Shi Yi, Sun Anna, Nan Hongmei, Yang Yuedi, Xu Jing, Eadon Michael T, Su Jing, Zhang Pengyue
Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN, USA.
Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA.
J Biomed Inform. 2025 May 31;168:104859. doi: 10.1016/j.jbi.2025.104859.
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.
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.
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.
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.
药物不良事件（ADE）对公共卫生构成重大挑战。由于已开发出数据挖掘方法以从真实世界数据中识别药物相互作用诱导（DDI诱导）或药物-宿主相互作用诱导（DHI诱导）的ADE信号，我们旨在开发一种新方法来检测不良药物相互作用，并特别关注患者特征。
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受试者内和受试者间对照的最佳选择的研究设计，用于从真实世界数据中检测ADE。我们分析了大规模的美国行政索赔数据并进行了模拟研究。
在行政索赔数据分析中，我们为包括急性肾损伤和胃肠道出血在内的潜在ADE开发了最佳匹配的病例对照数据集。我们发现，与传统的ADE检测设计相比，对照的最佳选择具有更高的AUC（AUC：0.79 - 0.80对0.56 - 0.76）。我们观察到TIM检测到的信号比参考方法更多（优势比：1.13 - 3.18，P < 0.01），并且发现TIM生成的所有信号中有36%是DDHI信号。在模拟研究中，我们证明TIM的经验性错误发现率（FDR）低于期望的0.05值，并且检测DDHI信号的概率比参考方法高>1.4倍。
TIM在高通量ADE挖掘中具有高概率识别不良DDI和DDHI信号，同时控制假阳性率。很大一部分药物组合仅在特定患者亚群中与ADE风险增加相关。受试者内和受试者间对照的最佳选择可以提高ADE数据挖掘的性能。
