Development and validation of a nomogram to predict plastic bronchitis in children with refractory Mycoplasma pneumoniae pneumonia

Abstract

Abstract Background Early identification of plastic bronchitis (PB) is of great importance and may aid in delivering appropriate treatment. This study aimed to develop and validate a nomogram for predicting PB in patients with refractory Mycoplasma pneumoniae pneumonia (RMPP). Methods A total of 547 consecutive children with RMPP who underwent fiberoptic bronchoscopy (FOB) intervention from January 2016 to June 2021 were enrolled in this study. Subsequently, 374 RMPP children (PB: 137, without PB: 237) from January 2016 to December 2019 were assigned to the development dataset to construct the nomogram to predict PB and 173 RMPP children from January 2020 to June 2021 were assigned to the validation dataset. The clinical, laboratory and radiological findings were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression was applied to construct a nomogram. The performance of the nomogram was evaluated by discrimination, calibration and clinical utility. Comparsion of ROC analysis and decision curve analysis (DCA) between nomogram and other models was performed to evaluate the discrimination ability and clinical utility. Results The development dataset included 374 patients with a mean age of 6.6 years and 185(49.5%) were men. The validation dataset included 173 patients and the mean age of the dataset was 6.7 years and 86 (49.7%) were men. From 26 potential predictors, LASSO regression identified 6 variables as significant predictive factors to construct the nomogram for predicting PB, including peak body temperature, neutrophil ratio (N%), platelet counts (PLT), interleukin-6 (IL-6), actic dehydrogenase (LDH) and pulmonary atelectasis. The nomogram showed good discrimination, calibration and clinical value. The mean AUC of the nomogram was 0.813 (95% CI 0.769–0.856) in the development dataset and 0.895 (95% CI 0.847–0.943) in the validation dataset. Through calibration plot and Hosmer–Lemeshow test, the predicted probability had a good consistency with actual probability both in the development dataset (P = 0.217) and validation dataset (P = 0.183), and DCA showed good clinical utility. ROC analysis indicated that the nomogram showed better discrimination ability compared with model of peak body temperature + pulmonary atelactsis and another model of N% + PLT + IL-6 + LDH, both in development dataset (AUC 0.813 vs 0.757 vs 0.754) and validation dataset (AUC 0.895 vs 0.789 vs 0.842). Conclusions In this study, a nomogram for predicting PB among RMPP patients was developed and validated. It performs well on discrimination ability, calibration ability and clinical value and may have the potential for the early identification of PB that will help physicians take timely intervention and appropriate management.