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

Autor:	Davies MR; QT-Informatics Ltd., Macclesfield, UK., Martinec M; PHC Data Science, Personalized Healthcare, Product Development, F. Hoffmann-La Roche AG, Basel, Switzerland., Walls R; PHC Data Science, Personalized Healthcare, Product Development, F. Hoffmann-La Roche AG, Basel, Switzerland., Schwarz R; Safety Analytics and Reporting, Drug Safety, Pharmaceutical Development, F. Hoffmann-La Roche AG, Basel, Switzerland., Mirams GR; Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK., Wang K; Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland., Steiner G; Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland., Surinach A; Genesis Research, Hoboken, NJ, USA., Flores C; Genesis Research, Hoboken, NJ, USA., Lavé T; Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland., Singer T; Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland., Polonchuk L; Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland.
Jazyk:	angličtina
Zdroj:	Cell reports. Medicine [Cell Rep Med] 2020 Aug 25; Vol. 1 (5), pp. 100076. Date of Electronic Publication: 2020 Aug 25 (Print Publication: 2020).
DOI:	10.1016/j.xcrm.2020.100076
Abstrakt:	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. Competing Interests: The authors declare no competing interests. (© 2020 The Authors.)
Databáze:	MEDLINE
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