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A Clinical Nomogram for Predicting Early Response to Biologics in Pediatric Allergic Asthma with Comorbid Allergic
Lixin Wang1, Zhaopeng Kang2, Jun Ma1
1Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital of Wannan Medical University), Wuhu, Anhui, People's Republic of China.
Insights
A new nomogram predicts early response to biologics in children with allergic asthma and rhinitis. This tool helps identify "super-responders" for better treatment decisions and resource use.
Area of Science:
- Pediatric Allergy and Immunology
- Respiratory Medicine
- Biologics and Therapeutics
Background:
- Allergic rhinitis (AR) and allergic asthma significantly impact children's health.
- Biologics offer new treatment avenues, but predicting individual responses remains challenging.
- A need exists for tools to forecast early therapeutic success in pediatric patients.
Purpose of the Study:
- To identify baseline predictors of early response to biologic therapy in children with AR and allergic asthma.
- To develop and validate a nomogram for personalized prediction of treatment outcomes.
Main Methods:
- Retrospective cohort study of 246 children with moderate-to-severe AR and allergic asthma treated with biologics (Omalizumab or Dupilumab).
- Early response assessed at 16 weeks using Asthma Control Test, nasal symptom VAS, and exacerbation history.
- Nomogram developed using LASSO and logistic regression, validated for performance (C-index, calibration, DCA).
Main Results:
- 66.7% of patients were early responders.
- Key predictors included fractional exhaled nitric oxide (FeNO), blood eosinophil count (EOS), total IgE, atopic dermatitis (AD) comorbidity, and BMI.
- The nomogram showed good predictive performance (C-index 0.88 training, 0.85 validation) and clinical utility.
Conclusions:
- A novel nomogram effectively predicts early biologic response in pediatric patients with comorbid AR and allergic asthma.
- The tool integrates type 2 inflammatory markers and AD comorbidity for personalized prediction.
- This nomogram aids clinicians in identifying potential responders, optimizing treatment strategies and resource allocation.
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.
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