Eur J Clin Microbiol Infect Dis. 2025 Nov 15. doi: 10.1007/s10096-025-05354-8. Online ahead of print.
ABSTRACT
PURPOSE: To develop a machine learning-based clinical prediction model for macrolide-resistant Mycoplasma pneumoniae pneumonia (MRMPP) in children, facilitating early identification of resistant cases and guiding targeted therapeutic interventions.
METHODS: In this retrospective, single-center study, we developed a stacking ensemble prediction model using demographic, laboratory, and inflammatory data from pediatric patients with MPP. A feature selection protocol was implemented to identify key predictors. The final model was validated using both internal cross-validation and an independent external temporal cohort. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).
RESULTS: The stacking ensemble model achieved an area under the curve (AUC) of 0.857 during internal validation, with a sensitivity of 0.769 and specificity of 0.841; the AUC during external validation was 0.812. Key predictive factors included interleukin-17 A (IL-17 A), interferon-gamma (IFN-γ), C-reactive protein (CRP), albumin-to-globulin ratio (A/G), History of pre-hospital macrolide use, and Pre-hospital course. The model is implemented as a web tool, facilitating rapid assessment of resistance risk.
CONCLUSION: The machine learning model developed in this study can initially identify children at high risk for MRMPP, serving as a data-driven decision-making tool for the rational use of antibiotics in clinical practice and demonstrating significant clinical translational value.
PMID:41240233 | DOI:10.1007/s10096-025-05354-8
