Enhanced Early Detection of Allergic Rhinitis: A Prospective Study on a Symptom-Based Predictive Model.

Abstract

Introduction: Allergic rhinitis (AR) and non-allergic rhinitis (NAR) share overlapping symptoms but differ in pathophysiology and treatment. Current AR diagnosis relies on skin prick testing (SPT) and serum IgE quantification, both of which are complex. This study aimed to develop a symptom-based model for early AR detection, explore allergen-symptom relationships, and evaluate its performance.

Material and methods: A prospective cohort study was conducted at Wuhan Tongji Hospital between June 2024 and October 2024, enrolling 1150 patients with clinically suspected AR. Participants completed a visual analogue scale (VAS) questionnaire evaluating nasal symptoms (itching, congestion, sneezing, rhinorrhea), ocular symptoms, and overall discomfort, and the final diagnosis of AR was confirmed by SPT. Patients were randomly divided into training and test cohorts (8:2). Logistic regression (LR), the classic artificial intelligence-machine learning algorithm, was used to build a prediction model after analyzing allergen-symptom associations, with evaluation of discrimination, calibration, and clinical utility.

Results: A total of 758 patients (65.9%) were confirmed AR cases, and showed more severe nasal/ocular symptoms than NAR. Dust mites were the most common allergen, correlated with animal dander (r > 0.45) and negatively with age (r = -0.27), while allergen-symptom specificity was generally low. Sneezing was the strongest AR predictor (AUC = 0.758) with the highest sensitivity, specificity, and F1 score, and the multivariable model combining all symptoms performed better (AUC = 0.771 training, 0.765 test), outperforming clinician experience. The model showed good calibration and stable performance across subgroups.

Discussion: This study developed a symptom-based AR predictive model that outperformed clinician experience. Sneezing demonstrated the highest AUC value for prediction, and the multivariable LR model using nasal and ocular symptoms further improved accuracy.

Conclusions: The findings support VAS-based screening as a practical, cost-effective tool for early AR detection, therapeutic interventions, and targeted patient education regarding allergen avoidance strategies, helping optimize AR management, minimizing diagnostic delays, and facilitating precision treatment decisions.