Risk prediction for secondary pulmonary fungal infection during acute exacerbation of chronic obstructive pulmonary disease

Background: Secondary fungal infections significantly affect the outcomes of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study aimed to develop and validate a clinically applicable prediction model for this complication. Methods: In this retrospective cohort study, we analyzed 225 consecutive patients with AECOPD who were admitted to The Fourth Affiliated Hospital of Soochow University (July 2022-July 2024). Patients were randomly allocated to the training (n=177) and validation (n=48) sets. Through multivariable logistic regression analysis, we identified independent risk factors and constructed a nomogram. Model performance was assessed using the area under the curve (AUC), calibration plots with the Hosmer-Lemeshow test, and decision curve analysis (DCA). Results: Three independent predictors were identified: the use of systemic glucocorticoids within 3 months before admission [odds ratio (OR) 2.943], admission to the hospital due to disease aggravation within the past year (OR 2.679), and the use of antibiotics for ≥14 days (OR 3.739). The nomogram demonstrated excellent discrimination {AUC 0.82 [95% confidence interval (CI): 0.75-0.88] in the training set; 0.80 (0.65-0.95) in the validation set} and good calibration (Hosmer-Lemeshow P>0.05). DCA confirmed the clinical utility across 10-80% risk thresholds. Conclusions: This validated nomogram, which incorporates three easily obtainable clinical parameters, provides reliable, individualized risk predictions for secondary pulmonary fungal infections in patients with AECOPD, facilitating early targeted interventions.