Key result
An integrative data science pipeline identified 26 novel drug-drug interactions associated with QT interval prolongation using adverse event reports and electronic health records.
Observational
An integrative data science pipeline successfully identified 26 novel drug-drug interactions associated with QT prolongation, highlighting a new method for pharmacovigilance.
Supports exploratory pharmacovigilance screening; leaves open clinical relevance pending prospective validation.
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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Lorberbaum et al. (2015) conducted an observational in Long QT syndrome (LQTS). Drug-drug interactions (QT-DDIs) was evaluated on Identification of novel QT-DDIs. An integrative data science pipeline identified 26 novel drug-drug interactions associated with QT interval prolongation using adverse event reports and electronic health records.
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