Chandak Payal, Tatonetti Nicholas P
Department of Computer Science, Columbia University, New York, NY 10027, USA.
Department of Biomedical Informatics, Columbia University, New York, NY 10027, USA.
Patterns (N Y). 2020 Oct 9;1(7). doi: 10.1016/j.patter.2020.100108. Epub 2020 Sep 22.
Adverse drug reactions are the fourth leading cause of death in the US. Although women take longer to metabolize medications and experience twice the risk of developing adverse reactions compared with men, these sex differences are not comprehensively understood. Real-world clinical data provide an opportunity to estimate safety effects in otherwise understudied populations, i.e., women. These data, however, are subject to confounding biases and correlated covariates. We present AwareDX, a pharmacovigilance algorithm that leverages advances in machine learning to predict sex risks. Our algorithm mitigates these biases and quantifies the differential risk of a drug causing an adverse event in either men or women. AwareDX demonstrates high precision during validation against clinical literature and pharmacogenetic mechanisms. We present a resource of 20,817 adverse drug effects posing sex-specific risks. AwareDX, and this resource, present an opportunity to minimize adverse events by tailoring drug prescription and dosage to sex.
药物不良反应是美国第四大死因。尽管女性代谢药物的时间更长，出现不良反应的风险是男性的两倍，但这些性别差异尚未得到全面了解。真实世界的临床数据为评估其他未充分研究的人群（即女性）的安全性影响提供了机会。然而，这些数据容易受到混杂偏倚和相关协变量的影响。我们提出了AwareDX，一种利用机器学习进展来预测性别风险的药物警戒算法。我们的算法减轻了这些偏倚，并量化了药物在男性或女性中导致不良事件的差异风险。在针对临床文献和药物遗传学机制进行验证时，AwareDX显示出高精度。我们提供了一份包含20817种具有性别特异性风险的药物不良反应的资源。AwareDX以及这份资源为根据性别调整药物处方和剂量以尽量减少不良事件提供了机会。
