Construction of a Nomogram Prediction Model for Individualized Prediction of the Risk of Pulmonary Fungal Infection in Lung Cancer
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 Chi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Dove Medical Press
2025-06-01
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| Series: | Infection and Drug Resistance |
| Subjects: | |
| Online Access: | https://www.dovepress.com/construction-of-a-nomogram-prediction-model-for-individualized-predict-peer-reviewed-fulltext-article-IDR |
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| Summary: | 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 |
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| ISSN: | 1178-6973 |