Cardiac arrest & resuscitation
The ability of machine learning algorithms to predict defibrillation success during cardiac arrest: A systematic review
Authors' conclusion
Machine learning algorithms, specifically Neural Networks, have been shown to have potential to predict defibrillation success for cardiac arrest with high sensitivity and specificity.Due to heterogeneity, inconsistent reporting, and high risk of bias, it is difficult to conclude which, if any, algorithm is optimal.
Study context
- Population
- In- and out-of-hospital cardiac arrest
- Participants
- 5,680 participants · 15 observational studies
- Comparison
- Machine learning (ML) algorithms for predicting defibrillation success
- Outcome
- Successful defibrillation (return of spontaneous circulation (ROSC), survival to hospital or discharge, or neurological status at discharge)
Methodological detailsPROBAST risk-of-bias assessment, no meta-analysis
- PROBAST risk-of-bias assessment
- No meta-analysis
Pooled result
As reported in the abstract
Accuracy of ML models in predicting defibrillation success
Best performing ML model accuracy (4 second ECG intervals)
98.6 (98.5 - 98.7)%
Algorithm incorporating end-tidal CO2, accuracy
83.3% (no CI reported)
Original publication
The ability of machine learning algorithms to predict defibrillation success during cardiac arrest: A systematic review. Resuscitation. Published 25 February 2023. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →