| Title: | Coronary Risk Estimation Based on Clinical Data in Electronic Health Records |
| Journal: | Journal of the American College of Cardiology |
| Published: | 1 Mar 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35331410/ |
| DOI: | https://doi.org/10.1016/j.jacc.2022.01.021 |
Publication 7588
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Abstract
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.9 Authors
- Ben O Petrazzini
- Kumardeep Chaudhary
- Carla Márquez-Luna
- Iain S Forrest
- Ghislain Rocheleau
- Judy Cho
- Jagat Narula
- Girish Nadkarni
- Ron Do
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