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. 2025 Jul 15;17(7):5411-5422.
doi: 10.62347/FBSN3075. eCollection 2025.

Early risk identification for recurrent wheezing in children with respiratory syncytial virus infections

Ying Wang  1 , Yalin Shi  2 , Lijuan Wang  2
Affiliations

Early risk identification for recurrent wheezing in children with respiratory syncytial virus infections

Ying Wang et al. Am J Transl Res. .

Abstract

Objective: To evaluate the predictive value of peripheral blood eosinophil (EOS) count and nasopharyngeal microbiota for recurrent wheezing in children following respiratory syncytial virus (RSV) lower respiratory tract infections.

Methods: This retrospective study included 614 children with RSV infection and an external validation cohort of 164 children. Clinical data, hematological parameters, and nasopharyngeal microbiota profiles were collected. Logistic regression was used to identify independent predictors of recurrent wheezing. A predictive model was developed and validated using receiver operating characteristic (ROC) and calibration curves.

Results: Peripheral blood EOS count, serum 25(OH)D and IgM levels, and nasopharyngeal bacterial colonization (notably Streptococcus pneumoniae and Haemophilus influenzae) were significantly associated with recurrent wheezing. The predictive model showed moderate-to-good diagnostic performance (AUC = 0.747) and consistent accuracy in the external validation cohort (AUC = 0.741).

Conclusion: Peripheral blood EOS count and nasopharyngeal microbiota composition are critical predictors of recurrent wheezing following RSV infection. The predictive model may aid in early risk stratification and personalized intervention to prevent recurrent wheezing in pediatric patients.

Keywords: Respiratory syncytial virus; children; eosinophils; nasopharyngeal microbiota; predictive model; recurrent wheezing.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
Comparison of dominant bacterial distribution between the recurrent wheezing and non-wheezing groups.
Figure 2
Figure 2
Nomogram for predicting recurrent wheezing risk based on multivariate logistic regression analysis. Note: 25(OH)D: 25-Hydroxy Vitamin D, EOS: Eosinophils, IgM: Immunoglobulin M.
Figure 3
Figure 3
ROC curve and calibration curve for the predictive model in model group. A. ROC Curve Assessing the Predictive Performance of the Developed Model. B. Calibration Curve Assessing the Predictive Accuracy of the Model. C. DCA Curve Assessing the model’s clinical benefit. Note: ROC: Receiver Operating Characteristic curve, AUC: Area Under Curve, DCA: Decision Curve Analysis.
Figure 4
Figure 4
ROC curve and calibration curve for the predictive model in external validation group. A. ROC Curve Assessing the Predictive Performance of the Developed Model. B. Calibration Curve Assessing the Predictive Accuracy of the Model. C. DCA Curve Assessing the model’s clinical benefit. Note: ROC: Receiver Operating Characteristic curve, AUC: Area Under Curve, DCA: Decision Curve Analysis.
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