Cardiac arrest & resuscitation
Prediction of survival after pediatric cardiac arrest using heart rate variability and machine learning
Authors' conclusion
Analysis of HRV at 24 h after ROSC may serve as a strong predictor of 12-month survival after pediatric CA.
Study context
- Population
- Children with cardiac arrest (CA) who achieved return of spontaneous circulation (ROSC) and were admitted to the pediatric intensive care unit (PICU) of a tertiary hospital, 2012–2021
- Participants
- 76 children
- Exposure
- Survival vs death
- Survivaln = 34reference
- Deathn = 42
- Outcome
- Survival or death (prediction from heart rate variability (HRV) parameters of a 5-min electrocardiogram (ECG) segment at 24 h after CA, random forest model)
- Follow-up
- 12 months
Methodological detailsmachine learning (random forest), single tertiary hospital
- Machine learning (random forest)
- Single tertiary hospital
Main finding
As reported in the abstract
Random forest model prediction of mortality
Accuracy
77.6%
PPV for mortality prediction
0.879
Observational data: associations, not effects.
Original publication
Prediction of survival after pediatric cardiac arrest using heart rate variability and machine learning. Resuscitation. Published 21 January 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →