Observational studyJournal of medical Internet research2024
Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.
Observational study in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 20 papers, 1 of them a synthesis 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.
Registry (Prospective Cohort) for Heart Failure in Korea (KorAHF)
Utilization of AI-ECG for Preoperative Cardiovascular Risk Stratification in General Surgery: A Randomized Controlled Trial
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in cardiovascular diagnostics: a systematic review and descriptive analysis of clinical applications and diagnostic performance.BMC cardiovascular disorders · 2025Pooled it
- Operationalizing AI-Enabled Cardiovascular Biomarkers: A Clinician-Centered Framework for Validation, Governance, and Workflow Integration.JMIR cardio · 2026Article
- Multitask Artificial Intelligence-Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs.Journal of medical Internet research · 2026Article
- External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.NPJ digital medicine · 2026Article
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- Validation of AI-enhanced ECG image analysis for identifying extreme cardiac magnetic resonance metrics in a cross-ethnic UK biobank study.Scientific reports · 2026Article
- Artificial intelligence-enhanced ECG score for perioperative risk assessment in non-cardiac surgery.European heart journal. Digital health · 2026Article
- Advances in Diagnosis and Treatment of Acute and Chronic Heart Failure: A Comprehensive Review.Journal of clinical medicine · 2026Review
- AI-derived prognostic model identifies high-risk gene signatures in pediatric gliomas.Frontiers in immunology · 2026Article
- Artificial intelligence in early heart failure detection: from algorithms to bedside integration.Frontiers in cardiovascular medicine · 2026Article
- ECGomics: An Open Platform for AI-ECG Digital Biomarker Discovery.Health data science · 2026Article
- The Evolution of Heart Failure Assessment in Asia.JACC. Asia · 2025Article
- Artificial Intelligence-Based Electrocardiogram Model as a Predictor of Postoperative Atrial Fibrillation Following Cardiac Surgery: Retrospective Cohort Study.Journal of medical Internet research · 2025Article
- Interethnic validation of electrocardiogram image analysis software for detecting left ventricular dysfunction in an emergency department population.Clinical and experimental emergency medicine · 2025Article
- Indirect Myocardial Injury in Polytrauma: Mechanistic Pathways and the Clinical Utility of Immunological Markers.Journal of cardiovascular development and disease · 2025Review
- Development and validation of an integrated prognostic model for all-cause mortality in heart failure: a comprehensive analysis combining clinical, electrocardiographic, and echocardiographic parameters.BMC cardiovascular disorders · 2025Article
- Non-Inferiority Analysis of Electrocardiography Analysis Application vs. Point-of-Care Ultrasound for Screening Left Ventricular Dysfunction.Yonsei medical journal · 2025Article
- Evolution of Research on Artificial Intelligence for Heart Failure: A Bibliometric and Visual Analysis.Journal of multidisciplinary healthcare · 2025Review
- AI derived ECG global longitudinal strain compared to echocardiographic measurements.Scientific reports · 2024Article
- Artificial intelligence in modern clinical practice (Review).Medicine internationalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability.
objectiveWe examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF.
methodsWe retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF).
resultsAmong the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001).
conclusionsPredicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients.
trial registrationClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.
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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.