ArticleCommunications medicine2024
An ECG-based artificial intelligence model for assessment of sudden cardiac death risk.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Electrocardiographic identification of drug effects: QT prolongation, arrhythmias, and clinical monitoring.European journal of clinical pharmacology · 2026Pooled it
- Population-based screening for conditions associated with juvenile sudden cardiac death: a systematic review and meta-analysis.European heart journal. Quality of care & clinical outcomes · 2026Pooled it
- Artificial intelligence in electrocardiogram signals for sudden cardiac death prediction: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- A Cumulative Electrical Risk Score Is Associated with Appropriate ICD Therapy in Primary-Prevention Patients with Reduced Ejection Fraction: A Single-Center Retrospective Study.Journal of clinical medicine · 2026Article
- Article
- From Molecules to Machines: An Integrative Framework Linking Molecular Pathogenesis, Multi-Factorial Risk, Risk Stratification, Clinical Management, and Artificial Intelligence in QT Prolongation and Sudden Cardiac Death.Clinical cardiology · 2026Review
- Artificial Intelligence-Enabled Electrocardiography for Prediction of Sudden Cardiac Death and Malignant Ventricular Arrhythmias: A Scoping Review.Journal of cardiovascular development and disease · 2026Review
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Performance of the 12-lead ECG in predicting short- and long-term risk of sudden cardiac death.NPJ digital medicine · 2026Article
- Article
- Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review.Korean circulation journal · 2026Review
- Telemedicine for the detection and management of in-hospital and out-of-hospital sudden cardiac arrest: Status quo and quo vadis.American heart journal plus : cardiology research and practice · 2025Review
- Markers of sudden cardiac death associated with the ventricular-paced 12-lead ECG.European heart journal open · 2025Article
- Interpretable Machine Learning Model Integrating Electrocardiographic and Acute Physiology Metrics for Mortality Prediction in Critical Ill Patients.Journal of clinical medicine · 2025Article
- Automated Characterization of Sudden Cardiac Death Using Locality Preserving Projection and Fuzzy Entropy Based on Empirical Mode Decomposition from ECG Signals.Journal of medical systems · 2025Article
- Temporal trends in left ventricular ejection fraction before sudden death in patients with heart failure.Heart rhythm · 2025Article
- Artificial intelligence in resuscitation: a scoping review.Resuscitation plus · 2025Review
- A Talk with ChatGPT: The Role of Artificial Intelligence in Shaping the Future of Cardiology and Electrophysiology.Journal of personalized medicine · 2025Article
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Heart Rate Variability Analysis in Congestive Heart Failure: The Need for Standardized Assessment Protocols.Reviews in cardiovascular medicine · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundConventional ECG-based algorithms could contribute to sudden cardiac death (SCD) risk stratification but demonstrate moderate predictive capabilities. Deep learning (DL) models use the entire digital signal and could potentially improve predictive power. We aimed to train and validate a 12 lead ECG-based DL algorithm for SCD risk assessment.
methodsOut-of-hospital SCD cases were prospectively ascertained in the Portland, Oregon, metro area. A total of 1,827 pre- cardiac arrest 12 lead ECGs from 1,796 SCD cases were retrospectively collected and analyzed to develop an ECG-based DL model. External validation was performed in 714 ECGs from 714 SCD cases from Ventura County, CA. Two separate control group samples were obtained from 1342 ECGs taken from 1325 individuals of which at least 50% had established coronary artery disease. The DL model was compared with a previously validated conventional 6 variable ECG risk model.
resultsThe DL model achieves an AUROC of 0.889 (95% CI 0.861-0.917) for the detection of SCD cases vs. controls in the internal held-out test dataset, and is successfully validated in external SCD cases with an AUROC of 0.820 (0.794-0.847). The DL model performs significantly better than the conventional ECG model that achieves an AUROC of 0.712 (0.668-0.756) in the internal and 0.743 (0.711-0.775) in the external cohort.
conclusionsAn ECG-based DL model distinguishes SCD cases from controls with improved accuracy and performs better than a conventional ECG risk model. Further detailed investigation is warranted to evaluate how the DL model could contribute to improved SCD risk stratification.
Identifiers
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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.