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.