Development and validation of a machine learning model for predicting mortality risk in veno-arterial extracorporeal membrane oxygenation patients.

ABSTRACT: This study aims to develop and validate a machine learning-based mortality risk prediction model for V-A ECMO patients to improve the precision of clinical decision-making. This multicenter retrospective cohort study included 280 patients receiving V-A ECMO from the Second Affiliated Hospital of Guangxi Medical University, Yulin First People's Hospital, and the MIMIC-IV database. The data from the Second Affiliated Hospital of Guangxi Medical University and the MIMIC-IV database were merged and randomly divided in a 7:3 ratio into a training set and an internal validation set, respectively. The dataset from Yulin First People's Hospital was reserved as an external validation cohort. The primary study outcome was defined as in-hospital mortality.Feature selection was conducted using Lasso r

SUBMITTER: Gao H 

PROVIDER: S-EPMC12644694 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature