A Clinical Nomogram for Predicting Early Response to Biologics in Pediatric Allergic Asthma with Comorbid Allergic Rhinitis.

Abstract

Background: The coexistence of allergic rhinitis (AR) and allergic asthma poses a significant burden on pediatric health. While biologics targeting type 2 inflammation have revolutionized treatment, individual responses vary significantly. There is a lack of practical tools to predict early therapeutic response before initiation. This study aimed to identify key baseline predictors and construct a nomogram to personalize the prediction of early response to biologics in children with AR and allergic asthma.

Methods:  This retrospective cohort study included 246 children (recruited between January 2021 and August 2025) diagnosed with moderate-to-severe allergic AR and allergic asthma who received biologic therapy (Omalizumab or Dupilumab). Early response was assessed at 16 weeks based on a composite outcome involving the Asthma Control Test (ACT), visual analog scale (VAS) for nasal symptoms, and exacerbation history. Independent predictors were identified using LASSO regression and multivariate logistic regression analysis to build a nomogram. The model's performance was evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA).

Results:  Of the 246 patients, 164 (66.7%) were classified as early responders. Multivariate analysis identified baseline fractional exhaled nitric oxide (FeNO), blood eosinophil count (EOS), total IgE levels, comorbidity of atopic dermatitis (AD), and Body Mass Index (BMI) as independent predictors. The developed nomogram demonstrated good discrimination with a C-index of 0.88 (95% CI: 0.83-0.93) in the training cohort and 0.85 (95% CI: 0.78-0.92) in the validation cohort. Calibration curves showed excellent agreement between predicted and observed probabilities. DCA indicated significant clinical net benefit across a wide range of threshold probabilities.

Conclusion:  We successfully developed a novel nomogram incorporating baseline type 2 inflammatory endotypes and AD comorbidity to predict early response to biologics in pediatric patients with allergic asthma and comorbid AR. This tool offers clinicians a practical method to screen potential "super-responders", thereby optimizing therapeutic decision-making and resource allocation.