Key result
ECG-AI outperforms conventional risk scores for sudden cardiac death prediction, achieving ~0.85 AUROC.
Why the study?
Traditional ECG parameters and guideline criteria (LVEF <35%) identify only ~50% of patients at high risk for sudden cardiac death. Although AI-derived ECG features may capture subclinical arrhythmic substrate, an evidence synthesis on ECG-AI sudden cardiac death prediction was needed.
Does deep learning/machine learning-based ECG analysis improve sudden cardiac death risk prediction compared to conventional ECG risk scores and guideline criteria in heart failure and general populations?
Meta-Analysis (n=18,500)
Does deep learning/machine learning-based ECG analysis improve sudden cardiac death risk prediction compared to conventional ECG risk scores and guideline criteria in heart failure and general populations?
ECG-based AI models significantly improve the discrimination of sudden cardiac death risk compared to conventional ECG risk scores and current guideline criteria, suggesting potential utility in refining primary prevention ICD selection.
Background Sudden cardiac death (SCD) remains a leading cause of mortality in heart failure populations. Traditional ECG parameters and guideline criteria (LVEF <35%) identify only ~50% of high-risk patients. AI-derived ECG features may capture subclinical arrhythmic substrate. Evidence synthesis on ECG-AI SCD prediction needed. Methods MEDLINE, Embase, clinicaltrials.gov (2018–January 2026) searched for prospective cohort studies using machine learning/deep learning on ECG signals to predict SCD. Inclusion: N≥100, ≥1-year follow-up, reported discrimination metrics (AUC/C-statistic) for SCD outcomes, comparison to conventional ECG risk scores. Exclusion: case-control designs, synthetic ECG data. Primary outcome: AUC for SCD prediction. Random-effects meta-analysis; subgroup analyses by patient population (HF vs population-based), ECG-AI architecture (CNN, LSTM, Vision Transformer). Results Thirty-two studies (n=18,500 patients; 1,247 SCD events; median 2.8-year follow-up) included. ECG-AI models achieved AUROC 0.75–0.90 (median 0.85) for SCD prediction, significantly outperforming conventional ECG risk scores (AUROC 0.62, p<0.001) and guideline-directed ICD criteria. In HF subgroup: ECG-AI combined with clinical variables achieved AUROC 0.90 (95% CI 0.86–0.94) versus conventional criteria 0.68 (p<0.001). Three-year SCD discrimination improved from 28.7% (conventional high-risk) to 36.1% (ECG-AI high-risk) (p for trend <0.001). External validation: minimal performance degradation (median ΔC-statistic −0.04). Conclusions ECG-based AI models significantly enhance SCD risk identification with improved discrimination over guideline criteria (AUROC 0.85 vs 0.62). Integration into ICD implantation protocols may improve primary prevention strategies. Prospective randomized trials assessing ICD therapy guided by ECG-AI warranted.
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Gohri et al. (2026) conducted a meta-analysis in Sudden cardiac death risk prediction in heart failure and general populations (n=18,500). Deep learning/machine learning-based ECG (ECG-AI) risk prediction models vs. Conventional ECG risk scores and guideline-directed ICD criteria was evaluated on AUC for sudden cardiac death (SCD) prediction (Median AUROC 0.85 (range 0.75-0.90) vs 0.62, p=<0.001). ECG-AI models significantly outperformed conventional ECG risk scores for sudden cardiac death prediction (median AUROC 0.85 vs 0.62, p<0.001).
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