实用心脑肺血管病杂志2026，Vol.34Issue(3)：32-35,4.DOI:10.12114/j.issn.1008-5971.2026.00.037
慢性阻塞性肺疾病急性加重期患者发生侵袭性肺部真菌感染的风险预测列线图模型构建
Nomogram Model Construction for Predicting the Risk of Invasive Pulmonary Fungal Infections in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease
摘要
Abstract
Objective To construct the nomogram model for predicting the risk of invasive pulmonary fungal infections in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Methods A total of 149 AECOPD patients admitted to Yixing People's Hospital from January 2022 to January 2024 were retrospectively selected as the research objects.Patients were divided into the infection group(n=41)and the non-infection group(n=108)according to whether they had invasive pulmonary fungal infections.Clinical data of the patients were collected,and multivariate Logistic regression analysis was used to discuss the influencing factors for invasive pulmonary fungal infections in patients with AECOPD.Based on the influencing factors,the risk predictive nomogram model was constructed.ROC curve and the Hosmer-Lemeshow goodness-of-fit test were used to assess the predictive value and calibration of the nomogram model.Results There were statistically significant differences between the two groups in terms of the proportion of smokers,incidence of diabetes mellitus,incidence of hypoalbuminemia,length of stay,proportion of patients in ICU,duration of antimicrobial use,antimicrobial use regimen,duration of glucocorticoid use,proportion of patients with invasive mechanical ventilation,and proportion of patients with patchy infiltration,consolidation,halo sign,and cavity(P<0.05).Multivariate Logistic regression analysis showed that prolonged length of stay,ICU admission,prolonged duration of antimicrobial use,use of dual antimicrobial agents,prolonged duration of glucocorticoid use,and invasive mechanical ventilation were independent risk factors for invasive pulmonary fungal infections in patients with AECOPD(P<0.05).The risk predictive nomogram model was constructed based on the above influencing factors.ROC curve analysis showed that the AUC of the nomogram model in predicting invasive pulmonary fungal infections in patients with AECOPD was 0.982[95%CI(0.966-0.998)].Hosmer-Lemeshow goodness-of-fit test results showed that the nomogram model fitted well(P>0.05).Conclusion The nomogram model for predicting the risk of invasive pulmonary fungal infections in patients with AECOPD constructed with length of stay,ICU admission,duration of antimicrobial use,antimicrobial use regimen,duration of glucocorticoid use,and invasive mechanical ventilation has a higher predictive value.关键词
肺疾病,慢性阻塞性/急性加重期/侵袭性真菌感染/列线图Key words
Pulmonary disease,chronic obstructive/Acute exacerbation/Invasive fungal infections/Nomograms分类
医药卫生引用本文复制引用
危慧敏,陆勤,杨妍,李雯雯..慢性阻塞性肺疾病急性加重期患者发生侵袭性肺部真菌感染的风险预测列线图模型构建[J].实用心脑肺血管病杂志,2026,34(3):32-35,4.基金项目
江苏省卫生健康委员会科研项目(M2022033) （M2022033）
