Coronary Risk Estimation Based on Clinical Data in Electronic Health Records

Ben O Petrazzini; Kumardeep Chaudhary; Carla Márquez-Luna; Iain S Forrest; Ghislain Rocheleau; Judy Cho; Jagat Narula; Girish Nadkarni; Ron Do

doi:10.1016/j.jacc.2022.01.021

Coronary Risk Estimation Based on Clinical Data in Electronic Health Records

J Am Coll Cardiol. 2022 Mar 29;79(12):1155-1166. doi: 10.1016/j.jacc.2022.01.021.

Authors

Ben O Petrazzini¹, Kumardeep Chaudhary², Carla Márquez-Luna², Iain S Forrest³, Ghislain Rocheleau², Judy Cho⁴, Jagat Narula⁵, Girish Nadkarni⁶, Ron Do⁷

Affiliations

¹ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA. Electronic address: https://twitter.com/OmegaPetrazzini.
² The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
³ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁴ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁵ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁶ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁷ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA. Electronic address: [email protected].

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.

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

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Cohort Studies
Coronary Artery Disease* / diagnosis
Coronary Artery Disease* / epidemiology
Electronic Health Records*
Genome-Wide Association Study
Humans
Risk Assessment / methods
Risk Factors

Abstract

Publication types

MeSH terms

Grants and funding