A deep learning model using 24-hour single-lead ambulatory ECGs accurately predicted sustained ventricular tachycardia over the next 13 days (internal validation AUC 0.957, 95% CI 0.943-0.971).
Observational (n=247,254)
Yes
Does a deep learning-based model applied to the first 24 hours of single-lead ambulatory ECG predict sustained VT occurrence in the subsequent 13 days?
A deep learning model applied to the first 24 hours of a single-lead ambulatory ECG can accurately predict the near-term risk of sustained ventricular arrhythmias over the subsequent 13 days.
Effect estimate: AUC 0.957 (95% CI 0.943-0.971)
BACKGROUND AND AIMS: Accurate near-term prediction of life-threatening ventricular arrhythmias would enable pre-emptive actions to prevent sudden cardiac arrest/death. A deep learning-enabled single-lead ambulatory electrocardiogram (ECG) may identify an ECG profile of individuals at imminent risk of sustained ventricular tachycardia (VT). METHODS: This retrospective study included 247 254, 14 day ambulatory ECG recordings from six countries. The first 24 h were used to identify patients likely to experience sustained VT occurrence (primary outcome) in the subsequent 13 days using a deep learning-based model. The development set consisted of 183 177 recordings. Performance was evaluated using internal (n = 43 580) and external (n = 20 497) validation data sets. Saliency mapping visualized features influencing the model's risk predictions. RESULTS: Among all recordings, 1104 (.5%) had sustained ventricular arrhythmias. In both the internal and external validation sets, the model achieved an area under the receiver operating characteristic curve of .957 95% confidence interval (CI) .943-.971 and .948 (95% CI .926-.967). For a specificity fixed at 97.0%, the sensitivity reached 70.6% and 66.1% in the internal and external validation sets, respectively. The model accurately predicted future VT occurrence of recordings with rapid sustained VT (≥180 b.p.m.) in 80.7% and 81.1%, respectively, and 90.0% of VT that degenerated into ventricular fibrillation. Saliency maps suggested the role of premature ventricular complex burden and early depolarization time as predictors for VT. CONCLUSIONS: A novel deep learning model utilizing dynamic single-lead ambulatory ECGs accurately identifies patients at near-term risk of ventricular arrhythmias. It also uncovers an early depolarization pattern as a potential determinant of ventricular arrhythmias events.
Fiorina et al. (2025) conducted an observational in Sustained ventricular arrhythmias (n=247,254). Deep learning-based model on single-lead ambulatory ECG was evaluated on Sustained VT occurrence in the subsequent 13 days (AUC 0.957, 95% CI 0.943-0.971). A deep learning model using 24-hour single-lead ambulatory ECGs accurately predicted sustained ventricular tachycardia over the next 13 days (internal validation AUC 0.957, 95% CI 0.943-0.971).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: