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Updated: Jul 9, 2025
Symptom Assessment of Patients with Allergic Rhinitis Using an Allergen Exposure Chamber
Published on: March 3, 2023
Developing and validating clinical models to identify candidates for allergic rhinitis pre-exposure prophylaxis
Wenting Luo1, Xiangqing Hou2, Yun Sun3
1Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
This study developed a clinical model to identify allergic rhinitis (AR) candidates for pre-exposure prophylaxis (PrEP). The model uses routine medical data for early screening, improving resource allocation and health management.
Area of Science:
- Allergy and Immunology
- Clinical Prediction Modeling
- Public Health
Background:
- Limited risk-forecasting models exist for allergic rhinitis (AR).
- Effective models are needed to guide AR pre-exposure prophylaxis (PrEP) in clinical practice.
- Routine medical questionnaires can potentially be used for AR risk assessment.
Purpose of the Study:
- To develop and validate a clinical model for identifying AR PrEP candidates.
- To utilize routine medical questionnaire data for AR risk prediction.
- To aid in the effective deployment of resources for AR management.
Main Methods:
- A cohort of 877 participants across 10 Chinese provinces was studied (2019-2021).
- Clinical characteristics and allergen exposure history were collected via interviews.
- Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression models were employed for risk factor identification and model construction.
Main Results:
- Two nomograms were developed to identify AR based on sensitization patterns.
- Models demonstrated an approximate area under the curve (AUC) of 0.7 for predictive power.
- Calibration curves showed good agreement, indicating model reliability.
Conclusions:
- The developed models show good performance in predicting AR.
- These models can serve as potential automatic screening tools for AR PrEP candidates.
- Early identification of AR candidates can optimize resource allocation and health management.
