ArticleInfection and drug resistance2025
Construction of a Nomogram Prediction Model for Individualized Prediction of the Risk of Pulmonary Fungal Infection in Lung Cancer.
Article in Infection and drug resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Independent Risk Factors and Nomogram-Based Prediction of Pulmonary Fungal Infection in Lung Cancer Inpatients: A Single-Center Retrospective Study.Cancer management and research · 2026Article
- A Clinical Prediction Model for Invasive Pulmonary Fungal Disease in Dialysis Patients: Development, Validation, and Pharmacist-Led Application.Infection and drug resistance · 2026Article
- Association Between Admission Prognostic Nutritional Index and Pulmonary Infection Status in Hospitalized Lung Cancer Patients: A Retrospective Cohort Study.Journal of inflammation research · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: 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.
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