Classification of Seasonal Allergic Rhinitis Patients by Prospective Patient‐Recorded Symptoms: The @ IT .2020 Project - S. Dramburg; C. J. Hernandez Toro; U. Grittner; E. Potapova; A. Travaglini; S. Tripodi; S. Arasi; S. Pelosi; F. Monnati; A. Acar Şahin; X. Aggelidis; A. Barbalace; et al. (Allergy, 2026)
ABSTRACT Background Seasonal allergic rhinitis (SAR) has been so far classified according to frequency and severity by retrospective information based on the ARIA (Allergic Rhinitis and its Impact on Asthma) classification. Electronic symptom diaries (e‐Diaries) are feasible tools to collect prospective patient‐reported data. Objective To test the consistency between prospectively recorded symptoms and retrospective ARIA classification and to generate a new SAR classification based on prospective e‐Diary data. Methods Within the @IT.2020 multicentre observational study, we recruited 815 children and adults with SAR in nine study centres from Mediterranean countries. Participants recorded daily organ‐specific (Rhinoconjunctivitis Total Symptom Score; RTSS) and general symptoms (visual analogue scale, VAS) plus quality‐of‐life measures via an e‐Diary app. Retrospective clinical questionnaires were performed at the beginning (T0) and end (T1) of the pollen season. Unsupervised clustering analyses around medoids were implemented to classify SAR by prospective patient‐reported data. Results One third (56/164, 34.1%) and almost half (173/366, 47.3%) of the patients retrospectively over‐ or underestimated the frequency of their symptoms, respectively. A moderate correlation was found between SAR severity reported at T1 and prospectively recorded symptom severity. An unsupervised clustering approach was able to identify distinct groups (mild, moderate, severe cluster) of patients based on the prospective severity of organ‐specific (RTSS) and overall symptoms (VAS), as well as quality of life criteria. Conclusions A significant recall bias regarding frequency and severity of symptoms emerged by comparing the retrospective ARIA classification with prospectively recorded data by an e‐Diary. A prospective approach to SAR classification, inspired by the retrospective ARIA classification, may facilitate a more precise evaluation of the patient for improved disease management.