TY - JOUR T1 - Data science identifies novel drug interactions that prolong the QT interval JF - bioRxiv DO - 10.1101/024745 SP - 024745 AU - Tal Lorberbaum AU - Kevin J. Sampson AU - Raymond L. Woosley AU - Robert S. Kass AU - Nicholas P. Tatonetti Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/08/16/024745.abstract N2 - 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 addresses 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. ER -