Developing and validating a machine learning model to predict multidrug-resistant <i>Klebsiella pneumoniae</i>-related septic shock.

ABSTRACT:

Background

Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.

Methods

A retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024. The cohort was randomly divided into a training set (n = 969) and a validation set (n = 416). Feature selection was performed using LASSO regression and the Boruta algorithm. Seven machine learning algorithms were evaluated, with logistic regression chosen for its op

SUBMITTER: Pan S 

PROVIDER: S-EPMC11757138 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature