title: Determination of colonoscopy indication from administrative claims data.
abstract: BACKGROUND
  Colonoscopy outcomes, such as polyp detection or complication rates, may differ by procedure indication.
  
  
  OBJECTIVE
  To develop methods to classify colonoscopy indications from administrative data, facilitating study of colonoscopy quality and outcomes.
  
  
  METHODS
  We linked 14,844 colonoscopy reports from the Clinical Outcomes Research Initiative, a national repository of endoscopic reports, to the corresponding Medicare Carrier and Outpatient File claims. Colonoscopy indication was determined from the procedure reports. We developed algorithms using classification and regression trees and linear discriminant analysis (LDA) to classify colonoscopy indication. Predictor variables included ICD-9CM and CPT/HCPCS codes present on the colonoscopy claim or in the 12 months prior, patient demographics, and site of colonoscopy service. Algorithms were developed on a training set of 7515 procedures, then validated using a test set of 7329 procedures.
  
  
  RESULTS
  Sensitivity was lowest for identifying average-risk screening colonoscopies, varying between 55% and 86% for the different algorithms, but specificity for this indication was consistently over 95%. Sensitivity for diagnostic colonoscopy varied between 77% and 89%, with specificity between 55% and 87%. Algorithms with classification and regression trees with 7 variables or LDA with 10 variables had similar overall accuracy, and generally lower accuracy than the algorithm using LDA with 30 variables.
  
  
  CONCLUSIONS
  Algorithms using Medicare claims data have moderate sensitivity and specificity for colonoscopy indication, and will be useful for studying colonoscopy quality in this population. Further validation may be needed before use in alternative populations.
authors.affiliation: *Department of Medicine, University of Washington †The VA Puget Sound Health Care System ‡Mountain-Whisper-Light Statistics §Department of Family Medicine ∥Department of Rehabilitation Medicine, University of Washington, Seattle, Washington.
authors.forename: Cynthia W
authors.initials: CW
authors.lastname: Ko
authors.forename: Jason A
authors.initials: JA
authors.lastname: Dominitz
authors.forename: Moni
authors.initials: M
authors.lastname: Neradilek
authors.forename: Nayak
authors.initials: N
authors.lastname: Polissar
authors.forename: Pam
authors.initials: P
authors.lastname: Green
authors.forename: William
authors.initials: W
authors.lastname: Kreuter
authors.forename: Laura-Mae
authors.initials: LM
authors.lastname: Baldwin
mesh_terms: D000368:Aged; D000465:Algorithms; D003113:Colonoscopy / Q000592:standards / Q000706:statistics & numerical data*; D015179:Colorectal Neoplasms / Q000175:diagnosis; D055088:Early Detection of Cancer / Q000592:standards / Q000706:statistics & numerical data; D006471:Gastrointestinal Hemorrhage / Q000175:diagnosis; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D010043:Outcome and Process Assessment, Health Care; D012680:Sensitivity and Specificity; D014481:United States