Risk Prediction Model for Pulmonary Fungal Infections in Patients with Lung Cancer
10.13471/j.cnki.j.sun.yat-sen.univ（med.sci）.2023.0617
- VernacularTitle:肺癌患者合并肺部真菌感染的风险预测模型
- Author:
Wei-wei DU
1
;
Wen-tao JI
1
;
Tian LUO
2
;
Jian-ping LIANG
2
;
Yan-hua LV
2
Author Information
1. Graduate School of Zunyi Medical University Zhuhai Campus， Zhuhai 519041， China
2. Department of Respiratory and Critical Care Medicine， Zhongshan City People's Hospital， Zhongshan 528499， China
- Publication Type:Journal Article
- Keywords:
lung cancer;
fungal infection of the lung;
risk factors;
nomogram;
model
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2023;44(6):1022-1029
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo 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 patients with lung cancer. MethodsWe conducted a retrospective study and collected information of 390 lung cancer patients 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 occurrence 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. ResultsLASSO regression identified 14 potential predictive factors， and further logistic regression analysis showed that hepatic injury， surgery， anemia， hypoalbuminemia， illness course， invasive operation， hospital stay at least 2 weeks and glucocorticoid used for at least 2 weeks were independent 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. ConclusionsThe 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 intervention or adjust treatment decisions.
