A nomogram for predicting plastic bronchitis in children with Mycoplasma pneumoniae pneumonia: a single-center retrospective study

Plastic bronchitis (PB) is a severe complication of Mycoplasma pneumoniae pneumonia (MPP). However, clinical factors predicting the occurrence of PB in MPP children have rarely been studied. The goal of this study was to establish a nomogram model to predict early PB in MPP children. We retrospectively analyzed data from MPP children who underwent electronic bronchoscopy at the Affiliated Xuzhou Children's Hospital of Xuzhou Medical University between January 2023 and June 2024. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen for risk factors for plastic bronchitis, and multivariate logistic regression analysis was used to construct the prediction model, which was visualized as a nomogram. The data were divided at random into a training cohort (70%) and a validation cohort (30%). A total of 212 children were included, of whom 72 (33.9%) developed PB following an MPP diagnosis. According to univariate analysis, thirteen factors were associated with PB. Six independent risk factors were identified in the training cohort using LASSO and multivariate logistic regression analyses: the neutrophil ratio (N%), the eosinophil ratio (E%), C-reactive protein (CRP), lactate dehydrogenase (LDH), D-dimer, and the neutrophil‒lymphocyte ratio (NLR). A graphical nomogram was subsequently developed. The nomogram demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.688 (95% confidence interval 0.597–0.780) in the training cohort and 0.705 (95% confidence interval 0.562–0.847) in the validation cohort. Decision curve analysis (DCA) validated the fitness and clinical application value of this nomogram. Internal validation revealed that the validation cohort was in good accordance with the training cohort. This nomogram prediction model aids in the early identification of PB among MPP children, thereby facilitating early management and improving clinical outcomes.