毒品类别
医学
药品
混淆
协变量
上市后监督
观察研究
人口
药物流行病学
不利影响
重症监护医学
临床试验
药物警戒
药理学
内科学
计算机科学
药方
机器学习
环境卫生
作者
Nicholas P. Tatonetti,Patrick P. Ye,Roxana Daneshjou,Russ B. Altman
出处
期刊:Science Translational Medicine
[American Association for the Advancement of Science (AAAS)]
日期:2012-03-14
卷期号:4 (125)
被引量:590
标识
DOI:10.1126/scitranslmed.3003377
摘要
Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.
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