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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 computatio...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659582/ https://www.ncbi.nlm.nih.gov/pubmed/33205069 http://dx.doi.org/10.1016/j.xcrm.2020.100076 |
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author | Davies, Mark R. Martinec, Michael Walls, Robert Schwarz, Roman Mirams, Gary R. Wang, Ken Steiner, Guido Surinach, Andy Flores, Carlos Lavé, Thierry Singer, Thomas Polonchuk, Liudmila |
author_facet | Davies, Mark R. Martinec, Michael Walls, Robert Schwarz, Roman Mirams, Gary R. Wang, Ken Steiner, Guido Surinach, Andy Flores, Carlos Lavé, Thierry Singer, Thomas Polonchuk, Liudmila |
author_sort | Davies, Mark R. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7659582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-76595822020-11-16 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 Lavé, Thierry Singer, Thomas Polonchuk, Liudmila Cell Rep Med Article 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. Elsevier 2020-08-25 /pmc/articles/PMC7659582/ /pubmed/33205069 http://dx.doi.org/10.1016/j.xcrm.2020.100076 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Davies, Mark R. Martinec, Michael Walls, Robert Schwarz, Roman Mirams, Gary R. Wang, Ken Steiner, Guido Surinach, Andy Flores, Carlos Lavé, Thierry Singer, Thomas Polonchuk, Liudmila Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk |
title | Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk |
title_full | Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk |
title_fullStr | Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk |
title_full_unstemmed | Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk |
title_short | Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk |
title_sort | use of patient health records to quantify drug-related pro-arrhythmic risk |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659582/ https://www.ncbi.nlm.nih.gov/pubmed/33205069 http://dx.doi.org/10.1016/j.xcrm.2020.100076 |
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