Risk factors for identifying pulmonary aspergillosis in pediatric patients.

OBJECTIVES

This study aimed to identify the independent risk factors and develop a predictive model for pulmonary aspergillosis (PA) in pediatric populations.

METHODS

This retrospective study compromised 97 pediatric patients with pulmonary infections (38 PA cases and 59 non-PA cases) at Children's Hospital Affiliated to Shandong University between January 2020 and October 2024. Multivariate binary logistic regression was used to identify PA-associated risk factors. Receiver operating characteristic (ROC) curves, calibration plots, and Brier scoring were used to evaluate the diagnostic model.

RESULTS

8 clinical variables significantly differed between the PA and non-PA groups. Multivariate binary logistic regression analysis identified six significant independent risk factors: a history of surgery (OR: 9.52; 95% CI: 1.96-46.23; = 0.005), hematologic diseases (OR: 11.68; 95% CI: 0.89-153.62; = 0.062), absence of fever (OR: 8.244; 95% CI: 1.84-36.932; = 0.006), viral coinfection (OR: 15.99; 95% CI: 3.55-72.00; < 0.001), elevated (1, 3) -β -D-glucan levels (BDG, > 61.28 pg/mL; OR: 7.38; 95% CI: 1.26-43.31; = 0.027), and shorter symptom-to-admission interval (< 4.5 days; OR: 38.68; 95% CI: 5.38-277.94; < 0.001) were risk factors for PA. The predictive model demonstrated excellent discrimination (AUC 0.93, 95% CI 0.88-0.98) and calibration (Hosmer-Lemeshow p=0.606, R²=0.96, Brier score 0.097). metagenomic next - generation sequencing (mNGS) revealed significantly higher rates of polymicrobial infections in PA cases (86.84% vs 18.64%, p<0.001).

CONCLUSIONS

This study established and validated a high-performance predictive model incorporating six clinically accessible parameters for the diagnosis of pediatric PA.