Journal
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
Volume 79, Issue 12, Pages 1155-1166Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jacc.2022.01.021
Keywords
biobank; coronary artery disease; electronic health record; machine learning; polygenic risk score; pooled cohort equations; prevention
Categories
Funding
- National Institute of General Medical Sciences of the National Institutes of Health (NIH) [T32-GM007280]
- NIH [R01-DK108803, U01-HG007278, U01-HG009610, U01-DK116100, K23-DK107908]
- Goldfinch Bio
- National Institute of General Medical Sciences of the NIH [R35-GM124836]
- National Heart, Lung, and Blood Institute of the NIH [R01-HL139865, R01-HL155915]
- AstraZeneca
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This study uses clinical features from electronic health records (EHRs) to build a complementary tool for predicting susceptibility to coronary artery disease (CAD). The findings show that an EHR score improves CAD prediction and reclassification, particularly in individuals with low CAD risk, compared to conventional clinical guidelines.
BACKGROUND Clinical features from electronic health records (EHRs) can be used to build a complementary tool to predict coronary artery disease (CAD) susceptibility. OBJECTIVES The purpose of this study was to determine whether an EHR score can improve CAD prediction and reclassification 1 year before diagnosis, beyond conventional clinical guidelines as determined by the pooled cohort equations (PCE) and a polygenic risk score for CAD. METHODS We applied a machine learning framework using clinical features from the EHR in a multiethnic, clinical care cohort (BioMe) comprising 555 CAD cases and 6,349 control subjects and in a population-based cohort (UK Biobank) comprising 3,130 CAD cases and 378,344 control subjects for external validation. RESULTS Compared with the PCE, the EHR score improved CAD prediction by 12% in the BioMe Biobank and by 9% in the UK Biobank. The EHR score reclassified 25.8% and 15.2% individuals in each cohort respectively, compared with the PCE score. We observed larger improvements in the EHR score over the PCE in a subgroup of individuals with low CAD risk, with 20% increased discrimination and 34.4% increased reclassification. In all models, the polygenic risk score for CAD did not improve CAD prediction, compared with the PCE or EHR score. CONCLUSIONS The EHR score resulted in increased prediction and reclassification for CAD, demonstrating its potential use for population health monitoring of short-term CAD risk in large health systems. (C) 2022 by the American College of Cardiology Foundation.
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