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Background: Although estimates of suboptimal adherence to oral corticosteroids in asthma range from 30% to 50%, no ideal method for measurement exists; the impact of poor adherence in severe asthma is likely to be particularly high. Research Questions: What is the prevalence of suboptimal adherence detected by self-reporting and direct measures? Is suboptimal adherence associated with disease activity? Study Design and Methods: Data were included from individuals with severe asthma taking part in the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study and prescribed daily oral corticosteroids. Participants completed the Medication Adherence Report Scale, a five-item questionnaire used to grade adherence on a scale from 1 to 5, and provided a urine sample for analysis of prednisolone and metabolites by liquid chromatography-mass spectrometry. Results: Data from 166 participants were included in this study: mean (SD) age, 54.2 (± 11.9) years; FEV 1, 65.1% (± 20.5%) predicted; female, 58%; 37% completing the Medication Adherence Report Scale reported suboptimal adherence; and 43% with urinary corticosteroid data did not have detectable prednisolone or metabolites in their urine. Good adherence by both methods was detected in 49 of the 142 (35%) of participants in whom both methods were performed; adherence detection did not match between methods in 53%. Self-reported high adherers had better asthma control and quality of life, whereas directly measured high adherers had lower blood eosinophil levels. Interpretation: Low adherence is a common problem in severe asthma, whether measured directly or self-reported. We report poor agreement between the two methods, suggesting some disassociation between self-assessment of medication adherence and regular oral corticosteroid use, which suggests that each approach may provide complementary information in clinical practice.

Purpose The coronavirus disease 2019 (COVID-19) pandemic resulted in accelerated market access to remdesivir worldwide. Therefore, data about complications experienced during use of the drug are limited. This is the first published case series (1 case report exists) to describe remdesivir infiltration in 3 patients with COVID-19. Summary In the first case, a 91-year-old woman experienced remdesivir infiltration resulting in edema, hematoma at the area of infiltration; on palpation, the affected area felt cooler than the surrounding areas. Swelling was still present after 6 weeks. In the second case, remdesivir infiltration occurred in a 72-year-old male, resulting in edema, hematoma, and pain at the area of infiltration. The hematoma lasted for 7 days. The third case concerned a 67-year-old woman, in whom remdesivir infiltration led to edema and a small hematoma. The hematoma regressed to a negligible size within 3 days. However, a week after infiltration, redness had reappeared. In 2 cases, the patient was immediately treated with hyaluronidase injections, but no specific treatments were provided in the other case. Conclusion Based on the product information provided by remdesivir’s manufacturer, we believe symptoms and signs observed in the 3 cases may have resulted from the low pH (~4) of the nonbuffered remdesivir solution, although the patients were not formally assessed for caustic injury. Previous experience with other noncytotoxic medications suggests that infusion-specific factors (eg, volume of leaked fluid) and patient-specific factors (eg, advanced age) may have a role in the outcome of remdesivir infiltration. The possibility of symptoms caused by cyclodextrins in the formulation or by intrinsic toxicity of remdesivir warrants exploration.

Background Asthma prevalence is 339 million globally. ‘Severe asthma’ (SA) comprises subjects with uncontrolled asthma despite proper management. Objectives To compare asthma from diverse ethnicities and environments. Methods A cross-sectional analysis of two adult cohorts, a Brazilian (ProAR) and a European (U-BIOPRED). U-BIOPRED comprised of 311 non-smoking with Severe Asthma (SAn), 110 smokers or ex-smokers with SA (SAs) and 88 mild to moderate asthmatics (MMA) while ProAR included 544 SA and 452 MMA. Although these projects were independent, there were similarities in objectives and methodology, with ProAR adopting operating procedures of U-BIOPRED. Results Among SA subjects, age, weight, proportion of former smokers and FEV1 pre-bronchodilator were similar. The proportion of SA with a positive skin prick tests (SPT) to aeroallergens, the scores of sino-nasal symptoms and quality of life were comparable. In addition, blood eosinophil counts (EOS) and the % of subjects with EOS > 300 cells/μl were not different. The Europeans with SA however, were more severe with a greater proportion of continuous oral corticosteroids (OCS), worse symptoms and more frequent exacerbations. FEV1/FVC pre- and post-bronchodilator were lower among the Europeans. The MMA cohorts were less comparable in control and treatment, but similar in the proportion of allergic rhinitis, gastroesophageal reflux disease and EOS >3%. Conclusions ProAR and U-BIOPRED cohorts, with varying severity, ethnicity and environment have similarities, which provide the basis for global external validation of asthma phenotypes. This should stimulate collaboration between asthma consortia with the aim of understanding SA, which will lead to better management.

Background: Adult-onset severe asthma is characterized by highly symptomatic disease despite high-intensity asthma treatments. Understanding of the underlying pathways of this heterogeneous disease is needed for the development of targeted treatments. Gene set variation analysis is a statistical technique used to identify gene profiles in heterogeneous samples. Objective: We sought to identify gene profiles associated with adult-onset severe asthma. Methods: This was a cross-sectional, observational study in which adult patients with adult-onset of asthma (defined as starting at age ≥18 years) as compared with childhood-onset severe asthma (

Background: Identification of treatable traits (TTs) permits a precision medicine strategy for chronic airways diseases that is label free and based on the identification of genetic, phenotypic or psychosocial characteristics for which there are evidence based therapeutic choices. We have previously shown that TTs are common in severe asthma. It remains unknown whether groups of patients with similar patterns of TTs exist. Methods: We conducted a prospective observational cohort study of adults with severe asthma as part of the U-BIOPRED project. 23 TTs were identified across 3 categories (pulmonary, extra-pulmonary and behavioural) and were included in the cluster analysis as dichotomous variables. Cluster identification was conducted using two-step cluster analysis. We explored the differences in clinical characteristics between the clusters using ANOVA. Results: We identified 6 clusters: i) “classic asthma” (high airways reversibility, high eosinophils); ii) pulmonary TT high (high prevalence of multiple pulmonary TTs); iii) steroid insensitive (high eosinophils, despite good medication adherence); iv) reflux and cough (high reflux, high cough); v) TT high (high prevalence of TTs across all categories), and vi) TT Low (low prevalence TT across categories). There were statistically significant and clinically relevant differences in exacerbation rates, asthma control and quality of life between clusters. Conclusions: We have applied a new approach for the characterisation of asthma, based on the identification of TTs, which we believe should be considered in treatment strategies. The association of trait profiles with clinically important endpoints merits further investigation.

BACKGROUND: Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalised management approach can be provided.OBJECTIVES: We stratified patients with moderate-to-severe asthma based on clinico-physiological parameters and performed an -omics analysis of sputum.METHODS: Partition-around-medoid clustering was applied to a training set of 266 asthma participants from the European U-BIOPRED adult cohort using 8 pre-specified clinic-physiological variables. This was repeated in a separate validation set of 152 asthmatics. The clusters were compared based on sputum proteomic and transcriptomic data.RESULTS: Four reproducible and stable clusters of asthmatics were identified. The training set cluster T1 consists of well-controlled moderate-to-severe asthmatics, while cluster T2 is a group of late-onset severe asthmatics with history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of non-smokers. Cluster T4 is predominantly composed of obese female uncontrolled severe asthmatics with increased exacerbations, but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters, T2, T3 and T4, had higher sputum eosinophilia than T1 with no differences in sputum neutrophil counts, exhaled nitric oxide and serum IgE levels.CONCLUSION: Clustering based on clinico-physiological parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.CLINICAL IMPLICATIONS: The definition of four distinct clusters of asthma linked to different pathobiological pathways provides a better template for the phenotyping and personalised treatment of severe asthma, where high unmet needs remain.CAPSULE SUMMARY: Unsupervised clustering of asthma on clinical features alone has led to the definition of four phenotypes. Sputum 'omics' analysis has revealed different biological pathways pointing towards potential new treatments.

Rationale: Unbiased discovery of gene signatures in sputum in the U-BIOPRED cohort revealed a possible role for mast cell gene expression in adult-onset severe asthma [Hekking et al , ERS 2015]. Therefore, we aimed to investigate whether enrichment scores of a mast cell gene signature (ESM) associate with phenotypic characteristics of adult-onset severe asthma. Methods: Patients with adult-onset severe asthma (onset ≥18yr) were selected from the U-BIOPRED cohort. ESM were calculated with Gene Set Variation Analysis (GSVA) based on expression of a set of 6 genes associated with mast cells. Clinical and inflammatory characteristics were compared between patients with high and low ESM (Fig.), using student t-tests, chi-square or Mann-Whitney U tests. Results: Patients with high ESM (n=33) were significantly older when asthma was first diagnosed (mean±SD; 43.2±14.3 vs 34.2±9.7 yr; P=0.02) and had higher % eosinophils in blood (median(IQR); 20.7(4.5-42.0) vs 1.2 (0.2-2.5); P e.g. age, gender and BMI) were not significantly different between the groups. Conclusion: These results reveal two distinct subphenotypes of adult-onset severe asthma based on high and low mast cell gene signature enrichment. Their association with age-of-onset and eosinophilia may have implications for clinical management and identification of endotypes. U-BIOPRED is funded by IMI (grant 115010)

Rationale: Severe asthma is a heterogeneous clinical condition including various pathophysiological pathways. Metabolomics of exhaled air is associated with airways inflammation in patients with asthma and COPD. Aim: To reveal severe asthma phenotypes by unbiased cluster analysis based on metabolomic fingerprints from exhaled breath by gas-chromatography/mass-spectrometry (GCMS) and to link results to electronic nose (eNose) data. Methods: This was a cross-sectional analysis in the U-BIOPRED cohort. Severe asthma was defined by IMI-criteria [Bel Thorax 2011]. Exhaled volatile organic compounds (VOCs) were trapped on two adsorption tubes per subject: one for GCMS and one for the U-BIOPRED eNose platform (Owlstone Lonestar, Cyranose C320, Comon Invent, Tor Vergata TEN). Data cleaning on both omic datasets included: normalization and data reduction by principal component analysis (PCA). Statistical analysis of GCMS data was performed using Ward clustering, followed by Similarity Profile Analysis. The between-cluster comparison of baseline variables and eNose PCs was done by ANOVA, Kruskal-Wallis or χ2 tests. Results: Shared GCMS-eNose data were available for 35 patients (age 50±15yr, 45% male, 46% (ex-)smokers). Four clusters of GCMS data were delineated, that differed significantly regarding: SNOT questionnaire (p=0.02), smoking pack years (p=0.04), sputum neutrophils (p=0.04), serum periostin (p=0.03) and 7 out of 12 eNose PCs (p Conclusions: Unbiased fingerprinting of exhaled air by GCMS provides clusters of severe asthma patients that differ regarding clinical and inflammatory parameters. Breath analysis by eNose technology is resembling, but not identical to GCMS in severe asthma.

For more than a century, clinicians have attempted to subdivide asthma into different phenotypes based on triggers that cause asthma attacks, the course of the disease, or the prognosis. The first phenotypes that were described included allergic asthma, intrinsic or nonallergic asthma, infectious asthma, and aspirin-exacerbated asthma. These phenotypes are being reviewed elsewhere in this issue of the journal. The present article focuses on developing and emerging clinical asthma phenotypes. First, asthma phenotypes that are associated with environmental exposures (occupational agents, cigarette smoke, air pollution, cold dry air); second, asthma phenotypes that are associated with specific symptoms or clinical characteristics (cough, obesity, adult onset of disease); and third, asthma phenotypes that are based on biomarkers. This latter approach is the most promising because it attempts to identify asthma phenotypes with different underlying mechanisms so that therapies can be better targeted toward disease-specific features and disease outcomes can be improved.