Original Investigation
Coronary Risk Estimation Based on Clinical Data in Electronic Health Records

https://doi.org/10.1016/j.jacc.2022.01.021Get rights and content
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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.

Key Words

biobank
coronary artery disease
electronic health record
machine learning
polygenic risk score
pooled cohort equations
prevention

Abbreviations and Acronyms

ASCVD
atherosclerotic cardiovascular disease
AUROC
area under the receiver-operating characteristic curve
CAD
coronary artery disease
EHR
electronic health record
ML
machine learning
NRI
net reclassification improvement
PCE
Pooled Cohort Equations
PRS
polygenic risk score

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Listen to this manuscript's audio summary by Editor-in-Chief Dr. Valentin Fuster on JACC.org.

Andrew DeFilippis, MD, MSc, served as Guest Associate Editor for this paper. Athena Poppas, MD, served as Guest Editor-in-Chief for this paper.

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