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

Abstract

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.

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

“…The design is well‐suited for drug interaction screening because: (i) the causal contrast is made within an individual and thus inherently controls for confounding by static factors over an individual’s observation period (e.g., genetics); (ii) the statistical model can (and does) control for dynamic factors; (iii) the approach is computationally efficient, as it is limited to persons experiencing an outcome; and (iv) there is ample precedent for the use of high‐throughput applications. Analogous screening studies have identified drugs associated with hypoglycemia in persons using insulin secretagogues, 33 rhabdomyolysis in persons using statins, 34 serious bleeding in persons using clopidogrel, 35 and anticoagulants, 34,36,37 and injury in persons using opioids 38 as examples. Methods detailed below were adapted from our prior work on opioids 38 …”

Drug‐Drug Interaction Surveillance Study: Comparing Self‐Controlled Designs in Five Empirical Examples in Real‐World Data

“…To evaluate DDIs, only person‐time on the object drug of interest was evaluated, and patients were required to be on the object drug continuously during the observation period ( Figure ). Such an approach controls for confounding by indication for the object drug and is often implemented in self‐controlled studies of DDIs 1,6,25 …”

“…Electronic healthcare databases are increasingly utilized to identify drug‐drug interactions (DDIs) and generate real‐world evidence (RWE) on their clinical impact 1–3 . Rigorous use of real‐world data (RWD), however, presents many challenges because treatments are not randomized in clinical practice, relevant information on exposures, confounders, or outcomes may be missing, and inappropriate study design or violations of assumptions required for valid causal inference may lead to biased estimates 4,5 …”

Pharmacoepidemiologic Screening of Potential Oral Anticoagulant Drug Interactions Leading to Thromboembolic Events

“…In the secondary analysis, we assumed a larger variance (0.67, corresponding to a 25-fold range). 25 For precipitants identified in both cohorts, we calculated the ratio of RR anticoagulant + precipitant vs. anticoagulant to RR pravastatin + precipitant vs. pravastatin . The variance of the ratio of RRs was calculated using the delta method.…”