基于循证理论构建重症监护病房患者多重耐药菌感染风险预测模型及外部验证研究  

Development and External Validation of an Evidence-based Risk Prediction Model for Multidrug-resistant Bacterial Infections in ICU Patients

作　　者：邹倩 耿苗苗 祝延红 ZOU Qian;GENG Miaomiao;ZHU Yanhong(School of Public Health,Shanghai Jiao Tong University,Shanghai 200025,China;Nosocomial Infection Department,Shanghai General Hospital,Shanghai 200080,China;Scientific Research Center,Shanghai General Hospital,Shanghai Jiao Tong University,Shanghai 200080,China)

机构地区：[1]上海交通大学医学院公共卫生学院,上海市200025 [2]上海交通大学附属第一人民医院医院感染科,上海市200080 [3]上海交通大学附属第一人民医院科研处,上海市200080

出　　处：《中国全科医学》2022年第12期1441-1448,共8页Chinese General Practice

基　　金：国家自然科学基金资助项目(71974127)。

摘　　要：背景既往研究证明多重耐药菌会在重症监护病房(ICU)患者之间交叉传播,患者获得多重耐药菌感染将影响其现有疾病的治疗效果;且临床上对多重耐药菌的检测速度较为缓慢。在此背景下,多重耐药菌感染预测研究应运而生。目的基于循证理论构建ICU患者多重耐药菌感染风险预测模型,并回顾性收集真实临床数据对模型进行验证。方法采用Meta分析的方法构建模型,即计算机检索PubMed、EMBase、the Cochrane Library、中国知网、万方数据知识服务平台、中文科技期刊数据库和中华医学期刊全文数据库2012年1月至2020年6月发表的有关ICU患者多重耐药菌感染的文献,提取可分析的危险因素,采用Stata/SE 12.0软件对纳入文献的数据进行Meta分析,确定ICU患者多重耐药菌感染的危险因素,并对各个危险因素的合并效应值进行β值转换构建ICU患者多重耐药菌感染风险预测模型。选取上海市第一人民医院2018年1月至2021年6月入住ICU的成年患者3908例,收集患者的临床资料,构建预测模型,绘制预测模型预测患者多重耐药菌感染的受试者工作特征(ROC)曲线,从而进行预测模型外部验证。结果共纳入31篇文献,确定17个危险因素。通过换算公式得到预测模型Logit(P)=-2.4763+0.086X_(1)〔性别(男)〕+0.191X_(2)(住院史)+0.392X_(3)(从外院转入)+1.723X_(4)(ICU住院天数)+0.315X_(5)(其他感染)+0.385X_(6)(慢性阻塞性肺疾病)+0.131X_(7)(糖尿病)+0.536X_(8)(肾脏疾病)+0.285X_(9)(肾衰竭)+0.565X_(10)(透析)+0.148X_(11)(机械通气)+0.742X_(12)(中央静脉导管)+0.336X_(13)(导尿管)+3.483X_(14)(抗菌药物使用种类)+0.174X_(15)(抗菌药物使用史)+0.975X_(16)(使用碳青霉烯类药物)+1.151X_(17)(使用氨基糖苷类药物)。将3908例患者数据代入预测模型中进行外部验证,结果显示,灵敏度为64.36%,特异度为80.39%,约登指数为0.4474,ROC曲线下面积为0.724。结论基于循证理论构建�Background Previous research has found that multi-drug resistant(MDR)bacteria can be transmitted between ICU patients,and the infections caused by MDR bacteria may negatively affect the efficacy of current treatment.As the speed of diagnostic testing to identify MDR bacteria is relatively slow in clinical practice,the research regarding the prediction of MDR bacteria infections has developed.Objective To develop an evidence-based risk prediction model for MDR bacterial infections in ICU patients,and to verify it using the real-world clinical data collected retrospectively.Methods Potential risk factors for MDR bacterial infections in ICU patients were identified by a meta-analysis of studies regarding MDR bacterial infections in ICU patients included in databases of PubMed,EMBase,the Cochrane Library,CNKI,Wanfang,China Science and Technology Journal Database,Ace Base of CMA during January 2012 to June 2020 using Stata/SE 12.0 software,and were used to develop a risk prediction model by transforming effect size to the standardized regression(β)coefficient.Next the model was fully established and externally verified using the clinical data of adult ICU patients(n=3908)recruited from Shanghai General Hospital from January 2018 to June 2021.ROC analysis was used to describe the predictive accuracy of the prediction model.Results Seventeen potential risk factors of MDR bacterial infections in ICU patients were identified through the meta-analysis of 31 included studies.The MDR bacterial infection risk prediction model incorporating these 17 factors with correspondingβvalue as coefficient(derived from converting the risk effect size of each factor)was developed:Logit(P)=-2.4763+0.086X_(1)〔gender(male)〕+0.191X_(2)(history of hospitalization)+0.392X_(3)(being transferred from another hospital)+1.723X_(4)(length of ICU stay)+0.315X_(5)(other infections)+0.385X_(6)(chronic obstructive pulmonary disease)+0.131X_(7)(diabetes)+0.536X_(8)(renal disease)+0.285X_(9)(renal failure)+0.565X_(10)(dialysis)+0.148X_(11)(mechanic

关 键 词：重症监护病房 多重耐药菌 LOGISTIC模型 预测模型 META分析 循证理论 

分 类 号：R37[医药卫生—病原生物学]