Classification of childhood asthma phenotypes and long-term clinical responses to inhaled anti-inflammatory medications
Howrylak, Judie A.
Strunk, Robert C.
Zeiger, Robert S.
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CitationHowrylak, Judie A., Anne L. Fuhlbrigge, Robert C. Strunk, Robert S. Zeiger, Scott T. Weiss, and Benjamin A. Raby. 2014. Classification of childhood asthma phenotypes and long-term clinical responses to inhaled anti-inflammatory medications. Journal of Allergy and Clinical Immunology 133, no. 5: 1289–1300.e12. doi:10.1016/j.jaci.2014.02.006.
AbstractBackground— Although recent studies have identified the presence of phenotypic clusters in asthmatic patients, the clinical significance and temporal stability of these clusters have not been explored.
Objective— Our aim was to examine the clinical relevance and temporal stability of phenotypic clusters in children with asthma.
Methods— We applied spectral clustering to clinical data from 1041 children with asthma participating in the Childhood Asthma Management Program. Posttreatment randomization follow-up data collected over 48 months were used to determine the effect of these clusters on
pulmonary function and treatment response to inhaled anti-inflammatory medication.
Results— We found 5 reproducible patient clusters that could be differentiated on the basis of 3 groups of features: atopic burden, degree of airway obstruction, and history of exacerbation. Cluster grouping predicted long-term asthma control, as measured by the need for oral prednisone (P < .0001) or additional controller medications (P = .001), as well as longitudinal differences in
pulmonary function (P < .0001). We also found that the 2 clusters with the highest rates of exacerbation had different responses to inhaled corticosteroids when compared with the other clusters. One cluster demonstrated a positive response to both budesonide (P = .02) and nedocromil (P = .01) compared with placebo, whereas the other cluster demonstrated minimal responses to both budesonide (P = .12) and nedocromil (P = .56) compared with placebo.
Conclusion— Phenotypic clustering can be used to identify longitudinally consistent and clinically relevant patient subgroups, with implications for targeted therapeutic strategies and clinical trials design.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:27005898
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