Application of decision tree model in nosocomial infection among intensive care unit patients
10.3969/j.issn.1671-8348.2024.20.008
- VernacularTitle:决策树模型在重症监护病房患者医院感染中的应用
- Author:
Huang HUANG
1
;
Chao HUANG
;
Yan LIU
;
Chunqiong XU
Author Information
1. 成都大学附属医院医院感染管理部,成都 610081
- Keywords:
intensive care unit;
nosocomial infection;
decision tree;
logistic regression;
prediction
- From:
Chongqing Medicine
2024;53(20):3084-3089
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish the nosocomial infection risk prediction model among the intensive care unit(ICU)patients by the decision tree model under the basis of regression analysis,and to investigate the classification rule of nosocomial infection in ICU.Methods The medical case data of the inpatients admit-ting to ICU＞48 h in a class3A hospital from Jan.2020 to Nov.2022 were collected.The variables with statis-tical difference in the logistic regression analysis results served as the predictive variables to construct the ICU nosocomial infection decision tree predictive model.The area under receiver operating characteristic(ROC)curve(AUC)was used to evaluate the accuracy of the model.Results A total of 1 704 study subjects were in-cluded in this study,among them there were 211 cases of nosocomial infection with a nosocomial infection rate of 12.4％.The decision tree model results showed that the risk factors of nosocomial infection among the ICU patients were the invasive ventilator use ≥7 d,central venous indwelling catheter ≥7 d,time of admitting to ICU ≥10 d and using antimicrobial drugs,in which the invasive ventilator use ≥7 d was the most important risk factor.AUC of the decision tree model ROC was 0.767(95％CI:0.730-0.805).Conclusion The combi-nation use of logistic regression analysis and decision tree model could effectively predict the risk of nosocomi-al infection occurrence under different factors combination,which provides the theoretical basis for reducing nosocomial infection rate among ICU patients.
