6%, having OPI overdose = 0.3%).

MEASUREMENTS: During the 6 months following OPI initiation (i.e. trajectory period), we identified OPI-BZD dose and duration patterns using group-based multi-trajectory models, based on average daily morphine milligram equivalents (MME) for OPIs and diazepam milligram equivalents (DME) for BZDs. To label dose levels in each trajectory, we defined OPI use as very low (< 25 MME), low (25-50 MME), moderate (51-90 MME), high (91-150 MME) and very high (>150 MME) dose. Similarly, we defined BZD use as very low (< 10 DME), low (10-20 DME), moderate (21-40 DME), high (41-60 DME) and very high (> 60 DME) dose. Our primary analysis was to estimate the risk of time to first hospital or emergency department visit for OPI overdose within 6 months following the trajectory period using inverse probability of treatment-weighted Cox proportional hazards models.

FINDINGS: We identified nine distinct OPI-BZD trajectories: group A: very low OPI (early discontinuation)-very low declining BZD (n = 10 598; 28.0% of the cohort); B: very low OPI (early discontinuation)-very low stable BZD (n = 4923; 13.0%); C: very low OPI (early discontinuation)-medium BZD (n = 4997; 13.2%); D: low OPI-low BZD (n = 5083; 13.4%); E: low OPI-high BZD (n = 3906; 10.3%); F: medium OPI-low BZD (n = 3948; 10.4%); G: very high OPI-high BZD (n = 1371; 3.6%); H: very high OPI-very high BZD (n = 957; 2.5%); and I: very high OPI-low BZD (n = 2096; 5.5%). Compared with group A, five trajectories (32.3% of the study cohort) were associated with increased 6-month OPI overdose risks: E: low OPI-high BZD [hazard ratio (HR) = 3.27, 95% confidence interval (CI) = 1.61-6.63]; F: medium OPI-low BZD (HR = 4.04, 95% CI = 2.06-7.95); G: very high OPI-high BZD (HR = 6.98, 95% CI = 3.11-15.64); H: very high OPI-very high BZD (HR = 4.41, 95% CI = 1.51-12.85); and I: very high OPI-low BZD (HR = 6.50, 95% CI = 3.15-13.42).

CONCLUSIONS: Patterns of concurrent opioid and benzodiazepine use most associated with overdose risk among fee-for-service US Medicare beneficiaries initiating opioid prescriptions include very high-dose opioid use (MME > 150), high-dose benzodiazepine use (DME > 40) or medium-dose opioid with low-dose benzodiazepine use.

Using 2003−2018 National Ambulatory Medical Care Survey data for office-based visits and 2003−2018 National Hospital Ambulatory Medical Care Survey data for emergency department (ED) visits, we conducted cross-sectional analyses to examine cough medication (CM) use trends in the United States (US) ambulatory care settings. We included adult (≥18 years) patient visits with respiratory-infection-related or non-infection-related cough as reason-for-visit or diagnosis without malignant cancer or benign respiratory tumor diagnoses. Using multivariable logistic regressions, we examined opioid antitussive, benzonatate, dextromethorphan-containing antitussive, and gabapentinoid use trends. From 2003−2005 to 2015−2018, opioid antitussive use decreased in office-based visits (8.8% to 6.4%, Ptrend = 0.03) but remained stable in ED visits (6.3% to 5.9%, Ptrend = 0.99). In both settings, hydrocodone-containing antitussive use declined over 50%. Benzonatate use more than tripled (office-based:1.6% to 4.8%; ED:1.5% to 8.0%; both Ptrend < 0.001). Dextromethorphan-containing antitussive use increased in ED visits (1.8% to 2.6%, Ptrend = 0.003) but stayed unchanged in office-based visits (3.8% to 2.7%; Ptrend = 0.60). Gabapentinoid use doubled in office-based visits (1.1% in 2006−2008 to 2.4% in 2015−2018, Ptrend < 0.001) but was negligible in ED visits. In US office-based and ED ambulatory care settings, hydrocodone-containing antitussive use substantially declined from 2003 to 2018, while benzonatate use more than tripled, and dextromethorphan-containing antitussive and gabapentinoid use remained low (<3%).

IMPORTANCE: State Medicaid programs have implemented initiatives to expand treatment coverage for opioid use disorder (OUD); however, some Medicaid programs still require prior authorizations (PAs) for filling buprenorphine prescriptions.

OBJECTIVE: To evaluate the changes in buprenorphine use for OUD among Medicaid enrollees in states that completely removed buprenorphine PA requirements.

DESIGN SETTING AND PARTICIPANTS: This retrospective cross-sectional study analyzed the immediate and trend changes on buprenorphine use during 2013 to 2020 associated with removal of PA requirements using a controlled interrupted time series analysis to account for autocorrelation. Data were collected from Medicaid State Drug Utilization Data for 2 states (California and Illinois) that completely removed a buprenorphine PA during the study period, and buprenorphine prescriptions for OUD treatment were identified among Medicaid enrollees.

MAIN OUTCOMES AND MEASURES: Quarterly total number of buprenorphine prescriptions for each state was calculated, and stratification analyses were conducted by dosage form (films and tablets).

RESULTS: Among the 2 state Medicaid programs (California and Illinois) that removed buprenorphine PAs, there was a total of 702 643 and 415 115 eligible buprenorphine prescription claims, respectively. After removing PA requirements for buprenorphine, there was an immediate increase that was not statistically significant (rate ratio [RR], 1.11; 95% CI, 0.76-1.61) in the number of all buprenorphine prescriptions in California and a statistically significant increase (RR, 6.99; 95% CI, 4.67-10.47) in the number of all buprenorphine prescriptions in Illinois relative to the change in the control states (Alabama, Florida, Idaho, Kansas, Mississippi, Nevada, South Dakota, and Wyoming). Additionally, there was a statistically significant decreasing trend in the number of all buprenorphine prescriptions in California (RR, 0.88; 95% CI, 0.82-0.94) and a statistically significant increasing trend in Illinois (RR, 1.11; 95% CI, 1.05-1.19) relative to the trend in control states.

CONCLUSIONS AND RELEVANCE: In this cross-sectional study, removal of buprenorphine PA requirements was associated with a statistically significant increase in the number of buprenorphine prescription fills among Medicaid populations in 1 of the 2 included states.

Suicide is a leading cause of death in the US. Patients with pain conditions have higher suicidal risks. In a systematic review searching observational studies from multiple sources (e.g., MEDLINE) from 1 January 2000-12 September 2020, we evaluated existing suicide prediction models' (SPMs) performance and identified risk factors and their derived data sources among patients with pain conditions. The suicide-related outcomes included suicidal ideation, suicide attempts, suicide deaths, and suicide behaviors. Among the 87 studies included (with 8 SPM studies), 107 suicide risk factors (grouped into 27 categories) were identified. The most frequently occurring risk factor category was depression and their severity (33%). Approximately 20% of the risk factor categories would require identification from data sources beyond structured data (e.g., clinical notes). For 8 SPM studies (only 2 performing validation), the reported prediction metrics/performance varied: C-statistics (n = 3 studies) ranged 0.67-0.84, overall accuracy(n = 5): 0.78-0.96, sensitivity(n = 2): 0.65-0.91, and positive predictive values(n = 3): 0.01-0.43. Using the modified Quality in Prognosis Studies tool to assess the risk of biases, four SPM studies had moderate-to-high risk of biases. This systematic review identified a comprehensive list of risk factors that may improve predicting suicidal risks for patients with pain conditions. Future studies need to examine reasons for performance variations and SPM's clinical utility.

PURPOSE: To conduct a systematic review to identify studies that assessed the association between CYP2C19 polymorphisms and clinical outcomes in peripheral artery disease (PAD) patients who took clopidogrel.

METHODS: We systematically searched Ovid EMBASE, PubMed, and Web of Science from November 1997 (inception) to September 2020. We included observational studies evaluating how CYP2C19 polymorphism is associated with clopidogrel's effectiveness and safety among patients with PAD. We extracted relevant information details from eligible studies (e.g., study type, patient population, study outcomes). We used the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) Tool to assess the risk of bias for included observational studies.

RESULTS: The outcomes of interest were the effectiveness and safety of clopidogrel. The effectiveness outcomes included clinical ineffectiveness (e.g., restenosis). The safety outcomes included bleeding and death related to the use of clopidogrel. We identified four observational studies with a sample size ranging from 50 to 278. Outcomes and comparison groups of the studies varied. Three studies (75%) had an overall low risk of bias. All included studies demonstrated that carrying CYP2C19 loss of function (LOF) alleles was significantly associated with reduced clinical effectiveness and safety of clopidogrel.

CONCLUSIONS: Our systematic review showed an association between CYP2C19 LOF alleles and reduced functions of clopidogrel. The use of CYP2C19 testing in PAD patients prescribed clopidogrel may help improve the clinical outcomes. However, based on the limited evidence, there is a need for randomized clinical trials in PAD patients to test both the effectiveness and safety outcomes of clopidogrel.

BACKGROUND AND AIMS: The time lag encountered when accessing health-care data is one major barrier to implementing opioid overdose prediction measures in practice. Little is known regarding how one's opioid overdose risk changes over time. We aimed to identify longitudinal patterns of individual predicted overdose risks among Medicaid beneficiaries after initiation of opioid prescriptions.

DESIGN, SETTING AND PARTICIPANTS: A retrospective cohort study in Pennsylvania, USA among Pennsylvania Medicaid beneficiaries aged 18-64 years who initiated opioid prescriptions between July 2017 and September 2018 (318 585 eligible beneficiaries (mean age = 39 ± 12 years, female = 65.7%, White = 62.2% and Black = 24.9%).

MEASUREMENTS: We first applied a previously developed and validated machine-learning algorithm to obtain risk scores for opioid overdose emergency room or hospital visits in 3-month intervals for each beneficiary who initiated opioid therapy, until disenrollment from Medicaid, death or the end of observation (December 2018). We performed group-based trajectory modeling to identify trajectories of these predicted overdose risk scores over time.

FINDINGS: Among eligible beneficiaries, 0.61% had one or more occurrences of opioid overdose in a median follow-up of 15 months. We identified five unique opioid overdose risk trajectories: three trajectories (accounting for 92% of the cohort) had consistent overdose risk over time, including consistent low-risk (63%), consistent medium-risk (25%) and consistent high-risk (4%) groups; another two trajectories (accounting for 8%) had overdose risks that substantially changed over time, including a group that transitioned from high- to medium-risk (3%) and another group that increased from medium- to high-risk over time (5%).

CONCLUSIONS: More than 90% of Medicaid beneficiaries in Pennsylvania USA with one or more opioid prescriptions had consistent, predicted opioid overdose risks over 15 months. Applying opioid prediction algorithms developed from historical data may not be a major barrier to implementation in practice for the large majority of individuals.

BACKGROUND AND AIMS: We aimed to develop and validate machine learning algorithms to predict direct-acting antiviral (DAA) treatment failure among patients with HCV infection.

APPROACH AND RESULTS: We used HCV-TARGET registry data to identify HCV-infected adults receiving all-oral DAA treatment and having virologic outcome. Potential pretreatment predictors (n = 179) included sociodemographic, clinical characteristics, and virologic data. We applied multivariable logistic regression as well as elastic net, random forest, gradient boosting machine (GBM), and feedforward neural network machine learning algorithms to predict DAA treatment failure. Training (n = 4894) and validation (n = 1631) patient samples had similar sociodemographic and clinical characteristics (mean age, 57 years; 60% male; 66% White; 36% with cirrhosis). Of 6525 HCV-infected adults, 95.3% achieved sustained virologic response, whereas 4.7% experienced DAA treatment failure. In the validation sample, machine learning approaches performed similarly in predicting DAA treatment failure (C statistic [95% CI]: GBM, 0.69 [0.64-0.74]; random forest, 0.68 [0.63-0.73]; feedforward neural network, 0.66 [0.60-0.71]; elastic net, 0.64 [0.59-0.70]), and all four outperformed multivariable logistic regression (0.51 [0.46-0.57]). Using the Youden index to identify the balanced risk score threshold, GBM had 66.2% sensitivity and 65.1% specificity, and 12 individuals were needed to evaluate to identify 1 DAA treatment failure. Over 55% of patients with treatment failure were classified by the GBM in the top three risk decile subgroups (positive predictive value: 6%-14%). The top 10 GBM-identified predictors included albumin, liver enzymes (aspartate aminotransferase, alkaline phosphatase), total bilirubin levels, sex, HCV viral loads, sodium level, HCC, platelet levels, and tobacco use.

CONCLUSIONS: Machine learning algorithms performed effectively for risk prediction and stratification of DAA treatment failure.