Machine learning prediction of in-hospital mortality and external validation in patients with cardiogenic shock: the RESCUE score.

Introduction And Objectives: Despite advances in mechanical circulatory support, mortality rates in cardiogenic shock (CS) remain high. A reliable risk stratification system could serve as a valuable guide in the clinical management of patients with CS. This study aimed to develop and externally validate a risk prediction model for in-hospital mortality in CS patients using machine learning (ML) algorithms.