title: Insight into Best Variables for COPD Case Identification: A Random Forests Analysis.
abstract: BACKGROUND
  This study is part of a larger, multi-method project to develop a questionnaire for identifying undiagnosed cases of chronic obstructive pulmonary disease (COPD) in primary care settings, with specific interest in the detection of patients with moderate to severe airway obstruction or risk of exacerbation.
  
  
  OBJECTIVE
  To examine 3 existing datasets for insight into key features of COPD that could be useful in the identification of undiagnosed COPD.
  
  
  METHODS
  Random forests analyses were applied to the following databases: COPD Foundation Peak Flow Study Cohort (N=5761), Burden of Obstructive Lung Disease (BOLD) Kentucky site (N=508), and COPDGene® (N=10,214). Four scenarios were examined to find the best, smallest sets of variables that distinguished cases and controls:(1) moderate to severe COPD (forced expiratory volume in 1 second [FEV1] <50% predicted) versus no COPD; (2) undiagnosed versus diagnosed COPD; (3) COPD with and without exacerbation history; and (4) clinically significant COPD (FEV1<60% predicted or history of acute exacerbation) versus all others.
  
  
  RESULTS
  From 4 to 8 variables were able to differentiate cases from controls, with sensitivity ≥73 (range: 73-90) and specificity >68 (range: 68-93). Across scenarios, the best models included age, smoking status or history, symptoms (cough, wheeze, phlegm), general or breathing-related activity limitation, episodes of acute bronchitis, and/or missed work days and non-work activities due to breathing or health.
  
  
  CONCLUSIONS
  Results provide insight into variables that should be considered during the development of candidate items for a new questionnaire to identify undiagnosed cases of clinically significant COPD.
authors.affiliation: Evidera, Bethesda, Maryland.
authors.forename: Nancy K
authors.initials: NK
authors.lastname: Leidy
authors.affiliation: Evidera, Bethesda, Maryland.
authors.forename: Karen G
authors.initials: KG
authors.lastname: Malley
authors.affiliation: Evidera, Bethesda, Maryland.
authors.forename: Anna W
authors.initials: AW
authors.lastname: Steenrod
authors.affiliation: University of Kentucky, Lexington, Kentucky.
authors.forename: David M
authors.initials: DM
authors.lastname: Mannino
authors.affiliation: National Jewish Health, Denver, Colorado.
authors.forename: Barry J
authors.initials: BJ
authors.lastname: Make
authors.affiliation: National Jewish Health, Denver, Colorado.
authors.forename: Russ P
authors.initials: RP
authors.lastname: Bowler
authors.affiliation: Columbia University, New York, New York.
authors.forename: Byron M
authors.initials: BM
authors.lastname: Thomashow
authors.affiliation: Columbia University, New York, New York.
authors.forename: R G
authors.initials: RG
authors.lastname: Barr
authors.affiliation: University of Nebraska, Omaha, Nebraska.
authors.forename: Stephen I
authors.initials: SI
authors.lastname: Rennard
authors.affiliation: University of Nebraska, Omaha, Nebraska.
authors.forename: Julia F
authors.initials: JF
authors.lastname: Houfek
authors.affiliation: Olmsted Medical Center, Rochester, Minnesota.
authors.forename: Barbara P
authors.initials: BP
authors.lastname: Yawn
authors.affiliation: University of Michigan, Ann Arbor, Michigan.
authors.forename: Meilan K
authors.initials: MK
authors.lastname: Han
authors.affiliation: University of Michigan, Ann Arbor, Michigan.
authors.forename: Catherine A
authors.initials: CA
authors.lastname: Meldrum
authors.affiliation: Evidera, Bethesda, Maryland.
authors.forename: Elizabeth D
authors.initials: ED
authors.lastname: Bacci
authors.affiliation: COPD Foundation, Washington, DC.
authors.forename: John W
authors.initials: JW
authors.lastname: Walsh
authors.affiliation: Weill Cornell Medical Center, New York, New York.
authors.forename: Fernando
authors.initials: F
authors.lastname: Martinez
keywords: COPD; case identification; chronic airways obstruction; data mining; primary care; random forests; screening
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