Browsing by Author "Costa, EM"

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    Comparison of rhinitis treatments using MASK-air® data and considering the minimal important difference
    Sousa-Pinto, B; Schünemann, HJ; Sá-Sousa, A; Vieira, RJ; Amaral, R; Anto, JM; Klimek, L; Czarlewski, W; Mullol, J; Pfaar, O; Bedbrook, A; Brussino, L; Kvedariene, V; Larenas-Linnemann, D; Okamoto, Y; Ventura, MT; Agache, I; Ansotegui, IJ; Bergmann, KC; Bosnic-Anticevich, S; Brozek, J; Canonica, GW; Cardona, V; Carreiro-Martins, P; Casale, T; Cecchi, L; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; Del Giacco, S; Devillier, P; Eklund, P; Fokkens, WJ; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Ispayeva, Z; Jutel, M; Kuna, P; Kaidashev, I; Khaitov, M; Kraxner, H; Laune, D; Lipworth, B; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mösges, R; Niedoszytko, M; Papadopoulos, NG; Patella, V; Pham-Thi, N; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sheikh, A; Sova, M; Todo-Bom, A; Taborda-Barata, L; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Vandenplas, O; Wallace, D; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonseca, JA; Bousquet, J
    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 (R) 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.
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    Consistent trajectories of rhinitis control and treatment in 16,177 weeks: The MASK-air® longitudinal study
    Sousa-Pinto, B; Schünemann, HJ; Sá-Sousa, A; Vieira, RJ; Amaral, R; Anto, JM; Klimek, L; Czarlewski, W; Mullol, J; Pfaar, O; Bedbrook, A; Brussino, L; Kvedariene, V; Larenas-Linnemann, DE; Okamoto, Y; Ventura, MT; Agache, I; Ansotegui, IJ; Bergmann, KC; Bosnic-Anticevich, S; Canonica, GW; Cardona, V; Carreiro-Martins, P; Casale, T; Cecchi, L; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; Del Giacco, S; Devillier, P; Eklund, P; Fokkens, WJ; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Ispayeva, Z; Jutel, M; Kuna, P; Kaidashev, I; Khaitov, M; Kraxner, H; Laune, D; Lipworth, B; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mösges, R; Niedoszytko, M; Papadopoulos, NG; Patella, V; Nhan, PT; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sheikh, A; Sova, M; Todo-Bom, A; Taborda-Barata, L; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Vandenplas, O; Wallace, D; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonseca, JA; Bousquet, J
    Introduction: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK-air (R), these data have only been analyzed cross-sectionally, without considering the changes of symptoms over time. We analyzed data from MASK-air (R) longitudinally, clustering weeks according to reported rhinitis symptoms. Methods: We analyzed MASK-air (R) data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7days. 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. [GRAPHICS] .
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    Patient-centered digital biomarkers for allergic respiratory diseases and asthma: The ARIA-EAACI approach - ARIA-EAACI Task Force Report
    Bousquet, J; Shamji, MH; Anto, JM; Schünemann, HJ; Canonica, GW; Jutel, M; Del Giacco, S; Zuberbier, T; Pfaar, O; Fonseca, JA; Sousa-Pinto, B; Klimek, L; Czarlewski, W; Bedbrook, A; Amaral, R; Ansotegui, IJ; Bosnic-Anticevich, S; Braido, F; Loureiro, CC; Gemicioglu, B; Haahtela, T; Kulus, M; Kuna, P; Kupczyk, M; Matricardi, PM; Regateiro, FS; Samolinski, B; Sofiev, M; Toppila-Salmi, S; Valiulis, A; Ventura, MT; Barbara, C; Bergmann, KC; Bewick, M; Blain, H; Bonini, M; Boulet, LP; Bourret, R; Brusselle, G; Brussino, L; Buhl, R; Cardona, V; Casale, T; Cecchi, L; Charpin, D; Cherrez-Ojeda, I; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; Devillier, P; Dramburg, S; Fokkens, WJ; Gotua, M; Heffler, E; Ispayeva, Z; Ivancevich, JC; Joos, G; Kaidashev, I; Kraxner, H; Kvedariene, V; Larenas-Linnemann, DE; Laune, D; Lourenço, O; Louis, R; Makela, M; Makris, M; Maurer, M; Melen, E; Micheli, Y; Morais-Almeida, M; Mullol, J; Niedoszytko, M; O'Hehir, R; Okamoto, Y; Olze, H; Papadopoulos, NG; Papi, A; Patella, V; Pétré, B; Pham-Thi, N; Puggioni, F; Quirce, S; Roche, N; Rouadi, PW; Sa-Sousa, A; Sagara, H; Sastre, J; Scichilone, N; Sheikh, A; Sova, M; Ulrik, CS; Taborda-Barata, L; Todo-Bom, A; Torres, MJ; Tsiligianni, I; Usmani, OS; Valovirta, E; Vasankari, T; Vieira, RJ; Wallace, D; Waserman, S; Zidarn, M; Yorgancioglu, A; Zhang, L; Chivato, T; Ollert, M
    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.
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    Next-generation Allergic Rhinitis and Its Impact on Asthma (ARIA) guidelines for allergic rhinitis based on Grading of Recommendations Assessment, Development and Evaluation (GRADE) and real-world evidence
    Bousquet, J; Schünemann, HJ; Togias, A; Bachert, C; Erhola, M; Hellings, PW; Klimek, L; Pfaar, O; Wallace, D; Ansotegui, I; Agache, I; Bedbrook, A; Bergmann, KC; Bewick, M; Bonniaud, P; Bosnic-Anticevich, S; Bossé, I; Bouchard, J; Boulet, LP; Brozek, J; Brusselle, G; Calderon, MA; Canonica, WG; Caraballo, L; Cardona, V; Casale, T; Cecchi, L; Chu, DK; Costa, EM; Cruz, AA; Czarlewski, W; D'Amato, G; Devillier, P; Dykewicz, M; Ebisawa, M; Fauquert, JL; Fokkens, WJ; Fonseca, JA; Fontaine, JF; Gemicioglu, B; van Wijk, RG; Haahtela, T; Halken, S; Ierodiakonou, D; Iinuma, T; Ivancevich, JC; Jutel, M; Kaidashev, I; Khaitov, M; Kalayci, O; Tebbe, JK; Kowalski, ML; Kuna, P; Kvedariene, V; La Grutta, S; Larenas-Linemann, D; Lau, S; Laune, D; Le, L; Lieberman, P; Carlsen, KCL; Lourenço, O; Marien, G; Carreiro-Martins, P; Melén, E; Menditto, E; Neffen, H; Mercier, G; Mosgues, R; Mullol, J; Muraro, A; Namazova, L; Novellino, E; O'Hehir, R; Okamoto, Y; Ohta, K; Park, HS; Panzner, P; Passalacqua, G; Nhan, PT; Price, D; Roberts, G; Roche, N; Rolland, C; Rosario, N; Ryan, D; Samolinski, B; Sanchez-Borges, M; Scadding, GK; Shamji, MH; Sheikh, A; Bom, AMT; Toppila-Salmi, S; Tsiligianni, I; Valentin-Rostan, M; Valiulis, A; Valovirta, E; Ventura, MT; Walker, S; Waserman, S; Yorgancioglu, A; Zuberbier, T
    The selection of pharmacotherapy for patients with allergic rhinitis aims to control the disease and depends on many factors. Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines have considerably improved the treatment of allergic rhinitis. However, there is an increasing trend toward use of real-world evidence to inform clinical practice, especially because randomized controlled trials are often limited with regard to the applicability of results. The Contre les Maladies Chroniques pour un Vieillissement Actif (MACVIA) algorithm has proposed an allergic rhinitis treatment by a consensus group. This simple algorithm can be used to step up or step down allergic rhinitis treatment. Next-generation guidelines for the pharmacologic treatment of allergic rhinitis were developed by using existing GRADE-based guidelines for the disease, real-world evidence provided by mobile technology, and additive studies (allergen chamber studies) to refine the MACVIA algorithm.
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    Concepts for the Development of Person-Centered, Digitally Enabled, Artificial Intelligence-Assisted ARIA Care Pathways (ARIA 2024)
    Bousquet, J; Schünemann, HJ; Sousa-Pinto, B; Zuberbier, T; Togias, A; Samolinski, B; Bedbrook, A; Czarlewski, W; Hofmann-Apitius, M; Litynska, J; Vieira, RJ; Anto, JM; Fonseca, JA; Brozek, J; Bognanni, A; Brussino, L; Canonica, GW; Cherrez-Ojeda, I; Cruz, AA; de las Vecillas, L; Dykewicz, M; Gemicioglu, B; Giovannini, M; Haahtela, T; Jacobs, M; Jacomelli, C; Klimek, L; Kvedariene, V; Larenas-Linnemann, DE; Louis, G; Lourenço, O; Leemann, L; Morais-Almeida, M; Neves, AL; Nadeau, KC; Nowak, A; Palamarchuk, Y; Palkonen, S; Papadopoulos, NG; Parmelli, E; Pereira, AM; Pfaar, O; Regateiro, FS; Savouré, M; Taborda-Barata, L; Toppila-Salmi, SK; Torres, MJ; Valiulis, A; Ventura, MT; Williams, S; Yepes-Nunez, JJ; Yorgancioglu, A; Zhang, L; Zuberbier, J; Latiff, AHA; Abdullah, B; Agache, I; Al-Ahmad, M; Al-Nesf, MA; Al Shaikh, NA; Amaral, R; Ansotegui, IJ; Asllani, J; Balotro-Torres, MC; Bergman, KC; Bernstein, JA; Bindslev-Jensen, C; Blaiss, MS; Bonaglia, C; Bonini, M; Bossé, I; Braido, F; Caballero-Fonseca, F; Camargos, P; Carreiro-Martins, P; Casale, T; Castillo-Vizuete, JA; Cecchi, L; Teixeira, MD; Chang, YS; Loureiro, CC; Christoff, G; Ciprandi, G; Cirule, I; Correia-de-Sousa, J; Costa, EM; Cvetkovski, B; de Vries, G; Del Giacco, S; Devillier, P; Dokic, D; Douagui, H; Durham, SR; Enecilla, ML; Fiocchi, A; Fokkens, WJ; Fontaine, JF; Gawlik, R; Gereda, JE; Gil-Mata, S; Giuliano, AFM; Gotua, M; Gradauskiene, B; Guzman, MA; Hossny, E; Hrubisko, M; Iinuma, T; Irani, C; Ispayeva, Z; Ivancevich, JC; Jartti, T; Jesenák, MS; Julge, K; Jutel, M; Kaidashev, I; Bennoor, KS; Khaltaev, N; Kirenga, B; Kraxner, H; Kull, I; Kulus, M; Kuna, P; Kupczyk, M; Kurchenko, A; La Grutta, S; Lane, S; Miculinic, N; Lee, SM; Tuyet, LLT; 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, CG; Neffen, HE; Nekam, K; Niedoszytko, M; Nunes, E; Nyembue, D; O'Hehir, R; Ollert, M; Ohta, K; Okamoto, Y; Okubo, K; Olze, H; Padukudru, MA; Palomares, O; Pali-Scholl, I; Panzner, P; Palosuo, K; Park, HS; Passalacqua, G; Patella, V; Pawankar, R; Pétré, B; Pitsios, C; Plavec, D; Popov, TA; Puggioni, F; Quirce, S; Raciborski, F; Ramonaité, A; Recto, M; Repka-Ramirez, S; Roberts, G; Robles-Velasco, K; Roche, N; Rodriguez-Gonzalez, M; Romualdez, JA; Rottem, M; Rouadi, PW; Salapatas, M; Sastre, J; Serpa, FS; Sayah, Z; Scichilone, N; Senna, G; Sisul, JC; Solé, D; Soto-Martinez, ME; Sova, M; Sozinova, O; Stevanovic, K; Ulrik, CS; Szylling, A; Tan, FM; Tantilipikorn, P; Todo-Bom, A; Tomic-Spiric, V; Tsaryk, V; Tsiligianni, I; Urrutia-Pereira, M; Rostan, MV; Sofiev, M; Valovirta, E; Van Eerd, M; Van Ganse, E; Vasankari, T; Vichyanond, P; Viegi, G; Wallace, D; Wang, DY; Waserman, S; Wong, G; Worm, M; Yusuf, OM; 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. (c) 2024 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
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    Allergen immunotherapy in MASK-air users in real-life: Results of a Bayesian mixed-effects model
    Sousa-Pinto, B; Azevedo, LF; Sá-Sousa, A; Vieira, RJ; Amaral, R; Klimek, L; Czarlewski, W; Anto, JM; Bedbrook, A; Kvedariene, V; Ventura, MT; Ansotegui, IJ; Bergmann, KC; Brussino, L; Canonica, GW; Cardona, V; Carreiro-Martins, P; Casale, T; Cecchi, L; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; De Feo, G; Devillier, P; Fokkens, WJ; Gaga, M; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Ispayeva, Z; Jutel, M; Kuna, P; Kaidashev, I; Kraxner, H; Larenas-Linnemann, DE; Laune, D; Lipworth, B; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mösges, R; Mullol, J; Odemyr, M; Okamoto, Y; Papadopoulos, NG; Patella, V; Nhân, PT; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sova, M; Todo-Bom, A; Taborda-Barata, L; Tomazic, PV; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Wallace, D; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonseca, JA; Bousquet, J; Pfaar, O
    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.
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    Behavioural patterns in allergic rhinitis medication in Europe: A study using MASK-air(R) real-world data
    Sousa-Pinto, B; Sá-Sousa, A; Vieira, RJ; Amaral, R; Klimek, L; Czarlewski, W; Antó, JM; Pfaar, O; Bedbrook, A; Kvedariene, V; Ventura, MT; Ansotegui, IJ; Bergmann, KC; Brussino, L; Canonica, GW; Cardona, V; Carreiro-Martins, P; Casale, T; Cecchi, L; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; De Feo, G; Devillier, P; Fokkens, WJ; Gaga, M; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Ispayeva, Z; Jutel, M; Kuna, P; Kaidashev, I; Kraxner, H; Larenas-Linnemann, DE; Laune, D; Lipworth, B; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mösges, R; Mullol, J; Odemyr, M; Okamoto, Y; Papadopoulos, NG; Patella, V; Pham-Thi, N; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sova, M; Todo-Bom, A; Taborda-Barata, L; Tomazic, PV; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Vandenplas, O; Wallace, D; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonseca, JA; Bousquet, J
    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(R) 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 H-1-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.
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    Relevance of individual bronchial symptoms for asthma diagnosis and control in patients with rhinitis: A MASK-air study
    Sousa-Pinto, B; Louis, G; Vieira, RJ; Czarlewski, W; Anto, JM; Amaral, R; Sá-Sousa, A; Brussino, L; Canonica, GW; Loureiro, CC; Cruz, AA; Gemicioglu, B; Haahtela, T; Kupczyk, M; Kvedariene, V; Larenas-Linnemann, DE; Pham-Thi, N; Puggioni, F; Regateiro, FS; Romantowski, J; Sastre, J; Scichilone, N; Taborda-Barata, L; Ventura, MT; Agache, I; Bedbrook, A; Benfante, A; Bergmann, KC; Bosnic-Anticevich, S; Bonini, M; Boulet, LP; Brusselle, G; Buhl, R; Cecchi, L; Charpin, D; Costa, EM; Del Giacco, S; Jutel, M; Klimek, L; Kuna, P; Laune, D; Makela, M; Morais-Almeida, M; Nadif, R; Niedoszytko, M; Papadopoulos, NG; Papi, A; Pfaar, O; Rivero-Yeverino, D; Roche, N; Samolinski, B; Shamji, MH; Sheikh, A; Ulrik, CS; Usmani, OS; Valiulis, A; Yorgancioglu, A; Zuberbier, T; Fonseca, JA; Pétré, B; Louis, R; Bousquet, J
    RationaleIt is unclear how each individual asthma symptom is associated with asthma diagnosis or control.ObjectivesTo assess the performance of individual asthma symptoms in the identification of patients with asthma and their association with asthma control.MethodsIn this cross-sectional study, we assessed real-world data using the MASK-air (R) app. We compared the frequency of occurrence of five asthma symptoms (dyspnea, wheezing, chest tightness, fatigue and night symptoms, as assessed by the Control of Allergic Rhinitis and Asthma Test [CARAT] questionnaire) in patients with probable, possible or no current asthma. We calculated the sensitivity, specificity and predictive values of each symptom, and assessed the association between each symptom and asthma control (measured using the e-DASTHMA score). Results were validated in a sample of patients with a physician-established diagnosis of asthma.Measurement and Main ResultsWe included 951 patients (2153 CARAT assessments), with 468 having probable asthma, 166 possible asthma and 317 no evidence of asthma. Wheezing displayed the highest specificity (90.5%) and positive predictive value (90.8%). In patients with probable asthma, dyspnea and chest tightness were more strongly associated with asthma control than other symptoms. Dyspnea was the symptom with the highest sensitivity (76.1%) and the one consistently associated with the control of asthma as assessed by e-DASTHMA. Consistent results were observed when assessing patients with a physician-made diagnosis of asthma.ConclusionsWheezing and chest tightness were the asthma symptoms with the highest specificity for asthma diagnosis, while dyspnea displayed the highest sensitivity and strongest association with asthma control.
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    Cutoff Values of MASK-air Patient-Reported Outcome Measures
    Sousa-Pinto, B; Sa-Sousa, A; Vieira, RJ; Amaral, R; Pereira, AM; Anto, JM; Klimek, L; Czarlewski, W; Mullol, J; Pfaar, O; Bedbrook, A; Brussino, L; Kvedariene, V; Larenas-Linnemann, DE; Okamoto, Y; Ventura, MT; Ansotegui, IJ; Bosnic-Anticevich, S; Canonica, GW; Cardona, V; Cecchi, L; Chivato, T; Cingi, C; Costa, EM; Cruz, AA; Del Giacco, S; Devillier, P; Fokkens, WJ; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Kuna, P; Kaidashev, I; Kraxner, H; Laune, D; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mosges, R; Niedoszytko, M; Papadopoulos, NG; Patella, V; Pham-Thi, N; Regateiro, FS; Reitsma, S; Rouadi, PW; 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, JA; 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 percen-tiles (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 sensitivityanalyses for individual countries and for VAS levels corre-sponding to full allergy control. Finally, we tested the different approaches using MASK-air real-world cross-sectional and lon-gitudinal 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 differenti-ating 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. (c) 2022 American Academy of Allergy, Asthma & Immunology (J Allergy Clin Immunol Pract 2023;11:1281-9)
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    Development and validation of combined symptom-medication scores for allergic rhinitis*
    Sousa-Pinto, B; Azevedo, LF; Jutel, M; Agache, I; Canonica, GW; Czarlewski, W; Papadopoulos, NG; Bergmann, KC; Devillier, P; Laune, D; Klimek, L; Anto, A; Anto, JM; Eklund, P; Almeida, R; Bedbrook, A; Bosnic-Anticevich, S; Brough, HA; Brussino, L; Cardona, V; Casale, T; Cecchi, L; Charpin, D; Chivato, T; Costa, EM; Cruz, AA; Dramburg, S; Durham, SR; De Feo, G; van Wijk, RG; Fokkens, WJ; Gemicioglu, B; Haahtela, T; Illario, M; Ivancevich, JC; Kvedariene, V; Kuna, P; Larenas-Linnemann, DE; Makris, M; Mathieu-Dupas, E; Melén, E; Morais-Almeida, M; Mösges, R; Mullol, J; Nadeau, KC; Nhan, PH; O'Hehir, R; Regateiro, FS; Reitsma, S; Samolinski, B; Sheikh, A; Stellato, C; Todo-Bom, A; Tomazic, PV; Toppila-Salmi, S; Valero, A; Valiulis, A; Ventura, MT; Wallace, D; Waserman, S; Yorgancioglu, A; Vries, G; Eerd, M; Zieglmayer, P; Zuberbier, T; Pfaar, O; Fonseca, JA; Bousquet, J
    Background Validated combined symptom-medication scores (CSMSs) are needed to investigate the effects of allergic rhinitis treatments. This study aimed to use real-life data from the MASK-air(R) app to generate and validate hypothesis- and data-driven CSMSs. Methods We used MASK-air(R) data to assess the concurrent validity, test-retest reliability and responsiveness of one hypothesis-driven CSMS (modified CSMS: mCSMS), one mixed hypothesis- and data-driven score (mixed score), and several data-driven CSMSs. The latter were generated with MASK-air(R) data following cluster analysis and regression models or factor analysis. These CSMSs were compared with scales measuring (i) the impact of rhinitis on work productivity (visual analogue scale [VAS] of work of MASK-air(R), and Work Productivity and Activity Impairment: Allergy Specific [WPAI-AS]), (ii) quality-of-life (EQ-5D VAS) and (iii) control of allergic diseases (Control of Allergic Rhinitis and Asthma Test [CARAT]). Results We assessed 317,176 days of MASK-air(R) use from 17,780 users aged 16-90 years, in 25 countries. The mCSMS and the factor analyses-based CSMSs displayed poorer validity and responsiveness compared to the remaining CSMSs. The latter displayed moderate-to-strong correlations with the tested comparators, high test-retest reliability and moderate-to-large responsiveness. Among data-driven CSMSs, a better performance was observed for cluster analyses-based CSMSs. High accuracy (capacity of discriminating different levels of rhinitis control) was observed for the latter (AUC-ROC = 0.904) and for the mixed CSMS (AUC-ROC = 0.820). Conclusion The mixed CSMS and the cluster-based CSMSs presented medium-high validity, reliability and accuracy, rendering them as candidates for primary endpoints in future rhinitis trials.
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    The Allergic Rhinitis and Its Impact on Asthma (ARIA) Approach of Value-Added Medicines: As-Needed Treatment in Allergic Rhinitis
    Bousquet, J; Toumi, M; Sousa-Pinto, B; Anto, JM; Bedbrook, A; Czarlewski, W; Valiulis, A; Ansotegui, IJ; Bosnic-Anticevich, S; Brussino, L; Canonica, W; Cecchi, L; Cherrez-Ojeda, I; Chivato, T; Costa, EM; Cruz, AA; Del Giacco, S; Fonseca, JA; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Jutel, M; Kaidashev, I; Klimek, L; Kvedariene, V; Kuna, P; Larenas-Linnemann, DE; Lipworth, B; Morais-Almeida, M; Mullol, J; Papadopoulos, NG; Patella, V; Pham-Thi, N; Regateiro, FS; Rouadi, PW; Samolinski, B; Sheikh, A; Taborda-Barata, L; Ventura, MT; 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 fo-cuses on digitally-enabled, patient-centered care pathways. (c) 2022 American Academy of Allergy, Asthma & Immunology (J Allergy Clin Immunol Pract 2022;10:2878-88)
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    Real-world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities
    Sousa-Pinto, B; Anto, A; Berger, M; Dramburg, S; Pfaar, O; Klimek, L; Jutel, M; Czarlewski, W; Bedbrook, A; Valiulis, A; Agache, I; Amaral, R; Ansotegui, IJ; Bastl, K; Berger, U; Bergmann, KC; Bosnic-Anticevich, S; Braido, F; Brussino, L; Cardona, V; Casale, T; Canonica, GW; Cecchi, L; Charpin, D; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; Devillier, P; Durham, SR; Ebisawa, M; Fiocchi, A; Fokkens, WJ; Gemicioglu, B; Gotua, M; Guzmán, MA; Haahtela, T; Ivancevich, JC; Kuna, P; Kaidashev, I; Khaitov, M; Kvedariene, V; Larenas-Linnemann, DE; Lipworth, B; Laune, D; Matricardi, PM; Morais-Almeida, M; Mullol, J; Naclerio, R; Neffen, H; Nekam, K; Niedoszytko, M; Okamoto, Y; Papadopoulos, NG; Park, HS; Passalacqua, G; Patella, V; Pelosi, S; Nhan, PT; Popov, TA; Regateiro, FS; Reitsma, S; Rodriguez-Gonzales, M; Rosario, N; Rouadi, PW; Samolinski, B; Sá-Sousa, A; Sastre, J; Sheikh, A; Ulrik, CS; Taborda-Barata, L; Todo-Bom, A; Tomazic, PV; Toppila-Salmi, S; Tripodi, S; Tsiligianni, I; Valovirta, E; Ventura, MT; Valero, AA; Vieira, RJ; Wallace, D; Waserman, S; Williams, S; Yorgancioglu, A; Zhang, L; Zidarn, M; Zuberbier, J; Olze, H; Antó, JM; Zuberbier, T; Fonseca, JA; Bousquet, J
    Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real-time, real-world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK-air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.
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    Adherence to inhaled corticosteroids and long-acting β2-agonists in asthma: A MASK-air study
    Sousa-Pinto, B; Louis, R; Anto, JM; Amaral, R; Sá-Sousa, A; Czarlewski, W; Brussino, L; Canonica, GW; Loureiro, CC; Cruz, AA; Gemicioglu, B; Haahtela, T; Kupczyk, M; Kvedariene, V; Larenas-Linnemann, DE; Okamoto, Y; Ollert, M; Pfaar, O; Pham-Thi, N; Puggioni, F; Regateiro, FS; Romantowski, J; Sastre, J; Scichilone, N; Taborda-Barata, L; Ventura, MT; Agache, I; Bedbrook, A; Becker, S; Bergmann, KC; Bosnic-Anticevich, S; Bonini, M; Boulet, LP; Brusselle, G; Buhl, R; Cecchi, L; Charpin, D; de Blay, F; Del Giacco, S; Ivancevich, JC; Jutel, M; Klimek, L; Kraxner, H; Kuna, P; Laune, D; Makela, M; Morais-Almeida, M; Nadif, R; Niedoszytko, M; Papadopoulos, NG; Papi, A; Patella, V; Pétré, B; Yeverino, DR; Cordeiro, CR; Roche, N; Rouadi, PW; Samolinski, B; Savouré, M; Shamji, MH; Sheikh, A; Ulrik, CS; Usmani, OS; Valiulis, A; Yorgancioglu, A; Zuberbier, T; Fonseca, JA; Costa, EM; Bousquet, J
    Introduction Adherence to controller medication is a major problem in asthma management, being difficult to assess and tackle. mHealth apps can be used to assess adherence. We aimed to assess the adherence to inhaled corticosteroids+long-acting beta 2-agonists (ICS+LABA) in users of the MASK-air((R)) app, comparing the adherence to ICS+formoterol (ICS+F) with that to ICS+other LABA. Materials and methods We analysed complete weeks of MASK-air((R)) data (2015-2022; 27 countries) from patients with self-reported asthma and ICS+LABA use. We compared patients reporting ICS+F versus ICS+other LABA on adherence levels, symptoms and symptom-medication scores. We built regression models to assess whether adherence to ICS+LABA was associated with asthma control or short-acting beta-agonist (SABA) use. Sensitivity analyses were performed considering the weeks with no more than one missing day. Results In 2598 ICS+LABA users, 621 (23.9%) reported 4824 complete weeks and 866 (33.3%) reported weeks with at most one missing day. Higher adherence (use of medication =80% of weekly days) was observed for ICS+other LABA (75.1%) when compared to ICS+F (59.3%), despite both groups displaying similar asthma control and work productivity. The ICS+other LABA group was associated with more days of SABA use than the ICS+F group (median=71.4% versus 57.1% days). Each additional weekly day of ICS+F use was associated with a 4.1% less risk in weekly SABA use (95%CI=-6.5;-1.6%;p=0.001). For ICS+other LABA, the percentage was 8.2 (95%CI=-11.6;-5.0%;p<0.001). Conclusions In asthma patients adherent to the MASK-air app, adherence to ICS+LABA was high. ICS+F users reported lower adherence but also a lower SABA use and a similar level of control.