ArticleJournal of the American College of Cardiology2022
Coronary Risk Estimation Based on Clinical Data in Electronic Health Records.
Article in Journal of the American College of Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Traditional Chinese and Western medicine-integrated risk prediction models for coronary heart disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Biomarkers and Risk Prediction in Cardiovascular-Kidney-Metabolic Syndrome: A Comprehensive Review.Reviews in cardiovascular medicine · 2026Review
- Everolimus-Eluting Stent (Espalier) in Coronary Artery Revascularization: A Retrospective, Investigator-Initiated, Multicenter Study.Cardiology research · 2026Article
- Three-year risk prediction of aortic stenosis using routine medical records: derivation and validation in 919 954 individuals from two cohorts.European heart journal. Digital health · 2026Article
- Development and validation of a neural network survival prediction model for ischemic heart disease.Cardiovascular diabetology · 2026Article
- Large language models improve transferability of electronic health record-based predictions across countries and coding systems.NPJ digital medicine · 2026Article
- Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.BMC cardiovascular disorders · 2026Article
- AI approaches for predicting progression to acute coronary syndrome among stable coronary heart disease patients.NPJ cardiovascular health · 2025Article
- The mortality of chronic kidney disease in Chinese subjects: a 12-year cohort based on clinical data in electronic health record.Annals of medicine · 2025Article
- CCL17 as an Inflammatory Biomarker Enhances Cardiovascular Risk Stratification: a Prospective Cohort Study.Journal of cardiovascular translational research · 2025Article
- Leveraging AI to Enhance Electronic Health Records.EJIFCC · 2025Review
- Gene therapy and genome editing for lipoprotein disorders.European heart journal · 2025Review
- Cross-biobank generalizability and accuracy of electronic health record-based predictors compared to polygenic scores.Nature genetics · 2025Article
- Machine Learning-Based Plasma Protein Risk Score Improves Atrial Fibrillation Prediction Over Clinical and Genomic Models.Circulation. Genomic and precision medicine · 2025Article
- Ningbo Schizophrenia Cohort (NSC)-a longitudinal ambispective cohort based on electronic health records: cohort profile.BMJ open · 2025Observational
- UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data.Nature communications · 2025Article
- Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.Journal of the American Heart Association · 2025Review
- Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.Atherosclerosis · 2025Article
- Article
- Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.European heart journal. Digital health · 2025Review
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundClinical 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 (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.
resultsCompared 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.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.