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

ABSTRACT:

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 ana

SUBMITTER: Gupta V 

PROVIDER: S-EPMC12035429 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature