Rationale: Patients with a history of asthma exacerbations are at a higher risk for future episodes of severe asthma exacerbations. Characterization of asthma phenotypes could help improve asthma management, including reducing exacerbations.
Aim: The aim of this study is to identify distinctive patient characteristics associated with a history of asthma exacerbations using cluster analysis.
Methods: We used data assessing asthma control from two cross-sectional surveys of adult and pediatric patients in the primary care setting. A supervised cluster analysis with recursive partitioning approach was applied to identify characteristics that maximized the differences across subgroups.
Results: The sample comprised 2205 adults and 2435 children and adolescents with asthma. Key predictors were identified in seven adult clusters including visiting an asthma specialist, number of hours worked, and excessive use of rescue medication. The rate ratio (RR) for having an exacerbation was significantly higher (2.88; 95% confidence interval (CI), 2.46-3.36) in Cluster 7, with more female patients reporting severe disease, high body mass index, sinus infections, gastroesophageal reflux disease, skin allergies, and lower asthma control score. Features identified in the six pediatric clusters included visiting an asthma specialist, missed school days, race/ethnicity, and age. The RR for having an exacerbation was higher in Cluster 6 (2.36; 95% CI, 2.11-2.64), with patients reporting more severe disease, sinus and skin allergies, and lower asthma control score.
Conclusions: Identification of specific risk factors can be enhanced by using supervised cluster analysis. This approach allows grouping of patients with unique characteristics to help identify patients at higher risk of exacerbations.