Browsing by Publisher "American Academy of Allergy, Asthma and Immunology"

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    Mobile Technology in Allergic Rhinitis: Evolution in Management or Revolution in Health and Care?
    (American Academy of Allergy, Asthma and Immunology, 2019) Bousquet J.; Ansotegui I.J.; Anto J.M.; Arnavielhe S.; Bachert C.; Basagaña X.; Bédard A.; Bedbrook A.; Bonini M.; Bosnic-Anticevich S.; Braido F.; Cardona V.; Czarlewski W.; Cruz A.A.; Demoly P.; De Vries G.; Dramburg S.; Mathieu-Dupas E.; Erhola M.; Fokkens W.J.; Fonseca J.A.; Haahtela T.; Hellings P.W.; Illario M.; Ivancevich J.C.; Jormanainen V.; Klimek L.; Kuna P.; Kvedariene V.; Laune D.; Larenas-Linnemann D.; Lourenço O.; Onorato G.L.; Matricardi P.M.; Melén E.; Mullol J.; Papadopoulos N.G.; Pfaar O.; Pham-Thi N.; Sheikh A.; Tan R.; To T.; Tomazic P.V.; Toppila-Salmi S.; Tripodi S.; Wallace D.; Valiulis A.; van Eerd M.; Ventura M.T.; Yorgancioglu A.; Zuberbier T.
    Smart devices and Internet-based applications (apps) are largely used in allergic rhinitis and may help to address some unmet needs. However, these new tools need to first of all be tested for privacy rules, acceptability, usability, and cost-effectiveness. Second, they should be evaluated in the frame of the digital transformation of health, their impact on health care delivery, and health outcomes. This review (1) summarizes some existing mobile health apps for allergic rhinitis and reviews those in which testing has been published, (2) discusses apps that include risk factors of allergic rhinitis, (3) examines the impact of mobile health apps in phenotype discovery, (4) provides real-world evidence for care pathways, and finally (5) discusses mobile health tools enabling the digital transformation of health and care, empowering citizens, and building a healthier society. © 2019 American Academy of Allergy, Asthma & Immunology
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    Global Initiative for Asthma Strategy 2021: Executive Summary and Rationale for Key Changes
    (American Academy of Allergy, Asthma and Immunology, 2022) Reddel H.K.; Bacharier L.B.; Bateman E.D.; Brightling C.E.; Brusselle G.G.; Buhl R.; Cruz A.A.; Duijts L.; Drazen J.M.; FitzGerald J.M.; Fleming L.J.; Inoue H.; Ko F.W.; Krishnan J.A.; Levy M.L.; Lin J.; Mortimer K.; Pitrez P.M.; Sheikh A.; Yorgancioglu A.A.; Boulet L.-P.
    The Global Initiative for Asthma (GINA) Strategy Report provides clinicians with an annually updated evidence-based strategy for asthma management and prevention, which can be adapted for local circumstances (e.g., medication availability). This article summarizes key recommendations from GINA 2021, and the evidence underpinning recent changes. GINA recommends that asthma in adults and adolescents should not be treated solely with short-acting β2-agonist (SABA), because of the risks of SABA-only treatment and SABA overuse, and evidence for benefit of inhaled corticosteroids (ICS). Large trials show that as-needed combination ICS–formoterol reduces severe exacerbations by ≥60% in mild asthma compared with SABA alone, with similar exacerbation, symptom, lung function, and inflammatory outcomes as daily ICS plus as-needed SABA. Key changes in GINA 2021 include division of the treatment figure for adults and adolescents into two tracks. Track 1 (preferred) has low-dose ICS–formoterol as the reliever at all steps: as needed only in Steps 1–2 (mild asthma), and with daily maintenance ICS–formoterol (maintenance-and-reliever therapy, “MART”) in Steps 3–5. Track 2 (alternative) has as-needed SABA across all steps, plus regular ICS (Step 2) or ICS–long-acting β2-agonist (Steps 3–5). For adults with moderate-to-severe asthma, GINA makes additional recommendations in Step 5 for add-on long-acting muscarinic antagonists and azithromycin, with add-on biologic therapies for severe asthma. For children 6–11 years, new treatment options are added at Steps 3–4. Across all age groups and levels of severity, regular personalized assessment, treatment of modifiable risk factors, self-management education, skills training, appropriate medication adjustment, and review remain essential to optimize asthma outcomes. © 2021 by the American Thoracic Society
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    The Allergic Rhinitis and Its Impact on Asthma (ARIA) Approach of Value-Added Medicines: As-Needed Treatment in Allergic Rhinitis
    (American Academy of Allergy, Asthma and Immunology, 2022) Bousquet J.; Toumi M.; Sousa-Pinto B.; Anto J.M.; Bedbrook A.; Czarlewski W.; Valiulis A.; Ansotegui I.J.; Bosnic-Anticevich S.; Brussino L.; Canonica G.W.; Cecchi L.; Cherrez-Ojeda I.; Chivato T.; Costa E.M.; Cruz A.A.; Del Giacco S.; Fonseca J.A.; Gemicioglu B.; Haahtela T.; Ivancevich J.C.; Jutel M.; Kaidashev I.; Klimek L.; Kvedariene V.; Kuna P.; Larenas-Linnemann D.E.; Lipworth B.; Morais-Almeida M.; Mullol J.; Papadopoulos N.G.; Patella V.; Pham-Thi N.; Regateiro F.S.; Rouadi P.W.; Samolinski B.; Sheikh A.; Taborda-Barata L.; Ventura M.T.; Yorgancioglu A.; Zidarn M.; Zuberbier T.
    Drug repurposing is a major field of value-added medicine. It involves investigating and evaluating existing drugs for new therapeutic purposes that address unmet healthcare needs. Several unmet needs in allergic rhinitis could be improved by drug repurposing. This could be game-changing for disease management. Current medications for allergic rhinitis are centered on continuous long-term treatment, and medication registration is based on randomized controlled trials carried out for a minimum of 14 days with adherence of 70% or greater. A new way of treating allergic rhinitis is to propose as-needed treatment depending on symptoms, rather than classical continuous treatment. This rostrum will discuss existing clinical trials on as-needed treatment for allergic rhinitis and real-world data obtained by the mobile health app MASK-air, which focuses on digitally-enabled, patient-centered care pathways. © 2022 American Academy of Allergy, Asthma & Immunology
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    Academic Productivity of Young People With Allergic Rhinitis: A MASK-air Study
    (American Academy of Allergy, Asthma and Immunology, 2022) Vieira R.J.; Pham-Thi N.; Anto J.M.; Czarlewski W.; Sá-Sousa A.; Amaral R.; Bedbrook A.; Bosnic-Anticevich S.; Brussino L.; Canonica G.W.; Cecchi L.; Cruz A.A.; Fokkens W.J.; Gemicioglu B.; Haahtela T.; Ivancevich J.C.; Klimek L.; Kuna P.; Kvedariene V.; Larenas-Linnemann D.; Morais-Almeida M.; Mullol J.; Niedoszytko M.; Okamoto Y.; Papadopoulos N.G.; Patella V.; Pfaar O.; Regateiro F.S.; Reitsma S.; Rouadi P.W.; Samolinski B.; Sheikh A.; Taborda-Barata L.; Toppila-Salmi S.; Sastre J.; Tsiligianni I.; Valiulis A.; Ventura M.T.; Waserman S.; Yorgancioglu A.; Zidarn M.; Zuberbier T.; Fonseca J.A.; Bousquet J.; Sousa-Pinto B.
    Background: Several studies have suggested an impact of allergic rhinitis on academic productivity. However, large studies with real-world data (RWD) are not available. Objective: To use RWD to assess the impact of allergic rhinitis on academic performance (measured through a visual analog scale [VAS] education and the Work Productivity and Activity Impairment Questionnaire plus Classroom Impairment Questions: Allergy Specific [WPAI+CIQ:AS] questionnaire), and to identify factors associated with the impact of allergic rhinitis on academic performance. Methods: We assessed data from the MASK-air mHealth app of users aged 13 to 29 years with allergic rhinitis. We assessed the correlation between variables measuring the impact of allergies on academic performance (VAS education, WPAI+CIQ:AS impact of allergy symptoms on academic performance, and WPAI+CIQ:AS percentage of education hours lost due to allergies) and other variables. In addition, we identified factors associated with the impact of allergic symptoms on academic productivity through multivariable mixed models. Results: A total of 13,454 days (from 1970 patients) were studied. VAS education was strongly correlated with the WPAI+CIQ:AS impact of allergy symptoms on academic productivity (Spearman correlation coefficient = 0.71 [95% confidence interval (CI) = 0.58; 0.80]), VAS global allergy symptoms (0.70 [95% CI = 0.68; 0.71]), and VAS nose (0.66 [95% CI = 0.65; 0.68]). In multivariable regression models, immunotherapy showed a strong negative association with VAS education (regression coefficient = −2.32 [95% CI = −4.04; −0.59]). Poor rhinitis control, measured by the combined symptom-medication score, was associated with worse VAS education (regression coefficient = 0.88 [95% CI = 0.88; 0.92]), higher impact on academic productivity (regression coefficient = 0.69 [95% CI = 0.49; 0.90]), and higher percentage of missed education hours due to allergy (regression coefficient = 0.44 [95% CI = 0.25; 0.63]). Conclusion: Allergy symptoms and worse rhinitis control are associated with worse academic productivity, whereas immunotherapy is associated with higher productivity. © 2022 American Academy of Allergy, Asthma & Immunology
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    Impairment of EQ-5D-5L Domains According to Allergic Rhinitis and Asthma Control: A MASK-air Real-World Study
    (American Academy of Allergy, Asthma and Immunology, 2023) Sousa-Pinto B.; Louis G.; Rodrigues J.; Giuliano A.F.M.; Baiardini I.; Braido F.; Czarlewski W.; Bedbrook A.; Haahtela T.; Valiulis A.; Brussino L.; Cecchi L.; Cruz A.A.; Gemicioglu B.; Fokkens W.J.; Ivancevich J.C.; Klimek L.; Kraxner H.; Kuna P.; Kupczyk M.; Kvedariene V.; Larenas-Linnemann D.; Louis R.; Nadif R.; Niedoszytko M.; Okamoto Y.; Ollert M.; Papadopoulos N.G.; Patella V.; Pawankar R.; Pham-Thi N.; Pfaar O.; Regateiro F.S.; Roche N.; Rouadi P.W.; Samolinski B.; Sastre J.; Savouré M.; Scichilone N.; Sheikh A.; Taborda-Barata L.; Toppila-Salmi S.; Yorgancioglu A.; Zidarn M.; Anto J.M.; Zuberbier T.; Canonica G.W.; Ventura M.T.; Fonseca J.A.; Pétré B.; Bousquet J.
    Background: EQ-5D-5L (EuroQOL, 5 Domains, 5 Levels) is a widely used health-related quality-of-life instrument, comprising 5 domains. However, it is not known how each domain is impacted by rhinitis or asthma control. Objective: To assess the association between rhinitis or asthma control and the different EQ-5D-5L domains using data from the MASK-air mHealth app. Methods: In this cross-sectional study, we assessed data from all MASK-air users (2015-2021; 24 countries). For the levels of each EQ-5D-5L domain, we assessed rhinitis and asthma visual analog scales (VASs) and the combined symptom-medication score (CSMS). We built ordinal multivariable models assessing the adjusted association between VAS/CSMS values and the levels of each EQ-5D-5L domain. Finally, we compared EQ-5D-5L data from users with rhinitis and self-reported asthma with data from users with rhinitis alone. Results: We assessed 5354 days from 3092 users. We observed an association between worse control of rhinitis or asthma (higher VASs and CSMS) and worse EQ-5D-5L levels. In multivariable models, all VASs and the CSMS were associated with higher levels of pain/discomfort and daily activities. For anxiety/depression, the association was mostly observed for rhinitis-related tools (VAS nose, VAS global, and CSMS), although the presence of self-reported asthma was also associated with worse anxiety/depression. Worse mobility (“walking around”) was particularly associated with VAS asthma and with the presence of asthma. Conclusions: A worse rhinitis control and a worse asthma control are associated with higher EQ-5D-5L levels, particularly regarding pain/discomfort and activity impairment. Worse rhinitis control is associated with worse anxiety/depression, and poor asthma control with worse mobility. © 2023 American Academy of Allergy, Asthma & Immunology
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    Cutoff Values of MASK-air Patient-Reported Outcome Measures
    (American Academy of Allergy, Asthma and Immunology, 2023) Sousa-Pinto B.; Sá-Sousa A.; Vieira R.J.; Amaral R.; Pereira A.M.; Anto J.M.; Klimek L.; Czarlewski W.; Mullol J.; Pfaar O.; Bedbrook A.; Brussino L.; Kvedariene V.; Larenas-Linnemann D.E.; Okamoto Y.; Ventura M.T.; Ansotegui I.J.; Bosnic-Anticevich S.; Canonica G.W.; Cardona V.; Cecchi L.; Chivato T.; Cingi C.; Costa E.M.; Cruz A.A.; Del Giacco S.; Devillier P.; Fokkens W.J.; Gemicioglu B.; Haahtela T.; Ivancevich J.C.; Kuna P.; Kaidashev I.; Kraxner H.; Laune D.; Louis R.; Makris M.; Monti R.; Morais-Almeida M.; Mösges R.; Niedoszytko M.; Papadopoulos N.G.; Patella V.; Pham-Thi N.; Regateiro F.S.; Reitsma S.; Rouadi P.W.; Samolinski B.; Sheikh A.; Sova M.; Taborda-Barata L.; Toppila-Salmi S.; Sastre J.; Tsiligianni I.; Valiulis A.; Yorgancioglu A.; Zidarn M.; Zuberbier T.; Fonseca J.A.; Bousquet J.
    Background: In clinical and epidemiological studies, cutoffs of patient-reported outcome measures can be used to classify patients into groups of statistical and clinical relevance. However, visual analog scale (VAS) cutoffs in MASK-air have not been tested. Objective: To calculate cutoffs for VAS global, nasal, ocular, and asthma symptoms. Methods: In a cross-sectional study design of all MASK-air participants, we compared (1) approaches based on the percentiles (tertiles or quartiles) of VAS distributions and (2) data-driven approaches based on clusters of data from 2 comparators (VAS work and VAS sleep). We then performed sensitivity analyses for individual countries and for VAS levels corresponding to full allergy control. Finally, we tested the different approaches using MASK-air real-world cross-sectional and longitudinal data to assess the most relevant cutoffs. Results: We assessed 395,223 days from 23,201 MASK-air users with self-reported allergic rhinitis. The percentile-oriented approach resulted in lower cutoff values than the data-driven approach. We obtained consistent results in the data-driven approach. Following the latter, the proposed cutoff differentiating “controlled” and “partly-controlled” patients was similar to the cutoff value that had been arbitrarily used (20/100). However, a lower cutoff was obtained to differentiate between “partly-controlled” and “uncontrolled” patients (35 vs the arbitrarily-used value of 50/100). Conclusions: Using a data-driven approach, we were able to define cutoff values for MASK-air VASs on allergy and asthma symptoms. This may allow for a better classification of patients with rhinitis and asthma according to different levels of control, supporting improved disease management. © 2022 American Academy of Allergy, Asthma & Immunology
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    Concepts for the Development of Person-Centered, Digitally Enabled, Artificial Intelligence–Assisted ARIA Care Pathways (ARIA 2024)
    (American Academy of Allergy, Asthma and Immunology, 2024) Bousquet J.; Schünemann H.J.; Sousa-Pinto B.; Zuberbier T.; Togias A.; Samolinski B.; Bedbrook A.; Czarlewski W.; Hofmann-Apitius M.; Litynska J.; Vieira R.J.; Anto J.M.; Fonseca J.A.; Brozek J.; Bognanni A.; Brussino L.; Canonica G.W.; Cherrez-Ojeda I.; Cruz A.A.; Vecillas L.D.L.; Dykewicz M.; Gemicioglu B.; Giovannini M.; Haahtela T.; Jacobs M.; Jacomelli C.; Klimek L.; Kvedariene V.; Larenas-Linnemann D.E.; Louis G.; Lourenço O.; Leemann L.; Morais-Almeida M.; Neves A.L.; Nadeau K.C.; Nowak A.; Palamarchuk Y.; Palkonen S.; Papadopoulos N.G.; Parmelli E.; Pereira A.M.; Pfaar O.; Regateiro F.S.; Savouré M.; Taborda-Barata L.; Toppila-Salmi S.K.; Torres M.J.; Valiulis A.; Ventura M.T.; Williams S.; Yepes-Nuñez J.J.; Yorgancioglu A.; Zhang L.; Zuberbier J.; Abdul Latiff A.H.; Abdullah B.; Agache I.; Al-Ahmad M.; Al-Nesf M.A.; Al Shaikh N.A.; Amaral R.; Ansotegui I.J.; Asllani J.; Balotro-Torres M.C.; Bergmann K.-C.; Bernstein J.A.; Bindslev-Jensen C.; Blaiss M.S.; Bonaglia C.; Bonini M.; Bossé I.; Braido F.; Caballero-Fonseca F.; Camargos P.; Carreiro-Martins P.; Casale T.; Castillo-Vizuete J.-A.; Cecchi L.; Teixeira M.D.C.; Chang Y.-S.; Loureiro C.C.; Christoff G.; Ciprandi G.; Cirule I.; Correia-de-Sousa J.; Costa E.M.; Cvetkovski B.; de Vries G.; Del Giacco S.; Devillier P.; Dokic D.; Douagui H.; Durham S.R.; Enecilla M.L.; Fiocchi A.; Fokkens W.J.; Fontaine J.-F.; Gawlik R.; Gereda J.E.; Gil-Mata S.; Giuliano A.F.M.; Gotua M.; Gradauskiene B.; Guzman M.A.; Hossny E.; Hrubiško M.; Iinuma T.; Irani C.; Ispayeva Z.; Ivancevich J.C.; Jartti T.; Jeseňák M.; Julge K.; Jutel M.; Kaidashev I.; Bennoor K.S.; Khaltaev N.; Kirenga B.; Kraxner H.; Kull I.; Kulus M.; Kuna P.; Kupczyk M.; Kurchenko A.; La Grutta S.; Lane S.; Miculinic N.; Lee S.M.; Le Thi Tuyet L.; Lkhagvaa B.; Louis R.; Mahboub B.; Makela M.; Makris M.; Maurer M.; Melén E.; Milenkovic B.; Mohammad Y.; Moniuszko M.; Montefort S.; Moreira A.; Moreno P.; Mullol J.; Nadif R.; Nakonechna A.; Navarro-Locsin C.G.; Neffen H.E.; Nekam K.; Niedoszytko M.; Nunes E.; Nyembue D.; O'Hehir R.; Ollert M.; Ohta K.; Okamoto Y.; Okubo K.; Olze H.; Padukudru M.A.; Palomares O.; Pali-Schöll I.; Panzner P.; Palosuo K.; Park H.S.; Passalacqua G.; Patella V.; Pawankar R.; Pétré B.; Pitsios C.; Plavec D.; Popov T.A.; Puggioni F.; Quirce S.; Raciborski F.; Ramonaité A.; Recto M.; Repka-Ramirez S.; Roberts G.; Robles-Velasco K.; Roche N.; Rodriguez-Gonzalez M.; Romualdez J.A.; Rottem M.; Rouadi P.W.; Salapatas M.; Sastre J.; Serpa F.S.; Sayah Z.; Scichilone N.; Senna G.; Sisul J.C.; Solé D.; Soto-Martinez M.E.; Sova M.; Sozinova O.; Stevanovic K.; Ulrik C.S.; Szylling A.; Tan F.M.; Tantilipikorn P.; Todo-Bom A.; Tomic-Spiric V.; Tsaryk V.; Tsiligianni I.; Urrutia-Pereira M.; Rostan M.V.; Sofiev M.; Valovirta E.; Van Eerd M.; Van Ganse E.; Vasankari T.; Vichyanond P.; Viegi G.; Wallace D.; Wang D.Y.; Waserman S.; Wong G.; Worm M.; Yusuf O.M.; Zaitoun F.; Zidarn M.
    The traditional healthcare model is focused on diseases (medicine and natural science) and does not acknowledge patients’ resources and abilities to be experts in their own lives based on their lived experiences. Improving healthcare safety, quality, and coordination, as well as quality of life, is an important aim in the care of patients with chronic conditions. Person-centered care needs to ensure that people's values and preferences guide clinical decisions. This paper reviews current knowledge to develop (1) digital care pathways for rhinitis and asthma multimorbidity and (2) digitally enabled, person-centered care.1 It combines all relevant research evidence, including the so-called real-world evidence, with the ultimate goal to develop digitally enabled, patient-centered care. The paper includes (1) Allergic Rhinitis and its Impact on Asthma (ARIA), a 2-decade journey, (2) Grading of Recommendations, Assessment, Development and Evaluation (GRADE), the evidence-based model of guidelines in airway diseases, (3) mHealth impact on airway diseases, (4) From guidelines to digital care pathways, (5) Embedding Planetary Health, (6) Novel classification of rhinitis and asthma, (7) Embedding real-life data with population-based studies, (8) The ARIA-EAACI (European Academy of Allergy and Clinical Immunology) strategy for the management of airway diseases using digital biomarkers, (9) Artificial intelligence, (10) The development of digitally enabled, ARIA person-centered care, and (11) The political agenda. The ultimate goal is to propose ARIA 2024 guidelines centered around the patient to make them more applicable and sustainable. © 2024 The Authors