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Predictive Model for Critical Illness Infection in Hospitalized Children with RSV Infection: A Retrospective Study

  † These authors contributed equally to this work.

Submitted:

29 April 2026

Posted:

07 May 2026

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Abstract
Background: Respiratory syncytial virus (RSV) is a leading cause of hospitalization in children, but predictors of critical illness remain poorly defined. This study aimed to identify risk factors for critical RSV pneumonia and to develop a predictive model. Methods: A retrospective analysis of 12,035 hospitalized children with RSV infection (2019-2025) yielded 304 eligible patients after exclusions. Based on critical illness criteria, 30 critical cases and 90 randomly selected non critical controls from the remaining patients were enrolled. Clinical characteristics, laboratory parameters, and co-infection patterns were compared. Univariate, Lasso, and multivariable logistic regression analyses were performed to identify independent predictors, which were then incorporated into a nomogram. Model performance was assessed using ROC curve, calibration plot, and decision curve analysis. Results: Among the 304 eligible children, 30 (9.9%) had critical illness. Co infection with three or more pathogens was most common in the critical group (43.3%), whereas single RSV infection predominated in the non critical group (38.9%). Multivariable logistic regression identified four independent predictors of critical illness: interleukin 6 (IL 6), creatine kinase MB (CK MB), serum bilirubin excretion (SBE), and neutrophil percentage. The nomogram combining these factors exhibited excellent discriminative ability (AUC = 0.921, 95% CI: 0.868-0.974). The calibration curve agreed well with the 45° reference line (Hosmer-Lemeshow χ² = 3.233, p = 0.919), and decision curve analysis demonstrated clinical benefit across threshold probabilities ranging from 0.01 to 0.99. Conclusions: Elevated IL-6, CK-MB, neutrophil percentage, and SBE are independent predictors of critical RSV infection in children. The nomogram based on these readily available biomarkers provides a robust tool for early risk stratification and clinical decision-making.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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