# A Clinical Prediction Model for Bacterial Coinfection in Children with Respiratory Syncytial Virus Infection: A Development and Validation Study

## TL;DR

This study created a model to identify bacterial coinfections in children with RSV using blood markers, helping reduce unnecessary antibiotic use.

## Contribution

A novel clinical prediction model using NLR, CRP, and SAA for bacterial coinfection in RSV-infected children.

## Key findings

- The model achieved an AUC of 0.832 in the training set and 0.811 in the test set.
- NLR, CRP, and SAA were identified as key predictors of bacterial coinfection.
- The model showed good calibration and clinical utility across various threshold probabilities.

## Abstract

Objectives: Respiratory syncytial virus (RSV) is a leading cause of hospitalization for acute lower respiratory tract infections (ALRIs) in children, with bacterial coinfection complicating diagnosis and often driving antibiotic overuse. This study aimed to develop and validate a clinical prediction model using common laboratory biomarkers to enable early, accurate identification of clinically significant bacterial coinfection in children with RSV infection. Methods: A single-center, retrospective cohort study was conducted at Fujian Children’s Hospital, enrolling 518 hospitalized children with RSV infection, which was confirmed via targeted next-generation sequencing (tNGS). Patients were randomly divided into a training set (n = 363) and a test set (n = 155) at a 7:3 ratio. The primary outcome, bacterial coinfection, was defined by a composite reference standard integrating etiological evidence from tNGS with clinical, inflammatory, and imaging data, and adjudicated by a blinded expert panel. LASSO regression identified independent predictors, followed by multivariable logistic regression modeling. Model performance was assessed via discrimination (AUC), calibration (Hosmer–Lemeshow test), and clinical utility (Decision Curve Analysis) in both sets. Results: Neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and serum amyloid A (SAA) were selected as predictors. The model achieved an AUC of 0.832 (95% CI: 0.788–0.875) in the training set and 0.811 (95% CI: 0.737–0.885) in the test set, with well-calibrated predictions (p > 0.05). Decision curve analysis demonstrated net clinical benefit across 10–80% threshold probabilities. A nomogram was developed for practical application. Conclusions: This study established a model integrating NLR, CRP, and SAA. It offers a reliable tool for the early detection of bacterial coinfection in RSV-infected children, enabling targeted antibiotic stewardship and improving clinical outcomes.