A Predictive Nomogram for Severe RSV Infection in Children: A Retrospective, Single-Center Development and Validation...

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.