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April 24, 2026SHILAP Revista de lepidopterología0 citationsOpen Access

Development of a predictive model based on clinical indicators for refractory Mycoplasma pneumoniae pneumonia in children: a case–control study

FYFeifei YuSichuan University YZY F ZhouDalian Medical University ZLZ X LuoChongqing Emergency Medical Center

Key Points

The research aims to identify predictors of refractory Mycoplasma pneumoniae pneumonia (RMPP) in children and establish a model for early identification.
Conducted a retrospective case-control analysis with 522 children diagnosed with pneumonia.
Collected 28 clinical indicators, including symptoms, hospitalization data, and laboratory results.
Developed a multivariate logistic regression model to identify independent risk factors for RMPP.
Identified key risk factors including duration of fever, pleural effusion, atelectasis, and extrapulmonary complications.
The prediction model demonstrated high accuracy with an AUC of 0.870 and sensitivity of 82.2%.
Created a visual nomogram for individualized risk prediction, aiding clinical decision-making.

Abstract

Objective This study aims to screen indicators for predicting the occurrence of refractory Mycoplasma pneumoniae pneumonia (RMPP) in children, determine the combined factors for predicting RMPP, and provide a basis for the early identification of children with RMPP and the determination of treatment plans. Methods This study was a retrospective case–control analysis. A total of 522 children with MPP and 28 clinical indicators were included. Clinical feature, hospitalization period, laboratory data, etc., were collected. The risk factors related to RMPP were screened through univariate analysis. A multivariate logistic regression model was established, and stepwise regression was used to screen out independent risk factors. The operating characteristic curve (ROC) of the combined predictor was drawn for predictive efficacy analysis. A visual nomogram model for predicting the probability of RMPP occurrence was constructed and validated. Results Differing from other results, there were no statistically significant differences in demographic indicators such as age and gender between the two groups. The multivariate logistic regression analysis showed that duration of fever (OR = 1.407), PLT (OR = 0.997), pleural effusion (OR = 2.084), atelectasis (OR = 3.116), and extrapulmonary complications (OR = 4.251) were independent risk factors for RMPP (P 0.05). MP antibody titer ≥1:320 (OR = 0.420) is a protective factor. The AUC of the prediction model was 0.870 (95%CI: 0.837, 0.904), the sensitivity of the prediction model was 82.2%, the specificity was 80.5%, and the prediction accuracy was relatively high. The calibration curve, close to the 45° line, exhibited good calibration. Conclusion We constructed and validated a visual and user-friendly model for individualized prediction of RMPP risk in children at initial presentation, to support clinical decision-making regarding macrolide therapy. This model provides a tool for identification of high-risk children, which may inform closer monitoring and prompt consideration of adjunctive therapies.

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