Coronary Risk Estimation Based on Clinical Data in Electronic Health Records.

<bold>Background: </bold>Clinical features from electronic health records (EHRs) can be used to build a complementary tool to predict coronary artery disease (CAD) susceptibility.<bold>Objectives: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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. [ABSTRACT FROM AUTHOR]