ArticleNature cardiovascular research2022
Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart.
Article in Nature cardiovascular research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT01076660 (Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of), which is not on this map. Cited by 42 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of: Functional Energetics and Imaging for Phenotypic Characterization of Patients at Risk for Sudden Cardiac Death, See Also NCT000181233]
Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it, 90 citations in OpenAlex.
- Artificial intelligence-enabled electrocardiography to predict impending episodes of ventricular tachycardia in individuals under continuous heart rhythm monitoring.The Cochrane database of systematic reviews · 2026Pooled it
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- The Hodgkin-Huxley-Katz Prize Lecture: Heart digital twins: A new paradigm in arrhythmia medicine.The Journal of physiology · 2026Article
- A Review of the Integration of Artificial Intelligence in Cardiac Electrophysiology.Journal of clinical medicine · 2026Review
- Improving arrhythmic risk prediction using cardiac magnetic resonance within deep learning in ischemic heart disease.NPJ cardiovascular health · 2026Article
- Predicting Sudden Cardiac Death in Heart Failure with Mildly Reduced/Preserved Left Ventricular Ejection Fraction: A Clinical Review.Journal of clinical medicine · 2026Review
- A generalizable deep learning system for cardiac MRI.Nature biomedical engineering · 2026Article
- Time and person sensitive foundation model for disease prediction and risk stratification.NPJ digital medicine · 2026Article
- Materials and System Design for Self-Decision Bioelectronic Systems.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Deep learning for cardiac MRI: performance evidence and barriers to clinical integration. A Systematic Review and Meta-Analysis.European heart journal. Imaging methods and practice · 2026Article
- Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review.Journal of personalized medicine · 2025Review
- Late gadolinium enhancement imaging and sudden cardiac death.European heart journal · 2025Review
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.Nature cardiovascular research · 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
- Cardiovascular imaging techniques for electrophysiologists.Nature cardiovascular research · 2025Review
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- Deep Learning-Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study.Journal of medical Internet research · 2025Article
- Deep-learning-driven optical coherence tomography analysis for cardiovascular outcome prediction in patients with acute coronary syndrome.European heart journal. Digital health · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 6 institutions in 1 country.
Funding
Abstract
Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. Here, we develop a novel deep learning (DL) approach that blends neural networks and survival analysis to predict patient-specific survival curves from contrast-enhanced cardiac magnetic resonance images and clinical covariates for patients with ischemic heart disease. The DL-predicted survival curves offer accurate predictions at times up to 10 years and allow for estimation of uncertainty in predictions. The performance of this learning architecture was evaluated on multi-center internal validation data and tested on an independent test set, achieving concordance index of 0.83 and 0.74, and 10-year integrated Brier score of 0.12 and 0.14. We demonstrate that our DL approach with only raw cardiac images as input outperforms standard survival models constructed using clinical covariates. This technology has the potential to transform clinical decision-making by offering accurate and generalizable predictions of patient-specific survival probabilities of arrhythmic death over time.
Identifiers
What OpenQuestion holds
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.