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

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC 0.921 for ROSC prediction

• Primary Outcome: ROSC within 2 minutes

• Nnt Or Sensitivity: Five ECG phenotypes identified with ROSC probabilities ranging from 0.5% (Phenotype 4) to 30.4% (Phenotype 1)

• Confidence Interval: 95% CI 0.897-0.945

Clinical Application

Requires sophisticated ECG analysis technology and trained personnel for implementation in emergency medical services Relevant to Australian paramedic services and emergency departments; would need validation in Australian OHCA population and integration with existing clinical protocols and equipment standards Adult patients with nontraumatic OHCA in prehospital setting with ECG monitoring capability

Abstract

INTRODUCTION: Electrocardiogram (ECG) signals during cardiac arrest contain detailed information on cardiac rhythm characteristics and have been associated with resuscitation outcomes. We aimed to develop a convolutional neural network (CNN) model to predict return of spontaneous circulation (ROSC) and identify novel ECG phenotypes in patients with out-of-hospital cardiac arrest (OHCA). METHODS: This retrospective cohort study used Korean OHCA Registry and ECG data from Seoul emergency medical services between July 2021 and December 2023. Adult patients with nontraumatic OHCA who had prehospital ECG signals were included. Five-second ECG segments obtained during resuscitation were analyzed. A one-dimensional CNN was developed to simultaneously predict the probability of ROSC within 2 min and to classify the concurrent ECG rhythm as shockable or non-shockable. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). ECG phenotypes were identified by applying K-means clustering to feature representations extracted from the trained CNN. RESULTS: 3452 patients (median age, 73 years [interquartile range, 61-81]; 34.4% female) were included. The AUCs for predicting ROSC within 2 min and shockable rhythm in the test set were 0.921 (95% confidence interval [CI], 0.897-0.945) and 0.983 (95% CI, 0.979-0.988), respectively. Five ECG phenotype categories with varying morphologies and ROSC probabilities were identified. Phenotype 1 consisted of shockable rhythms with high ROSC probability (30.4%), whereas Phenotype 2 showed shockable rhythms with low ROSC probability (4.8%). Phenotypes 3 and 4 included pulseless electrical activities with relatively high (5.2%) and lower (0.5%) ROSC probabilities, respectively. Phenotype 5 primarily consisted of asystole or near-asystole rhythms. Transitions between ECG phenotypes were associated with CPR quality. CONCLUSIONS: 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.

References

1. 1.Choi, D. H., Hong, K. J., Kim, K. H., Kim, Y., Kim, M. W., Shin, S. D., Song, K. J., Ro, Y. S., Park, J. H., Kim, T. H., & Jeong, J. (2026). Deep learning-based ROSC prediction and ECG phenotyping in out-of-hospital cardiac arrest. Resuscitation, Article 111035. https://doi.org/10.1016/j.resuscitation.2026.111035