ArticleEuropean heart journal2025
Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.
Article in European heart journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07163767 (Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images), which is not on this map. Cited by 48 papers, 2 of them syntheses that pooled it.
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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.
Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images
Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Applied to Electrocardiogram-Based Cardiovascular Risk Assessment: A Systematic Review.Arquivos brasileiros de cardiologia · 2026Pooled it
- Artificial intelligence in electrocardiogram-based prediction of heart failure: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Innovative strategies to reduce heart failure hospitalizations: insights from the ESC heart failure congress 2026.Heart failure reviews · 2026Review
- Artificial intelligence and the evolution of the electrocardiogram: from cardiovascular diagnostic tool to digital biomarker.European heart journal. Digital health · 2026Review
- Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis.Annals of medicine and surgery (2012) · 2026Article
- A bibliometric analysis of artificial intelligence applications in heart failure.Journal of thoracic disease · 2026Article
- Digital phenotyping of aortic stenosis-related remodeling reveals complementary structural, electrical, and hemodynamic signatures.medRxiv : the preprint server for health sciences · 2026Article
- Differentiating HFmr/rEF from HFpEF using standard 12-lead ECG measurements: an interpretable machine learning study.Open heart · 2026Article
- Deep learning analysis of single-lead electrocardiograms enables pragmatic heart failure risk assessment in the general population.European heart journal. Digital health · 2026Article
- AI-ECG for Detecting Left Ventricular Systolic Dysfunction in Chagas Disease: Diagnostic and Prognostic Value.JACC. Advances · 2026Article
- Modeling day-long ECG signals to predict heart failure risk with explainable AI.NPJ digital medicine · 2026Article
- Artificial Intelligence-Derived Electrocardiogram Analysis for Identification of Carbon Monoxide-Induced Cardiomyopathy: A Retrospective Study.Medicina (Kaunas, Lithuania) · 2026Article
- Artificial Intelligence-Enhanced Electrocardiogram for Longitudinal Evaluation of Right Ventricular Dysfunction After Acute Pulmonary Embolism: Rational and Design of CURES-CARE Study.Pulmonary circulation · 2026Article
- The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.Current heart failure reports · 2026Review
- Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease.Current heart failure reports · 2026Review
- Wearable-Echo-FM: an ECG echo foundation model for 1-lead electrocardiography.European heart journal. Digital health · 2026Article
- Multimodal deep learning with missing data robustness for enhanced early diagnosis of coronary artery disease using CCTA, clinical, and ECG data.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence-Enabled ECG Analysis to Predict Incident Heart Failure.Circulation. Heart failure · 2026Article
- Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations.Health science reports · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
BACKGROUND AND
aimsCurrent heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk.
methodsAcross multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization. An AI-ECG model that defines cross-sectional left ventricular systolic dysfunction from 12-lead ECG images was used, and its association with incident HF was evaluated. Discrimination was assessed using Harrell's C-statistic. Pooled cohort equations to prevent HF (PCP-HF) were used as a comparator.
resultsAmong 231 285 YNHHS patients, 4472 had primary HF hospitalizations over 4.5 years (inter-quartile range 2.5-6.6). In UKB and ELSA-Brasil, among 42 141 and 13 454 people, 46 and 31 developed HF over 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years. A positive AI-ECG screen portended a 4- to 24-fold higher risk of new-onset HF [age-, sex-adjusted hazard ratio: YNHHS, 3.88 (95% confidence interval 3.63-4.14); UKB, 12.85 (6.87-24.02); ELSA-Brasil, 23.50 (11.09-49.81)]. The association was consistent after accounting for comorbidities and the competing risk of death. Higher probabilities were associated with progressively higher HF risk. Model discrimination was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. In YNHHS and ELSA-Brasil, incorporating AI-ECG with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.
conclusionsAn AI model applied to a single ECG image defined the risk of future HF, representing a digital biomarker for stratifying HF risk.
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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.