Aug 16, 2015

Data science identifies novel drug interactions that prolong the QT interval

BioRxiv : the Preprint Server for Biology
Tal LorberbaumNicholas P Tatonetti

Abstract

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.

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Mentioned in this Paper

Study
United States Food and Drug Administration
Electronic Health Records
Torsades De Pointes
KCNQ1OT1 gene
Desmosine
Evaluation
Adverse Event
Long QT Syndrome
Pharmacologic Substance

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