Eosinophilic and non-eosinophilic asthma: an expert consensus framework to characterize phenotypes in a global real-life severe asthma cohort.

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Phenotypic characteristics of eosinophilic and non-eosinophilic asthma patients are not well-characterized in global, real life severe asthma cohorts.What is the prevalence of eosinophilic and non-eosinophilic phenotypes in the severe asthma population, and can they be differentiated by clinical and biomarker variables?This was an historical, registry study. Adult severe asthma patients with available blood eosinophil count (BEC) from 11 countries enrolled into the International Severe Asthma Registry (01/01/2015 to 09/30/2019) were categorized according to likelihood of eosinophilic phenotype using a pre-defined gradient eosinophilic algorithm based on highest BEC, long-term oral corticosteroid use, elevated fractional exhaled nitric oxide, nasal polyps, and adult-onset asthma. Demographic/clinical characteristics were defined at baseline (i.e. 1-year prior or closest to date of BEC).1,716 patients with prospective data were included; 83.8% were identified as "most likely" (Grade 3), 8.3% were "likely" (Grade 2), and 6.3% "least likely" (Grade 1) to have an eosinophilic phenotype. 1.6% of patients had a non-eosinophilic phenotype (Grade 0). Eosinophilic phenotype patients (i.e. Grade 2 or 3) had later asthma onset (29.1 vs 6.7 yrs; p<0.001), and worse lung function (post-bronchodilator % predicted FEV1: 76.1% vs 89.3%; p=0.027) than those with a non-eosinophilic phenotype. Non-eosinophilic-phenotype patients were more likely to be female (81.5% vs 62.9%; p=0.047), have eczema (20.8% vs 8.5%; p=0.003) and use anti-IgE (32.1% vs 13.4%; p=0.004) and leukotriene receptor antagonists (50.0% vs 28.0%; p=0.011) add-on therapy.According to this multi-component, 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 phenotypically distinct. This pragmatic gradient algorithm utilizes 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.None.


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Authors: Liam G Heaney, Luis Perez de Llano, Mona Al-Ahmad, Vibeke Backer, John Busby, Giorgio Walter Canonica, George C Christoff, Borja G Cosio, J Mark FitzGerald, Enrico Heffler, Takashi Iwanaga, David J Jackson, Andrew N Menzies-Gow, Nikolaos G Papadopoulos, Andriana I Papaioannou, Paul E Pfeffer, Todor A Popov, Celeste M Porsbjerg, Chin Kook Rhee, Mohsen Sadatsafavi, Yuji Tohda, Eileen Wang, Michael E Wechsler, Marianna Alacqua, Alan Altraja, Leif Bjermer, Unnur S Björnsdóttir, Arnaud Bourdin, Guy G Brusselle, Roland Buhl, Richard W Costello, Mark Hew, Mariko Koh Siyue, Sverre Lehmann, Lauri Lehtimäki, Matthew Peters, Camille Taillé, Christian Taube, Trung N Tran, James Zangrilli, Lakmini Bulathsinhala, Victoria A Carter, Isha Chaudhry, Neva Eleangovan, Naeimeh Hosseini, Marjan Kerkhof, Ruth B Murray, Chris A Price, David B Price

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