Risk factors for plastic bronchitis complicating severe Mycoplasma pneumoniae pneumonia in children and development of a machine learning-based predictive model.

BACKGROUND

Plastic bronchitis (PB) is a rare but life-threatening complication of severe Mycoplasma pneumoniae pneumonia (SMPP) in children, and effective tools for its early prediction remain limited. This study aimed to identify risk factors for PB complicating SMPP and develop a machine learning-based predictive model.

METHODS

This retrospective cohort study included 166 children (aged 1-14 years) with SMPP admitted between January 2022 and December 2024. Patients were classified into PB (n=51) and non-PB (n=115) groups based on bronchoscopy and randomly split into training and validation sets (7:3 ratio). Eight machine learning algorithms were developed and evaluated based on the area under the receiver operating characteristic curve (AUC), precision-recall curve (PRC), decision curve analysis (DCA), accuracy, and F1-score. The optimal model was selected and interpreted using SHapley Additive exPlanations (SHAP). Candidate risk factors were identified and further analyzed using multivariable logistic regression to develop a nomogram. The predictive model was assessed using both sets and further validated by bootstrap resampling.

RESULTS

Generalized linear model (GLM) achieved the highest performance [AUC: 0.951, cross-validation AUC: 0.915, accuracy 0.880, F1-score: 0.825, area under the precision-recall curve (AUPRC): 0.856] in the training set, representing the optimal model. Fever duration, serum prealbumin, and D-dimer were the most influential features in SHAP analysis. Multivariable analysis confirmed these as independent risk factors: fever duration [odds ratio (OR): 1.550, 95% confidence interval (CI): 1.297-1.908, P<0.001], prealbumin (OR: 0.959, 95% CI: 0.939-0.976, P<0.001), and D-dimer (OR: 1.723, 95% CI: 1.157-2.627, P=0.009). The nomogram exhibited acceptable discriminative ability [C-index: 0.933 (95% CI: 0.887-0.980) in the training set; 0.831 (95% CI: 0.717-0.946) in the validation set; 0.913 (95% CI: 0.869-0.957) in bootstrap validation] and calibration (Brier score: 0.106, 0.142, and 0.103, respectively). The Hosmer-Lemeshow goodness-of-fit test yielded χ2=5.983 for internal validation [P=0.649 (>0.05)], suggesting acceptable model fit, with DCA confirming clinical utility across 0-90% threshold probabilities.

CONCLUSIONS

Fever duration, serum prealbumin, and D-dimer are independent risk factors for PB in children with SMPP. This internally derived nomogram achieved moderate discrimination and acceptable calibration in our limited single-center cohort. Further external validation in independent patient cohorts is therefore necessary to verify its generalizability and clinical utility.