Development and validation of a model for predicting in-hospital mortality in patients with sepsis-associated kidney injury receiving renal replacement therapy: a retrospective cohort study based on the MIMIC-IV database.

ABSTRACT:

Background

SAKI is a common and serious complication of sepsis, contributing significantly to high morbidity and mortality, especially in patients requiring RRT. Early identification of high-risk patients enables timely interventions and improvement in clinical outcomes. The objective of this study was to develop and validate a predictive model for in-hospital mortality in patients with SAKI receiving RRT.

Methods

Patients with SAKI receiving RRT from the MIMIC-IV database were retrospectively enrolled and randomly assigned to either the training cohort or the testing cohort in a 7:3 ratio. LASSO regression and Boruta algorithm were utilized for feature selection. Subsequently, three machine learning models-CART, SVM and LR-were constructed, and their predictive efficacy was

SUBMITTER: Li C 

PROVIDER: S-EPMC11570588 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature