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OA
Nomogram Model Construction for Predicting the Risk of Invasive Pulmonary Fungal Infections in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease
目的 构建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.方法 回顾性选取2022年1月—2024年1月宜兴市人民医院收治的149例慢性阻塞性肺疾病急性加重期患者作为研究对象,根据患者是否发生侵袭性肺部真菌感染将其分为感染组(n=41)和未感染组(n=108).收集患者的临床资料,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影响因素采用多因素Logistic回归分析,基于影响因素构建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,采用ROC曲线、Hosmer-Lemeshow拟合优度检验分析该列线图模型的预测价值及拟合程度.结果 两组有吸烟史者占比、糖尿病发生率、低蛋白血症发生率、住院时间、入住ICU者占比、抗菌药物使用时间、抗菌药物治疗方案、糖皮质激素使用时间、行有创机械通气者占比及伴肺部片状浸润影、实变影、晕轮征、空洞征者占比比较,差异有统计学意义(P＜0.05).多因素Logistic回归分析结果显示,住院时间延长、入住ICU、抗菌药物使用时间延长、使用二联抗菌药物治疗、糖皮质激素使用时间延长、有创机械通气是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独立危险因素(P＜0.05).基于上述影响因素构建风险预测列线图模型,ROC曲线分析结果显示,该列线图模型预测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AUC为0.982[95%CI(0.966～0.998)].Hosmer-Lemeshow拟合优度检验结果显示,该列线图模型的拟合程度良好(P＞0.05).结论 基于住院时间、入住ICU、抗菌药物使用时间、抗菌药物使用方案、糖皮质激素使用时间、有创机械通气构建的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具有较高预测价值.
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.
危慧敏;陆勤;杨妍;李雯雯
214206 江苏省宜兴市人民医院呼吸与危重症医学科214206 江苏省宜兴市人民医院呼吸与危重症医学科214206 江苏省宜兴市人民医院呼吸与危重症医学科214206 江苏省宜兴市人民医院呼吸与危重症医学科
医药卫生
肺疾病,慢性阻塞性急性加重期侵袭性真菌感染列线图
Pulmonary disease,chronic obstructiveAcute exacerbationInvasive fungal infectionsNomograms
《实用心脑肺血管病杂志》 2026 (3)
32-35,4
江苏省卫生健康委员会科研项目(M2022033)
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