Machine learning-based evaluation of risk factors for carbapenem-resistant <i>Klebsiella pneumoniae</i> dissemination in neonatal units.

ABSTRACT: Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dissemination have not been comprehensively investigated. This study aims to track HAI transmission pathways in NICUs and identify key risk factors contributing to the persistence and spread of carbapenem-resistant Klebsiella pneumoniae (CRKP). We analyzed CRKP epidemiology and population dynamics in neonatal patients at a pediatric hospital in China over 8 years. Random forest models identified primary risk factors for CRKP persistence and outbreaks, focusing on clonal spread, healthcare groups (HGs), and plasmid dynamics. Three major clon

SUBMITTER: Liu X 

PROVIDER: S-EPMC12542634 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature