Deep learning-based ROSC prediction and ECG phenotyping in out-of-hospital cardiac arrest

doi:10.1016/j.resuscitation.2026.111035

Acute cardiac care & resuscitationCohort study

Deep learning-based return of spontaneous circulation (ROSC) prediction and electrocardiogram (ECG) phenotyping in out-of-hospital cardiac arrest

ResuscitationPublished 6 Mar 2026

retrospective cohort study
one-dimensional convolutional neural network (CNN)
K-means clustering

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Authors' conclusion

The CNN-based model accurately predicted ROSC and shockable rhythm from ECG signals and identified five novel ECG phenotypes in OHCA. These findings can enable accurate ROSC prediction and individualized prehospital resuscitation for patients with OHCA.

Study context

Population: Adult patients with nontraumatic OHCA who had prehospital ECG signals, Korean OHCA Registry and Seoul emergency medical services ECG data, July 2021 to December 2023patients included
Participants: 3,452 patients

Exposure: CNN model predicting ROSC within 2 min and classifying shockable vs non-shockable rhythm from 5-second ECG segments, evaluated by AUC

Main finding

As reported in the abstract

Prediction of ROSC within 2 min (AUC) · test set

AUC

0.921 (95% confidence interval [CI], 0.897-0.945)

Observational data: associations, not effects.

Further findings

Prediction of shockable rhythm (AUC) · test set

AUC: 0.983 (95% CI, 0.979-0.988)

ECG phenotypes (K-means clustering)

Phenotype 1: shockable rhythms with high ROSC probability: 30.4%
Phenotype 2: shockable rhythms with low ROSC probability: 4.8%
Phenotype 3: pulseless electrical activities with relatively high ROSC probability: 5.2%
Phenotype 4: pulseless electrical activities with lower ROSC probability: 0.5%
Five ECG phenotype categories with varying morphologies and ROSC probabilities
Phenotype 5: primarily asystole or near-asystole rhythms
Transitions between ECG phenotypes were associated with CPR quality

Original publication

Deep learning-based ROSC prediction and ECG phenotyping in out-of-hospital cardiac arrest. Resuscitation. Published 6 March 2026. doi:10.1016/j.resuscitation.2026.111035

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Summarised from the published abstract; every number is checked against it