Allergen immunotherapy in MASK-air users in real-life: Results of a Bayesian mixed-effects model

Background Evidence regarding the effectiveness of allergen immunotherapy (AIT) on allergic rhinitis has been provided mostly by randomised controlled trials, with little data from real-life studies. Objective To compare the reported control of allergic rhinitis symptoms in three groups of users of the MASK-air(R) app: those receiving sublingual AIT (SLIT), those receiving subcutaneous AIT (SCIT), and those receiving no AIT. Methods We assessed the MASK-air(R) data of European users with self-reported grass pollen allergy, comparing the data reported by patients receiving SLIT, SCIT and no AIT. Outcome variables included the daily impact of allergy symptoms globally and on work (measured by visual analogue scales-VASs), and a combined symptom-medication score (CSMS). We applied Bayesian mixed-effects models, with clustering by patient, country and pollen season. Results We analysed a total of 42,756 days from 1,093 grass allergy patients, including 18,479 days of users under AIT. Compared to no AIT, SCIT was associated with similar VAS levels and CSMS. Compared to no AIT, SLIT-tablet was associated with lower values of VAS global allergy symptoms (average difference = 7.5 units out of 100; 95% credible interval [95%CrI] = -12.1;-2.8), lower VAS Work (average difference = 5.0; 95%CrI = -8.5;-1.5), and a lower CSMS (average difference = 3.7; 95%CrI = -9.3;2.2). When compared to SCIT, SLIT-tablet was associated with lower VAS global allergy symptoms (average difference = 10.2; 95%CrI = -17.2;-2.8), lower VAS Work (average difference = 7.8; 95%CrI = -15.1;0.2), and a lower CSMS (average difference = 9.3; 95%CrI = -18.5;0.2). Conclusion In patients with grass pollen allergy, SLIT-tablet, when compared to no AIT and to SCIT, is associated with lower reported symptom severity. Future longitudinal studies following internationally-harmonised standards for performing and reporting real-world data in AIT are needed to better understand its 'real-world' effectiveness

Allergen Immunotherapy in MASK‐Air Users in Real‐Life: Results of a Bayesian Mixed‐Effects Model

Background: Evidence regarding the effectiveness of allergen immunotherapy (AIT) on allergic rhinitis has been provided mostly by randomised controlled trials, with little data from real-life studies. Objective: To compare the reported control of allergic rhinitis symptoms in three groups of users of the MASK-air® app: those receiving sublingual AIT (SLIT), those receiving subcutaneous AIT (SCIT), and those receiving no AIT. Methods: We assessed the MASK-air® data of European users with self-reported grass pollen allergy, comparing the data reported by patients receiving SLIT, SCIT and no AIT. Outcome variables included the daily impact of allergy symptoms globally and on work (measured by visual analogue scales-VASs), and a combined symptom-medication score (CSMS). We applied Bayesian mixed-effects models, with clustering by patient, country and pollen season. Results: We analysed a total of 42,756 days from 1,093 grass allergy patients, including 18,479 days of users under AIT. Compared to no AIT, SCIT was associated with similar VAS levels and CSMS. Compared to no AIT, SLIT-tablet was associated with lower values of VAS global allergy symptoms (average difference = 7.5 units out of 100; 95% credible interval [95%CrI] = -12.1;-2.8), lower VAS Work (average difference = 5.0; 95%CrI = -8.5;-1.5), and a lower CSMS (average difference = 3.7; 95%CrI = -9.3;2.2). When compared to SCIT, SLIT-tablet was associated with lower VAS global allergy symptoms (average difference = 10.2; 95%CrI = -17.2;-2.8), lower VAS Work (average difference = 7.8; 95%CrI = -15.1;0.2), and a lower CSMS (average difference = 9.3; 95%CrI = -18.5;0.2). Conclusion: In patients with grass pollen allergy, SLIT-tablet, when compared to no AIT and to SCIT, is associated with lower reported symptom severity. Future longitudinal studies following internationally-harmonised standards for performing and reporting real-world data in AIT are needed to better understand its 'real-world' effectiveness.info:eu-repo/semantics/publishedVersio

Consistent Trajectories of Rhinitis Control and Treatment in 16,177 Weeks: The MASK‐air® Longitudinal Study

Introduction: 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. Methods: 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. 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. Conclusions: 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.info:eu-repo/semantics/publishedVersio

Behavioural Patterns in Allergic Rhinitis Medication in Europe: A Study Using MASK‐Air ® Real‐World Data

Background: Co-medication is common among patients with allergic rhinitis (AR), but its dimension and patterns are unknown. This is particularly relevant since AR is understood differently across European countries, as reflected by rhinitis-related search patterns in Google Trends. This study aims to assess AR co-medication and its regional patterns in Europe, using real-world data. Methods: We analysed 2015-2020 MASK-air® European data. We compared days under no medication, monotherapy and co-medication using the visual analogue scale (VAS) levels for overall allergic symptoms ('VAS Global Symptoms') and impact of AR on work. We assessed the monthly use of different medication schemes, performing separate analyses by region (defined geographically or by Google Trends patterns). We estimated the average number of different drugs reported per patient within 1 year. Results: We analysed 222,024 days (13,122 users), including 63,887 days (28.8%) under monotherapy and 38,315 (17.3%) under co-medication. The median 'VAS Global Symptoms' was 7 for no medication days, 14 for monotherapy and 21 for co-medication (p < .001). Medication use peaked during the spring, with similar patterns across different European regions (defined geographically or by Google Trends). Oral H1 -antihistamines were the most common medication in single and co-medication. Each patient reported using an annual average of 2.7 drugs, with 80% reporting two or more. Conclusions: Allergic rhinitis medication patterns are similar across European regions. One third of treatment days involved co-medication. These findings suggest that patients treat themselves according to their symptoms (irrespective of how they understand AR) and that co-medication use is driven by symptom severity.info:eu-repo/semantics/publishedVersio

Comparison of Rhinitis Treatments Using MASK-air ® Data and Considering the Minimal Important Difference

Background: Different treatments exist for allergic rhinitis (AR), including pharmacotherapy and allergen immunotherapy (AIT), but they have not been compared using direct patient data (i.e., "real-world data"). We aimed to compare AR pharmacological treatments on (i) daily symptoms, (ii) frequency of use in co-medication, (iii) visual analogue scales (VASs) on allergy symptom control considering the minimal important difference (MID) and (iv) the effect of AIT. Methods: We assessed the MASK-air® app data (May 2015-December 2020) by users self-reporting AR (16-90 years). We compared eight AR medication schemes on reported VAS of allergy symptoms, clustering data by the patient and controlling for confounding factors. We compared (i) allergy symptoms between patients with and without AIT and (ii) different drug classes used in co-medication. Results: We analysed 269,837 days from 10,860 users. Most days (52.7%) involved medication use. Median VAS levels were significantly higher in co-medication than in monotherapy (including the fixed combination azelastine-fluticasone) schemes. In adjusted models, azelastine-fluticasone was associated with lower average VAS global allergy symptoms than all other medication schemes, while the contrary was observed for oral corticosteroids. AIT was associated with a decrease in allergy symptoms in some medication schemes. A difference larger than the MID compared to no treatment was observed for oral steroids. Azelastine-fluticasone was the drug class with the lowest chance of being used in co-medication (adjusted OR = 0.75; 95% CI = 0.71-0.80). Conclusion: Median VAS levels were higher in co-medication than in monotherapy. Patients with more severe symptoms report a higher treatment, which is currently not reflected in guidelines.info:eu-repo/semantics/publishedVersio

Consistent trajectories of rhinitis control and treatment in 16,177 weeks: The MASK-air® longitudinal study

Introduction: 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. Methods: 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. Results: We analyzed 113,239 days (16,177 complete weeks) from 2590 patients (mean age ± SD&nbsp;=&nbsp;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. Conclusions: 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

Patient-centered digital biomarkers for allergic respiratory diseases and asthma: The ARIA-EAACI approach – ARIA-EAACI Task Force Report

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

Rhinitis associated with asthma is distinct from rhinitis alone: The ARIA-MeDALL hypothesis

Asthma, rhinitis, and atopic dermatitis (AD) are interrelated clinical phenotypes that partly overlap in the human interactome. The concept of “one-airway-one-disease,” coined over 20 years ago, is a simplistic approach of the links between upper- and lower-airway allergic diseases. With new data, it is time to reassess the concept. This article reviews (i) the clinical observations that led to Allergic Rhinitis and its Impact on Asthma (ARIA), (ii) new insights into polysensitization and multimorbidity, (iii) advances in mHealth for novel phenotype definitions, (iv) confirmation in canonical epidemiologic studies, (v) genomic findings, (vi) treatment approaches, and (vii) novel concepts on the onset of rhinitis and multimorbidity. One recent concept, bringing together upper- and lower-airway allergic diseases with skin, gut, and neuropsychiatric multimorbidities, is the “Epithelial Barrier Hypothesis.” This review determined that the “one-airway-one-disease” concept does not always hold true and that several phenotypes of disease can be defined. These phenotypes include an extreme “allergic” (asthma) phenotype combining asthma, rhinitis, and conjunctivitis. Rhinitis alone and rhinitis and asthma multimorbidity represent two distinct diseases with the following differences: (i) genomic and transcriptomic background (Toll-Like Receptors and IL-17 for rhinitis alone as a local disease; IL-33 and IL-5 for allergic and non-allergic multimorbidity as a systemic disease), (ii) allergen sensitization patterns (mono- or pauci-sensitization versus polysensitization), (iii) severity of symptoms, and (iv) treatment response. In conclusion, rhinitis alone (local disease) and rhinitis with asthma multimorbidity (systemic disease) should be considered as two distinct diseases, possibly modulated by the microbiome, and may be a model for understanding the epidemics of chronic and autoimmune diseases

Rhinitis associated with asthma is distinct from rhinitis alone: TARIA‐MeDALL hypothesis

Asthma, rhinitis, and atopic dermatitis (AD) are interrelated clinical phenotypes that partly overlap in the human interactome. The concept of “one-airway-one-disease,” coined over 20 years ago, is a simplistic approach of the links between upper- and lower-airway allergic diseases. With new data, it is time to reassess the concept. This article reviews (i) the clinical observations that led to Allergic Rhinitis and its Impact on Asthma (ARIA), (ii) new insights into polysensitization and multimorbidity, (iii) advances in mHealth for novel phenotype definitions, (iv) confirmation in canonical epidemiologic studies, (v) genomic findings, (vi) treatment approaches, and (vii) novel concepts on the onset of rhinitis and multimorbidity. One recent concept, bringing together upper- and lower-airway allergic diseases with skin, gut, and neuropsychiatric multimorbidities, is the “Epithelial Barrier Hypothesis.” This review determined that the “one-airway-one-disease” concept does not always hold true and that several phenotypes of disease can be defined. These phenotypes include an extreme “allergic” (asthma) phenotype combining asthma, rhinitis, and conjunctivitis.info:eu-repo/semantics/publishedVersio