Pharmacoepidemiologic Methods for Studying the Health Effects of Drug–Drug Interactions

Background Drug interactions, particularly those involving warfarin, are a major clinical and public health problem. Minimizing serious bleeding caused by anticoagulants is a recent major focus of the United States (US) Department of Health and Human Services. This study quantified the risk of gastrointestinal bleeding (GIB) and intracranial hemorrhage (ICH) among concomitant users of warfarin and individual antihyperlipidemics. Methods The authors conducted a high-dimensional propensity score-adjusted cohort study of new concomitant users of warfarin and an antihyperlipidemic, among US Medicaid beneficiaries from five states during 1999–2011. Exposure was defined by concomitant use of warfarin plus one of eight antihyperlipidemics. The primary outcome measure was a composite of GIB/ICH within the first 30 days of concomitant use. As a secondary outcome measure, GIB/ICH was examined within the first 180 days of concomitant use. Results Among 236,691 persons newly-exposed to warfarin and an antihyperlipidemic, the crude incidence of GIB/ICH was 13.2 (95% confidence interval 12.7 to 13.8) per 100 person-years. Users were predominantly older, female, and Caucasian. Adjusted hazard ratios (aHRs) for warfarin and individual statins were consistent with no association. Warfarin + gemfibrozil was associated with an 80% increased risk of GIB/ICH within the first month of concomitant use (aHR = 1.8, 1.4 to 2.4). Warfarin + fenofibrate was associated with a similar increased risk (aHR = 1.8, 1.2 to 2.7), yet with an onset during the second month of concomitant use. Conclusions Among warfarin-treated persons, the use of fibrates—but not statins—increases the risk of hospital presentation for GIB/ICH.

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“…[34] Persons entered the cohort without regard to the initiation order of warfarin and the antihyperlipidemic ( Figure 1 ). [35]…”

Section: Methodsmentioning

confidence: 99%

Gastrointestinal bleeding and intracranial hemorrhage in concomitant users of warfarin and antihyperlipidemics

Leonard

Brensinger

Bilker

et al. 2017

International Journal of Cardiology

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“…For instance, nested case‐control design with a 30‐day drug exposure can be used to investigate acute ADEs, as well as the case‐crossover design . Hennessy et al provides detailed guidance for investigating DDI from EHR databases . We would like to point out that our proposed method has the potential to analyze EHR data with proper epidemiology designs.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System

Wang

Feng

et al. 2019

Statistics in Medicine

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With increasing trend of polypharmacy, drug‐drug interaction (DDI)‐induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score‐adjusted three‐component mixture model (PS‐3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug‐drug‐ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS‐3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS‐3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS‐3CMM prioritizes DDI signals differently. PS‐3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS‐3CMM is a new method that is complement to the existing DDI signal detection methods.

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“…Second, we identified DDI signals by performing confounder‐adjusted self‐controlled case series studies for clopidogrel plus precipitant (i.e., interacting drug) pairs, with hospital presentation for serious bleeding as the study outcome. To help distinguish native bleeding effects of a precipitant drug from a DDI involving clopidogrel, we repeated these steps for pravastatin, which served as a quantitative comparator (i.e., negative control object drug) . Pravastatin was selected because it is a widely used cardiovascular drug that does not affect the risk of serious bleeding, minimally inhibits human carboxylesterase 1, and lacks substantive CYP‐based effects that could affect other drugs’ bleeding risk.…”

Section: Methodsmentioning

confidence: 99%

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

Leonard

Zhou

Brensinger

et al. 2019

Clin Pharma and Therapeutics

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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.

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Pharmacoepidemiologic Methods for Studying the Health Effects of Drug–Drug Interactions

Cited by 71 publications

References 30 publications

Gastrointestinal bleeding and intracranial hemorrhage in concomitant users of warfarin and antihyperlipidemics

Gastrointestinal bleeding and intracranial hemorrhage in concomitant users of warfarin and antihyperlipidemics

Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System

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

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