Abstract
The global rise in polypharmacy has increased both the necessity and complexity of drug-drug interaction (DDI) assessments, given the growing potential for interactions involving more than two drugs. Leveraging large-scale healthcare claims data, we piloted a semi-automated, high-throughput case-crossover-based approach for drug-drug-drug interaction (3DI) screening. Cases were direct-acting oral anticoagulant (DOAC) users with either a major bleeding event during ongoing dispensings for potentially interacting, enzyme-inhibiting antihypertensive drugs (AHDs) (Study 1), or a thromboembolic event during ongoing dispensings for potentially interacting, enzyme-inducing antiseizure medications (ASMs) (Study 2). 3DI detection was based on screening for additional drug exposures that served as acute outcome triggers. To mitigate direct effects and confounding by concomitant drugs, self-controlled estimates were adjusted using negative cases (external "control" DOAC users with the same outcomes but co-dispensings for non-interacting AHDs or ASMs). Signal thresholds were set based on P-values and false discovery rate q-values to address multiple comparisons. Study 1: 285 drugs were examined among 3,306 episodes. Self-controlled assessments with q-value thresholds yielded 9 3DI signals (cases) and 40 DDI signals (negative cases). External adjustment generated 10 3DI signals from the P-value threshold and no signals from the q-value threshold. Study 2: 126 drugs were examined among 604 episodes. Assessments with P-value thresholds yielded 3 3DI and 26 DDI signals following self-control, as well as 4 3DI signals following adjustment. No 3DI signals met the q-value threshold. The presented self- and externally-controlled approach aimed to advance paradigms for real-world higher order drug interaction screening among high-susceptibility populations with pre-existent DDI risk.