创伤病人术后多重耐药菌医院感染风险模型的构建  

Construction of nosocomial multi⁃drug resistant bacterias infection risk model of trauma patients undergoing surgery

作　　者：郭磊磊 秦红英 武珍珍 张艺 赵智琛 GUO Leilei;QIN Hongying;WU Zhenzhen;ZHANG Yi;ZHAO Zhichen(Zhengzhou Central Hospital Affiliated to Zhengzhou University,Henan 450007 China)

机构地区：[1]郑州大学附属郑州中心医院,河南450007

出　　处：《护理研究》2025年第3期361-367,共7页Chinese Nursing Research

基　　金：河南省医学科技攻关计划联合共建项目,编号:LHGJ20220855;中华预防医学会医院感染控制分会2024年度医院感染学科发展青年人才托举项目,编号:CPMA-HAIC-20240129001。

摘　　要：目的:应用Lasso-Logistic回归分析和分类树(CHAID)算法分析创伤病人术后多重耐药菌(MDRO)医院感染的危险因素,构建风险预测模型并比较结果的优劣性。方法:回顾性分析2019年1月—2022年1月郑州大学附属郑州中心医院创伤住院病人的临床资料,应用CHAID算法和Lasso-Logistic回归分别建立风险预测模型,采用拟合优度检验评价模型效果,使用受试者工作特征(ROC)曲线下面积(AUC)比较两种预测模型的优劣。结果:共纳入821例创伤病人,其中创伤合并多重耐药菌感染191例,感染率为23.26%,分类树模型和Logistic回归结果均显示,急性生理学及慢性健康状况评分系统(APACHEⅡ)评分≥20分、发热时间≥3 d、住院时间≥10 d、入院时降钙素原(PCT)≥0.5 ng/L是创伤病人术后多重耐药菌感染的独立危险因素。分类树模型的风险预测正确率为79.2%,模型拟合效果较好;Lasso-Logistic回归模型Hosmer-Lemeshow拟合优度检验显示模型拟合较好(P=0.146),Bootstrap内部验证模型预测能力较好。分类树模型的AUC为0.792[95%CI(0.763,0.819)],Lasso-Logistic回归模型的AUC为0.862[95%CI(0.836,0.885)],两种模型的预测价值中等,通过比较两种模型预测价值差异有统计学意义(P<0.001)。净重分类指数(net reclassification index,NRI)评价提示Lasso-Logistic回归模型优于分类树模型(NRI=0.1536)。结论:Lasso-Logistic回归分析与分类树模型均能提供较为直观的呈现形式,两种模型互补结合使用可以从不同角度早期识别创伤病人术后多重耐药菌感染的风险因素,应采取有效防控措施降低多重耐药菌医院感染发生率。Objective:To analyze the risk factors of nosocomial multi⁃drug resistant bacterias(MDRO)infection in trauma patients undergoing surgery by Lasso⁃Logistic regression analysis and classification tree(CHAID)algorithm,build a risk prediction model and compare the results.Methods:The clinical data of trauma inpatients in Zhengzhou University Affiliated Zhengzhou Central Hospital from January 2019 to January 2022 were retrospectively analyzed.The risk prediction models were established by CHAID algorithm and Lasso-Logistic regression,respectively.The goodness of fit test was used to evaluate the effect of the model,and the area under the receiver operating characteristic curve(ROC)curve(AUC)was used to compare the advantages and disadvantages of the two prediction models.Results:A total of 821 trauma patients were included as the modeling group,including 191 trauma patients with MDRO 23.26%;Classification tree model and logistic regression showed that APACHEⅡscore≥20 scores,fever days≥3 days,hospitalization days≥10 days,PCT level≥0.5 ng/L on admission were independent risk factors for postoperative MDRO infection in trauma patients.The risk prediction accuracy of classification tree model was 79.2%,and the model fit effect was good.The Hosmer⁃Lemeshow goodness of fit test for Lasso⁃Logistic regression showed that the fitting effect of the model was relatively good(P=0.146).And the Bootstrap internal validation showed that the prediction ability of the model was good.The AUC of classification tree model was 0.792(95%CI 0.763⁃0.819),and the AUC of Lasso⁃Logistic regression model was 0.862(95%CI 0.836⁃0.885),the predictive value of the two models were medium.The difference between the two models was statistically significant(P<0.001).Net Reclassification Index(NRI)evaluation indicated that the Lasso⁃Logistic regression model was superior to the classification tree model(NRI=0.1536).Conclusion:Both models could provide a more intuitive form of presentation.The complementary combination of the two

关 键 词：创伤 多重耐药菌 医院感染 危险因素 Lasso-Logistic回归 分类树 预测模型 调查研究 

分 类 号：R473.6[医药卫生—护理学]