Original Article
 

Application of Subcutaneous Immunotherapy in Pediatric Patients with Allergic Rhinitis and Analysis of Logistic Regression Models

Abstract

This study aimed to investigate the clinical efficacy of subcutaneous immunotherapy (SCIT) in pediatric patients with allergic rhinitis (AR) and to analyze influencing factors associated with treatment outcomes.
A total of 153 pediatric AR patients receiving SCIT were retrospectively enrolled. Patients were categorized into effective and ineffective groups according to treatment response. Clinical characteristics were collected and compared between groups. Differential variables were subjected to multicollinearity analysis and subsequently included in logistic regression analysis to identify risk factors for poor treatment response. A nomogram prediction model was established, and its predictive performance was internally validated using receiver operating characteristic (ROC) analysis.
Following SCIT treatment, 20 patients (13.07%) showed no therapeutic response, while the overall response rate reached 86.93%. Significant differences between groups were observed in sex, family history, place of residence, total immunoglobulin E (IgE), dust mite-specific IgE, and eosinophil count (EOS). Variance inflation factor (VIF) analysis demonstrated no multicollinearity among variables. Logistic regression identified all above indicators as independent risk factors for treatment failure (OR>1). The nomogram model showed excellent predictive performance, with an AUC of 0.924 (95% CI, 0.856–0.991).
SCIT demonstrated favorable clinical efficacy in pediatric AR patients. Gender, family history, place of residence, total IgE, dust mite sIgE, and EOS were independent predictors of poor treatment outcomes and may facilitate early identification of high-risk populations.

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Keywords
Allergic rhinitis Influencing factors Prediction model Subcutaneous immunotherapy

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How to Cite
1.
Zhang S, Zhao X, Ke J, Shen X, Yang M, Xu M. Application of Subcutaneous Immunotherapy in Pediatric Patients with Allergic Rhinitis and Analysis of Logistic Regression Models. Iran J Allergy Asthma Immunol. 2026;:1-11.