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✅   Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis

Health

Objective In-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). Design A nationwide, registry-based observational study. Setting Data were sourced from the Swedish Cardiopulmonary Resuscitation Registry (2010–2020), merged with the Patient Registry. Participants We analysed 25 905 IHCA cases with attempted resuscitation, of which 8166 patients survived for 30 days.

BMJ     Apr 28, 2025