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The asthma disease activity score: a discriminating, responsive measure predicts future asthma attacks
Steven Greenberg1, Nancy Liu, Amarjot Kaur
1Clinical Research, Merck Sharp & Dohme Corp, Whitehouse Station, NJ, USA. steven_greenberg@merck.com
Background:
Classifying asthma severity or activity has evolved, but there are no published weighted composite measures of asthma disease activity that account for the relative importance of the many individual clinical variables that are widely used.
Objectives:
We sought to develop a weighted and responsive measure of asthma disease activity.
Methods:
Discriminant and multiple regression analyses based on 2 previously conducted clinical trials were used to develop the Asthma Disease Activity Score (ADAS-6).
Results:
The ADAS-6 demonstrated content validity because its components assess different manifestations of asthma: FEV(1) (percent predicted), Asthma Quality of Life Questionnaire-Symptom domain, rescue β-agonist use, nocturnal awakenings, peak expiratory flow diurnal variability, and rescue β-agonist use diurnal variability. The ADAS-6 demonstrated cross-sectional and longitudinal validity. It was discriminating: it distinguished levels of disease activity and response to different treatment intensities (P < .0001). Similar results were obtained with an independent clinical trial. The ADAS-6 was highly responsive to treatment effects, with a standardized effect size exceeding that of other widely used outcome measures. Using ADAS-6 as the primary end point in the montelukast pivotal trials would have significantly reduced the sample size needed to detect a comparable change in outcome. Furthermore, increments in the ADAS-6 predicted the risk of future asthma attacks. A simplified Asthma Disease Activity Score 4-variable version (ADAS-4) demonstrated similar measurement properties.
Conclusions:
The ADAS-6 and ADAS-4 are novel, weighted, and responsive measures of asthma disease activity. Use of these measures in clinical trials might better separate treatment effects, predict future asthma attacks, and substantially reduce sample size.
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