预测老年慢性阻塞性肺疾病患者肺部真菌感染列线图的建立及验证

Establishment and Verification of a Pulmonary Fungal Infection Nomogram for Predicting Chronic Obstructive Pulmonary Disease in Elderly Patients

目的:探索影响老年慢性阻塞性肺疾病患者肺部真菌感染的危险因素,并建立可视化临床预测模型。方法:回顾性分析2020年1月—2023年12月入住我院感染呼吸科且符合纳入标准的老年慢性阻塞性肺病患者的基线资料、人口学资料和临床资料,共496例纳入研究,使用R软件对纳入患者进行无放回随机抽样分组,其中70%患者纳入训练集,30%纳入验证集,对训练集数据先进行Lasso分析,后对筛选出变量进行多因素logistic分析并建立临床预测模型,后进行模型可视化绘制列线图。最后在训练集及验证集分别进行拟合优度检验及受试者工作特征曲线(ROC)检验,做内部验证。结果:多因素分析显示年龄、侵入性操作、白蛋白含量≤30g/L、机械通气、过去一年入院次数≥4次、抗生素使用三种以上和COPD分级为Ⅲ~Ⅳ期是老年COPD患者合并肺部侵袭性真菌感染的独立危险因素。训练集AUC为0.856(95%CI:0.798~0.914);验证集AUC为0.835(95%CI:0.730~0.939),及拟合优度检验结果为χ^(2)=3.506,P=0.898。结论:本研究建立的老年COPD患者合并肺部侵袭性真菌感染预测模型对真菌感染的发生有很强的预测能力,可以在临床治疗阶段做到早发现、早预防、早治疗,从而改善患者的预后。

Objective:To explore the risk factors of pulmonary fungal infection in elderly patients with chronic obstructive pulmonary disease and establish a visual clinical prediction model.Methods:The baseline,demographic and clinical data of 496 elderly patients with COPD who met the inclusion criteria and were admitted to the infectious department of our hospital from January 2020 to December 2023 were retrospectively analyzed.The included patients were randomly sampled without retracting using R software,and 70%of them were included in the training set and 30%were included in the validation set.Lasso analysis was carried out on the training set data,then multivariate logistic analysis was carried out on the screened variables and clinical prediction model was established,which then was the drawn as the Nomogram for visualization.Finally in the training set and validation set separately carried on the test of goodness of fit and the receiver-operating characteristic curve(ROC)test,for internal validation.Results:Multivariate analysis showed that age,invasive operation,albumin level less than or equal to 30g/L,mechanic ventilation,the number of hospital admissions in the past year more than or equal to 4,antibiotic use more the three types and COPD stageⅢ~Ⅳage were independent risk factors for pulmonary invasive fungal infection in elderly patients with COPD.Training set AUC 0.856(95%CI:0.798~0.914);validation set AUC 0.835(95%CI:0.730~0.939)and the results of goodness-of-fit test forχ2=3.506,P=0.898.Conclusion:The prediction model of pulmonary invasive fungal infection in elderly patients with COPD established in this study had a strong ability to predict the occurrence of fungal infection,which can achieve early detection,early prevention and early treatment in clinical treatment stage,so as to improve the prognosis of patients.