Observational studyEuropean heart journal2025
Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome.
Observational study in European heart journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Diagnostic performance of the ECG-SMART AI score for detecting angiographically significant coronary artery disease in patients undergoing elective coronary angiography.PLOS digital health · 2026Article
- Article
- Large language models for acute coronary syndrome triage at first medical contact in emergency departments.NPJ digital medicine · 2026Article
- Electrical risk score and electrocardiographic markers in acute cholecystitis: a prospective cross-sectional study using explainable machine learning.BMC cardiovascular disorders · 2026Article
- Sources of Variation in Cardiovascular Care Cascades.JAMA health forum · 2026Article
- A parsimonious six-predictor model for memory decline: cross-population validation in Chinese and Japanese aging cohorts.GeroScience · 2026Article
- An efficient deep CNN based BiLSTM framework with RanA optimization for accurate cardiac arrhythmia detection.Scientific reports · 2026Article
- Machine learning-based risk factor analysis and prediction model construction for mortality in chronic heart failure.Journal of global health · 2025Article
- Research status, hotspots and perspectives of artificial intelligence applied to pain management: a bibliometric and visual analysis.Updates in surgery · 2025Article
- Diagnostic Value of Long Non-Coding RNA NORAD in Acute Coronary Syndrome: A Diagnostic Biomarker Study.Journal of inflammation research · 2025Article
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Authors and funding
18 authors.
Funding
Abstract
BACKGROUND AND
aimsThe importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.
methodsThis was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain. All-cause death was ascertained from multiple sources, including the CDC National Death Index registry. Six machine learning models were trained for survival analysis using 73 morphological electrocardiogram features (80% training with 10-fold cross-validation and 20% testing), followed by a variational Bayesian Gaussian mixture model to define distinct risk groups. The resulting classification performance was compared against the HEART score.
resultsThe derivation cohort included 4015 patients (age 59 ± 16 years, 47% women). The mortality rate was 20.3% after a median follow-up period of 3.05 years (interquartile range 1.75-5.32). Extra Survival Trees outperformed other forecasting models, and the derived risk groups successfully classified patients into low-, moderate-, and high-risk groups (log-rank test statistic = 121.14, P < .001). This model outperformed the HEART score, reducing the rate of missed events by >90% with a negative predictive value and sensitivity of 93.4% and 85.9%, compared to 89.0% and 75.0%, respectively. In an independent external testing cohort (N = 3095, age 59 ± 15 years, 44% women, 30-day mortality 3.5%), patients in the moderate [odds ratio 3.62 (1.35-9.74)] and high [odds ratio 6.12 (2.38-15.75)] risk groups had significantly higher odds of mortality compared to those in the low-risk group.
conclusionsThe externally validated machine learning-based model, exclusively utilizing features from the 12-lead electrocardiogram, outperformed the HEART score in stratifying the mortality risk of patients with acute chest pain. This may have the potential to impact the precision of care delivery and the allocation of resources to those at highest risk of adverse events.
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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.