Page 84 - Comprehensive treatment of patients with glucocorticoid-dependent severe asthma

Chapter 4
Table 4. Independent associations between patient characteristics and outcomes (multiple regression analysis)
Outcome 1: Change in OCS dose
ACQ
Age Eosinophil AQLQ score
Outcome 2: Change in AQLQ
ACQ score
CIS fatigue score
Outcome 3: Change in FEV1
Outcome 4: Change in blood eosinophils
ICS dose Total IgE OCS dose
Outcome domain 5: Change in BMI
CIS fatigue score
FEV1 % predicted
B* (95% CI)* p-value
-8.23 -14.22 to -2.23 <0.01 0.24 0.04 to 0.49 0.02 0.02 0.01 to 0.03 <0.01 -1.57 -7.67 to 4.51 0.60
-0.30 -0.12 to -0.48 <0.01 0.01 -0.01 to 0.03 0.23 -- -- --
-0.07 -0.14 to -0.01 0.03 -0.09 -0.13 to -0.06 <0.01
2.57 -0.26 to 5.41 0.07
0.04 0.01 to 0.06 0.01 -0.01 -0.02 to 0.00 0.05
Footnote table 4: Values from multiple linear regression analysis between baseline characteristics and improvement in each outcome domain. The model included univariate predictors of improvement with a p-value <0.05.
Discussion
In this study we categorized the benefits of high altitude climate therapy for patients with severe asthma in five different outcome domains represented as reduction in daily oral steroid use, improvements in asthma related quality of life, FEV1, blood eosinophils and weight loss. Each of these outcomes was independently associated with specific patient characteristics, except for the improvement in asthma related quality of life and FEV1, which was similar for all patients with severe asthma.
Nearly all patients with severe asthma participating in this study showed a significant improvement in their asthma related quality of life scores after high altitude climate therapy. No patient characteristics could predict this benefit, which suggests that non-specific aspects of high altitude climate therapy played an important role, such as better air quality, improved adherence with the drug regimen or removal from stress at home or at the workplace [27].
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