Risk factor analysis of carbapenem-resistant enterobacteriaceae infection based on machine learning
10.3969/j.issn.1673-4130.2024.01.015
- VernacularTitle:基于机器学习的耐碳青霉烯类肠杆菌感染风险因素分析
- Author:
Chunhai XIAO
1
;
Shuang LIANG
;
Xianglu LIU
;
Juanfang WU
;
Huimin MA
;
Shan ZHONG
Author Information
1. 上海市第六人民医院金山分院检验科,上海 201500
- Keywords:
risk factors;
carbapenem resistance;
Enterobacteriaceae;
machine learning
- From:
International Journal of Laboratory Medicine
2024;45(1):79-83
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the machine learning model and risk factor analysis for hospital infection caused by carbapenem-resistant enterobacteriaceae(CRE).Methods The clinical data of totally 451 patients infected with extended-spectrum β-lactamases(ESBL)producing Enterobacteriaceae treated in the hospital from 2018 to 2022 were retrospectively collected.The patients were divided into CRE group(115 cases)and sensitive group(336 cases)according to the susceptibility of carbapenem.Four machine learning methods in-cluding Logistic regression analysis,random forest,support vector machine,and neural network were used to build prediction models and receiver operating characteristic curve was used to evaluate.Based on the predic-tion model with the best performance,risk factors for CRE infection were analyzed.Results Random forest model had the best performance,with the area under the curve of 0.952 3.The risk factors for predicting CRE infection by the random forest model included 15 clinical data items,namely fever for more than 3 days,cere-bral injury,drainage fluid sample,trunk surgery,first-level or special-level nursing,ICU treatment,procalcito-nin,anti-anaerobic bacteria,the use of third-generation cephalosporins,age,pre-albumin,creatinine,white blood cell count,and albumin.Conclusion The CRE prediction model developed in this study has good predic-tive value and the risk factors have guiding significance for the early prevention and treatment of CRE infec-tion in clinical practice.
