title: Application of the asthma phenotype algorithm from the Severe Asthma Research Program to an urban population.
abstract: BACKGROUND
  Identification and characterization of asthma phenotypes are challenging due to disease complexity and heterogeneity. The Severe Asthma Research Program (SARP) used unsupervised cluster analysis to define 5 phenotypically distinct asthma clusters that they replicated using 3 variables in a simplified algorithm. We evaluated whether this simplified SARP algorithm could be used in a separate and diverse urban asthma population to recreate these 5 phenotypic clusters.
  
  
  METHODS
  The SARP simplified algorithm was applied to adults with asthma recruited to the New York University/Bellevue Asthma Registry (NYUBAR) to classify patients into five groups. The clinical phenotypes were summarized and compared.
  
  
  RESULTS
  Asthma subjects in NYUBAR (n = 471) were predominantly women (70%) and Hispanic (57%), which were demographically different from the SARP population. The clinical phenotypes of the five groups generated by the simplified SARP algorithm were distinct across groups and distributed similarly to those described for the SARP population. Groups 1 and 2 (6 and 63%, respectively) had predominantly childhood onset atopic asthma. Groups 4 and 5 (20%) were older, with the longest duration of asthma, increased symptoms and exacerbations. Group 4 subjects were the most atopic and had the highest peripheral eosinophils. Group 3 (10%) had the least atopy, but included older obese women with adult-onset asthma, and increased exacerbations.
  
  
  CONCLUSIONS
  Application of the simplified SARP algorithm to the NYUBAR yielded groups that were phenotypically distinct and useful to characterize disease heterogeneity. Differences across NYUBAR groups support phenotypic variation and support the use of the simplified SARP algorithm for classification of asthma phenotypes in future prospective studies to investigate treatment and outcome differences between these distinct groups.
  
  
  BACKGROUND
  Clinicaltrials.gov NCT00212537.
authors.affiliation: Division of Pulmonary and Critical Care Medicine, Department of Medicine, New York University School of Medicine, New York, New York, United States of America.
authors.forename: Paru
authors.initials: P
authors.lastname: Patrawalla
authors.forename: Angeliki
authors.initials: A
authors.lastname: Kazeros
authors.forename: Linda
authors.initials: L
authors.lastname: Rogers
authors.forename: Yongzhao
authors.initials: Y
authors.lastname: Shao
authors.forename: Mengling
authors.initials: M
authors.lastname: Liu
authors.forename: Maria-Elena
authors.initials: ME
authors.lastname: Fernandez-Beros
authors.forename: Shulian
authors.initials: S
authors.lastname: Shang
authors.forename: Joan
authors.initials: J
authors.lastname: Reibman
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001249:Asthma / Q000145:classification / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D013147:Spirometry; D011795:Surveys and Questionnaires; D055815:Young Adult