ArticleNature communications2020
Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
84 citing papers in PubMed, 3 syntheses or guidelines pooled it, 184 citations in OpenAlex.
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Pooled it
- Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020.Frontiers in physiology · 2021Pooled it
- Improved Detection of Acute Coronary Occlusion Myocardial Infarction by an Artificial Intelligence Electrocardiogram Model in Swedish Emergency Departments.Journal of the American College of Emergency Physicians open · 2026Article
- Clinical Applications, Opportunities, and Implementation Challenges of AI in Emergency Medicine: A Narrative Review.Cureus · 2026Review
- Artificial intelligence-based ECG as a triage tool for acute myocardial infarction: a diagnostic systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Beyond Clinical Acuity: Cardiac Comorbidity and Complication Profiles and the Prehospital Hospitalization/Transport Decision in Ischemic Heart Disease-A Five-Year Retrospective Emergency Medical Service Study in Astana, Kazakhstan.Healthcare (Basel, Switzerland) · 2026Article
- Interpretable machine learning for predicting one-year prognosis in acute ischemic stroke.BMC neurology · 2026Article
- Artificial intelligence in prehospital assessment of acute coronary syndrome: a scoping review.BMC emergency medicine · 2026Article
- Novel machine learning fusion architectures integrating electrocardiogram representations: applications to acute coronary event detection.European heart journal. Digital health · 2026Article
- Artificial intelligence-enhanced electrocardiography for acute myocardial infarction detection: a systematic review.Cardiovascular diagnosis and therapy · 2026Review
- Adversarial Debiasing for Equitable and Fair Detection of Acute Coronary Syndrome Using 12-Lead ECG.IEEE transactions on bio-medical engineering · 2026Article
- Review
- Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.Scientific reports · 2026Article
- Sex-specific differences in the prognostic value of ischemic pre-hospital ECGs.Frontiers in cardiovascular medicine · 2026Article
- QTc interval dynamics and heart rate variability response to submaximal exercise in normotensive young adults with and without parental hypertension.Journal of education and health promotion · 2026Article
- Impact of Prehospital Administration of Aspirin and Unfractionated Heparin on In-Hospital Outcomes in Patients with Suspected Myocardial Infarction: A Retrospective Cohort Study.Open access emergency medicine : OAEM · 2026Article
- Flexible dry electrodes for electrocardiographic monitoring: Working principles, design strategies, and recent advances.iScience · 2025Review
- AI-guided refinement of coronary revascularization need in patients suspected of acute coronary syndrome.European heart journal. Digital health · 2025Article
- The Accuracy of Artificial Intelligence-Based Models Applied to 12-Lead Electrocardiograms for the Diagnosis of Acute Coronary Syndrome: A Systematic Review.Journal of the American College of Emergency Physicians open · 2025Review
24 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.
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Registered trials
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.