Data science identifies novel drug interactions that prolong the QT interval
Tal Lorberbaum, Kevin J. Sampson, Raymond L. Woosley, Robert S. Kass, Nicholas P. Tatonetti · bioRxiv (Cold Spring Harbor Laboratory) · 2015
Drug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia called Torsades de Pointes (TdP). 180 drugs with both cardiac and non-cardiac indications have been found to increase risk for TdP, but drug-drug interactions contributing to LQTS (QT-DDIs) remain poorly characterized. Traditional methods for mining observational healthcare data are poorly equipped to detect QT-DDI signals due to low reporting numbers and a lack of direct evidence for LQTS. In this study we present an integrative data science pipeline that effectively circumvents these limitations by identifying latent signals for QT-DDIs in the FDA?s Adverse Event Reporting System and retrospectively validating these predictions using electrocardiogram data in electronic health records. We present 26 novel QT-DDIs flagged using this method that warrant further investigation.