Comparative Cardiovascular Effectiveness of Empagliflozin Versus Dapagliflozin in Adults With Treated Type 2 Diabetes: A Target Trial Emulation.

BACKGROUND: Empagliflozin and dapagliflozin have proven cardiovascular benefits in people with type 2 diabetes at high cardiovascular risk, but their comparative effectiveness is unknown. METHODS: This study used nationwide, population-based Danish health registries to emulate a hypothetical target trial comparing empagliflozin versus dapagliflozin initiation, in addition to standard care, among people with treated type 2 diabetes from 2014 through 2020. The outcome was a composite of myocardial infarction, ischemic stroke, heart failure (HF), or cardiovascular death (major adverse cardiovascular event). Participants were followed until an outcome, emigration, or death occurred; 6 years after initiation; or December 31, 2021, whichever occurred first. Logistic regression was used to compute inverse probability of treatment and censoring weights, controlling for 57 potential confounders. In intention-to-treat analyses, 6-year adjusted risks, risk differences, and risk ratios considering noncardiovascular death competing risks were estimated. Analyses were stratified by coexisting atherosclerotic cardiovascular disease and HF. A per-protocol design was performed as a secondary analysis. RESULTS: There were 36 670 eligible empagliflozin and 20 606 eligible dapagliflozin initiators. In the intention-to-treat analysis, the adjusted 6-year absolute risk of major adverse cardiovascular event was not different between empagliflozin and dapagliflozin initiators (10.0% versus 10.0%; risk difference, 0.0% [95% CI, -0.9% to 1.0%]; risk ratio, 1.00 [95% CI, 0.91 to 1.11]). The findings were consistent in people with atherosclerotic cardiovascular disease (risk difference, -2.3% [95% CI, -8.2% to 3.5%]; risk ratio, 0.92 [95% CI, 0.74 to 1.14]) and without atherosclerotic cardiovascular disease (risk difference, 0.3% [95% CI, -0.6% to 1.2%]; risk ratio, 1.04 [95% CI, 0.93 to 1.16]) and in people with HF (risk difference, 1.1% [95% CI, -6.5% to 8.6%]; risk ratio, 1.04 [95% CI, 0.79 to 1.37]) and without HF (risk difference, -0.1% [95% CI, -1.0% to 0.8%]; risk ratio, 0.99 [95% CI, 0.90 to 1.09]). The 6-year risks of major adverse cardiovascular event were also not different in the per-protocol analysis (9.1% versus 8.8%; risk difference, 0.2% [95% CI, -2.1% to 2.5%]; risk ratio, 1.03 [95% CI, 0.80 to 1.32]). CONCLUSIONS: Empagliflozin and dapagliflozin initiators had no differences in 6-year cardiovascular outcomes in adults with treated type 2 diabetes with or without coexisting atherosclerotic cardiovascular disease or HF.

Grace periods and exposure misclassification in self-controlled case-series studies of drug-drug interactions.

The self-controlled case-series (SCCS) research design is increasingly used in pharmacoepidemiologic studies of drug-drug interactions (DDIs), with the target of inference being the incidence rate ratio (IRR) associated with concomitant exposure to the object plus precipitant drug versus the object drug alone. While day-level drug exposure can be inferred from dispensing claims, these inferences may be inaccurate, leading to biased IRRs. Grace periods (periods assuming continued treatment impact after days' supply exhaustion) are frequently used by researchers, but the impact of grace period decisions on bias from exposure misclassification remains unclear. Motivated by an SCCS study examining the potential DDI between clopidogrel (object) and warfarin (precipitant), we investigated bias due to precipitant or object exposure misclassification using simulations. We show that misclassified precipitant treatment always biases the estimated IRR toward the null, whereas misclassified object treatment may lead to bias in either direction or no bias, depending on the scenario. Further, including a grace period for each object dispensing may unintentionally increase the risk of misclassification bias. To minimize such bias, we recommend 1) avoiding the use of grace periods when specifying object drug exposure episodes; and 2) including a washout period following each precipitant exposed period.

Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia.

PURPOSE: High-dimensional propensity score (hdPS) is a semiautomated method that leverages a vast number of covariates available in healthcare databases to improve confounding adjustment. A novel combined Super Learner (SL)-hdPS approach was proposed to assist with selecting the number of covariates for propensity score inclusion, and was found in plasmode simulation studies to improve bias reduction and precision compared to hdPS alone. However, the approach has not been examined in the applied setting. METHODS: We compared SL-hdPS's performance with that of several hdPS models, each with prespecified covariates and a different number of empirically-identified covariates, using a cohort study comparing real-world bleeding rates between ibrutinib- and bendamustine-rituximab (BR)-treated individuals with chronic lymphocytic leukemia in Optum's de-identified Clinformatics® Data Mart commercial claims database (2013-2020). We used inverse probability of treatment weighting for confounding adjustment and Cox proportional hazards regression to estimate hazard ratios (HRs) for bleeding outcomes. Parameters of interest included prespecified and empirically-identified covariate balance (absolute standardized difference [ASD] thresholds of <0.10 and <0.05) and outcome HR precision (95% confidence intervals). RESULTS: We identified 2423 ibrutinib- and 1102 BR-treated individuals. Including >200 empirically-identified covariates in the hdPS model compromised covariate balance at both ASD thresholds. SL-hdPS balanced more covariates than all individual hdPS models at both ASD thresholds. The bleeding HR 95% confidence intervals were generally narrower with SL-hdPS than with individual hdPS models. CONCLUSION: In a real-world application, hdPS was sensitive to the number of covariates included, while use of SL for covariate selection resulted in improved covariate balance and possibly improved precision.

Adherence with statins and all-cause mortality in days with high temperature.

PURPOSE: It has been suggested that statins may exert thermo-protective effects that can reduce mortality on hot days. We aimed to examine the relationship between statin adherence and mortality in days with high temperature. METHODS: Utilizing data from a prior historical new-user cohort study, we analyzed a cohort of 229 918 individuals within a state-mandated health provider in Israel who initiated statin therapy between 1998 and 2006. Adherence to statins was assessed through the mean proportion of days covered (PDC) with statins during the follow-up period. The study's primary outcome was all-cause mortality during hot days. RESULTS: During the study follow-up period, a total of 13 165 individuals (5.7%) died. In a multivariable model, a 10% increase in PDC with statins was associated with an HR of (0.85; 95% CI: 0.72-1.00) for deaths (n = 16) in extremely hot days (≥39°C). This association was numerically stronger compared to HR = 0.94 (0.93-0.94) in cooler days and displayed a significant difference between sexes. In males, the fully-adjusted HR for a 10% increase in PDC with statins was 0.66 (0.45-0.95), while in women, it was 0.98 (0.78-1.23). In contrast, no such effect modification was observed for death in cooler days. CONCLUSIONS: These findings align with earlier research, supporting the notion that adherence with statin treatment may be associated with a reduced risk of death during extremely hot days, particularly among men.

Real-World Trends in the Evaluation of Medical Products.

There is a compelling need to evaluate the real-world health effects of medical products outside of tightly controlled preapproval clinical trials. This is done through pharmacoepidemiology, which is the study of the health effects of medical products (including drugs, biologicals, and medical devices and diagnostics) in populations, often using nonrandomized designs. Recent developments in pharmacoepidemiology span changes in the focus of research questions, research designs, data used, and statistical analysis methods. Developments in these areas are thought to improve the value of the evidence produced by such studies, and are prompting greater use of real-world evidence to inform clinical, regulatory, and reimbursement decisions.

Rejoinder to "Instrumented difference-in-differences".

We thank all the discussants for the careful reading and insightful comments. In our rejoinder, we extend the discussion of how the assumptions of instrumented difference-in-differences (iDID) compare to the assumptions of the standard instrumental variable method. We also make additional comments on how iDID is related to the fuzzy DID. We highlight future research directions to enhance the utility of iDID, including extensions to adjust for covariate shift in two-sample iDID design, and generalization of iDID to multiple time points and a multi-valued instrumental variable for DID.

Instrumented difference-in-differences.

Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variables and difference-in-differences, we propose a new method called instrumented difference-in-differences that explicitly leverages exogenous randomness in an exposure trend to estimate the average and conditional average treatment effect in the presence of unmeasured confounding. We develop the identification assumptions using the potential outcomes framework. We propose a Wald estimator and a class of multiply robust and efficient semiparametric estimators, with provable consistency and asymptotic normality. In addition, we extend the instrumented difference-in-differences to a two-sample design to facilitate investigations of delayed treatment effect and provide a measure of weak identification. We demonstrate our results in simulated and real datasets.

Robust causal inference of drug-drug interactions.

There is growing interest in developing causal inference methods for multi-valued treatments with a focus on pairwise average treatment effects. Here we focus on a clinically important, yet less-studied estimand: causal drug-drug interactions (DDIs), which quantifies the degree to which the causal effect of drug A is altered by the presence versus the absence of drug B. Confounding adjustment when studying the effects of DDIs can be accomplished via inverse probability of treatment weighting (IPTW), a standard approach originally developed for binary treatments and later generalized to multi-valued treatments. However, this approach generally results in biased results when the propensity score model is misspecified. Motivated by the need for more robust techniques, we propose two empirical likelihood-based weighting approaches that allow for specifying a set of propensity score models, with the second method balancing user-specified covariates directly, by incorporating additional, nonparametric constraints. The resulting estimators from both methods are consistent when the postulated set of propensity score models contains a correct one; this property has been termed multiple robustness. In this paper, we derive two multiply-robust estimators of the causal DDI, and develop inference procedures. We then evaluate their finite sample performance through simulation. The results demonstrate that the proposed estimators outperform the standard IPTW method in terms of both robustness and efficiency. Finally, we apply the proposed methods to evaluate the impact of renin-angiotensin system inhibitors (RAS-I) on the comparative nephrotoxicity of nonsteroidal anti-inflammatory drugs (NSAID) and opioids, using data derived from electronic medical records from a large multi-hospital health system.

Group sequential testing under instrumented difference-in-differences approach.

Unmeasured confounding is a major obstacle to reliable causal inference based on observational studies. Instrumented difference-in-differences (iDiD), a novel idea connecting instrumental variable and standard DiD, ameliorates the above issue by explicitly leveraging exogenous randomness in an exposure trend. In this article, we utilize the above idea of iDiD, and propose a novel group sequential testing method that provides valid inference even in the presence of unmeasured confounders. At each time point, we estimate the average or conditional average treatment effect under iDiD setting using the data accumulated up to that time point, and test the significance of the treatment effect. We derive the joint distribution of the test statistics under the null using the asymptotic properties of M-estimation, and the group sequential boundaries are obtained using the α $$ \alpha $$ -spending functions. The performance of our proposed approach is evaluated on both synthetic data and Clinformatics Data Mart Database (OptumInsight, Eden Prairie, MN) to examine the association between rofecoxib and acute myocardial infarction, and our method detects significant adverse effect of rofecoxib much earlier than the time when it was finally withdrawn from the market.

Core concepts in pharmacoepidemiology: Key biases arising in pharmacoepidemiologic studies.

Pharmacoepidemiology has an increasingly important role in informing and improving clinical practice, drug regulation, and health policy. Therefore, unrecognized biases in pharmacoepidemiologic studies can have major implications when study findings are translated to the real world. We propose a simple taxonomy for researchers to use as a starting point when thinking through some of the most pervasive biases in pharmacoepidemiology. We organize this discussion according to biases best assessed with respect to the study population (including confounding by indication, channeling bias, healthy user bias, and protopathic bias), the study design (including prevalent user bias and immortal time bias), and the data source (including misclassification bias and missing data/loss to follow up). This tutorial defines, provides a curated list of recommended references, and illustrates through relevant case examples these key biases to consider when planning, conducting, or evaluating pharmacoepidemiologic studies.

Apixaban Versus Rivaroxaban in Patients With Atrial Fibrillation and Valvular Heart Disease : A Population-Based Study.

BACKGROUND: Although apixaban and rivaroxaban are commonly used in patients with atrial fibrillation (AF) and valvular heart disease (VHD), there is limited evidence comparing the 2 drugs in these patients. OBJECTIVE: To emulate a target trial of effectiveness and safety of apixaban and rivaroxaban in patients with AF and VHD. DESIGN: New-user, active comparator, cohort study design. SETTING: Commercial health insurance database from 1 January 2013 to 31 December 2020. PATIENTS: New users of apixaban or rivaroxaban who had a diagnosis of AF and VHD before initiation of anticoagulant therapy. MEASUREMENTS: The primary effectiveness outcome was a composite of ischemic stroke or systemic embolism. The primary safety outcome was a composite of gastrointestinal or intracranial bleeding. Cox proportional hazards regression with a robust variance estimator was used to estimate hazard ratios (HRs) and 95% CIs. RESULTS: When compared with rivaroxaban in a propensity score-matched cohort of 19 894 patients (9947 receiving each drug), apixaban was associated with a lower rate of ischemic stroke or systemic embolism (HR, 0.57 [95% CI, 0.40 to 0.80]) and bleeding (HR, 0.51 [CI, 0.41 to 0.62]). The absolute reduction in the probability of stroke or systemic embolism with apixaban compared with rivaroxaban was 0.0026 within 6 months and 0.011 within 1 year of treatment initiation. The absolute reduction in the probability of bleeding events with apixaban compared with rivaroxaban was 0.012 within 6 months and 0.019 within 1 year of treatment initiation. LIMITATION: Short follow-up time and inability to ascertain some types of VHD. CONCLUSION: In this study of patients with AF and VHD, patients receiving apixaban had a lower risk for ischemic stroke or systemic embolism and for bleeding when compared with those receiving rivaroxaban. PRIMARY FUNDING SOURCE: National Institutes of Health.

Risk of Serious Infection With Low-dose Glucocorticoids in Patients With Rheumatoid Arthritis: An Instrumental Variable Analysis.

BACKGROUND: Low-dose glucocorticoids are commonly used in the treatment of rheumatoid arthritis (RA). Observational studies have found an increased risk of serious infection associated with low-dose glucocorticoids, but concerns about residual confounding remain. METHODS: We identified adults with RA on stable immunomodulatory therapy for >6 months receiving no glucocorticoids or ≤5 mg/day using Medicare data from 2006 to 2015. We used provider preference for glucocorticoids as an instrumental variable (IV) to assess associations between low-dose glucocorticoid use and the risk of infection requiring hospitalization using a cause-specific proportional hazards model. RESULTS: We identified 163,603 qualifying treatment episodes among 120,656 patients. Glucocorticoids ≤5 mg/day were used by 25,373/81,802 (31.0%) of patients seen by a rheumatologist with low provider preference for glucocorticoids and by 36,087/81,801 (44.1%) of patients seen by a rheumatologist with high provider preference for glucocorticoids (adjusted odds ratio 1.81, 95% confidence interval 1.77, 1.84 for association between provider preference and glucocorticoids). Chronic obstructive pulmonary disease, opioids, antibiotics, previous emergency department visits, hospitalizations, and infections requiring hospitalization infections were unbalanced with regard to exposure but not to the IV. The incidence of infection requiring hospitalization was 8.0/100 person-years among patients unexposed to glucocorticoids versus 11.7/100 person-years among those exposed. The association between glucocorticoids and infection requiring hospitalization from IV analysis (hazard ratio 1.26 [1.02-1.56]) was similar to results from a standard multivariable model (hazard ratio 1.24 [1.21-1.28]). CONCLUSIONS: Among patients with RA on stable immunomodulatory therapy, IV analysis based on provider preference demonstrated an increased risk of infection requiring hospitalization associated with low-dose glucocorticoids, similar to a traditional analysis.

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.

Very high PD-L1 expression as a prognostic indicator of overall survival among patients with advanced non-small cell lung cancer receiving anti-PD-(L)1 monotherapies in routine practice.

PURPOSE: Programmed death or ligand-1 (PD-(L)1) pathway inhibitors confer improved survival as the first-line treatment for advanced non-small cell lung cancer (aNSCLC) in patients with PD-L1 expression (PD-L1 + e ≥ 50%) compared to platinum-doublet chemotherapy and have become a standard therapy. Some recent evidence suggests that among aNSCLC patients with PD-L1 + e of ≥50% receiving pembrolizumab monotherapy, very high levels of PD-L1 + e (≥90%) may be associated with better outcomes. We sought to assess whether very high PD-L1 + e (≥90%) compared to high PD-L1 + e (50%-89%) is associated with an overall survival benefit in aNSCLC patients receiving anti-PD-(L)1 monotherapies. METHODS: We conducted a single-site retrospective cohort study of aNSCLC patients who initiated PD-(L)1 inhibitor monotherapy as the first-line treatment from October 24, 2016, to August 25, 2021, and had a PD-L1 + e ≥ 50%. The primary outcome was overall survival, measured from the start of the first-line PD-(L)1 inhibitor monotherapy (index date) to date of death or last confirmed activity prior to the cohort exit date. Propensity score-based inverse probability weighting (IPW) was used to control for confounding in Kaplan-Meier curves and Cox proportional hazard regression analysis. RESULTS: One hundred sixty-six patients with aNSCLC receiving PD-(L)1 inhibitor monotherapy met inclusion criteria. 54% were female, 90% received pembrolizumab, median age was 68 years, 70% had non-squamous cell carcinoma, 94% had a history of smoking, 29% had a KRAS mutation, and 37% had very high PD-L1 + e. Unweighted covariates at cohort entry were similar between groups (absolute standardized mean differences [SMDs] <0.1) except for race (SMD = 0.2); age at therapy initiation (SMD = 0.13); smoking status (SMD = 0.13), and BRAF mutation status (SMD = 0.11). After weighting, baseline covariates were well balanced (all absolute SMDs <0.1). In the weighted analysis, having a very high PD-L1 + e was associated with lower mortality (weighted hazard ratio 0.57, 95% CI 0.36-0.90) and longer median survival: 3.85 versus 1.49 years. CONCLUSIONS: Very high PD-L1 + e (≥90%) was associated with an overall survival benefit over high PD-L1 + e (50%-89%) in patients receiving the first-line PD-(L)1 inhibitor monotherapy in a model controlling for potential confounders. These findings should be confirmed in a larger real-world data set.

Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users.

BACKGROUND: Fluoroquinolones are associated with central (CNS) and peripheral (PNS) nervous system symptoms, and predicting the risk of these outcomes may have important clinical implications. Both LASSO and random forest are appealing modeling methods, yet it is not clear which method performs better for clinical risk prediction. PURPOSE: To compare models developed using LASSO versus random forest for predicting neurological dysfunction among fluoroquinolone users. METHODS: We developed and validated risk prediction models using claims data from a commercially insured population. The study cohort included adults dispensed an oral fluoroquinolone, and outcomes were CNS and PNS dysfunction. Model predictors included demographic variables, comorbidities and medications known to be associated with neurological symptoms, and several healthcare utilization predictors. We assessed the accuracy and calibration of these models using measures including AUC, calibration curves, and Brier scores. RESULTS: The underlying cohort contained 16 533 (1.18%) individuals with CNS dysfunction and 46 995 (3.34%) individuals with PNS dysfunction during 120 days of follow-up. For CNS dysfunction, LASSO had an AUC of 0.81 (95% CI: 0.80, 0.82), while random forest had an AUC of 0.80 (95% CI: 0.80, 0.81). For PNS dysfunction, LASSO had an AUC of 0.75 (95% CI: 0.74, 0.76) versus an AUC of 0.73 (95% CI: 0.73, 0.74) for random forest. Both LASSO models had better calibration, with Brier scores 0.17 (LASSO) versus 0.20 (random forest) for CNS dysfunction and 0.20 (LASSO) versus 0.25 (random forest) for PNS dysfunction. CONCLUSIONS: LASSO outperformed random forest in predicting CNS and PNS dysfunction among fluoroquinolone users, and should be considered for modeling when the cohort is modest in size, when the number of model predictors is modest, and when predictors are primarily binary.

Association of COVID-19 vs Influenza With Risk of Arterial and Venous Thrombotic Events Among Hospitalized Patients.

Importance: The incidence of arterial thromboembolism and venous thromboembolism in persons with COVID-19 remains unclear. Objective: To measure the 90-day risk of arterial thromboembolism and venous thromboembolism in patients hospitalized with COVID-19 before or during COVID-19 vaccine availability vs patients hospitalized with influenza. Design, Setting, and Participants: Retrospective cohort study of 41 443 patients hospitalized with COVID-19 before vaccine availability (April-November 2020), 44 194 patients hospitalized with COVID-19 during vaccine availability (December 2020-May 2021), and 8269 patients hospitalized with influenza (October 2018-April 2019) in the US Food and Drug Administration Sentinel System (data from 2 national health insurers and 4 regional integrated health systems). Exposures: COVID-19 or influenza (identified by hospital diagnosis or nucleic acid test). Main Outcomes and Measures: Hospital diagnosis of arterial thromboembolism (acute myocardial infarction or ischemic stroke) and venous thromboembolism (deep vein thrombosis or pulmonary embolism) within 90 days. Outcomes were ascertained through July 2019 for patients with influenza and through August 2021 for patients with COVID-19. Propensity scores with fine stratification were developed to account for differences between the influenza and COVID-19 cohorts. Weighted Cox regression was used to estimate the adjusted hazard ratios (HRs) for outcomes during each COVID-19 vaccine availability period vs the influenza period. Results: A total of 85 637 patients with COVID-19 (mean age, 72 [SD, 13.0] years; 50.5% were male) and 8269 with influenza (mean age, 72 [SD, 13.3] years; 45.0% were male) were included. The 90-day absolute risk of arterial thromboembolism was 14.4% (95% CI, 13.6%-15.2%) in patients with influenza vs 15.8% (95% CI, 15.5%-16.2%) in patients with COVID-19 before vaccine availability (risk difference, 1.4% [95% CI, 1.0%-2.3%]) and 16.3% (95% CI, 16.0%-16.6%) in patients with COVID-19 during vaccine availability (risk difference, 1.9% [95% CI, 1.1%-2.7%]). Compared with patients with influenza, the risk of arterial thromboembolism was not significantly higher among patients with COVID-19 before vaccine availability (adjusted HR, 1.04 [95% CI, 0.97-1.11]) or during vaccine availability (adjusted HR, 1.07 [95% CI, 1.00-1.14]). The 90-day absolute risk of venous thromboembolism was 5.3% (95% CI, 4.9%-5.8%) in patients with influenza vs 9.5% (95% CI, 9.2%-9.7%) in patients with COVID-19 before vaccine availability (risk difference, 4.1% [95% CI, 3.6%-4.7%]) and 10.9% (95% CI, 10.6%-11.1%) in patients with COVID-19 during vaccine availability (risk difference, 5.5% [95% CI, 5.0%-6.1%]). Compared with patients with influenza, the risk of venous thromboembolism was significantly higher among patients with COVID-19 before vaccine availability (adjusted HR, 1.60 [95% CI, 1.43-1.79]) and during vaccine availability (adjusted HR, 1.89 [95% CI, 1.68-2.12]). Conclusions and Relevance: Based on data from a US public health surveillance system, hospitalization with COVID-19 before and during vaccine availability, vs hospitalization with influenza in 2018-2019, was significantly associated with a higher risk of venous thromboembolism within 90 days, but there was no significant difference in the risk of arterial thromboembolism within 90 days.

Trends in post-discharge prophylactic anticoagulant use among stroke patients in the United States between 2006 and 2019.

BACKGROUND: Stroke is an independent risk factor for venous thromboembolism (VTE). Although the risk of VTE persists after hospital discharge, information on the utilization of anticoagulants among stroke patients after discharge remains limited. OBJECTIVE: To evaluate changes in post-discharge thromboprophylaxis among stroke patients between 2006 and 2019. METHODS: We conducted a retrospective repeated cross-sectional analysis using a commercial healthcare insurance database in the United States. We included patients aged ≥ 18 years with incident stroke diagnosis and assessed prophylactic use of anticoagulants in the 30 days following hospital discharge including low-molecular-weight heparin (enoxaparin ≤40 mg/day, dalteparin ≤5000 IU/day), unfractionated heparin ≤5000 IU/ twice daily or 3 times a day, apixaban 2.5 mg twice daily, and rivaroxaban 10 mg/day. Patients with atrial fibrillation, VTE, mechanical heart valves, cancer, antiphospholipid antibody syndrome, and users of therapeutic doses of anticoagulants were excluded. We used the Cochrane-Armitage test to assess changes in the use of anticoagulants across the study period. RESULTS: There was a small increase in the overall use of post-discharge prophylactic anticoagulants among stroke patients between 2006 and 2019 from 0.5% to 1.9%. The use of heparin decreased from 0.5% in 2006 to 0.3% in 2019 (P-value for trend = 0.001). In contrast, the use of apixaban or rivaroxaban increased from 0.1% in 2013 to 1.6% in 2019 (P-value for trend < 0.001). Apixaban was more commonly used than rivaroxaban. CONCLUSIONS: In this population-based study of stroke patients, we found that post-discharge anticoagulant use remains low through 2019. Prophylactic use of heparin or rivaroxaban was relatively low but the use of apixaban increased over the study period. Further research is needed to determine if these agents are safe and effective for VTE prevention in stroke patients.

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.

Preoperative Sedative-hypnotic Medication Use and Adverse Postoperative Outcomes.

OBJECTIVE: To determine the association between preoperative benzodiazepine and nonbenzodiazepine receptor agonist ("Z-drugs") use and adverse outcomes after surgery. BACKGROUND: Prescriptions for benzodiazepines and Z-drugs have increased over the past decade. Despite this, the association of preoperative benzodiazepines and Z-drug receipt with adverse outcomes after surgery is unknown. METHODS: Using the Optum Clinformatics Datamart, we performed a retrospective cohort study of adults 18 years or older who underwent any of 10 common surgical procedures between 2010 and 2015. The principal exposure was one or more filled prescriptions for a benzodiazepine or Z-drug in the 90 days before surgery. The primary outcome was any emergency department visit or hospital admission for either (1) a drug related adverse medical event or overdose or (2) a traumatic injury in the 30 days after surgery. RESULTS: Of 785,346 patients meeting inclusion criteria, 94,887 (12.1%) filled a preoperative prescription for a benzodiazepine or Z-drug. From multivariable logistic regression, benzodiazepine or Z-drug use was associated with an increased odds of an adverse postoperative event [odds ratio 1.13; 95% confidence interval: 1.08-1.18). In a separate regression, coprescription of benzodiazepines or Z-drugs with opioids was associated with a 1.45 odds of an adverse postoperative event (95% confidence interval: 1.37-1.53). CONCLUSIONS: Preoperative benzodiazepines and Z-drug use is common and associated with increased odds of adverse outcomes after surgery, particularly when coprescribed with opioids. Counseling on appropriate benzodiazepine and Z-drug use in advance of elective surgery may potentially increase the safety of surgical care.

Annual Prevalence of Use of Potentially Inappropriate Medications for Treatment of Affective Disorders in Parkinson's Disease.

OBJECTIVE: To examine the national prevalence of pharmacological treatment of affective disorders in older adults with Parkinson's disease (PD), and determine the prevalence and risk factors for receipt of an American Geriatrics Society Beers Criteria® defined potentially inappropriate medication (PIM) for affective disorder treatment. DESIGN: Cross-sectional analysis of 2014 Medicare data. SETTING: Research Identifiable File data from the Centers for Medicare and Medicaid Services. PARTICIPANTS: Individuals ≥65 years of age with PD whose inpatient, outpatient, and prescription care is administered through the U.S. Medicare Program. MEASUREMENTS: The 2014 prevalence of affective (i.e., depressive and anxiety) disorders was calculated. We assessed prescription fills for affective disorder treatment and classified prescriptions according to PIM status. Patient and clinician factors associated with PIM prescriptions were determined. RESULTS: Of 84,323 beneficiaries with PD, 15.1% had prevalent depression only, 7.5% had anxiety only, and 8.5% had comorbid depression and anxiety. Among those with depression only, 80.7% were treated in 2014 (12.8% of treated received at least one PIM). The annual treatment prevalence was 62.9% (75.9% PIM) and 93.1% (63.9% PIM) in the anxiety only and comorbid group, respectively. In most groups, PIM use was less likely among men and those with dementia; geriatricians were less likely to prescribe PIMs. CONCLUSION: Treatment of affective disorders in persons diagnosed with PD is high. PIM use is also common, particularly in persons with anxiety. Future research will quantify the potential effects of these PIMs on clinical and patient outcomes.