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A Predictive Nomogram for Severe RSV Infection in Children: A Retrospective, Single-Center Development and Validation
Wanyi Li1, Shuying Wang2, Xuelin Wang1
1Children's Hospital, Tianjin University/Tianjin Children's Hospital, Tianjin, People's Republic of China.
Insights
A new nomogram model can predict severe respiratory syncytial virus (RSV) infection in children using factors like NLR, age, and hypoxemia. This tool aids early identification of high-risk pediatric patients for timely intervention.
Area of Science:
- Pediatrics
- Infectious Diseases
- Medical Informatics
Background:
- Respiratory syncytial virus (RSV) is a major cause of severe lower respiratory tract infections in children.
- Early identification of children at high risk for severe RSV infection is crucial for effective management and improved outcomes.
Purpose of the Study:
- To retrospectively analyze clinical factors associated with severe RSV infection in children.
- To develop and validate a nomogram model for early prediction of severe RSV infection in pediatric patients.
Main Methods:
- Retrospective analysis of clinical characteristics of 2595 children with RSV infection, divided into severe and non-severe groups.
- Variable selection using Elastic Net regression and multivariable logistic regression on multiply imputed datasets.
- Internal validation using bootstrap resampling to assess model discrimination and calibration.
Main Results:
- Five independent risk factors for severe RSV infection were identified: Neutrophil-to-Lymphocyte Ratio (NLR), age, winter onset, hypoxemia, and preterm status.
- The nomogram demonstrated good predictive performance with a pooled AUC of 0.847 and an optimism-corrected AUC of 0.843.
- Internal validation indicated low overfitting risk, with a calibration slope of 0.975.
Conclusions:
- A validated nomogram incorporating NLR, age, winter onset, hypoxemia, and preterm status can effectively predict severe RSV infection in children.
- This predictive tool assists clinicians in the timely identification of high-risk pediatric patients.
- Early identification facilitates prompt intervention, potentially improving clinical outcomes for children with severe RSV infection.
Background:
Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract infections in children, and early identification of high-risk patients is critical for improving outcomes. This study aimed to retrospectively study the clinical factors of children with severe RSV infection and, on this basis, develop and verify the nomogram model to identify independent risk factors for early prediction of children with severe RSV infection.
Methods:
This study retrospectively analyzed the clinical characteristics of children diagnosed with respiratory syncytial virus infection and divided the children into a severe group and a non-severe group. Based on five multiply imputed datasets, variable selection was performed using Elastic Net regression combined with clinical knowledge, and a multivariable logistic regression model was constructed. Internal validation was conducted using 500 bootstrap resamples to obtain optimism-corrected AUC and calibration slope, which was subsequently applied as a shrinkage factor to adjust regression coefficients for overfitting.
Results:
Of the 2595 children, 160 were in the severe group and 2435 were in the non-severe group. Five predictors were retained in the final model: Neutrophil-to-Lymphocyte Ratio (NLR), age, winter onset, hypoxemia, and preterm. The pooled area under the ROC curve was 0.847 (95% CI: 0.833-0.860), and the optimism-corrected AUC after bootstrap validation was 0.843. The calibration slope was 0.975, indicating low overfitting risk after shrinkage correction.
Conclusion:
A nomogram incorporating five predictors (NLR, age, winter onset, hypoxemia, and preterm) was developed to predict severe RSV infection in children. Bootstrap internal validation showed good discrimination and indicated low overfitting. This tool can assist clinicians in timely identifying high-risk patients for early intervention.
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