High-throughput signal detection of hepatotoxic drug-drug interactions in hospitalized elderly patients: an NLP-driven pharmacovigilance study.

BACKGROUND: Elderly patients are highly susceptible to drug-drug interaction (DDI)-induced liver injury, yet comprehensive real-world evidence remains scarce. This study aimed to leverage a natural language processing (NLP) model to efficiently identify hepatotoxicity-associated DDI signals in this population. METHODS: This retrospective study analyzed electronic health records from 109,263 elderly inpatients. An integrated approach combining laboratory thresholds and NLP was utilized to precisely identify liver injury cases. High-throughput case-control analyses were conducted using additive and multiplicative interaction models to screen for DDI signals. To minimize false positives, time-dependent causality inference and sensitivity analyses were performed, followed by validation using in vivo animal experiments. RESULTS: Among 3,227 drug combinations evaluated, 111 signals were identified, 58 of which demonstrated consistent time-dependent risk trends. Notably, among the top 20 DDI signals with the strongest associations, a marked risk elevation was observed for cardiovascular medications such as aspirin (additive: 1.16; multiplicative: 2.49), clopidogrel (additive: 1.12; multiplicative: 2.12), and atorvastatin (additive: 0.52; multiplicative: 1.88) when co-administered with the antimicrobial agent piperacillin/tazobactam. Animal experiments and sensitivity analyses further corroborated these findings. CONCLUSION: We established a robust NLP-based framework to efficiently evaluate DDI-induced hepatotoxicity in elderly inpatients. By identifying novel high-risk combinations, this study provides critical insights for optimizing pharmacotherapy and highlights the potential of artificial intelligence technologies in real-world pharmacovigilance study.