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