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Cited by 639Artificial Intelligence in HealthcareCardiac Imaging and DiagnosticsCardiovascular Health and Risk Factors

Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

Open access
Ambale-Venkatesh, Bharath, Yang, Xiaoying, Wu, Colin O., Liu, Kiang, Hundley, W. Gregory, McClelland, Robyn, Gomes, Antoinette S., Folsom, Aaron R. +4 more·2017·Circulation Research
DOI 10.1161/circresaha.117.311312PMC PMC5640485
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Abstract

Rationale: Machine learning may be useful to characterize cardiovascular risk, predict outcomes, and identify biomarkers in population studies. Objective: To test the ability of random survival forests, a machine learning technique, to predict 6 cardiovascular outcomes in comparison to standard cardiovascular risk scores. Methods and Results: We included participants from the MESA (Multi-Ethnic Study of Atherosclerosis). Baseline measurements were used to predict cardiovascular outcomes over 12 years of follow-up. MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of cardiovascular disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across the United States were included. Seven-hundred thirty-five variables from imaging and noninvasive tests, questionnaires, and biomarker panels were obtained. We used the random survival forests technique to identify the top-20 predictors of each outcome. Imaging, electrocardiography, and serum biomarkers featured heavily on the top-20 lists as opposed to traditional cardiovascular risk factors. Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary Artery Calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function and cardiac troponin-T were among the top predictors for incident heart failure. Creatinine, age, and ankle-brachial index were among the top predictors of atrial fibrillation. TNF-α (tissue necrosis factor-α) and IL (interleukin)-2 soluble receptors and NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) levels were important across all outcomes. The random survival forests technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10%–25%). Conclusions: Machine learning in conjunction with deep phenotyping improves prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights on subclinical disease markers without apriori assumptions of causality. Clinical Trial Registration: URL: http://www.clinicaltrials.gov . Unique identifier: NCT00005487.

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  • AI-CAC could support evolution toward a more informative, physiologically grounded CAC score, preserving clinical familiarity and, combined with radiomics features or additional image-derived markers such as epicardial adipose tissue, cardiac chamber volumetry, as well as complementary biomarkers such as thoracic aortic calcification and body composition markers if these are shown to be predictive

    “AI-CAC could support evolution toward a more informative, physiologically grounded CAC score, preserving clinical familiarity and, combined with radiomics features or additional image-derived markers such as epicardial adipose tissue, cardiac chamber volumetry, as well as complementary biomarkers such as thoracic aortic calcification and body composition markers if these are shown to be predictive”

    From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [57]

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Authors
Ambale-Venkatesh, B., Yang, X., Wu, C. O., Liu, K., Hundley, W. G., McClelland, R., Gomes, A. S., Folsom, A. R., Shea, S., Guallar, E., Bluemke, D. A., & Lima, J. A. C.
Affiliations
  • From the Department of Radiology (B.A.-V.), Bloomberg School of Public Health (E.G.), and Department of Medicine, Cardiology and Radiology (J.A.C.L.), Johns Hopkins University, Baltimore, MD; George Washington University, DC (X.Y.); Office of Biostatistics, NHLBI, NIH, Bethesda, MD (C.O.W.); Department of Preventive Medicine, Northwestern University Medical School, Chicago, IL (K.L.); Department of Cardiology, Wake Forest University Health Sciences, Winston-Salem, NC (W.G.H.); Department of...
  • Northwestern University (US)
ORCID
Venue
Circulation Research
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2017-10-13
Volume
121
Issue
9
Pages
1092-1101
ISSN
  • 0009-7330 (ISSN-L)
  • 1524-4571
DOI
10.1161/circresaha.117.311312
PMC
PMC5640485
OpenAlex
W2743269518
Cited by
639
References
6
Citations by year
2026: 66 · 2025: 73 · 2024: 91 · 2023: 81 · 2022: 102 · 2021: 102 · 2020: 65 · 2019: 39 · 2018: 17 · 2017: 3
FWCI
46.55
Type
journal article
Language
en
Open access
Locations
  • Circulation Research (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (n01hc95164-17-0-1, n01hc95165-34-0-1, n01hc95161-24-0-1, n01hc095169-003, n01hc95166-21-0-1, n01hc95162-31-0-1, n01hc95168-27-0-1, n01hc95160-18-0-1, n01hc095167-000, UL1TR001079, n01hc95163-22-0-1, n01hc95159-40-0-1, UL1TR000040)
  • National Heart, Lung, and Blood Institute (N01-HC095169, N01-HC-095165, N01 HC095163, N01-HC-095159, N01 HC095168, N01-HC-095162, N01-HC-095161, N01 HC095167, N01-HC-095160, N01 HC095166, N01 HC095164, R01HL066075)
  • National Center for Advancing Translational Sciences
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Keywords
cardiovascular event prediction, random survival forest, cardiovascular risk score, Multi-Ethnic Study of Atherosclerosis, all-cause mortality, stroke, coronary artery disease, heart failure, atrial fibrillation, carotid ultrasound, coronary artery calcium, NT-proBNP, TNF-α, subclinical atherosclerosis, biomarkers
MeSH terms
Machine Learning, Aged, Aged, 80 and over, Cardiovascular Diseases, Ethnicity, Female, Follow-Up Studies, Humans, Male, Middle Aged, Predictive Value of Tests, Cohort Studies, Survival Rate, Atherosclerosis

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