A Case‐Crossover–Based Screening Approach to Identifying Clinically Relevant Drug–Drug Interactions in Electronic Healthcare Data. Issue 1 (18th March 2019) - Bykov, Katsiaryna; Schneeweiss, Sebastian; Glynn, Robert J.; Mittleman, Murray A.; Gagne, Joshua J.
Abstract : We sought to develop a semiautomated screening approach using electronic healthcare data to identify drug–drug interactions (DDIs) that result in clinical outcomes. Using a case‐crossover design with 30‐day hazard and referent windows, we evaluated codispensed drugs (potential precipitants) in 7, 801 patients who experienced rhabdomyolysis while on cytochrome P450 (CYP)3A4‐metabolized statins and in 15, 147 who experienced bleeding while on dabigatran. Estimates of direct associations between precipitant drugs and outcomes were used to adjust for bias and precipitants' direct effects. The P values were adjusted for multiple testing using the false discovery rate (FDR). From among 460 drugs codispensed with statins, 1 drug (clarithromycin) generated an alert (adjusted odds ratio (OR) 5.83, FDR < 0.05). From among 485 drugs codispensed with dabigatran, 2 drugs (naproxen and enoxaparin, ORs 2.50 and 2.75; FDR < 0.05) generated an alert. All three signals reflected known pharmacologic interactions, confirming the potential of case‐crossover–based approaches for DDI screening in electronic healthcare data.