Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk
Summary: 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 c...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-08-01
|
| Series: | Cell Reports Medicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666379120300975 |
| _version_ | 1819206186048684032 |
|---|---|
| author | Mark R. Davies Michael Martinec Robert Walls Roman Schwarz Gary R. Mirams Ken Wang Guido Steiner Andy Surinach Carlos Flores Thierry Lavé Thomas Singer Liudmila Polonchuk |
| author_facet | Mark R. Davies Michael Martinec Robert Walls Roman Schwarz Gary R. Mirams Ken Wang Guido Steiner Andy Surinach Carlos Flores Thierry Lavé Thomas Singer Liudmila Polonchuk |
| author_sort | Mark R. Davies |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-12-23T05:03:35Z |
| format | Article |
| id | doaj.art-8ab556035a2b4ff38523463e38599500 |
| institution | Directory Open Access Journal |
| issn | 2666-3791 |
| language | English |
| last_indexed | 2024-12-23T05:03:35Z |
| publishDate | 2020-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports Medicine |
| spelling | doaj.art-8ab556035a2b4ff38523463e385995002022-12-21T17:59:09ZengElsevierCell Reports Medicine2666-37912020-08-0115100076Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic RiskMark R. Davies0Michael Martinec1Robert Walls2Roman Schwarz3Gary R. Mirams4Ken Wang5Guido Steiner6Andy Surinach7Carlos Flores8Thierry Lavé9Thomas Singer10Liudmila Polonchuk11QT-Informatics Ltd., Macclesfield, UK; Corresponding authorPHC Data Science, Personalized Healthcare, Product Development, F. Hoffmann-La Roche AG, Basel, SwitzerlandPHC Data Science, Personalized Healthcare, Product Development, F. Hoffmann-La Roche AG, Basel, SwitzerlandSafety Analytics and Reporting, Drug Safety, Pharmaceutical Development, F. Hoffmann-La Roche AG, Basel, SwitzerlandCentre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UKRoche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, SwitzerlandRoche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, SwitzerlandGenesis Research, Hoboken, NJ, USAGenesis Research, Hoboken, NJ, USARoche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, SwitzerlandRoche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, SwitzerlandRoche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2666379120300975drug developmentarrhythmiacardiac safetyin silico modelsreal world data |
| spellingShingle | Mark R. Davies Michael Martinec Robert Walls Roman Schwarz Gary R. Mirams Ken Wang Guido Steiner Andy Surinach Carlos Flores Thierry Lavé Thomas Singer Liudmila Polonchuk Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk Cell Reports Medicine drug development arrhythmia cardiac safety in silico models real world data |
| 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 | drug development arrhythmia cardiac safety in silico models real world data |
| url | http://www.sciencedirect.com/science/article/pii/S2666379120300975 |
| work_keys_str_mv | AT markrdavies useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT michaelmartinec useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT robertwalls useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT romanschwarz useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT garyrmirams useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT kenwang useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT guidosteiner useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT andysurinach useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT carlosflores useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT thierrylave useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT thomassinger useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk AT liudmilapolonchuk useofpatienthealthrecordstoquantifydrugrelatedproarrhythmicrisk |