Predicting macrolide resistance in pediatric Mycoplasma pneumoniae pneumonia: A machine learning modeling study
Shuo Yang1,2, Xinying Liu1,2, Huizhe Wang1,2
1First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, China.
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.
Insights
A new machine learning model can identify children with macrolide-resistant Mycoplasma pneumoniae pneumonia (MRMPP). This tool aids early detection and guides antibiotic treatment, improving clinical decision-making.
Area of Science:
- Pediatric Infectious Diseases
- Machine Learning in Medicine
- Clinical Prediction Modeling
Background:
- Macrolide-resistant Mycoplasma pneumoniae pneumonia (MRMPP) poses a significant challenge in pediatric care.
- Early identification of MRMPP is crucial for effective treatment and preventing antimicrobial resistance.
- Current diagnostic methods may not always facilitate timely identification of resistant cases.
Purpose of the Study:
- To develop and validate a machine learning-based clinical prediction model for identifying pediatric MRMPP.
- To facilitate early detection of resistant cases and guide targeted therapeutic interventions.
- To provide a data-driven tool for rational antibiotic use in pediatric pneumonia.
Main Methods:
- A retrospective, single-center study utilizing demographic, laboratory, and inflammatory data from pediatric patients with Mycoplasma pneumoniae pneumonia (MPP).
- Development of a stacking ensemble prediction model with feature selection to identify key predictors.
- Validation using internal cross-validation and an independent external temporal cohort; SHapley Additive exPlanations (SHAP) for model interpretability.
Main Results:
- The stacking ensemble model demonstrated strong performance with an AUC of 0.857 (internal validation) and 0.812 (external validation).
- Key predictors identified include IL-17A, IFN-γ, CRP, A/G ratio, prior macrolide use, and pre-hospital course.
- The model is accessible via a web tool for rapid assessment of resistance risk.
Conclusions:
- The developed machine learning model effectively identifies children at high risk for MRMPP.
- This tool serves as a valuable decision-making aid for clinicians regarding antibiotic use.
- The model shows significant clinical translational value in managing pediatric pneumonia.
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