Thromboembolic Events in Users of Warfarin Treated with Different Skeletal Muscle Relaxants.

Background and Objectives: Warfarin and a skeletal muscle relaxant are co-treatments in nearly a quarter-million annual United States (US) office visits. Despite international calls to minimize patient harm arising from anticoagulant drug interactions, scant data exist on clinical outcomes in real-world populations. We examined effects of concomitant use of warfarin and individual muscle relaxants on rates of hospitalization for thromboembolism among economically disadvantaged persons. Materials and Methods: Using 1999−2012 administrative data of four US state Medicaid programs, we conducted 16 retrospective self-controlled case series studies: half included concomitant users of warfarin + one of eight muscle relaxants; half included concomitant users of an inhaled corticosteroid (ICS) + one of eight muscle relaxants. The ICS analyses served as negative control comparisons. In each study, we calculated incidence rate ratios (IRRs) comparing thromboembolism rates in the co-exposed versus warfarin/ICS-only exposed person-time, adjusting for time-varying confounders. Results: Among ~70 million persons, we identified 8693 warfarin-treated subjects who concomitantly used a muscle relaxant, were hospitalized for thromboembolism, and met all other inclusion criteria. Time-varying confounder-adjusted IRRs ranged from 0.31 (95% confidence interval: 0.13−0.77) for metaxalone to 3.44 (95% confidence interval: 1.53−7.78) for tizanidine. The tizanidine finding was robust after quantitatively adjusting for negative control ICS findings, and in numerous prespecified secondary analyses. Conclusions: We identified a potential >3-fold increase in the rate of hospitalized thromboembolism in concomitant users of warfarin + tizanidine vs. warfarin alone. Alternative explanations for this finding include confounding by indication, a native effect of tizanidine, or chance.

Identifying Clinically Relevant Drug-Drug Interactions With Methadone and Buprenorphine: A Translational Approach to Signal Detection.

Methadone and buprenorphine have pharmacologic properties that are concerning for a high risk of drug-drug interactions (DDIs). We performed high-throughput screening for clinically relevant DDIs with methadone or buprenorphine by combining pharmacoepidemiologic and pharmacokinetic approaches. We conducted pharmacoepidemiologic screening via a series of self-controlled case series studies (SCCS) in Optum claims data from 2000 to 2019. We included persons 18 years or older who experienced an outcome of interest during target drug treatment. Exposures were all overlapping medications (i.e., the candidate precipitants) during target drug treatment. Outcomes were opioid overdose, non-overdose adverse effects, and cardiac arrest. We used conditional Poisson regression to calculate rate ratios, accounting for multiple comparisons with semi-Bayes shrinkage. We explored the impact of key study design choices in analyses that varied the exposure definitions of the target drugs and the candidate precipitant drugs. Pharmacokinetic screening was conducted by incorporating published data on CYP enzyme metabolism into an equation-based static model. In SCCS analysis, 1,432 events were included from 248,069 new users of methadone or buprenorphine. In the primary analysis, statistically significant DDIs included gabapentinoids with either methadone or buprenorphine; baclofen with methadone; and benzodiazepines with methadone. In sensitivity analysis, additional statistically significant DDIs included methocarbamol, quetiapine, or simvastatin with methadone. Pharmacokinetic screening identified two moderate-to-strong potential DDIs (clonidine and fluconazole with buprenorphine). The combination of clonidine and buprenorphine was also associated with a significantly increased risk of opioid overdose in pharmacoepidemiologic screening. These DDI signals may be the most important targets for future confirmation studies.

Skeletal muscle relaxant drug-drug-drug interactions and unintentional traumatic injury: Screening to detect three-way drug interaction signals.

AIM: The aim of this study was to identify skeletal muscle relaxant (SMR) drug-drug-drug interaction (3DI) signals associated with increased rates of unintentional traumatic injury. METHODS: We conducted automated high-throughput pharmacoepidemiologic screening of 2000-2019 healthcare data for members of United States commercial and Medicare Advantage health plans. We performed a self-controlled case series study for each drug triad consisting of an SMR base-pair (i.e., concomitant use of an SMR with another medication), and a co-dispensed medication (i.e., candidate interacting precipitant) taken during ongoing use of the base-pair. We included patients aged ≥16 years with an injury occurring during base-pair-exposed observation time. We used conditional Poisson regression to calculate adjusted rate ratios (RRs) with 95% confidence intervals (CIs) for injury with each SMR base-pair + candidate interacting precipitant (i.e., triad) versus the SMR-containing base-pair alone. RESULTS: Among 58 478 triads, 29 were significantly positively associated with injury; confounder-adjusted RRs ranged from 1.39 (95% CI = 1.01-1.91) for tizanidine + omeprazole with gabapentin to 2.23 (95% CI = 1.02-4.87) for tizanidine + diclofenac with alprazolam. Most identified 3DI signals are new and have not been formally investigated. CONCLUSION: We identified 29 SMR 3DI signals associated with increased rates of injury. Future aetiologic studies should confirm or refute these SMR 3DI signals.

Population-based signals of benzodiazepine drug interactions associated with unintentional traumatic injury.

Benzodiazepine receptor agonists and related medications, such as Z-drugs and dual orexin receptor antagonists (BZDs), have been associated with unintentional traumatic injury due to their central nervous system (CNS)-depressant effects. Drug-drug interactions (DDIs) may contribute to the known relationship between BZD use and unintentional traumatic injury, yet evidence is still lacking. We conducted high-throughput pharmacoepidemiologic screening using the self-controlled case series design in a large US commercial health insurance database to identify potentially clinically relevant DDI signals among new users of BZDs. We used conditional Poisson regression to estimate rate ratios (RRs) between each co-exposure (vs. not) and unintentional traumatic injury (primary outcome), typical hip fracture (secondary outcome), and motor vehicle crash (secondary outcome). We identified 48 potential DDI signals (1.1%, involving 39 unique co-dispensed drugs), i.e., with statistically significant elevated adjusted RRs for injury. Signals were strongest for DDI pairs involving zolpidem, lorazepam, temazepam, alprazolam, eszopiclone, triazolam, and clonazepam. We also identified four potential DDI signals for typical hip fracture, but none for motor vehicle crash. Many signals have biologically plausible explanations through additive or synergistic pharmacodynamic effects of co-dispensed antidepressants, opioids, or muscle relaxants on CNS depression, impaired psychomotor and cognitive function, and/or somnolence. While other signals that lack an obvious mechanism may represent true associations that place patients at risk of injury, it is also prudent to consider the roles of chance, reverse causation, and/or confounding by indication, which merit further exploration. Given the high-throughput nature of our investigation, findings should be interpreted as hypothesis generating.

Signals of Muscle Relaxant Drug Interactions Associated with Unintentional Traumatic Injury: A Population-Based Screening Study.

BACKGROUND: Use of muscle relaxants is rapidly increasing in the USA. Little is understood about the role of drug interactions in the known association between muscle relaxants and unintentional traumatic injury, a clinically important endpoint causing substantial morbidity, disability, and death. OBJECTIVE: We examined potential associations between concomitant drugs (i.e., precipitants) taken with muscle relaxants (affected drugs, i.e., objects) and hospital presentation for unintentional traumatic injury. METHODS: In a series of self-controlled case series studies, we screened to identify drug interaction signals for muscle relaxant + precipitant pairs and unintentional traumatic injury. We used Optum's de-identified Clinformatics® Data Mart Database, 2000-2019. We included new users of a muscle relaxant, aged 16-90 years, who were dispensed at least one precipitant drug and experienced an unintentional traumatic injury during the observation period. We classified each observation day as precipitant exposed or precipitant unexposed. The outcome was an emergency department or inpatient discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to estimate rate ratios adjusting for time-varying confounders and then accounted for multiple estimation via semi-Bayes shrinkage. RESULTS: We identified 74,657 people who initiated muscle relaxants and experienced an unintentional traumatic injury, in whom we studied concomitant use of 2543 muscle relaxant + precipitant pairs. After adjusting for time-varying confounders, 16 (0.6%) pairs were statistically significantly and positively associated with injury, and therefore deemed signals of a potential drug interaction. Among signals, semi-Bayes shrunk, confounder-adjusted rate ratios ranged from 1.29 (95% confidence interval 1.04-1.62) for baclofen + sertraline to 2.28 (95% confidence interval 1.14-4.55) for methocarbamol + lamotrigine. CONCLUSIONS: Using real-world data, we identified several new signals of potential muscle relaxant drug interactions associated with unintentional traumatic injury. Only one among 16 signals is currently reported in a major drug interaction knowledge base. Future studies should seek to confirm or refute these signals.

Comparative Safety of Dipeptidyl Peptidase-4 Inhibitors and Sudden Cardiac Arrest and Ventricular Arrhythmia: Population-Based Cohort Studies.

In vivo studies suggest that arrhythmia risk may be greater with less selective dipeptidyl peptidase-4 inhibitors, but evidence from population-based studies is missing. We aimed to compare saxagliptin, sitagliptin, and linagliptin with regard to risk of sudden cardiac arrest (SCA)/ventricular arrhythmia (VA). We conducted high-dimensional propensity score (hdPS) matched, new-user cohort studies. We analyzed Medicaid and Optum Clinformatics separately. We identified new users of saxagliptin, sitagliptin (both databases), and linagliptin (Optum only). We defined SCA/VA outcomes using emergency department and inpatient diagnoses. We identified and then controlled for confounders via a data-adaptive, hdPS approach. We generated marginal hazard ratios (HRs) via Cox proportional hazards regression using a robust variance estimator while adjusting for calendar year. We identified the following matched comparisons: saxagliptin vs. sitagliptin (23,895 vs. 96,972) in Medicaid, saxagliptin vs. sitagliptin (48,388 vs. 117,383) in Optum, and linagliptin vs. sitagliptin (36,820 vs. 78,701) in Optum. In Medicaid, use of saxagliptin (vs. sitagliptin) was associated with an increased rate of SCA/VA (adjusted HR (aHR), 2.01, 95% confidence interval (CI) 1.24-3.25). However, in Optum data, this finding was not present (aHR, 0.79, 95% CI 0.41-1.51). Further, we found no association between linagliptin (vs. sitagliptin) and SCA/VA (aHR, 0.65, 95% CI 0.36-1.17). We found discordant results regarding the association between SCA/VA with saxagliptin compared with sitagliptin in two independent datasets. It remains unclear whether these findings are due to heterogeneity of treatment effect in the different populations, chance, or unmeasured confounding.

Population-Based Signals of Antidepressant Drug Interactions Associated With Unintentional Traumatic Injury.

Antidepressants are very widely used and associated with traumatic injury, yet little is known about their potential for harmful drug interactions. We aimed to identify potential drug interaction signals by assessing concomitant medications (precipitant drugs) taken with individual antidepressants (object drugs) that were associated with unintentional traumatic injury. We conducted pharmacoepidemiologic screening of 2000-2015 Optum Clinformatics data, identifying drug interaction signals by performing self-controlled case series studies for antidepressant + precipitant pairs and injury. We included persons aged 16-90 years codispensed an antidepressant and ≥ 1 precipitant drug(s), with an injury during antidepressant therapy. We classified antidepressant person-days as either precipitant-exposed or precipitant-unexposed. The outcome was an emergency department or inpatient discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to calculate confounder adjusted rate ratios (RRs) and accounted for multiple estimation via semi-Bayes shrinkage. We identified 330,884 new users of antidepressants who experienced an injury. Among such persons, we studied concomitant use of 7,953 antidepressant + precipitant pairs. Two hundred fifty-six (3.2%) pairs were positively associated with injury and deemed potential drug interaction signals; 22 of these signals had adjusted RRs > 2.00. Adjusted RRs ranged from 1.06 (95% confidence interval: 1.00-1.12, P = 0.04) for citalopram + gabapentin to 3.06 (1.42-6.60) for nefazodone + levonorgestrel. Sixty-five (25.4%) signals are currently reported in a seminal drug interaction knowledgebase. We identified numerous new population-based signals of antidepressant drug interactions associated with unintentional traumatic injury. Future studies, intended to test hypotheses, should confirm or refute these potential interactions.

National outpatient medication utilization for opioid and alcohol use disorders from 2014 to 2016.

BACKGROUND AND AIMS: Research has recommended a combination of pharmacotherapy and behavioral therapy to treat opioid use disorder (OUD) or alcohol use disorder (AUD). The objective of this study was to estimate the prevalence of U.S. outpatient visits in which patients had a documented OUD or AUD and in what proportion of these visits the patient was receiving medication for OUD (MOUD) or AUD (MAUD), alone or in combination with behavioral therapy. DESIGN: Cross-sectional analysis of the National Ambulatory Medical Care Survey (NAMCS) from 2014 to 2016. SETTING: NAMCS provides national estimates based on the latest census data, for all U.S. outpatient medical visits. PARTICIPANTS/CASES: All visits involving patients aged ≥18 years with an OUD or AUD diagnosis. MEASUREMENT: Medications for OUD included buprenorphine, buprenorphine/naloxone, or naltrexone; medications for AUD included acamprosate, disulfiram, or naltrexone. We defined behavioral therapy as provision of psychosocial therapy, mental health counseling, or stress management. We also compared annualized data between 2014 and 2016 using the Chi-square test. FINDINGS: From 2014 to 2016, NAMCS recorded nearly 2.3 billion adult outpatient visits, including 17.1 million and 21.7 million visits involving patients with an OUD or AUD diagnosis, respectively. From 2014 to 2016, a decreased prevalence of annual visits involved AUD (11.7 vs. 9.9/1000, P < 0.0001), while those for OUD increased (9.3 vs. 13.3/1000, P < 0.0001). Among office visits with an OUD diagnosis, a MOUD was documented in 14.2 million (83.1%) visits and behavioral therapy was provided in 4.4 million (25.6%). Among office visits with an AUD diagnosis, an MAUD was documented in approximately 800,000 (3.6%) and behavioral therapy in 5.4 million (24.8%). CONCLUSION: These data highlight an opportunity to increase the use of MAUD and offer behavioral therapy to those with OUD and/or AUD.

Screening to identify signals of opioid drug interactions leading to unintentional traumatic injury.

BACKGROUND: Efforts to minimize harms from opioid drug interactions may be hampered by limited evidence on which drugs, when taken concomitantly with opioids, result in adverse clinical outcomes. OBJECTIVE: To identify signals of opioid drug interactions by identifying concomitant medications (precipitant drugs) taken with individual opioids (object drugs) that are associated with unintentional traumatic injury DESIGN: We conducted pharmacoepidemiologic screening of Optum Clinformatics Data Mart, identifying drug interaction signals by performing confounder-adjusted self-controlled case series studies for opioid + precipitant pairs and injury. SETTING: Beneficiaries of a major United States-based commercial health insurer during 2000-2015 PATIENTS: Persons aged 16-90 years co-dispensed an opioid and ≥1 precipitant drug(s), with an unintentional traumatic injury event during opioid therapy, as dictated by the case-only design EXPOSURE: Precipitant-exposed (vs. precipitant-unexposed) person-days during opioid therapy. OUTCOME: Emergency department or inpatient International Classification of Diseases discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to generate confounder adjusted rate ratios. We accounted for multiple estimation via semi-Bayes shrinkage. RESULTS: We identified 25,019, 12,650, and 10,826 new users of hydrocodone, tramadol, and oxycodone who experienced an unintentional traumatic injury. Among 464, 376, and 389 hydrocodone-, tramadol-, and oxycodone-precipitant pairs examined, 20, 17, and 16 (i.e., 53 pairs, 34 unique precipitants) were positively associated with unintentional traumatic injury and deemed potential drug interaction signals. Adjusted rate ratios ranged from 1.23 (95 % confidence interval: 1.05-1.44) for hydrocodone + amoxicillin-clavulanate to 4.21 (1.88-9.42) for oxycodone + telmisartan. Twenty (37.7 %) of 53 signals are currently reported in a major drug interaction knowledgebase. LIMITATIONS: Potential for reverse causation, confounding by indication, and chance CONCLUSIONS: We identified previously undescribed and/or unappreciated signals of opioid drug interactions associated with unintentional traumatic injury. Subsequent etiologic studies should confirm (or refute) and elucidate these potential drug interactions.

Assessment of Physician Prescribing of Muscle Relaxants in the United States, 2005-2016.

Importance: Little is known to date about national trends in the prescribing of skeletal muscle relaxants (SMRs), the use of which is associated with important safety concerns, especially in older adults and in those who use concomitant opioids. Objective: To measure national trends in SMR prescribing over a 12-year period. Design, Setting, and Participants: This cross-sectional study used data from the National Ambulatory Medical Care Survey from January 2005 to December 2016. Data were analyzed from August 21, 2018, to July 18, 2019. The study included patients with ambulatory care visits who had encounters with non-federally funded, office-based physicians in the United States. Exposures: SMR use, categorized as newly prescribed or continued therapy at the office visit. Main Outcomes and Measures: Ambulatory care visits-overall and stratified by calendar year, geographic region, and patient age, sex, and race-in which an SMR was newly prescribed or continued were quantified. Among office visits in which an SMR was newly prescribed, diagnoses were assessed. Concomitant medications were quantified for all office visits, stratified by new or continued therapy. Survey visit weights were used to estimate nationally representative measures, and age-standardized rates were generated by geographic region using US Census data. Results: This study included a total of 314 970 308 office visits (mean [SD] age, 53.5 [15.2] years; 194 621 102 [61.8%] men and 120 349 206 [38.2%] women). In 2016, there were 30 730 262 (95% CI, 30 626 464-30 834 060) US ambulatory care visits in which an SMR was either newly prescribed or continued as ongoing therapy. Patients in these visits were most frequently female (58.2% [95% CI, 57.9%-58.6%]), white (53.7% [95% CI, 53.4%-54.0%]), and aged 45 to 64 years (48.5% [95% CI, 48.2%-48.9%]). During the study period, office visits with a prescribed SMR nearly doubled from 15.5 million (95% CI, 15.4-15.6 million) in 2005 to 30.7 million (95% CI, 30.6-30.8 million) in 2016. Although visits for new SMR prescriptions remained stable, office visits with continued SMR drug therapy tripled from 8.5 million (95% CI, 8.4-8.5 million) visits in 2005 to 24.7 million (95% CI, 24.6-24.8 million) visits in 2016. Older adults accounted for 22.2% (95% CI, 21.8%-22.6%) of visits with an SMR prescription. Concomitant use of an opioid was recorded in 67.2% (95% CI, 62.0%-72.5%) of all visits with a continuing SMR prescription. Conclusions and Relevance: This study found that SMR use increased rapidly between 2005 and 2016, which is a concern given the prominent adverse effects and limited long-term efficacy data associated with their use. These findings suggest that approaches are needed to limit the long-term use of SMRs, especially in older adults, similar to approaches to limit long-term use of opioids and benzodiazepines.

Risk of sudden cardiac arrest and ventricular arrhythmia with sulfonylureas: An experience with conceptual replication in two independent populations.

Sulfonylureas are commonly used to treat type 2 diabetes mellitus. Despite awareness of their effects on cardiac physiology, a knowledge gap exists regarding their effects on cardiovascular events in real-world populations. Prior studies reported sulfonylurea-associated cardiovascular death but not serious arrhythmogenic endpoints like sudden cardiac arrest (SCA) or ventricular arrhythmia (VA). We assessed the comparative real-world risk of SCA/VA among users of second-generation sulfonylureas: glimepiride, glyburide, and glipizide. We conducted two incident user cohort studies using five-state Medicaid claims (1999-2012) and Optum Clinformatics commercial claims (2000-2016). Outcomes were SCA/VA events precipitating hospital presentation. We used Cox proportional hazards models, adjusted for high-dimensional propensity scores, to generate adjusted hazard ratios (aHR). We identified 624,406 and 491,940 sulfonylurea users, and 714 and 385 SCA/VA events, in Medicaid and Optum, respectively. Dataset-specific associations with SCA/VA for both glimepiride and glyburide (vs. glipizide) were on opposite sides of and could not exclude the null (glimepiride: aHRMedicaid 1.17, 95% CI 0.96-1.42; aHROptum 0.84, 0.65-1.08; glyburide: aHRMedicaid 0.87, 0.74-1.03; aHROptum 1.11, 0.86-1.42). Database differences in data availability, populations, and documentation completeness may have contributed to the incongruous results. Emphasis should be placed on assessing potential causes of discrepancies between conflicting studies evaluating the same research question.

The risk of sudden cardiac arrest and ventricular arrhythmia with rosiglitazone versus pioglitazone: real-world evidence on thiazolidinedione safety.

BACKGROUND: The low cost of thiazolidinediones makes them a potentially valuable therapeutic option for the > 300 million economically disadvantaged persons worldwide with type 2 diabetes mellitus. Differential selectivity of thiazolidinediones for peroxisome proliferator-activated receptors in the myocardium may lead to disparate arrhythmogenic effects. We examined real-world effects of thiazolidinediones on outpatient-originating sudden cardiac arrest (SCA) and ventricular arrhythmia (VA). METHODS: We conducted population-based high-dimensional propensity score-matched cohort studies in five Medicaid programs (California, Florida, New York, Ohio, Pennsylvania | 1999-2012) and a commercial health insurance plan (Optum Clinformatics | 2000-2016). We defined exposure based on incident rosiglitazone or pioglitazone dispensings; the latter served as an active comparator. We controlled for confounding by matching exposure groups on propensity score, informed by baseline covariates identified via a data adaptive approach. We ascertained SCA/VA outcomes precipitating hospital presentation using a validated, diagnosis-based algorithm. We generated marginal hazard ratios (HRs) via Cox proportional hazards regression that accounted for clustering within matched pairs. We prespecified Medicaid and Optum findings as primary and secondary, respectively; the latter served as a conceptual replication dataset. RESULTS: The adjusted HR for SCA/VA among rosiglitazone (vs. pioglitazone) users was 0.91 (0.75-1.10) in Medicaid and 0.88 (0.61-1.28) in Optum. Among Medicaid but not Optum enrollees, we found treatment effect heterogeneity by sex (adjusted HRs = 0.71 [0.54-0.93] and 1.16 [0.89-1.52] in men and women respectively, interaction term p-value = 0.01). CONCLUSIONS: Rosiglitazone and pioglitazone appear to be associated with similar risks of SCA/VA.

Clopidogrel Drug Interactions and Serious Bleeding: Generating Real-World Evidence via Automated High-Throughput Pharmacoepidemiologic Screening.

Few population-based studies have examined bleeding associated with clopidogrel drug-drug interactions (DDIs). We sought to identify precipitant drugs taken concomitantly with clopidogrel (an object drug) that increased serious bleeding rates. We screened 2000-2015 Optum commercial health insurance claims to identify DDI signals. We performed self-controlled case series studies for clopidogrel plus precipitant pairs, examining associations with gastrointestinal bleeding or intracranial hemorrhage. To distinguish native bleeding effects of a precipitant, we reexamined associations using pravastatin as a negative control object drug. Among 431 analyses, 28 clopidogrel plus precipitant pairs were statistically significantly positively associated with serious bleeding. Ratios of rate ratios ranged from 1.13-3.94. Among these pairs, 13 were expected given precipitant drugs alone increased and/or were harbingers of serious bleeding. The remaining 15 pairs constituted new DDI signals, none of which are currently listed in two major DDI knowledge bases.