A Diagnostic Nomogram for Early Prediction of Post-Infectious Bronchiolitis Obliterans in Severe Pneumonia

The study aimed to set up and validate a predictive nomogram for post-infectious bronchiolitis obliterans in severe pneumonia.

We retrospectively analyzed data of 228 patients diagnosed with severe pneumonia and constructed a prediction nomogram. The least absolute shrinkage and selection operator (LASSO) regression model was utilized to optimize the selection of features for the clinical characteristics of post-infectious bronchiolitis obliterans. Individual nomograms of bronchiolitis obliterans incorporating clinical factors were developed using the multivariate logistic model. The C-index, calibration plot, and decision curve analysis were used to verify the calibration, discrimination, and clinical utility. The bootstrapping method was used for the internal validation of the model.

Predictors in the individualized predictive nomogram included age of patients (odds ratio [OR], 0.994; 95% confidence interval; [CI], 0.990-0.998), length of stay (OR, 1.043; 95% CI: 1.015-1.073), mechanical ventilation (OR, 1.865; 95% CI: 1.236-2.817), human adenoviral infection (OR, 1.671; 95%, CI: 1.201-2.326), and the level of interleukin (IL)-2 (OR, 0.947; 95% CI: 0.901-0.955). The model discriminated reasonably well, with a C-index of 0.907 (C-index, 0.888 and 0.926) with good calibration and internal validation, which was not statistically significant by the Hosmer-Lemeshow test (P = 0.5443). Decision curve analysis showed that nomograms were useful in clinical settings.

In this study, a model was developed and presented as a nomogram with relatively good accuracy to help clinicians accurately and early diagnose post-infectious bronchiolitis obliterans in children with severe pneumonia.