What question did this study set out to answer?

This study aims to develop a prediction model for severe Mycoplasma pneumoniae pneumonia (SMPP) and assess personalized treatments based on mutation status.

May 29, 2026Infection and Drug Resistance0 citationsOpen Access

Development of a Prediction Model for Severe Pediatric Mycoplasma pneumoniae Pneumonia: A Single-Center Retrospective Study

ZZZeyu Zeng GLGuang Li YXYanbing Xu

Key Points

This study aims to develop a prediction model for severe Mycoplasma pneumoniae pneumonia (SMPP) and assess personalized treatments based on mutation status.
Retrospective analysis of 2381 children diagnosed with Mycoplasma pneumoniae pneumonia from November 2019 to December 2023.
Clinical characteristics and A2063/2064G mutation status were compared between SMPP and general MPP groups.
A predictive model was developed using multivariate logistic regression, evaluated by ROC curve analysis.
71.3% of patients developed severe mycoplasma pneumonia (SMPP).
A2063/2064G mutation was found in 46.9% of cases, with a higher rate in the SMPP group (54.7% vs. 27.5%, P < 0.001).
The seven-indicator model achieved an AUC of 0.899, sensitivity of 0.827, and specificity of 0.861.

Abstract

Background: Mycoplasma pneumoniae pneumonia (MPP) is a common pediatric respiratory infection, with 10– 40% of cases progressing to severe MPP (SMPP). Macrolide-resistant Mycoplasma pneumoniae (MRMP) harboring the A2063/2064G mutation is closely associated with disease severity and treatment failure, posing a major clinical challenge. This study aimed to establish an early prediction model for SMPP and explore personalized treatment strategies for children with A2063/2064G-mutated infections. Methods: A total of 2381 children diagnosed with MPP at Shanghai Children’s Hospital between November 2019 and December 2023 were retrospectively analyzed. Clinical characteristics, laboratory indices, and A2063/2064G mutation status were compared between SMPP and general MPP groups. A predictive model for SMPP was developed using multivariate logistic regression, and its performance was evaluated by receiver operating characteristic (ROC) curve analysis. Medication patterns and length of hospital stay in patients with A2063/2064G mutations were further assessed to formulate personalized treatment strategies. Results: Of 2381 patients, 71.3% developed SMPP; 46.9% of all cases carried the A2063/2064G mutation, and the mutation rate was significantly higher in the SMPP group (54.7% vs. 27.5%, P < 0.001). The seven-indicator model (fever duration, lactate dehydrogenase (LDH), albumin (ALB), creatine kinase-MB (CK-MB), neutrophil percentage (Neu%), white blood cell (WBC) count and D-dimer) exhibited excellent performance (area under the curve (AUC) = 0.899, 95% confidence interval (CI) = 0.861, 0.937, sensitivity = 0.827, specificity = 0.861). In mutation-positive patients, those requiring tetracyclines (TCs)/fluoroquinolones (FQs) had higher SMPP rates than macrolide antibiotics (MACs)-responsive cases (89.6% vs. 78.0%, P < 0.001). Early TCs/FQs shortened hospital stay (7.30 ± 1.96 vs. 8.38 ± 2.20 days, P < 0.001). The model performed consistently across groups, and age-stratified analysis showed the highest TCs/FQs usage in patients with both mutation and model-predicted SMPP. Conclusion: The prediction model effectively identifies SMPP and guides interventions when combined with mutation status. Early TCs/FQs may benefit children with A2063/2064G-mutated MP when predicted as SMPP. Keywords: Mycoplasma pneumoniae pneumonia, clinical prediction model, A2063/2064G mutation, macrolide resistance, personalized treatment

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