Browsing by Author "Sa-Sousa, A"

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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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    Digitally-enabled, patient-centred care in rhinitis and asthma multimorbidity: The ARIA-MASK-air® approach
    Bousquet, J; Anto, JM; Sousa-Pinto, B; Czarlewski, W; Bedbrook, A; Haahtela, T; Klimek, L; Pfaar, O; Kuna, P; Kupczyk, M; Regateiro, FS; Samolinski, B; Valiulis, A; Yorgancioglu, A; Arnavielhe, S; Basagaña, X; Bergmann, KC; Bosnic-Anticevich, S; Brussino, L; Canonica, GW; Cardona, V; Cecchi, L; Chaves-Loureiro, C; Costa, E; Cruz, AA; Gemicioglu, B; Fokkens, WJ; Ivancevich, JC; Kraxner, H; Kvedariene, V; Larenas-Linnemann, DE; Laune, D; Louis, R; Makris, M; Maurer, M; Melén, E; Micheli, Y; Morais-Almeida, M; Mullol, J; Niedoszytko, M; Okamoto, Y; Papadopoulos, NG; Patella, V; Pham-Thi, N; Rouadi, PW; Sastre, J; Scichilone, N; Sheikh, A; Sofiev, M; Taborda-Barata, L; Toppila-Salmi, S; Tsiligianni, I; Valovirta, E; Ventura, MT; Vieira, RJ; Zidarn, M; Amaral, R; Ansotegui, IJ; Bédard, A; Benveniste, S; Bewick, M; Bindslev-Jensen, C; Blain, H; Bonini, M; Bourret, R; Braido, F; Carreiro-Martins, P; Charpin, D; Cherrez-Ojeda, I; Chivato, T; Chu, DK; Cingi, C; Del Giacco, S; de Blay, F; Devillier, P; De Vries, G; Doulaptsi, M; Doyen, V; Dray, G; Fontaine, JF; Gomez, RM; Hagemann, J; Heffler, E; Hofmann, M; Jassem, E; Jutel, M; Keil, T; Kritikos, V; Kull, I; Kulus, M; Lourenço, O; Mathieu-Dupas, E; Menditto, E; Mösges, R; Murray, R; Nadif, R; Neffen, H; Nicola, S; O'Hehir, R; Olze, H; Palamarchuk, Y; Pepin, JL; Pétré, B; Picard, R; Pitsios, C; Puggioni, F; Quirce, S; Raciborski, F; Reitsma, S; Roche, N; Rodriguez-Gonzalez, M; Romantowski, J; Sa-Sousa, A; Serpa, FS; Savouré, M; Shamji, MH; Sova, M; Sperl, A; Stellato, C; Todo-Bom, A; Tomazic, PV; Vandenplas, O; Van Eerd, M; Vasankari, T; Viart, F; Waserman, S; Fonseca, JA; Zuberbier, T
    MASK-air((R)), a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK-air((R)) is a Good Practice of DG Sante on digitally-enabled, patient-centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co-operation and Development). MASK-air((R)) data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK-air((R)) data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as-needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom-medication score (ARIA-EAACI-CSMS) has been validated for clinical practice and trials. The implications of the novel MASK-air((R)) results should lead to change management in rhinitis and asthma.
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    Academic Productivity of Young People With Allergic Rhinitis: A MASK-air Study
    Vieira, RJ; Pham-Thi, N; Anto, JM; Czarlewski, W; Sa-Sousa, A; Amaral, R; Bedbrook, A; Bosnic-Anticevich, S; Brussino, L; Canonica, GW; Cecchi, L; Cruz, AA; Fokkens, WJ; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Klimek, L; Kuna, P; Kvedariene, V; Larenas-Linnemann, D; Morais-Almeida, M; Mullol, J; Niedoszytko, M; Okamoto, Y; Papadopoulos, NG; Patella, V; Pfaar, O; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sheikh, A; Taborda-Barata, L; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Ventura, MT; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonesca, JA; 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 [WPAIDCIQ: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. (C) 2022 American Academy of Allergy, Asthma & Immunology
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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)