Why the study?
Evaluating computational models that predict proarrhythmic risk requires robust assessment of clinical outcomes.
Can electronic healthcare claims and adverse event reports be used to quantify drug-related pro-arrhythmic risk?
Can electronic healthcare claims and adverse event reports be used to quantify drug-related pro-arrhythmic risk?
Electronic health records and claims data provide a complementary resource for evaluating drug-induced proarrhythmic risk and validating computational cardiac safety models.
Real-world claims data benchmark proarrhythmic models; leaves open whether predictions improve clinical decision-making or outcomes.
There is an increasing expectation that computational approaches may supplement existing human decision-making. Frontloading of models for cardiac safety prediction is no exception to this trend, and ongoing regulatory initiatives propose use of high-throughput in vitro data combined with computational models for calculating proarrhythmic risk. Evaluation of these models requires robust assessment of the outcomes. Using FDA Adverse Event Reporting System reports and electronic healthcare claims data from the Truven-MarketScan US claims database, we quantify the incidence rate of arrhythmia in patients and how this changes depending on patient characteristics. First, we propose that such datasets are a complementary resource for determining relative drug risk and assessing the performance of cardiac safety models for regulatory use. Second, the results suggest important determinants for appropriate stratification of patients and evaluation of additional drug risk in prescribing and clinical support algorithms and for precision health.
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Davies et al. (2020) studied this question.
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