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Published on: November 4, 2010
Predictors of benefit from high-altitude climate therapy in adults with severe asthma
S Hashimoto1, L H Rijssenbeek-Nouwens, K B Fieten
1Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, the Netherlands.
Background:
High-altitude climate therapy has been shown to benefit patients with severe asthma but it is not known which patients benefit most from this treatment. In the current study we aimed to identify clinical, functional and inflammatory predictors of favourable outcome of high-altitude climate therapy.
Methods:
This is a secondary analysis of a prospective cohort including 136 adult patients with a diagnosis of severe refractory asthma, referred to the Dutch Asthma Centre in Davos (1600 metres above sea level), Switzerland. They had assessments of medication usage, asthma-related quality of life (Asthma-related Quality of Life Questionnaire, AQLQ), asthma control, body mass index (BMI), sino-nasal symptoms, fatigue, lung function (forced expiratory volume in one second, FEV1), exercise tolerance, allergy and inflammation (fraction of exhaled nitric oxide, blood eosinophils) at entry and after 12 weeks of treatment. Five clinically relevant outcomes were considered: AQLQ, oral corticosteroid dose, FEV1, body mass index and blood eosinophils. Independent predictors of beneficial outcome were identified by multiple linear regression analysis.
Results:
Lower blood eosinophil counts (p < 0.01), younger age (p = 0.02) and poorer asthma control (p < 0.01) were independently associated with greater reduction in the dose of oral corticosteroids. Lower fatigue score at baseline (p = 0.01) was associated with greater weight loss (reduction in BMI). Higher levels of total IgE at baseline (p < 0.01), and higher doses of inhaled corticosteroids (p = 0.03) were associated with greater decreases in blood eosinophils. There were no predictors for improvement in AQLQ or FEV1.
Conclusions:
The beneficial effect of high-altitude climate therapy in adults with severe asthma can be predicted by patient characteristics, such as age, blood eosinophils and degree of asthma control before admission.
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