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

Davies, Mark R.; Martinec, Michael; Walls, Robert; Schwarz, Roman; Mirams, Gary R.; Wang, Ken; Steiner, Guido; Surinach, Andy; Flores, Carlos; Lav�, Thierry; Singer, Thomas; Polonchuk, Liudmila

Abstract

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. 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.

Citation

Davies, M. R., Martinec, M., Walls, R., Schwarz, R., Mirams, G. R., Wang, K., …Polonchuk, L. (2020). Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk. Cell Reports Medicine, 1(5), Article 100076. https://doi.org/10.1016/j.xcrm.2020.100076

• Journal Article Type: Article
• Acceptance Date: Jul 29, 2020
• Online Publication Date: Aug 25, 2020
• Publication Date: Aug 25, 2020
• Deposit Date: Jan 4, 2022
• Publicly Available Date: Jan 4, 2022
• Journal: Cell Reports Medicine
• Print ISSN: 2666-3791
• Electronic ISSN: 2666-3791
• Publisher: Cell Press
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Issue: 5
• Article Number: 100076
• DOI: https://doi.org/10.1016/j.xcrm.2020.100076
• Public URL: https://nottingham-repository.worktribe.com/output/4869877
• Publisher URL: https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(20)30097-5?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2666379120300975%3Fshowall%3Dtrue
• Related Public URLs: https://www.sciencedirect.com/science/article/pii/S2666379120300975
• Additional Information: This article is maintained by: Elsevier; Article Title: Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk; Journal Title: Cell Reports Medicine; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.xcrm.2020.100076; Content Type: article; Copyright: © 2020 The Authors.