Adherence to inhaled corticosteroids and long-acting ß2-agonists in asthma: A MASK-air study.

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 ß2-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.

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.

Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app.

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.

Home administration of biological treatment in severe asthma in real-life experience: impact on asthma control and quality of life.

Summary: Introduction. Several biological agents for the treatment of severe asthma have been approved for self-administration on an outpatient basis in the last years. However, data on the impact of home administration in outcomes such as asthma control and quality of life in real-life settings are sparse. Being this knowledge crucial for clinical practice, this study aimed to assess asthma control and quality of life in patients who transitioned from day hospital administration of biological therapy to home administration. Methods. A single-center prospective analysis of 33 patients treated with biologics for severe asthma, who switched from hospital to home treatment was performed. Asthma Control Test (ACT), Control of Allergic Rhinitis and Asthma Test (CARAT), Asthma Life Quality (ALQ) and the number of exacerbations were assessed 3 months before and 3 and 6 months after of home-use. Results. ACT and CARAT did not show statistical differences comparing to the baseline values (21.8 ± 2.7 and 23.8 ± 5.5) within 3 months (22.1 ± 2.4, p = 0.609; 23.2 ± 5.3, p = 0.572) or 6 months (23.4 ± 0.9, p = 0.553; 23.7 ± 6.2, p = 0.149) of home administration. Also, ALQ score did not show meaningful variations between baseline (9.5 ± 3.2) and after 3 months (11.2 ± 4.4, p = 0.275) and 6 months (10.3 ± 3.8, p = 0.209) of home-use. Regarding asthma exacerbations, we did not record a significant difference comparing to the baseline values of 3 months/patient exacerbations (0.2 ± 0.4) and after 3 months (0.2 ± 0.5, p = 0.786) or 6 months (0.2 ± 0.4, p = 1.000) of change in modality treatment. There was no cases of anaphylaxis or other serious adverse effects in those patients treated at home. Conclusions. Transition of day hospital administration of biologic treatment for severe asthma to home administration did not lead to any deterioration of asthma control or quality of life. Our results emphasized the efficacy and safety of home administration of biologic treatment and provide support on changing the paradigm of the administration of biological treatment in severe asthma.

ConectAR: Collaborative network of patients with asthma and carers actively involved in health research: a protocol for patient and public involvement.

Summary: Background. Patients and Public Involvement in every stage of the patient-centered health research cycle is the key to the development of innovative solutions with an impact on patients' care. Methods. This protocol describes the development of ConectAR, a network to promote the involvement of patients with asthma and their carers in the health research cycle. Results. This protocol comprehends 4 tasks: 1) define the mission, vision, governance and activities of the network through focus groups; 2) establish the communication strategy and tools; 3) test the feasibility of the network in a Delphi study on the research priorities for asthma in Portugal; 4) coordination and dissemination activities. Conclusions. This network will improve research by ensuring that patients and carers have an active role in the co-creation of impactful solutions for asthma.

Paradoxical reaction of blood pressure on sleep apnoea patients treated with Positive Airway Pressure.

INTRODUCTION: Obstructive Sleep Apnoea Syndrome (OSAS) patients may develop hypertension and Positive Airway Pressure (PAP) is an effective treatment in blood pressure (BP) control. OBJECTIVES: Analyse a hypertensive OSAS population with unexpected BP rise after PAP usage and verify correlations between BP rise, either with OSAS severity index or nocturnal ventilatory support compliance. METHODS: Descriptive, retrospective analysis of 30 patients with PAP treated OSA, for one year, on average, and with previous controlled hypertension, who developed a rise in BP, defined as augmentation of > 5 mmHg in systolic (SBP) and/or diastolic BP (DBP), after PAP usage. Co-relational analysis of BP increase, with OSAS severity indexes and therapy compliance, using Pearson coefficient. RESULTS: Of 508 consecutive patients followed in our Department, treated with nocturnal ventilatory support, 30 evolved with BP rise after initiating treatment (age 58 ± 10.8 years; Apnoea-Hypopnoea Index [AHI], 46.1 ± 18.68). After PAP usage, mean blood pressure (MBP), Systolic BP (SBP) and Diastolic BP (DBP) variation was 16 ± 15 mmHg, 20 ± 25 mmHg and 6 ± 19.4 mmHg, respectively. No patient showed significant BMI increase. Epworth Sleepiness Scale (ESS) value decreased 8.9 ± 5.48 points. MBP, SBP and DBP variations were not correlated with P90/P95, residual AHI, leaks or PAP compliance. CONCLUSIONS: No specific characteristics were identified in the group who developed a rise in BP with PAP usage. No correlations were found between rises in BP and OSAS severity indexes or PAP compliance. Neither BMI nor variation in wakefulness status explained the rise in BP. Studies relate polymorphisms of ß1-adrenoreceptors with different BP responses to ventilatory support. More studies are needed to clarify the cause of this paradoxical response.