Acute cardiac care & 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. If these results can be validated, this model could potentially be used clinically to assess the risk of another cardiac arrest in survivors of IHCA.
Study context
- Population
- Patients (>18 year) discharged alive after in-hospital cardiac arrest (IHCA) in the Swedish registry for cardiopulmonary resuscitation (SRCR) from 2010 to 2021
- Participants
- 7,302 included patientsEvents: 22%
- Predictors
- Data related to the index IHCA, comorbidities, socioeconomic data and prescription medication obtained from national Swedish registries; XGBoost model
- Predicted outcome
- Recurrent cardiac arrest or death within one year after IHCA
Methodological detailsregistry-based, Extreme gradient boosting (XGBoost)
- Registry-based
- Extreme gradient boosting (XGBoost)
Model performance
As reported in the abstract
ROC-AUC
Best performing model with 1241 variables
0.72(95% CI 0.70-0.75)
Further findings
- Features of greatest importance were highest serum creatinine measured before IHCA, age and unknown civil status
- Comorbidity and cardiac arrest circumstances were important classes of features
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.
Composed by AI from the published abstract and checked against it. How Briefs are made →