Observational studyEuropace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology2023
Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography.
Observational study in 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, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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
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Who cites it
27 citing papers in PubMed, 40 citations in OpenAlex.
- Prediction of cardiac arrest in patients with heart failure in Sweden: a registry study with development of a machine learning model.BMJ open · 2026Observational
- Artificial Intelligence-Enabled Electrocardiography for Prediction of Sudden Cardiac Death and Malignant Ventricular Arrhythmias: A Scoping Review.Journal of cardiovascular development and disease · 2026Review
- Article
- AI Tools for Heart Failure Management: A Comprehensive Review of Potential, Pitfalls, and Predictive Analytics.Cureus · 2025Review
- EHRA perspective on the digital data revolution in arrhythmia management: insights from the association's annual summit.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 · 2025Review
- Deep Convolutional Generative Adversarial Network for Improved Cardiac Image Classification in Heart Disease Diagnosis.Journal of imaging informatics in 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
- A hybrid algorithm-based ECG risk prediction model for cardiovascular disease.European heart journal. Digital health · 2025Article
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- The Timing for Primary Prevention for ICD in the Current Era of Pharmacotherapy.Cardiac failure review · 2025Review
- Characteristics of Sudden Cardiac Arrest in Young Athletes: A Web-Based Survey of Athletes in Japanese College Sports.Cardiology research and practice · 2025Article
- Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study.Journal of cardiovascular development and disease · 2024Article
- Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation.NPJ digital medicine · 2024Article
- AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions.Diagnostics (Basel, Switzerland) · 2024Review
- Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias.Scientific reports · 2024Article
- Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs.Cardiovascular digital health journal · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Decreased plasma ELABELA level as a novel screening indicator for heart failure: a cohort and observational study.Scientific reports · 2024Observational
- Implantable Cardioverter Defibrillator Tachycardia Therapies: Past, Present and Future Directions.Journal of cardiovascular development and disease · 2024Review
- An ECG-based artificial intelligence model for assessment of sudden cardiac death risk.Communications medicine · 2024Article
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
Abstract
aimsAvailable predictive models for sudden cardiac death (SCD) in heart failure (HF) patients remain suboptimal. We assessed whether the electrocardiography (ECG)-based artificial intelligence (AI) could better predict SCD, and also whether the combination of the ECG-AI index and conventional predictors of SCD would improve the SCD stratification among HF patients. METHODS AND
resultsIn a prospective observational study, 4 tertiary care hospitals in Tokyo enrolled 2559 patients hospitalized for HF who were successfully discharged after acute decompensation. The ECG data during the index hospitalization were extracted from the hospitals' electronic medical record systems. The association of the ECG-AI index and SCD was evaluated with adjustment for left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and competing risk of non-SCD. The ECG-AI index plus classical predictive guidelines (i.e. LVEF ≤35%, NYHA Class II and III) significantly improved the discriminative value of SCD [receiver operating characteristic area under the curve (ROC-AUC), 0.66 vs. 0.59; P = 0.017; Delong's test] with good calibration (P = 0.11; Hosmer-Lemeshow test) and improved net reclassification [36%; 95% confidence interval (CI), 9-64%; P = 0.009]. The Fine-Gray model considering the competing risk of non-SCD demonstrated that the ECG-AI index was independently associated with SCD (adjusted sub-distributional hazard ratio, 1.25; 95% CI, 1.04-1.49; P = 0.015). An increased proportional risk of SCD vs. non-SCD with an increasing ECG-AI index was also observed (low, 16.7%; intermediate, 18.5%; high, 28.7%; P for trend = 0.023). Similar findings were observed in patients aged ≤75 years with a non-ischaemic aetiology and an LVEF of >35%.
conclusionTo improve risk stratification of SCD, ECG-based AI may provide additional values in the management of patients with HF.
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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.