Browsing by Author "Usmani O.S."

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    Patient-centered digital biomarkers for allergic respiratory diseases and asthma: The ARIA-EAACI approach – ARIA-EAACI Task Force Report
    (John Wiley and Sons Inc, 2023) Bousquet J.; Shamji M.H.; Anto J.M.; Schünemann H.J.; Canonica G.W.; Jutel M.; Del Giacco S.; Zuberbier T.; Pfaar O.; Fonseca J.A.; Sousa-Pinto B.; Klimek L.; Czarlewski W.; Bedbrook A.; Amaral R.; Ansotegui I.J.; Bosnic-Anticevich S.; Braido F.; Chaves Loureiro C.; Gemicioglu B.; Haahtela T.; Kulus M.; Kuna P.; Kupczyk M.; Matricardi P.; Regateiro F.S.; Samolinski B.; Sofiev M.; Toppila-Salmi S.; Valiulis A.; Ventura M.T.; Barbara C.; Bergmann K.C.; Bewick M.; Blain H.; Bonini M.; Boulet L.-P.; Bourret R.; Brusselle G.; Brussino L.; Buhl R.; Cardona V.; Casale T.; Cecchi L.; Charpin D.; Cherrez-Ojeda I.; Chu D.K.; Cingi C.; Costa E.M.; Cruz A.; Devillier P.; Dramburg S.; Fokkens W.; Gotua M.; Heffler E.; Ispayeva Z.; Ivancevich J.C.; Joos G.; Kaidashev I.; Kraxner H.; Kvedariene V.; Larenas-Linnemann D.E.; Laune D.; Lourenço O.; Louis R.; Makela M.; Makris M.; Maurer M.; Melén E.; Micheli Y.; Morais-Almeida M.; Mullol J.; Niedoszytko M.; O'Hehir R.; Okamoto Y.; Olze H.; Papadopoulos N.G.; Papi A.; Patella V.; Pétré B.; Pham-Thi N.; Puggioni F.; Quirce S.; Roche N.; Rouadi P.; Sá-Sousa A.; Sagara H.; Sastre J.; Scichilone N.; Sheikh A.; Sova M.; Suppli Ulrik C.; Taborda-Barata L.; Todo-Bom A.; Torres M.; Tsiligianni I.; Usmani O.S.; Valovirta E.; Vasankari T.; Vieira R.J.; 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. © 2023 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.
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    Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app
    (Elsevier Espana S.L.U, 2023) Bousquet J.; Sousa-Pinto B.; Anto J.M.; Amaral R.; Brussino L.; Canonica G.W.; Cruz A.A.; Gemicioglu B.; Haahtela T.; Kupczyk M.; Kvedariene V.; Larenas-Linnemann D.E.; Louis R.; Pham-Thi N.; Puggioni F.; Regateiro F.S.; Romantowski J.; Sastre J.; Scichilone N.; Taborda-Barata L.; Ventura M.T.; Agache I.; Bedbrook A.; Bergmann K.C.; Bosnic-Anticevich S.; Bonini M.; Boulet L.-P.; Brusselle G.; Buhl R.; Cecchi L.; Charpin D.; Chaves-Loureiro C.; Czarlewski W.; de Blay F.; Devillier P.; Joos G.; Jutel M.; Klimek L.; Kuna P.; Laune D.; Pech J.L.; Makela M.; Morais-Almeida M.; Nadif R.; Niedoszytko M.; Ohta K.; Papadopoulos N.G.; Papi A.; Yeverino D.R.; Roche N.; Sá-Sousa A.; Samolinski B.; Shamji M.H.; Sheikh A.; Suppli Ulrik C.; Usmani O.S.; Valiulis A.; Vandenplas O.; Yorgancioglu A.; Zuberbier T.; Fonseca J.A.
    Background: The self-reporting of asthma frequently leads to patient misidentification in epidemiological studies. Strategies combining the triangulation of data sources may help to improve the identification of people with asthma. We aimed to combine information from the self-reporting of asthma, medication use and symptoms to identify asthma patterns in the users of an mHealth app. Methods: We studied MASK-air® users who reported their daily asthma symptoms (assessed by a 0-100 visual analogue scale – “VAS Asthma”) at least three times (either in three different months or in any period). K-means cluster analysis methods were applied to identify asthma patterns based on: (i) whether the user self-reported asthma; (ii) whether the user reported asthma medication use and (iii) VAS asthma. Clusters were compared by the number of medications used, VAS asthma levels and Control of Asthma and Allergic Rhinitis Test (CARAT) levels. Findings: We assessed a total of 8,075 MASK-air® users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncontrolled asthma despite treatment (11.9-16.1% of MASK-air® users); (ii) treated and partly-controlled asthma (6.3-9.7%); (iii) treated and controlled asthma (4.6-5.5%); (iv) untreated uncontrolled asthma (18.2-20.5%); (v) untreated partly-controlled asthma (10.1-10.7%); (vi) untreated controlled asthma (6.7-8.5%) and (vii) no evidence of asthma (33.0-40.2%). This classification was validated in a study of 192 patients enrolled by physicians. Interpretation: We identified seven profiles based on the probability of having asthma and on its level of control. mHealth tools are hypothesis-generating and complement classical epidemiological approaches in identifying patients with asthma. © 2022 Sociedade Portuguesa de Pneumologia
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    Adherence to inhaled corticosteroids and long-acting β2-agonists in asthma: A MASK-air study
    (Elsevier Espana S.L.U, 2023) Sousa-Pinto B.; Louis R.; Anto J.M.; Amaral R.; Sá-Sousa A.; Czarlewski W.; Brussino L.; Canonica G.W.; Chaves Loureiro C.; Cruz A.A.; Gemicioglu B.; Haahtela T.; Kupczyk M.; Kvedariene V.; Larenas-Linnemann D.E.; Okamoto Y.; Ollert M.; Pfaar O.; Pham-Thi N.; Puggioni F.; Regateiro F.S.; Romantowski J.; Sastre J.; Scichilone N.; Taborda-Barata L.; Ventura M.T.; Agache I.; Bedbrook A.; Becker S.; Bergmann K.C.; Bosnic-Anticevich S.; Bonini M.; Boulet L.-P.; Brusselle G.; Buhl R.; Cecchi L.; Charpin D.; de Blay F.; Del Giacco S.; Ivancevich J.C.; Jutel M.; Klimek L.; Kraxner H.; Kuna P.; Laune D.; Makela M.; Morais-Almeida M.; Nadif R.; Niedoszytko M.; Papadopoulos N.G.; Papi A.; Patella V.; Pétré B.; Rivero Yeverino D.; Robalo Cordeiro C.; Roche N.; Rouadi P.W.; Samolinski B.; Savouré M.; Shamji M.H.; Sheikh A.; Suppli Ulrik C.; Usmani O.S.; Valiulis A.; Yorgancioglu A.; Zuberbier T.; Fonseca J.A.; Costa E.M.; 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 β2-agonists (ICS+LABA) in users of the MASK-air® 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® 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. © 2023 Sociedade Portuguesa de Pneumologia
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    Development and validation of an electronic daily control score for asthma (e-DASTHMA): a real-world direct patient data study
    (Elsevier Ltd, 2023) Sousa-Pinto B.; Jácome C.; Pereira A.M.; Regateiro F.S.; Almeida R.; Czarlewski W.; Kulus M.; Shamji M.H.; Boulet L.-P.; Bonini M.; Brussino L.; Canonica G.W.; Cruz A.A.; Gemicioglu B.; Haahtela T.; Kupczyk M.; Kvedariene V.; Larenas-Linnemann D.; Louis R.; Niedoszytko M.; Pham-Thi N.; Puggioni F.; Romantowski J.; Sastre J.; Scichilone N.; Taborda-Barata L.; Ventura M.T.; Vieira R.J.; Agache I.; Bedbrook A.; Bergmann K.C.; Amaral R.; Azevedo L.F.; Bosnic-Anticevich S.; Brusselle G.; Buhl R.; Cecchi L.; Charpin D.; Loureiro C.C.; de Blay F.; Del Giacco S.; Devillier P.; Jassem E.; Joos G.; Jutel M.; Klimek L.; Kuna P.; Laune D.; Luna Pech J.; Makela M.; Morais-Almeida M.; Nadif R.; Neffen H.E.; Ohta K.; Papadopoulos N.G.; Papi A.; Pétré B.; Pfaar O.; Yeverino D.R.; Cordeiro C.R.; Roche N.; Sá-Sousa A.; Samolinski B.; Sheikh A.; Ulrik C.S.; Usmani O.S.; Valiulis A.; Vandenplas O.; Vieira-Marques P.; Yorgancioglu A.; Zuberbier T.; Anto J.M.; Fonseca J.A.; Bousquet J.
    Background: Validated questionnaires are used to assess asthma control over the past 1–4 weeks from reporting. However, they do not adequately capture asthma control in patients with fluctuating symptoms. Using the Mobile Airways Sentinel Network for airway diseases (MASK-air) app, we developed and validated an electronic daily asthma control score (e-DASTHMA). Methods: We used MASK-air data (freely available to users in 27 countries) to develop and assess different daily control scores for asthma. Data-driven control scores were developed based on asthma symptoms reported by a visual analogue scale (VAS) and self-reported asthma medication use. We included the daily monitoring data from all MASK-air users aged 16–90 years (or older than 13 years to 90 years in countries with a lower age of digital consent) who had used the app in at least 3 different calendar months and had reported at least 1 day of asthma medication use. For each score, we assessed construct validity, test–retest reliability, responsiveness, and accuracy. We used VASs on dyspnoea and work disturbance, EQ-5D-VAS, Control of Allergic Rhinitis and Asthma Test (CARAT), CARAT asthma, and Work Productivity and Activity Impairment: Allergy Specific (WPAI:AS) questionnaires as comparators. We performed an internal validation using MASK-air data from Jan 1 to Oct 12, 2022, and an external validation using a cohort of patients with physician-diagnosed asthma (the INSPIRERS cohort) who had had their diagnosis and control (Global Initiative for Asthma [GINA] classification) of asthma ascertained by a physician. Findings: We studied 135 635 days of MASK-air data from 1662 users from May 21, 2015, to Dec 31, 2021. The scores were strongly correlated with VAS dyspnoea (Spearman correlation coefficient range 0·68–0·82) and moderately correlated with work comparators and quality-of-life-related comparators (for WPAI:AS work, we observed Spearman correlation coefficients of 0·59–0·68). They also displayed high test–retest reliability (intraclass correlation coefficients range 0·79–0·95) and moderate-to-high responsiveness (correlation coefficient range 0·69–0·79; effect size measures range 0·57–0·99 in the comparison with VAS dyspnoea). The best-performing score displayed a strong correlation with the effect of asthma on work and school activities in the INSPIRERS cohort (Spearman correlation coefficients 0·70; 95% CI 0·61–0·78) and good accuracy for the identification of patients with uncontrolled or partly controlled asthma according to GINA (area under the receiver operating curve 0·73; 95% CI 0·68–0·78). Interpretation: e-DASTHMA is a good tool for the daily assessment of asthma control. This tool can be used as an endpoint in clinical trials as well as in clinical practice to assess fluctuations in asthma control and guide treatment optimisation. Funding: None. © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license
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    Ensuring availability of respiratory medicines in times of European drug shortages
    (European Respiratory Society, 2024) van Boven J.F.M.; Yorgancioglu A.; Roche N.; Usmani O.S.
    [No abstract available]
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    Relevance of individual bronchial symptoms for asthma diagnosis and control in patients with rhinitis: A MASK-air study
    (John Wiley and Sons Inc, 2024) Sousa-Pinto B.; Louis G.; Vieira R.J.; Czarlewski W.; Anto J.M.; Amaral R.; Sá-Sousa A.; Brussino L.; Canonica G.W.; Loureiro C.C.; Cruz A.A.; Gemicioglu B.; Haahtela T.; Kupczyk M.; Kvedariene V.; Larenas-Linnemann D.E.; Pham-Thi N.; Puggioni F.; Regateiro F.S.; Romantowski J.; Sastre J.; Scichilone N.; Taborda-Barata L.; Ventura M.T.; Agache I.; Bedbrook A.; Benfante A.; Bergmann K.C.; Bosnic-Anticevich S.; Bonini M.; Boulet L.-P.; Brusselle G.; Buhl R.; Cecchi L.; Charpin D.; Costa E.M.; Del Giacco S.; Jutel M.; Klimek L.; Kuna P.; Laune D.; Makela M.; Morais-Almeida M.; Nadif R.; Niedoszytko M.; Papadopoulos N.G.; Papi A.; Pfaar O.; Rivero-Yeverino D.; Roche N.; Samolinski B.; Shamji M.H.; Sheikh A.; Ulrik C.S.; Usmani O.S.; Valiulis A.; Yorgancioglu A.; Zuberbier T.; Fonseca J.A.; Pétré B.; Louis R.; Bousquet J.
    Rationale: It is unclear how each individual asthma symptom is associated with asthma diagnosis or control. Objectives: To assess the performance of individual asthma symptoms in the identification of patients with asthma and their association with asthma control. Methods: In this cross-sectional study, we assessed real-world data using the MASK-air® 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 Results: We 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. Conclusions: Wheezing 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. © 2024 The Authors. Clinical and Translational Allergy published by John Wiley & Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology.
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    Adherence to Treatment in Allergic Rhinitis During the Pollen Season in Europe: A MASK-air Study
    (John Wiley and Sons Inc, 2025) Sousa-Pinto B.; Costa E.M.; Vieira R.J.; Klimek L.; Czarlewski W.; Pfaar O.; Bedbrook A.; Amaral R.; Brussino L.; Kvedariene V.; Larenas-Linnemann D.E.; Iinuma T.; Pham-Thi N.; Regateiro F.S.; Taborda-Barata L.; Ventura M.T.; Ansotegui I.J.; Bergmann K.C.; Canonica G.W.; Cardona V.; Cecchi L.; Cherrez-Ojeda I.; Cingi C.; Cruz A.A.; Del Giacco S.; Devillier P.; Fokkens W.J.; Gemicioglu B.; Haahtela T.; Ivancevich J.C.; Kuna P.; Kraxner H.; Laune D.; Louis R.; Makris M.; Morais-Almeida M.; Mösges R.; Niedoszytko M.; Papadopoulos N.G.; Patella V.; Pereira A.M.; Reitsma S.; Robles-Velasco K.; Rouadi P.W.; Samolinski B.; Sova M.; Toppila-Salmi S.K.; Sastre J.; Valiulis A.; Yorgancioglu A.; Zidarn M.; Zuberbier T.; Fonseca J.A.; Bousquet J.; Anto J.M.; Kupczyk M.; Kulus M.; Roche N.; Scichilone N.; Almeida R.; Bosnic-Anticevich S.; Braido F.; Loureiro C.C.; de Vries G.; Giuliano A.F.M.; Jácome C.; Kaidashev I.; Louis G.; Lourenço O.; Makela M.; Maurer M.; Mullol J.; Nadif R.; O’Hehir R.; Okamoto Y.; Ollert M.; Olze H.; Pétré B.; Puggioni F.; Romantowski J.; Rivero-Yeverino D.; Rodriguez-Gonzalez M.; Sá-Sousa A.; Savouré M.; Serpa F.S.; Shamji M.H.; Sheikh A.; Ulrik C.S.; Sofiev M.; Sperl A.; Todo-Bom A.; Tsiligianni I.; Valovirta E.; van Eerd M.; Blain H.; Boulet L.-P.; Brusselle G.; Buhl R.; Charpin D.; Casale T.; Chivato T.; Correia-de-Sousa J.; Corrigan C.; de Blay F.; Dykewicz M.; Fiocchi A.; Giovannini M.; Jassem E.; Jutel M.; Keil T.; La Grutta S.; Lipworth B.; Papi A.; Pépin J.-L.; Quirce S.; Cordeiro C.R.; Torres M.J.; Usmani O.S.; Bonini M.; Gradauskiene B.; Brightling C.
    Background: Adherence to rhinitis treatment has been insufficiently assessed. We aimed to use data from the MASK-air mHealth app to assess adherence to oral antihistamines (OAH), intra-nasal corticosteroids (INCS) or azelastine-fluticasone in patients with allergic rhinitis. Methods: We included regular European MASK-air users with self-reported allergic rhinitis and reporting at least 1 day of OAH, INCS or azelastine-fluticasone. We assessed weeks during which patients answered the MASK-air questionnaire on all days. We restricted our analyses to data provided between January and June, to encompass the pollen seasons across the different assessed countries. We analysed symptoms using visual analogue scales (VASs) and the combined symptom-medication score (CSMS), performing stratified analyses by weekly adherence levels. Medication adherence was computed as the proportion of days in which patients reported rhinitis medication use. Sensitivity analyses were performed considering all weeks with at most 1 day of missing data and all months with at most 4 days of missing data. Results: We assessed 8212 complete weeks (1361 users). Adherence (use of medication > 80% days) to specific drug classes ranged from 31.7% weeks for azelastine-fluticasone to 38.5% weeks for OAH. Similar adherence to rhinitis medication was found in users with or without self-reported asthma, except for INCS (better adherence in asthma patients). VAS and CSMS levels increased from no adherence to full adherence, except for INCS. A higher proportion of days with uncontrolled symptoms was observed in weeks with higher adherence. In full adherence weeks, 41.2% days reported rhinitis co-medication. The sensitivity analyses displayed similar results. Conclusions: A high adherence was found in patients reporting regular use of MASK-air. Different adherence patterns were found for INCS compared to OAH or azelastine-fluticasone that are likely to impact guidelines. © 2025 The Author(s). Clinical & Experimental Allergy published by John Wiley & Sons Ltd.