• Wei Wu, Eugene Bleecker, Wendy Moore, William W Busse, Mario Castro, Kian Fan Chung, William J Calhoun, Serpil Erzurum, Benjamin Gaston, Elliot Israel, Douglas Curran-Everett, and Sally E Wenzel.
• Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pa. Electronic address: weiwu2@cs.cmu.edu.
• J. Allergy Clin. Immunol. 2014 May 1;133(5):1280-8.

BackgroundPrevious studies have identified asthma phenotypes based on small numbers of clinical, physiologic, or inflammatory characteristics. However, no studies have used a wide range of variables using machine learning approaches.ObjectivesWe sought to identify subphenotypes of asthma by using blood, bronchoscopic, exhaled nitric oxide, and clinical data from the Severe Asthma Research Program with unsupervised clustering and then characterize them by using supervised learning approaches.MethodsUnsupervised clustering approaches were applied to 112 clinical, physiologic, and inflammatory variables from 378 subjects. Variable selection and supervised learning techniques were used to select relevant and nonredundant variables and address their predictive values, as well as the predictive value of the full variable set.ResultsTen variable clusters and 6 subject clusters were identified, which differed and overlapped with previous clusters. Patients with traditionally defined severe asthma were distributed through subject clusters 3 to 6. Cluster 4 identified patients with early-onset allergic asthma with low lung function and eosinophilic inflammation. Patients with later-onset, mostly severe asthma with nasal polyps and eosinophilia characterized cluster 5. Cluster 6 asthmatic patients manifested persistent inflammation in blood and bronchoalveolar lavage fluid and exacerbations despite high systemic corticosteroid use and side effects. Age of asthma onset, quality of life, symptoms, medications, and health care use were some of the 51 nonredundant variables distinguishing subject clusters. These 51 variables classified test cases with 88% accuracy compared with 93% accuracy with all 112 variables.ConclusionThe unsupervised machine learning approaches used here provide unique insights into disease, confirming other approaches while revealing novel additional phenotypes.Copyright © 2014 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

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