Background: The severity of asthma (SOA) score is based on a validated disease-specific questionnaire that addresses frequency of asthma symptoms, use of systemic corticosteroids, use of other asthma medications, and history of hospitalization/intubation for asthma. SOA does not require measurements of pulmonary function. This study compared the ability of SOA to predict clinical outcomes in the EXCELS (Epidemiological Study of Xolair [omalizumab]: Evaluating Clinical Effectiveness and Long-term Safety in Patients with Moderate to Severe Asthma) patient population vs three other asthma assessment tools. EXCELS is a large, ongoing, observational study of patients with moderate to severe persistent asthma and reactivity to perennial aeroallergens.
Methods: Baseline scores for SOA, asthma control test (ACT), work productivity and impairment index-asthma (WPAI-A), and FEV(1) % predicted were compared for their ability to predict five prespecified adverse clinical outcomes in asthma: serious adverse events (SAEs) reported as exacerbations, SAEs leading to hospitalizations, the incidence of unscheduled office visits, ED visits, and po or IV corticosteroid bursts related to asthma. Logistic regression analysis, area under receiver operating characteristic curves (AUCROCs), and classification and regression tree (CART) analysis were used to evaluate the ability of the four tools to predict adverse clinical outcomes using baseline and 1-year data from 2,878 patients enrolled in the non-omalizumab cohort of EXCELS.
Results: SOA was the only assessment tool contributing significantly in all five statistical models of adverse clinical outcomes by logistic regression analysis (full model AUCROC range, 0.689-0.783). SOA appeared to be a stand-alone predictor for four of five outcomes (reduced model AUCROC range, 0.689-0.773). CART analysis showed that SOA had the greatest variable importance for all five outcomes.
Conclusions: SOA score was a powerful predictor of adverse clinical outcomes in moderate to severe asthma, as evaluated by either logistic regression analysis or CART analysis.
Trial registry: ClinicalTrials.gov; No.: NCT00252135; URL: www.clinicaltrials.gov.