ORCID Profile
0000-0002-4517-1749
Current Organisation
Università di Cagliari
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Publisher: Wiley
Date: 30-06-2023
DOI: 10.1111/ALL.15740
Abstract: Biomarkers for the diagnosis, treatment and follow‐up of patients with rhinitis and/or asthma are urgently needed. Although some biologic biomarkers exist in specialist care for asthma, they cannot be largely used in primary care. There are no validated biomarkers in rhinitis or allergen immunotherapy (AIT) that can be used in clinical practice. The digital transformation of health and health care (including mHealth) places the patient at the center of the health system and is likely to optimize the practice of allergy. Allergic Rhinitis and its Impact on Asthma (ARIA) and EAACI (European Academy of Allergy and Clinical Immunology) developed a Task Force aimed at proposing patient‐reported outcome measures (PROMs) as digital biomarkers that can be easily used for different purposes in rhinitis and asthma. It first defined control digital biomarkers that should make a bridge between clinical practice, randomized controlled trials, observational real‐life studies and allergen challenges. Using the MASK‐air app as a model, a daily electronic combined symptom‐medication score for allergic diseases (CSMS) or for asthma (e‐DASTHMA), combined with a monthly control questionnaire, was embedded in a strategy similar to the diabetes approach for disease control. To mimic real‐life, it secondly proposed quality‐of‐life digital biomarkers including daily EQ‐5D visual analogue scales and the bi‐weekly RhinAsthma Patient Perspective (RAAP). The potential implications for the management of allergic respiratory diseases were proposed.
Publisher: Wiley
Date: 20-11-2023
DOI: 10.1111/ALL.15574
Abstract: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK‐air®, these data have only been analyzed cross‐sectionally, without considering the changes of symptoms over time. We analyzed data from MASK‐air® longitudinally, clustering weeks according to reported rhinitis symptoms. We analyzed MASK‐air® data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7 days. We firstly used k‐means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used the latent class mixture model to assess the robustness of results. We analyzed 113,239 days (16,177 complete weeks) from 2590 patients (mean age ± SD = 39.1 ± 13.7 years). The first clustering algorithm identified ten clusters among weeks with medication use: seven with low variability in rhinitis control during the week and three with highly‐variable control. Clusters with poorly‐controlled rhinitis displayed a higher frequency of rhinitis co‐medication, a more frequent change of medication schemes and more pronounced seasonal patterns. Six clusters were identified in weeks when no rhinitis medication was used, displaying similar control patterns. The second clustering method provided similar results. Moreover, patients displayed consistent levels of rhinitis control, reporting several weeks with similar levels of control. We identified 16 patterns of weekly rhinitis control. Co‐medication and medication change schemes were common in uncontrolled weeks, reinforcing the hypothesis that patients treat themselves according to their symptoms.
Publisher: Wiley
Date: 03-2021
DOI: 10.1111/ALL.14453
Publisher: Wiley
Date: 10-10-2023
DOI: 10.1111/ALL.15902
No related grants have been discovered for Stefano R. Del Giacco.