一个基于轨迹的模型,用于从真实世界的数据中检测药物-药物-宿主相互作用
Yi Shi1, Anna Sun1, Hongmei Nan2
1Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN, USA.
Journal of biomedical informatics
|June 2, 2025
概括
一种新的轨迹信息模型 (TIM) 能够有效地检测药物药物相互作用 (DDI) 和药物药物主体相互作用 (DDHI),即使是在特定的患者群体中. 这种方法优于传统的方法,用于识别药物诱导的不良事件.
科学领域:
- 药监和药物安全 药监和药物安全
- 数据挖掘和机器学习
- 现实世界的证据分析分析.
背景情况:
- 药物不良事件 (ADEs) 构成了重大的公共卫生挑战.
- 现有的数据挖掘方法从现实数据中识别药物相互作用 (DDI) 诱导的或药物主体相互作用 (DHI) 诱导的ADEs.
- 需要在ADE检测中考虑患者特征的方法.
研究的目的:
- 开发一种新的方法,即轨迹信息模型 (TIM),用于检测有害药物相互作用 (DDI),特别注意患者特征 (药物-药物-宿主相互作用,DDHI).
- 提出一个最佳的研究设计,使用主体内和主体间的控制来加强从现实数据中检测ADE.
主要方法:
- 开发轨迹信息模型 (TIM) 来识别DDHI信号.
- 在案例控制研究中实施最佳控制选择策略.
- 对美国大型行政索赔数据和模拟研究的分析.
主要成果:
- 与传统设计相比,最佳对照选择改善了ADE检测曲线下的面积 (AUC) (AUC:0.79-0.80与0.56-0.76).
- TIM发现的信号比参考方法更多 (赔率比:1.13-3.18,P<0.01),其中36%是DDHI信号.
- 在模拟研究中,TIM证明了经验错误发现率 (FDR) <0.05和DDHI信号的更高检测概率.
结论:
- 在高通量分析中,TIM有效地检测到ADE信号,包括DDHIs,同时控制虚假阳性率.
- 药物-药物组合可以增加特定患者亚群的ADE风险.
- 最佳的控制选择可以提高ADE数据挖掘的性能.
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