Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.

ABSTRACT: Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 72 h. A total of 721 patients were extracted from the Medical Information Mart for Intensive Care IV database, divided into the training set (n = 576) and the internal validation set (n = 145). The external validation set containing 856 cases were collected from four tertiary hospitals in Zhejiang Province. The primary outcome was in-hospital mortality. Eleven ML algorithms were utilized to establish prediction models based on data from 72 h after return of spontaneous circulation (ROSC). The results indicate that the CatBoost model exhibited th

SUBMITTER: Ni P 

PROVIDER: S-EPMC11907063 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature