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

IF 4.6 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2025-09-15 DOI:10.1128/msystems.00909-25
Xiao Liu, Muxiu Jiang, Jinzhi Zhang, Heng Li, Yina Liu, Jiaqi Zhang, Xia Chen, Jun Bu, Shichang Xie, Menghan Zhang, Ning Dong, Qing Cao, Zhemin Zhou
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引用次数: 0

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 clonal outbreaks involving ST14 and ST433 strains were identified, highlighting the critical role of clonal dissemination in NICUs. Complex transmission patterns, characterized by periods of dormancy and resurgence, suggest the existence of underlying reservoirs. HGs were implicated in the short-term transmission of CRKP, with >80% of infection clusters involving patients from the same HG. Plasmids emerged as critical factors in the long-term persistence of CRKP, with shifts in plasmid prevalence corresponding to outbreak periods. This study advances our understanding of CRKP transmission dynamics in NICUs, highlighting the multifaceted roles of clonal dissemination, HGs, and plasmid-mediated persistence. Our findings emphasize the need for enhanced infection control measures targeting both intra- and inter-group transmissions and plasmid surveillance.IMPORTANCEThis study provides a detailed analysis of carbapenem-resistant Klebsiella pneumoniae (CRKP) transmission dynamics in neonatal intensive care units (NICUs) over eight years, utilizing 64 isolates and applying machine learning to identify risk factors associated with persistence and spread. Through phylogenetic analyses, we uncovered three clonal outbreaks and linked healthcare group (HG) interactions, bacterial genotypes, and plasmid prevalence to short- and long-term CRKP transmission. We identified that HGs are primary mediators of rapid, short-term transmission, while specific plasmids play an extended role in maintaining CRKP presence across multiple patient cohorts and bacterial strains. This finding suggests the existence of latent reservoirs or periodic reintroductions from external sources, thus reshaping the understanding of NICU-associated pathogen transmission and persistence.

基于机器学习的新生儿单位耐碳青霉烯肺炎克雷伯菌传播危险因素评估。
医疗保健相关感染(HAIs),特别是在新生儿重症监护病房(NICUs),由于新生儿的脆弱性和感染的快速传播,构成了重大挑战。然而,促进病原体持续存在和传播的危险因素尚未得到全面调查。本研究旨在追踪新生儿重症监护室中HAI的传播途径,并确定导致耐碳青霉烯肺炎克雷伯菌(CRKP)持续存在和传播的关键危险因素。我们分析了中国一家儿科医院8年来新生儿患者的CRKP流行病学和人口动态。随机森林模型确定了CRKP持续存在和爆发的主要危险因素,重点关注克隆传播、医疗保健群体(HGs)和质粒动力学。发现了涉及ST14和ST433菌株的三次主要克隆暴发,突出了克隆传播在nicu中的关键作用。以休眠期和复苏期为特征的复杂传播模式表明存在地下储层。HG与CRKP的短期传播有关,约80%的感染聚集涉及来自同一HG的患者。质粒成为CRKP长期持续的关键因素,质粒流行率的变化与爆发期相对应。这项研究促进了我们对新生儿重症监护病房中CRKP传播动力学的理解,强调了克隆传播、HGs和质粒介导的持久性的多方面作用。我们的研究结果强调需要加强针对群体内和群体间传播和质粒监测的感染控制措施。本研究详细分析了8年来新生儿重症监护病房(NICUs)耐碳青霉烯类肺炎克雷伯菌(CRKP)的传播动态,利用64株分离株并应用机器学习识别与持久性和传播相关的危险因素。通过系统发育分析,我们发现了三次克隆爆发和相关的医疗保健组(HG)相互作用、细菌基因型和质粒流行与短期和长期CRKP传播的关系。我们发现,HGs是快速、短期传播的主要媒介,而特定的质粒在维持CRKP在多个患者群体和细菌菌株中的存在方面发挥着广泛的作用。这一发现表明潜伏宿主的存在或外部来源的周期性重新引入,从而重塑了对新生儿重症监护病房相关病原体传播和持久性的理解。
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来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
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