Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population

Abstract The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics. It is unknown if machine learning (ML) models can improve ASCVD risk prediction...

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Autores principales:	Andrew Ward, Ashish Sarraju, Sukyung Chung, Jiang Li, Robert Harrington, Paul Heidenreich, Latha Palaniappan, David Scheinker, Fatima Rodriguez
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/8af9c8a7db8d4652b878ad12571c3ae7
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spelling	oai:doaj.org-article:8af9c8a7db8d4652b878ad12571c3ae72021-12-02T18:48:41ZMachine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population10.1038/s41746-020-00331-12398-6352https://doaj.org/article/8af9c8a7db8d4652b878ad12571c3ae72020-09-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00331-1https://doaj.org/toc/2398-6352Abstract The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics. It is unknown if machine learning (ML) models can improve ASCVD risk prediction across broader diverse, real-world populations. We developed ML models for ASCVD risk prediction for multi-ethnic patients using an electronic health record (EHR) database from Northern California. Our cohort included patients aged 18 years or older with no prior CVD and not on statins at baseline (n = 262,923), stratified by PCE-eligible (n = 131,721) or PCE-ineligible patients based on missing or out-of-range variables. We trained ML models [logistic regression with L2 penalty and L1 lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups. A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825–0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755–0.794). Among patients aged 40–79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759–0.808) and after (AUC 0.790, 95% CI: 0.765–0.814) incorporating additional EHR data. Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction.Andrew WardAshish SarrajuSukyung ChungJiang LiRobert HarringtonPaul HeidenreichLatha PalaniappanDavid ScheinkerFatima RodriguezNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-7 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Andrew Ward Ashish Sarraju Sukyung Chung Jiang Li Robert Harrington Paul Heidenreich Latha Palaniappan David Scheinker Fatima Rodriguez Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
description	Abstract The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics. It is unknown if machine learning (ML) models can improve ASCVD risk prediction across broader diverse, real-world populations. We developed ML models for ASCVD risk prediction for multi-ethnic patients using an electronic health record (EHR) database from Northern California. Our cohort included patients aged 18 years or older with no prior CVD and not on statins at baseline (n = 262,923), stratified by PCE-eligible (n = 131,721) or PCE-ineligible patients based on missing or out-of-range variables. We trained ML models [logistic regression with L2 penalty and L1 lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups. A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825–0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755–0.794). Among patients aged 40–79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759–0.808) and after (AUC 0.790, 95% CI: 0.765–0.814) incorporating additional EHR data. Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction.
format	article
author	Andrew Ward Ashish Sarraju Sukyung Chung Jiang Li Robert Harrington Paul Heidenreich Latha Palaniappan David Scheinker Fatima Rodriguez
author_facet	Andrew Ward Ashish Sarraju Sukyung Chung Jiang Li Robert Harrington Paul Heidenreich Latha Palaniappan David Scheinker Fatima Rodriguez
author_sort	Andrew Ward
title	Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
title_short	Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
title_full	Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
title_fullStr	Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
title_full_unstemmed	Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
title_sort	machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/8af9c8a7db8d4652b878ad12571c3ae7
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Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population

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