Stratify severe risk in children with respiratory syncytial virus pneumonia-A retrospective study based on machine
Jun-An Pan1,2,3, Wen-Hao Yang1,2,3, Chao-Fen Wu1,2,3
1Department of Pediatric Pulmonology, West China Second University Hospital, Sichuan University, Chengdu, China.
Insights
Machine learning identified key risk factors for severe pediatric respiratory syncytial virus (RSV) pneumonia. Prolonged fever, diarrhea, and low hemoglobin predict severe illness, aiding early intervention for children with RSV.
Area of Science:
- Pediatric Infectious Diseases
- Computational Biology
- Clinical Informatics
Background:
- Respiratory syncytial virus (RSV) is a major cause of severe lower respiratory tract infections in children globally.
- Current tools for early identification and risk stratification of severe RSV pneumonia in children are insufficient.
- RSV infection can lead to severe inflammatory responses and mortality if not managed promptly.
Purpose of the Study:
- To identify high-risk factors for severe pediatric RSV pneumonia using machine learning.
- To develop predictive models for individualized prevention, diagnosis, and treatment strategies.
- To improve clinical outcomes for children affected by severe RSV pneumonia.
Main Methods:
- Variable screening via univariate and multivariate logistic regression analyses.
- Comparison of five machine learning models, with XGBoost selected for its superior performance.
- Application of Shapley Additive Explanations (SHAP) for clinical interpretability of the XGBoost model.
Main Results:
- Twelve key variables were identified as significant predictors of severe RSV pneumonia in children.
- The XGBoost model achieved high performance with AUC values of 0.949 (training) and 0.818 (test).
- SHAP analysis highlighted fever duration, diarrhea, hemoglobin, rhinorrhea, age, and neutrophil-to-lymphocyte ratio as crucial predictive factors.
Conclusions:
- Prolonged fever duration, presence of diarrhea, decreased hemoglobin, absence of rhinorrhea, age under 3 months, and elevated neutrophil-to-lymphocyte ratio are significant predictors of severe RSV pneumonia in children.
- These identified factors can aid in the early identification and risk stratification of severe cases.
- The study provides a foundation for developing targeted interventions to mitigate the severity of RSV pneumonia in pediatric populations.
Background:
Respiratory syncytial virus (RSV) is the primary pathogen causing severe lower respiratory tract infections in children, imposing a significant disease burden worldwide. The clinical manifestation of respiratory syncytial virus is not highly specific, in severe cases, it may cause a severe inflammatory response in the organism, potentially resulting in mortality. Currently, early identification and risk stratification tools for severe RSV-related pneumonia remain inadequate. This study identified potential high-risk factors contributing to severe cases in children with respiratory syncytial virus pneumonia by screening variables and establishing machine learning models, aiming to achieve individualized prevention, diagnosis, and treatment for these patients.
Methods:
Our study conducted variable screening through univariate analysis and multivariate logistic regression analysis. The performance of five machine learning models in the training and test sets was compared using receiver operating characteristic curves, and the XGBOOST model with the best overall performance was selected as the final model. Finally, shapley additive explanations (SHAP) was employed to quantify and perform clinically interpretable analysis on this black-box model.
Results:
Twelve key variables were identified in patients with severe respiratory syncytial virus pneumonia. XGBoost demonstrated the best overall performance, selected as the final model for the study, which achieving AUC values of 0.949 and 0.818 in the training and test sets respectively. By SHapley Additive exPlanations (SHAP), it was found that fever duration, diarrhea, hemoglobin concentration, rhinorrhea, age, neutrophil-to-lymphocyte ratio, gestational age, neutrophil count, mode of delivery, and lymphocyte count may be the most important predictive variables for children with severe RSV pneumonia.
Conclusion:
Our findings demonstrated that prolonged fever duration, presence of diarrhea, decreased hemoglobin concentration (HGB), absence of rhinorrhea, age under 3 months (Age<3 m), and elevated neutrophil-to-lymphocyte ratio (NLR) were predictors of severe cases among children with RSV pneumonia.
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