Objective: Adverse drug event (ADE) is a significant challenge to public health. Since data mining methods have been developed to identify signals of drug-drug interaction-induced (DDI-induced) or drug-host interaction-induced (DHI-induced) ADE from real-world data, we aim to develop a new method to detect adverse drug-drug interaction with a special awareness on patient characteristics.

Methods: We developed a trajectory-informed model (TIM) to identify signals of adverse DDI with a special awareness on patient characteristics (i.e., drug-drug-host interaction [DDHI]). We also proposed a study design based on an optimal selection of within-subject and between-subjects controls for detecting ADEs from real-world data. We analyzed a large-scale US administrative claims data and conducted a simulation study.

Results: In administrative claims data analysis, we developed optimally matched case-control datasets for potential ADEs including acute kidney injury and gastrointestinal bleeding. We identified that an optimal selection of controls had a higher AUC compared to traditional designs for ADE detection (AUCs: 0.79-0.80 vs. 0.56-0.76). We observed that TIM detected more signals than reference methods (odds ratios: 1.13-3.18, P < 0.01), and found that 36 % of all signals generated by TIM were DDHI signals. In a simulation study, we demonstrated that TIM had an empirical false discovery rate (FDR) less than the desired value of 0.05, as well as > 1.4-fold higher probabilities of detection of DDHI signals than reference methods.

Conclusions: TIM had a high probability to identify signals of adverse DDI and DDHI in a high-throughput ADE mining while controlling false positive rate. A significant portion of drug-drug combinations were associated with an increased risk of ADEs only in specific patient subpopulations. Optimal selection of within-subject and between-subjects controls could improve the performance of ADE data mining.

Keywords: Adverse drug event; Drug-drug interaction; Drug-drug-host interaction; Drug-host interaction; Patient characteristics.

Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.

目标：不良药物事件（ADE）是公共卫生的一个重要挑战。由于数据挖掘方法已经被开发出来，可以从真实世界的数据中识别出由药物-药物相互作用引起的（DDI诱导的）或药物-宿主相互作用引起的（DHI诱导的）ADE信号，我们旨在开发一种新的方法来检测不良药物相互作用，并特别关注患者的特征。

方法：我们开发了一种轨迹信息模型（TIM），以识别不良DDI信号并特别关注患者特征（即药物-药物-宿主相互作用[DDHI]）。我们还提出了一种基于最佳选择受试者内部和不同受试者对照的实证设计，用于从真实世界数据中检测ADE。我们分析了大规模美国行政索赔数据，并进行了模拟研究。

结果：在行政索赔数据分析中，我们开发了优化匹配的病例对照数据集来识别潜在的急性肾损伤和胃肠道出血等不良事件。我们发现，最优选择对照组比传统设计具有更高的ADE检测AUC（AUCs：0.79-0.80 vs 0.56-0.76）。TIM检测到的信号比参考方法多（优势比1.13-3.18, P

结论：TIM能够在高通量ADE挖掘过程中以控制假阳性率为前提高效识别不良DDI和DDHI信号。相当一部分药物组合仅在特定患者亚群中与增加的不良事件风险相关联。最佳选择内部受试者和不同受试者的对照能够提高ADE数据挖掘的表现。

关键词：不良药物事件；药物-药物相互作用；药物-药物-宿主相互作用；药物-宿主相互作用；患者特征。

版权 © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.