An Integrative Data Science Pipeline to Identify Novel Drug Interactions that Prolong the QT Interval.

ABSTRACT:

Introduction

Drug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia known as torsades de pointes (TdP). Over 40 drugs with both cardiac and non-cardiac indications are associated with increased risk of 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 lack of direct evidence for LQTS.

Objective

We hypothesized that LQTS could be identified latently using an adverse event (AE) fingerprint of more commonly reported AEs. We aimed to generate an integrated data science pipeline that addresses current limitat

SUBMITTER: Lorberbaum T 

PROVIDER: S-EPMC4835515 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature