Development and Validation of a Nomogram for Predicting Bronchiolitis Obliterans in Children with Severe Adenovirus...

This study aimed to identify the risk factors for bronchiolitis obliterans (BO) development in children with severe adenovirus pneumonia (SAP) and to construct and validate a nomogram prediction model.

This retrospective study included 152 pediatric patients with SAP between January 2019 and December 2023. We categorized these patients as having developed BO (n=36) and non-BO (n=116) based on long-term follow-up outcomes. Key clinical features were optimized using the least absolute shrinkage and selection operator (LASSO) regression and a nomogram was developed using logistic regression. Model performance was assessed and validated through receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA).

The LASSO regression analysis initially identified nine potential clinical predictors. Subsequent univariable and multivariable logistic regression revealed four independent risk factors significantly associated with BO development, namely, younger age, Odds ratio (OR) =0.94, 95% CI, 0.90-0.99, p=0.010; longer duration of fever, OR=2.27, 95% CI, 1.52-3.39, p<0.001; requirement for tracheoscopy, OR=5.25, 95% CI, 1.06-26.09, p=0.040; and extended oxygen therapy, OR=1.64, 95% CI, 1.10-2.43, p=0.010. The final prediction model incorporated three key predictors (months of age, fever duration, and oxygen therapy duration) into a clinically practical nomogram. The model demonstrated excellent discrimination, with an area under the curve (AUC) of 0.95, 95% CI, 0.91-0.98, a sensitivity of 0.83, and a specificity of 0.93. The Hosmer-Lemeshow test, χ2=5.24, p=0.732 indicated good calibration, and the DCA demonstrated positive clinical benefits.

We developed and validated a clinically practical nomogram, incorporating three key predictors mainly, months of age, fever duration, and oxygen therapy duration in predicting BO in children with SAP.The model demonstrates strong discriminatory power, reliable calibration, and clinical utility. This tool enables early risk stratification, facilitating timely intervention for high-risk pediatric SAP patients.