Risk factors and construction of prediction model for plastic bronchitis in children with Mycoplasma pneumoniae pneumonia.

BACKGROUND: Children's health is at serious risk due to the possibility of developing life-threatening plastic bronchitis (PB) from severe Mycoplasma pneumoniae pneumonia (SMPP). It is critical to detect PB early. A risk prediction model was developed and evaluated in this study to enable early warning and intervention for high-risk pediatric patients. METHODS: A total of 180 children with SMPP were the subject of a retrospective analysis. Patients were divided into a PB group (60 cases) and a non-PB group (120 cases) based on the results of fiberoptic bronchoscopies. Imaging results, laboratory data, and clinical traits were compared between groups. Candidate predictors of PB were initially screened using univariable analysis and the least absolute shrinkage and selection operator (LASSO) regression. Independent risk factors were subsequently identified through multivariate logistic regression, and a prediction model was constructed. Internal validation was performed on the model, and its discriminatory power, calibration, and clinical value were assessed using receiver operating characteristic (ROC), calibration, and clinical decision curves. RESULTS: Independent risk variables for PB in children with SMPP were history of intensive care unit (ICU) admission [odds ratio (OR): 4.32; 95% confidence interval (CI): 1.41-13.82; P=0.01], fever duration (OR: 1.26; 95% CI: 1.09-1.47; P=0.002), neutrophil percentage (OR: 1.05; 95% CI: 1.01-1.09; P=0.03), and lactate dehydrogenase (OR: 1.01; 95% CI: 1.01-1.01; P<0.001). The predictive model built using these parameters had a sensitivity of 81.7% and a specificity of 85.0%, with an area under the curve (AUC) of 0.871 (95% CI: 0.813-0.929). The model's strong consistency and practical applicability were validated using calibration curves and clinical decision-support analysis. CONCLUSIONS: The goal of the study was to develop and validate a nomogram for predicting PB in children with SMPP, thereby facilitating early identification of PB in clinical practice.