Cardiac arrest & resuscitation
Predicting recurrent cardiac arrest within one year after surviving in-hospital cardiac arrest using a machine learning model
Authors' conclusion
In this dataset, an XGBoost model could predict recurrent cardiac arrest or death within one year after IHCA with a modest performance and good accuracy.
Study context
- Population
- Adults discharged alive after in-hospital cardiac arrest (IHCA) in the Swedish registry for cardiopulmonary resuscitation, 2010–2021
- Participants
- 7,302 adults
- Outcome
- Recurrent cardiac arrest or death
- Follow-up
- Within one year after IHCA
Methodological detailsmachine learning (XGBoost), national registry
- Machine learning (XGBoost)
- National registry
Performance
As reported in the abstract
Prediction of recurrent cardiac arrest or death (XGBoost model) · within one year
Patients developing the outcome
22%
Receiver operating characteristics and area under the curve (ROC-AUC) of best performing model (1241 variables), 95% CI 0.70-0.75
0.72
Original publication
Predicting recurrent cardiac arrest within one year after surviving in-hospital cardiac arrest using a machine learning model. Resuscitation. Published 6 February 2026. doi:10.
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