Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk

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. [Display omitted] In vitro data and computational models can assist with calculating pro-arrhythmic riskWe use patient health records and FDA Adverse Event Reporting System reportsUse of such datasets helps assess relative drug risk and cardiac safety modelsWe quantify how patient characteristics can affect arrhythmia incidence Davies et al. analyze patient health records and FDA Adverse Event Reporting System reports to demonstrate how patient subtypes affect the incidence of drug-related arrhythmia. Using such real-world data to understand background arrhythmia can further validate cardiac risk models for regulatory use and help stratify patients when evaluating drug risk.