Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
Open accessAbstract
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 L 2 penalty and L 1 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.
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ML models integrating clinical, genetic, and lifestyle factors have been developed to predict an individual's risk of developing cardiovascular disease.
“Researchers have developed ML models that integrate clinical, genetic, and lifestyle factors to predict an individual’s risk of developing cardiovascular disease [4–7].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [7]
Ward et al. (2020) used EHRs of 262,923 individuals to train and validate ML models, demonstrating performance comparable to or better than traditional equations for atherosclerotic cardiovascular disease risk.
“Ward et al. (2020) used EHRs of 262,923 individuals to train and validate ML models, demonstrating performance that was comparable to or better than traditional equations for atherosclerotic cardiovascular disease risk [7].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [7]
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- Authors
- Ward, A., Sarraju, A., Chung, S., Li, J., Harrington, R., Heidenreich, P., Palaniappan, L., Scheinker, D., & Rodriguez, F.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Palo Alto Medical Foundation (US)
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2020-09-23
- Volume
- 3
- Issue
- 1
- Pages
- 125-125
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC7511400
- OpenAlex
- W3088053597
- Cited by
- 112
- References
- 27
- Citations by year
- 2026: 18 · 2025: 17 · 2024: 22 · 2023: 28 · 2022: 16 · 2021: 9 · 2020: 1
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (1K01HL144607)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Keywords
- atherosclerotic cardiovascular disease, cardiovascular risk prediction, Pooled Cohort Equations, machine learning, gradient boosting, extreme gradient boosting, random forest, electronic health records, multi-ethnic population, Asian patients, Hispanic patients, risk discrimination, statin-naive patients
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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