A Web-Based Dynamic Nomogram for Predicting Refractory Mycoplasma pneumoniae Pneumonia in Children

Refractory Mycoplasma pneumoniae pneumonia (RMPP) in children is associated with stronger inflammatory responses, more complex management, and a higher risk of pulmonary complications. Early identification of high-risk children may support timely treatment adjustment. This retrospective study included 500 hospitalized children with Mycoplasma pneumoniae pneumonia from November 2022 to November 2023. The cohort was randomly divided into a training set (n = 375) and a validation set (n = 125). Univariable and multivariable logistic regression analyses identified six independent predictors of RMPP: fever duration before admission, peak body temperature, lactate dehydrogenase level, sputum plugs or pleural effusion, lung consolidation, and hypoxemia. A web-based dynamic nomogram was constructed using these variables. The model showed good discrimination, with an area under the receiver operating characteristic curve/C-index of 0.840 (95% CI, 0.785-0.892) in the training set and 0.831 (95% CI, 0.743-0.906) in the validation set. Calibration curves showed good agreement between predicted and observed risks, and decision curve analysis suggested clinical net benefit across most threshold probabilities. Children with prolonged fever, high peak temperature, elevated lactate dehydrogenase, hypoxemia, and CT evidence of lung consolidation or sputum plugs/pleural effusion should be considered at increased risk of RMPP and may require closer monitoring and timely treatment adjustment.