Acute cardiac care & resuscitation
Early Identification of Recurrent Out-of-hospital Cardiac Arrest Using Clinical ECG Features
Authors' conclusion
Established ECG features achieved good prediction for rearrest with identification improving closer to time of rearrest. Future work leveraging novel features or more advanced artificial intelligence models may further improve prediction.
Study context
- Population
- Adult out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) between 2018–2024analyzed, of 1,489 OHCA patients achieving ROSC
- Participants
- 1,298 participantsEvents: 528 (40.7%)
- Predictors
- ECG features (QRS width, standard deviation of NN intervals, root mean square of successive NN interval differences, and percentage of NN intervals that differ by more than 50 milliseconds) within individual 15-second segments throughout a 5-minute window prior to rearrest, and their temporal trends
- Predicted outcome
- Recurrent cardiac arrest (rearrest)
Model performance
As reported in the abstract
AUROC
1 minute prior to rearrest (highest)
0.74
Models using temporal trends
0.67
- Identification declining beyond 3 minutes
Further findings
Characteristics and outcomes of patients with vs without rearrest
Bystander CPR
Survival
Favorable neurologic outcomes
- Characteristics and outcomes of patients with vs without rearrest: all p<0.001
Rearrest, models using temporal trends
- Strongest predictors: heart rate variability measures and QRS width
Characteristics and outcomes of patients with vs without rearrest
- Rearrest patients were older
ECG features in those with rearrest
- Lower heart rate, wider QRS, and higher HR variability in those with rearrest
Original publication
Early Identification of Recurrent Out-of-hospital Cardiac Arrest Using Clinical ECG Features. Resuscitation. Published 29 October 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →