Coronary Risk Estimation Based on Clinical Data in Electronic Health Records

• Published in: Journal of the American College of Cardiology Vol. 79; no. 12; pp. 1155 - 1166
• Main Authors: Petrazzini, Ben O., BS, Chaudhary, Kumardeep, PhD, Márquez-Luna, Carla, PhD, Forrest, Iain S., BS, Rocheleau, Ghislain, PhD, Cho, Judy, MD, Narula, Jagat, MD, PhD, Nadkarni, Girish, MD, Do, Ron, PhD
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 29.03.2022
• Subjects: biobank; Cardiovascular; Cohort Studies; coronary artery disease; Coronary Artery Disease - diagnosis; Coronary Artery Disease - epidemiology; electronic health record; Electronic Health Records; Genome-Wide Association Study; Humans; machine learning; polygenic risk score; pooled cohort equations; prevention; Risk Assessment - methods; Risk Factors; coronary artery disease; NRI; ASCVD; CAD; net reclassification improvement; AUROC; atherosclerotic cardiovascular disease; machine learning; EHR; PRS; pooled cohort equations; PCE; area under the receiver-operating characteristic curve; biobank; electronic health record; polygenic risk score; prevention; ML
• ISSN: 0735-1097; 1558-3597; 1558-3597
• DOI: 10.1016/j.jacc.2022.01.021

AbstractBackgroundClinical features from electronic health records (EHRs) can be used to build a complementary tool to predict coronary artery disease (CAD) susceptibility. ObjectivesThe 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. MethodsWe applied a machine learning framework using clinical features from the EHR in a multiethnic, clinical care cohort (Bio Me) 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. ResultsCompared with the PCE, the EHR score improved CAD prediction by 12% in the Bio Me 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. ConclusionsThe 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.