ReviewHeart rhythm2025
Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.
Review in Heart rhythm, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 4 syntheses or guidelines pooled 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
- Risk prediction model performances for sarcopenia in middle-aged and elderly people: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled it
- 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 electrocardiogram signals for sudden cardiac death prediction: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Artificial Intelligence-Enabled Electrocardiography for Prediction of Sudden Cardiac Death and Malignant Ventricular Arrhythmias: A Scoping Review.Journal of cardiovascular development and disease · 2026Review
- Understanding patient preferences, experiences and engagement with ambulatory heart rhythm monitoring: a scoping review.BMJ open · 2026Article
- Predicting Sudden Cardiac Death in Heart Failure with Mildly Reduced/Preserved Left Ventricular Ejection Fraction: A Clinical Review.Journal of clinical medicine · 2026Review
- Implementation of Machine Learning in Heart Failure Trials.Current heart failure reports · 2026Review
- Modifiable lifestyle factors and sudden cardiac death: mechanisms, evidence, and prevention strategies.Frontiers in cardiovascular medicine · 2026Review
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Machine Learning-Based Risk Stratification for Sudden Cardiac Death Using Clinical and Device-Derived Data.Sensors (Basel, Switzerland) · 2025Article
- Improving Risk Stratification in Sudden Cardiac Death Using Interpretable Machine Learning: A Clinical Perspective.Healthcare (Basel, Switzerland) · 2025Article
- The "Now" and "Next" in Precision Cardiovascular Medicine.Biomedicines · 2025Article
- Current and emerging innovations in technology, strategy, and artificial intelligence: a new era in cardiac arrest and cardiopulmonary resuscitation.World journal of emergency medicine · 2025Article
- Review
- Molecular Mechanisms of Cardiac Adaptation After Device Deployment.Journal of cardiovascular development and disease · 2025Review
- Cardiac rhythm devices in heart failure with reduced ejection fraction - role, timing, and optimal use in contemporary practice. European Journal of Heart Failure expert consensus document.European journal of heart failure · 2025Review
- Deep Learning-Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study.Journal of medical Internet research · 2025Article
- Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort.International journal of cardiology. Heart & vasculature · 2025Article
- Left Ventricular Non-Compaction: Evolving Concepts.Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each year globally. The heterogeneity among people who suffer a SCD, ranging from individuals with severe heart failure to seemingly healthy individuals, poses a significant challenge for effective risk assessment. Conventional risk stratification, which primarily relies on left ventricular ejection fraction, has resulted in only modest efficacy of implantable cardioverter-defibrillators for SCD prevention. In response, artificial intelligence (AI) holds promise for personalized SCD risk prediction and tailoring preventive strategies to the unique profiles of individual patients. Machine and deep learning algorithms have the capability to learn intricate nonlinear patterns between complex data and defined end points, and leverage these to identify subtle indicators and predictors of SCD that may not be apparent through traditional statistical analysis. However, despite the potential of AI to improve SCD risk stratification, there are important limitations that need to be addressed. We aim to provide an overview of the current state-of-the-art of AI prediction models for SCD, highlight the opportunities for these models in clinical practice, and identify the key challenges hindering widespread adoption.
Indexed as
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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.