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Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis
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Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis
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Journal Article
Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis
2025
Overview
ObjectiveIn-hospital cardiac arrest (IHCA) presents a critical challenge with low survival rates and limited prediction tools. Despite advances in resuscitation, predicting 30-day survival remains difficult, and current methods lack interpretability for timely decision-making. This study developed a machine learning (ML) model to predict 30-day survival after IHCA, using peri-arrest variables available on the rescue team’s arrival, while ensuring a balance between predictive accuracy and clinical interpretability through Shapley Additive Explanations (SHAP).DesignA nationwide, registry-based observational study.SettingData were sourced from the Swedish Cardiopulmonary Resuscitation Registry (2010–2020), merged with the Patient Registry.ParticipantsWe analysed 25 905 IHCA cases with attempted resuscitation, of which 8166 patients survived for 30 days.Outcome measure and analysis30-day survival after IHCA was the outcome measure. An ML model was developed using fivefold cross-validation. Key predictors were identified through in-built variable importance and validated using SHAP. Model performance was evaluated with metrics such as area under the receiver operating characteristics (AUROC), calibration, sensitivity, specificity, false negative rate (FNR) and F-score.ResultsThe CatBoost model achieved an AUROC of 0.9136 (95% CI 0.9075 to 0.9191) with all features, and 0.9034 (95% CI 0.8955 to 0.9037) with the top 15 features, along with Brier scores of 0.1028 and 0.1103, respectively. Performance plateaued after including the top 15 predictors, with few key variables, such as epinephrine administration, age, initial rhythm, ROSC within 15 min, breathing on rescue team arrival and witnessed cardiac arrest, being most influential. The model showed strong calibration for patients with low predicted survival probabilities and demonstrated high sensitivity with a low FNR across relevant survival thresholds.ConclusionThe CatBoost model provides an effective and interpretable tool for predicting 30-day survival after IHCA. Key predictors such as epinephrine administration, age and initial rhythm inform clinical decision-making. This model has strong clinical utility and can be externally validated via the open-access Application Programming Interface (API) at www.gocares.se
