Distinguishing characteristics of difficult-to-control asthma in inner-city children and adolescents

Abstract

Background: Treatment levels required to control asthma vary greatly across a population with asthma. The factors that contribute to variability in treatment requirements of inner-city children have not been fully elucidated.

Objective: We sought to identify the clinical characteristics that distinguish difficult-to-control asthma from easy-to-control asthma.

Methods: Asthmatic children aged 6 to 17 years underwent baseline assessment and bimonthly guideline-based management visits over 1 year. Difficult-to-control and easy-to-control asthma were defined as daily therapy with 500 μg of fluticasone or greater with or without a long-acting β-agonist versus 100 μg or less assigned on at least 4 visits. Forty-four baseline variables were used to compare the 2 groups by using univariate analyses and to identify the most relevant features of difficult-to-control asthma by using a variable selection algorithm. Nonlinear seasonal variation in longitudinal measures (symptoms, pulmonary physiology, and exacerbations) was examined by using generalized additive mixed-effects models.

Results: Among 619 recruited participants, 40.9% had difficult-to-control asthma, 37.5% had easy-to-control asthma, and 21.6% fell into neither group. At baseline, FEV1 bronchodilator responsiveness was the most important characteristic distinguishing difficult-to-control asthma from easy-to-control asthma. Markers of rhinitis severity and atopy were among the other major discriminating features. Over time, difficult-to-control asthma was characterized by high exacerbation rates, particularly in spring and fall; greater daytime and nighttime symptoms, especially in fall and winter; and compromised pulmonary physiology despite ongoing high-dose controller therapy.

Conclusions: Despite good adherence, difficult-to-control asthma showed little improvement in symptoms, exacerbations, or pulmonary physiology over the year. In addition to pulmonary physiology measures, rhinitis severity and atopy were associated with high-dose asthma controller therapy requirement.

Keywords: Child; IgE; allergen sensitization; asthma; asthma exacerbations; asthma morbidity; asthma phenotype; asthma severity; inner-city asthma; pulmonary function; rhinitis.

Copyright © 2016 American Academy of Allergy, Asthma & Immunology. All rights reserved.

Figures

Figure 1

Schematic of APIC study design: Flow diagram illustrating: 1) screening visit; 2) clinic visits; 3) definition of easy-to-control, indeterminate and difficult-to-control groups based on controller treatment step throughout the study; and 4) domains used to group characteristics measured on each participant throughout the study.

Figure 2

CONSORT diagram: Flow diagram illustrating the number of individuals screened as well as the number of participants in the full APIC cohort and included in the analytic sample.

Figure 3

Controller treatment step for easy-to-control and difficult-to-control groups over 12 months. Green and blue lines (shaded areas) indicate mean (95% CI) of controller treatment step for easy-to-control and difficult-to-control groups respectively over 12 months.

Figure 4

Seasonal variation in clinical severity measures in easy-to-control, indeterminate and difficult-to-control groups. Green and blue lines (shaded areas) indicate mean or probability (95% CI) of clinical severity measure for easy-to-control and difficult-to-control groups respectively over 12 months. Clinical severity measures are: A) CASI component measuring day symptoms and albuterol use in the last two weeks (0 to 3 points); B) CASI component measuring night symptoms and albuterol use in the last two weeks (0 to 3 points); C) FEV1/FVC (×100); and D) Monthly incidence of exacerbations, as defined by prednisone use.

Figure 5

Variable importance plot of characteristics (with and without pulmonary physiology variables) distinguishing difficult-to-control from easy-to-control groups. Circles plot the median z-score for each variable, a measure of importance obtained from Boruta, a feature selection algorithm where higher values indicate a higher level of importance. Solid blue circles indicate variables that are confirmed as being relevant to distingushing difficult- from easy-to-control groups. Pink and green circle indicate variables that are tentative or rejected respectively and are not relevant to distingushing between difficult- from easy-to-control groups.