Cluster Analyses From the Real-World NOVELTY Study: Six Clusters Across the Asthma-COPD Spectrum.

BACKGROUND: Asthma and chronic obstructive pulmonary disease (COPD) are complex diseases, the definitions of which overlap. OBJECTIVE: To investigate clustering of clinical/physiological features and readily available biomarkers in patients with physician-assigned diagnoses of asthma and/or COPD in the NOVEL observational longiTudinal studY (NOVELTY; NCT02760329). METHODS: Two approaches were taken to variable selection using baseline data: approach A was data-driven, hypothesis-free and used the Pearson dissimilarity matrix; approach B used an unsupervised Random Forest guided by clinical input. Cluster analyses were conducted across 100 random resamples using partitioning around medoids, followed by consensus clustering. RESULTS: Approach A included 3796 individuals (mean age, 59.5 years; 54% female); approach B included 2934 patients (mean age, 60.7 years; 53% female). Each identified 6 mathematically stable clusters, which had overlapping characteristics. Overall, 67% to 75% of patients with asthma were in 3 clusters, and approximately 90% of patients with COPD were in 3 clusters. Although traditional features such as allergies and current/ex-smoking (respectively) were higher in these clusters, there were differences between clusters and approaches in features such as sex, ethnicity, breathlessness, frequent productive cough, and blood cell counts. The strongest predictors of the approach A cluster membership were age, weight, childhood onset, prebronchodilator FEV1, duration of dust/fume exposure, and number of daily medications. CONCLUSIONS: Cluster analyses in patients from NOVELTY with asthma and/or COPD yielded identifiable clusters, with several discriminatory features that differed from conventional diagnostic characteristics. The overlap between clusters suggests that they do not reflect discrete underlying mechanisms and points to the need for identification of molecular endotypes and potential treatment targets across asthma and/or COPD.

Characterization of Patients in the International Severe Asthma Registry with High Steroid Exposure Who Did or Did Not Initiate Biologic Therapy.

Background: Many severe asthma patients with high oral corticosteroid exposure (HOCS) often do not initiate biologics despite being eligible. This study aimed to compare the characteristics of severe asthma patients with HOCS who did and did not initiate biologics. Methods: Baseline characteristics of patients with HOCS (long-term maintenance OCS therapy for at least 1 year, or ≥4 courses of steroid bursts in a year) from the International Severe Asthma Registry (ISAR; https://isaregistries.org/), who initiated or did not initiate biologics (anti-lgE, anti-IL5/5R or anti-IL4R), were described at the time of biologic initiation or registry enrolment. Statistical relationships were tested using Pearson's chi-squared tests for categorical variables, and t-tests for continuous variables, adjusting for potential errors in multiple comparisons. Results: Between January 2015 and February 2021, we identified 1412 adult patients with severe asthma from 19 countries that met our inclusion criteria of HOCS, of whom 996 (70.5%) initiated a biologic and 416 (29.5%) did not. The frequency of biologic initiation varied across geographical regions. Those who initiated a biologic were more likely to have higher blood eosinophil count (483 vs 399 cells/µL, p=0.003), serious infections (49.0% vs 13.3%, p<0.001), nasal polyps (35.2% vs 23.6%, p<0.001), airflow limitation (56.8% vs 51.8%, p=0.013), and uncontrolled asthma (80.8% vs 73.2%, p=0.004) despite greater conventional treatment adherence than those who did not start a biologic. Both groups had similar annual asthma exacerbation rates in the previous 12 months (5.7 vs 5.3, p=0.147). Conclusion: Around one third of severe HOCS asthma patients did not receive biologics despite a similar high burden of asthma exacerbations as those who initiated a biologic therapy. Other disease characteristics such as eosinophilic phenotype, serious infectious events, nasal polyps, airflow limitation and lack of asthma control appear to dictate biologic use.

Eosinophilic and Noneosinophilic Asthma: An Expert Consensus Framework to Characterize Phenotypes in a Global Real-Life Severe Asthma Cohort.

BACKGROUND: Phenotypic characteristics of patients with eosinophilic and noneosinophilic asthma are not well characterized in global, real-life severe asthma cohorts. RESEARCH QUESTION: What is the prevalence of eosinophilic and noneosinophilic phenotypes in the population with severe asthma, and can these phenotypes be differentiated by clinical and biomarker variables? STUDY DESIGN AND METHODS: This was an historical registry study. Adult patients with severe asthma and available blood eosinophil count (BEC) from 11 countries enrolled in the International Severe Asthma Registry (January 1, 2015-September 30, 2019) were categorized according to likelihood of eosinophilic phenotype using a predefined gradient eosinophilic algorithm based on highest BEC, long-term oral corticosteroid use, elevated fractional exhaled nitric oxide, nasal polyps, and adult-onset asthma. Demographic and clinical characteristics were defined at baseline (ie, 1 year before or closest to date of BEC). RESULTS: One thousand seven hundred sixteen patients with prospective data were included; 83.8% were identified as most likely (grade 3), 8.3% were identified as likely (grade 2), and 6.3% identified as least likely (grade 1) to have an eosinophilic phenotype, and 1.6% of patients showed a noneosinophilic phenotype (grade 0). Eosinophilic phenotype patients (ie, grades 2 or 3) showed later asthma onset (29.1 years vs 6.7 years; P < .001) and worse lung function (postbronchodilator % predicted FEV1, 76.1% vs 89.3%; P = .027) than those with a noneosinophilic phenotype. Patients with noneosinophilic phenotypes were more likely to be women (81.5% vs 62.9%; P = .047), to have eczema (20.8% vs 8.5%; P = .003), and to use anti-IgE (32.1% vs 13.4%; P = .004) and leukotriene receptor antagonists (50.0% vs 28.0%; P = .011) add-on therapy. INTERPRETATION: According to this multicomponent, consensus-driven, and evidence-based eosinophil gradient algorithm (using variables readily accessible in real life), the severe asthma eosinophilic phenotype was more prevalent than previously identified and was phenotypically distinct. This pragmatic gradient algorithm uses variables readily accessible in primary and specialist care, addressing inherent issues of phenotype heterogeneity and phenotype instability. Identification of treatable traits across phenotypes should improve therapeutic precision.