Construction of a Nomogram Prediction Model for Individualized Prediction of the Risk of Pulmonary Fungal Infection in Lung Cancer - Lai Q; Liao K; Kuang G; Liao W; Zhang S (Infection and Drug Resistance, 2025)
Qixun Lai,1 Kaifu Liao,1 Guangzhi Kuang,1 Weijie Liao,2 Shengrui Zhang2 1Department of Thoracic Surgery, Ganzhou Fifth People’s Hospital, Ganzhou City, 341000, People’s Republic of China; 2Department of Critical Care Medicine, Ganzhou People’s Hospital, Ganzhou City, 341000, People’s Republic of ChinaCorrespondence: Shengrui Zhang, Department of Critical Care Medicine, Ganzhou People’s Hospital, No. 16 Meiguan Avenue, Ganzhou City, Jiangxi Province, 341000, People’s Republic of China, Tel +8615279719190, Email a13755016764@126.comObjective: To construct a nomogram model for individualized prediction of pulmonary fungal infection risk in lung cancer patients.Methods: A total of 483 lung cancer patients hospitalized between August 2021 and August 2024 were retrospectively analyzed and randomly divided into a modeling group (n=338) and validation group (n=145). Patients in the modeling group were categorized based on the presence or absence of pulmonary fungal infection. Clinical data were analyzed using logistic regression, and a nomogram was developed using R software. Model performance was assessed using ROC curves, the Hosmer-Lemeshow (H-L) test, and Decision Curve Analysis (DCA).Results: Pulmonary fungal infections occurred in 99 out of 483 patients (20.50%). In the modeling group, the infection rate was 21.30%. Multivariate logistic regression identified age, smoking history, diabetes, glucocorticoid use, type of antimicrobial agents, invasive procedures, and length of hospitalization as independent risk factors (P< 0.05). The Area Under the Curve (AUC) was 0.933 in the modeling group and 0.954 in the validation group. H-L tests indicated good model calibration (P> 0.05). DCA demonstrated high clinical utility when the predicted probability ranged from 0.08 to 0.93.Conclusion: The nomogram based on key clinical factors effectively predicts the risk of pulmonary fungal infection in lung cancer patients and is a promising tool for assisting in early identification and intervention.Keywords: lung cancer, pulmonary fungal infection, influencing factors, nomogram