中国感染控制杂志2025，Vol.24Issue(5)：674-681,8.DOI:10.12138/j.issn.1671-9638.20256898
肺移植受者术后医院感染Nomogram预测模型的构建与验证
Construction and validation of nomogram predictive model for postopera-tive healthcare-associated infection in lung transplant recipients
摘要
Abstract
Objective To explore the risk factors for healthcare-associated infection(HAI)in lung transplant re-cipients(LTRs),and construct a predictive nomogram model.Methods Clinical data of patients who underwent lung transplant in Wuxi People's Hospital from January 2019 to December 2023 were analyzed retrospectively.The patients were divided into a training set(n=506)and a validation set(n=218).Independent risk factors were screened through LASSO regression,and multivariate logistic regression was included to construct a nomogram pre-diction model.The discrimination,calibration,and clinical applicability of the model were evaluated using receiver operating characteristic(ROC)curves,Hosmer-Lemeshow goodness-of-fit,and decision curves.Results Among the 506 LTRs,201 developed HAIs,with an incidence of 39.72％.The major infection site was lower respiratory tract,and the major pathogen were Gram-negative bacilli(Acinetobacter baumannii).Older age,use of extracorpo-real membrane oxygenation(ECMO),double-lung transplant,surgery duration＞3 hours,long duration of contin-uous fever,frequent abnormal blood routine examination,and long duration of combined use of antimicrobial agents were identified as independent risk factors for HAI after lung transplant.The ROC curve analysis results showed that the areas under the curve(AUCs)of the training set and the validation set were 0.74(95％CI:0.70-0.78)and 0.71(95％CI:0.64-0.78),respectively.The Hosmer-Lemeshow test results showed that there was no sta-tistically significant difference between the predictive and actual probability of HAI(P＞0.05).The clinical decision curve results indicated that the model had clinical benefits at a threshold probability value of 7％-71％.Conclusion The nomogram prediction model constructed in this study can effectively evaluate the risk of postoperative infection in LTRs.The model is stable and has high clinical application value,providing scientific reference for postoperative infection prevention and control.关键词
肺移植受者/手术后感染/医院感染/nomogram预测模型/LASSO回归/多因素logistic回归/预测模型验证Key words
lung transplant recipient/postoperative infection/healthcare-associated infection/nomogram predic-tive model/LASSO regression/multivariate logistic regression/predictive model validation分类
医药卫生引用本文复制引用
仇桑桑,许琴芬,邵君飞,黄琴红,吴波,胡春晓,陈静瑜..肺移植受者术后医院感染Nomogram预测模型的构建与验证[J].中国感染控制杂志,2025,24(5):674-681,8.基金项目
江苏省科技重点研发计划社会发展基金项目(BE2022697) （BE2022697）
江苏省医院协会医院管理创新研究基金项目(JSYGY3-2023-326) （JSYGY3-2023-326）
