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Machine learning prediction of oxygen therapy in pediatric Mycoplasma pneumoniae pneumonia
Claudio Coppola1,2, Judith Jeyafreeda Andrew3,4, Martino Ruggieri5
1Postgraduate Training Program in Pediatrics, University of Catania, Catania, Italy.
Insights
Machine learning accurately predicts oxygen therapy needs in pediatric Mycoplasma pneumoniae pneumonia. Models using routine data identify high-risk children early, improving clinical decisions for respiratory infections.
Area of Science:
- Pediatric Pulmonology
- Medical Informatics
- Artificial Intelligence in Medicine
Background:
- Mycoplasma pneumoniae pneumonia is a common cause of pediatric community-acquired pneumonia.
- Clinical presentation varies from mild to severe, necessitating respiratory support.
- Predicting oxygen therapy needs in children with this condition is challenging with current methods.
Purpose of the Study:
- To develop and validate machine learning models for predicting oxygen therapy requirements in pediatric Mycoplasma pneumoniae pneumonia.
- To identify key clinical and laboratory features predictive of oxygen therapy need.
- To assess the performance of various machine learning algorithms in this prediction task.
Main Methods:
- A multicenter retrospective study of 206 pediatric patients with Mycoplasma pneumoniae pneumonia.
- Development and validation of nine machine learning algorithms using routine admission data.
- Performance evaluation using AUC, precision, recall, and F1-score; feature importance assessed with SHAP analysis.
Main Results:
- 42 (20.4%) patients required oxygen therapy.
- Support Vector Machine (SVM) showed the highest performance (AUC 0.97).
- Key predictors included C-reactive protein (CRP), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), and respiratory distress.
Conclusions:
- Machine learning models effectively predict oxygen therapy needs in pediatric Mycoplasma pneumoniae pneumonia using routine data.
- Interpretable AI can aid in early risk stratification for pediatric respiratory infections.
- Improved clinical decision-making is possible through AI-driven insights.
Background:
Mycoplasma pneumoniae pneumonia represents a significant cause of community-acquired pneumonia in children, with clinical presentations ranging from mild to severe forms requiring respiratory support. Early identification of children at risk for oxygen therapy remains challenging using conventional clinical and laboratory parameters.
Methods:
We conducted a multicenter retrospective study involving 206 pediatric patients (aged 1 month to 18 years) with confirmed Mycoplasma pneumoniae pneumonia admitted to three Italian hospitals between 2023 and 2025. Nine machine learning algorithms were developed and validated using routine admission data including demographics, clinical presentation, laboratory tests, and imaging findings. The primary outcome was the need for oxygen therapy during hospitalization. Model performance was evaluated using area under the curve (AUC), precision, recall, and F1-score metrics. Feature importance was assessed using SHAP (Shapley Additive Explanations) analysis.
Results:
Among the 206 patients, 42 (20.4%) required oxygen therapy during hospitalization. The cohort had a mean age of approximately 4.6 years (SD ≈ 3.5), with comorbidities present in approximately 40% of cases. Support Vector Machine (SVM) achieved the highest performance with an AUC of 0.97, precision of 0.93, recall of 0.93, and F1-score of 0.92. Logistic Regression (AUC 0.95), XGBoost (AUC 0.94), and LightGBM (AUC 0.93) also demonstrated strong predictive performance. SHAP analysis consistently identified C-reactive protein (CRP), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), neutrophil percentage, and respiratory distress as the most important predictive features across models.
Conclusion:
Machine learning models using routine admission data can accurately predict oxygen therapy requirements in pediatric Mycoplasma pneumoniae pneumonia. The integration of interpretable artificial intelligence approaches may enable earlier risk stratification and improve clinical decision-making in pediatric respiratory infections.
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