Objective To explore the establishment of a risk prediction model for concurrent bronchiolitis obliterans(BO)in children with refractory Mycoplasma pneumoniae pneumonia(RMPP).Methods A retrospective study included 116 RMPP children treated in the Department of Pediatrics of Xiangya Changde Hospital from June 2021 to December 2023.Eighty-one cases were allocated to the training set and thirty-five cases to the validation set based on a 7:3 ratio.Among them,26 cases in the training set developed BO,while 55 did not.The multivariate logistic regression was used to select variable factors for constructing the BO risk prediction model.Nomograms were drawn,and the receiver operating characteristic(ROC)curve was used to assess the discriminative ability of the model,while calibration curves and Hosmer-Lemeshow tests evaluated the model's calibration.Results Multivariate logistic regression analysis indicated that several factors were significantly associated with concurrent BO in RMPP children,including length of hospital stay,duration of fever,atelectasis,neutrophil percentage(NEUT%),peak lactate dehydrogenase(LDH),ferritin,peak C reactive protein(CRP),oxygenation index(PaO2/FiO2),≥2/3 lung lobe consolidation,pleural effusion,bronchial mucous plugs,bronchial mucosal necrosis,and arterial oxygen partial pressure(PaO2)(P<0.05).ROC curve analysis for the training set indicated an area under the curve of 0.904 with 88%sensitivity and 83%specificity;the validation set showed an area under the curve of 0.823 with 76%sensitivity and 93%specificity.The Hosmer-Lemeshow test's Chi-square values for the training and validation sets were 2.17 and 1.92,respectively,with P values of 0.221 and 0.196,respectively.Conclusions The risk prediction model for BO in RMPP children based on logistic regression has good performance.Variables such as length of hospital stay,duration of fever,atelectasis,peak LDH,peak CRP,NEUT%,ferritin,≥2/3 lung lobe consolidation,pleural effusion,bronchial mucous plugs,bronchial mucosal necrosis,PaO2/FiO2,and PaO2 can be used as predictors.