中山大学学报（医学科学版）2023，Vol.44Issue(6)：1022-1029,8.
肺癌患者合并肺部真菌感染的风险预测模型
Risk Prediction Model for Pulmonary Fungal Infections in Patients with Lung Cancer
摘要
Abstract
[Objective]To investigate the risk factors for pulmonary fungal infection in lung cancer patients,construct and validate a risk prediction model using available clinical data to predict the risk of pulmonary fungal infections in pa-tients with lung cancer.[Methods]We conducted a retrospective study and collected information of 390 lung cancer pa-tients treated at Zhongshan People's Hospital from January 2021 to March 2023.Demographic and clinical characteristics of the patients with and without pulmonary fungal infections were used to construct column line graphs to predict the occur-rence of pulmonary fungal infections.All enrolled patients were randomly assigned to training set and internal validation set in the ratio of 7:3.For the modelling group,LASSO regression was applied to screen variables and select predictors,and multivariate logistic regression with a training set was used to construct the Noe column line graph model.The judgment ability of the model was determined by calculating the area under the curve(AUC),and in addition,calibration analysis and decision curve analysis(DCA)were performed on the model.[Results]LASSO regression identified 14 potential pre-dictive factors,and further logistic regression analysis showed that hepatic injury,surgery,anemia,hypoalbuminemia,ill-ness course,invasive operation,hospital stay at least 2 weeks and glucocorticoid used for at least 2 weeks were indepen-dent predictors for the occurrence of pulmonary fungal infection in lung cancer patients.A predictive model was established based on these variables,with an AUC95%CI of 0.980(0.973,0.896)for the training set and an AUC95%CI of 0.956(0.795,1.000)for internal validation,indicating high discriminative ability.The calibration curves for both the training set and validation set were distributed along the 45°line,and the decision curve analysis(DCA)showed net benefit for threshold probabilities greater than 0.03.[Conclusions]The construction and validation of a predictive model for the risk of lung fungal infections in lung cancer patients will help clinical practitioners to identify high-risk groups and give timely in-tervention or adjust treatment decisions.关键词
肺癌/肺部真菌感染/危险因素/列线图/模型Key words
lung cancer/fungal infection of the lung/risk factors/nomogram/model分类
医药卫生引用本文复制引用
杜伟伟,季文涛,罗甜,梁剑平,吕燕华..肺癌患者合并肺部真菌感染的风险预测模型[J].中山大学学报（医学科学版）,2023,44(6):1022-1029,8.基金项目
国家自然科学基金(82200038) （82200038）
