晚期早产儿肺部感染病原菌分布及其预测风险模型的早期诊断价值  

Pathogens isolated from late preterm infants with pulmonary infections and early diagnostic value of predictive risk model

作　　者：陈芳芳[1] 张静[1] 韩紫敏 王周洁 陈思思[1] 李崇寿[2] CHEN Fang-fang;ZHANG Jing;HAN Zi-min;WANG Zhou-Jie;CHEN Si-si;LI Chong-shou(The Second Affiliated Hospital of Wenzhou Medical University,Wenzhou,Zhejiang 325027,China)

机构地区：[1]温州医科大学附属第二医院新生儿科,浙江温州325027 [2]温州医科大学附属第二医院超声科,浙江温州325027

出　　处：《中华医院感染学杂志》2022年第15期2378-2381,共4页Chinese Journal of Nosocomiology

基　　金：温州市科技局科研基金资助项目(Y20170112)。

摘　　要：目的探究晚期早产儿肺部感染病原菌分布特点及预测风险模型的建立和早期诊断价值。方法回顾性选取温州医科大学附属第二医院2018年1月-2020年12月晚期早产儿肺部感染患儿63例作为研究组,另按照1∶2的匹配原则选择同期无肺部感染的晚期早产儿126例作为对照组。分析肺部感染患儿病原菌分布特点及其影响因素,并建立Logistic多因素回归预测模型,采用受试者工作特征(ROC)曲线评价模型早期诊断价值。结果63例肺部感染患儿中,病毒感染14例,细菌感染49例;Logistic回归显示,出生5 min Apgar评分是晚期早产儿肺部感染的独立保护因素,胎膜早破、气管插管操作史、PCT、CRP、Lac是晚期早产儿肺部感染的独立危险因素(P<0.05);ROC曲线分析,Logistic多因素回归模型诊断晚期早产儿肺部感染的AUC为0.983,敏感度为92.06%,特异度为96.03%。结论晚期早产儿肺部感染以细菌感染为主,出生5 min Apgar评分、胎膜早破、气管插管操作史、PCT、CRP、Lac均是其独立影响因素,根据上述因素构建Logistic回归模型能有效诊断肺部感染,为临床防控提供指导。OBJECTIVE To investigate the characteristics of the distribution of pathogenic bacteria of pulmonary infections in late preterm infants and to establish a predictive risk model and evaluate the value of early diagnosis.METHODS Sixty-three cases of late preterm infants with lung infection in the Second Hospital of Wenzhou Medical University from Jan 2018 to Dec 2020 were enrolled as the study group,and another 126 cases of late preterm infants without lung infection during the same period were recruited in the control group according to the matching principle of 1∶2.The distribution characteristics of pathogenic bacteria in children with pulmonary infection and the influencing factors were analyzed.Multivariate Logistic regression analysis model was established,and the receiver operating characteristic(ROC)curve was used to evaluate the early diagnostic value of the model.RESULTS Among 63 children with pulmonary infections,14 cases were viral infection;49 cases were bacterial infection.Multivariate Logistic regression showed that Apgar score at 5 min after birth was an independent protective factor for pulmonary infection in late preterm infants,while premature rupture of membranes,history of tracheal intubation,PCT,CRP and Lac were independent risk factors for pulmonary infection in late preterm infants(P<0.05).ROC curve analysis showed that AUC of multivariate Logistic regression model for diagnosis of lung infection in late preterm infants was 0.983,with the sensitivity of 92.06%and specificity of 96.03%.CONCLUSION Bacterial infection were the mainly infection in late preterm pulmonary infections.Apgar score at 5 min after birth,premature rupture of membranes,history of tracheal intubation,PCT,CRP and Lac are the independent influencing factors,and the logistic regression model constructed based on the above factors can effectively diagnose pulmonary infections and provide guidance for clinical prevention and control.

关 键 词：晚期早产儿 肺部感染 病原菌 影响因素 预测风险模型 诊断价值 

分 类 号：R725.6[医药卫生—儿科]