title: Billing code algorithms to identify cases of peripheral artery disease from administrative data.
abstract: OBJECTIVE
  To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD).
  
  
  METHODS
  We extracted all encounters and line item details including PAD-related billing codes at Mayo Clinic Rochester, Minnesota, between July 1, 1997 and June 30, 2008; 22 712 patients evaluated in the vascular laboratory were divided into training and validation sets. Multiple logistic regression analysis was used to create an integer code score from the training dataset, and this was tested in the validation set. We applied a model-based code algorithm to patients evaluated in the vascular laboratory and compared this with a simpler algorithm (presence of at least one of the ICD-9 PAD codes 440.20-440.29). We also applied both algorithms to a community-based sample (n=4420), followed by a manual review.
  
  
  RESULTS
  The logistic regression model performed well in both training and validation datasets (c statistic=0.91). In patients evaluated in the vascular laboratory, the model-based code algorithm provided better negative predictive value. The simpler algorithm was reasonably accurate for identification of PAD status, with lesser sensitivity and greater specificity. In the community-based sample, the sensitivity (38.7% vs 68.0%) of the simpler algorithm was much lower, whereas the specificity (92.0% vs 87.6%) was higher than the model-based algorithm.
  
  
  CONCLUSIONS
  A model-based billing code algorithm had reasonable accuracy in identifying PAD cases from the community, and in patients referred to the non-invasive vascular laboratory. The simpler algorithm had reasonable accuracy for identification of PAD in patients referred to the vascular laboratory but was significantly less sensitive in a community-based sample.
authors.affiliation: Geriatric Cardiovascular Department, Chinese PLA General Hospital, Beijing, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Fan
authors.forename: Adelaide M
authors.initials: AM
authors.lastname: Arruda-Olson
authors.forename: Cynthia L
authors.initials: CL
authors.lastname: Leibson
authors.forename: Carin
authors.initials: C
authors.lastname: Smith
authors.forename: Guanghui
authors.initials: G
authors.lastname: Liu
authors.forename: Kent R
authors.initials: KR
authors.lastname: Bailey
authors.forename: Iftikhar J
authors.initials: IJ
authors.lastname: Kullo
mesh_terms: D000465:Algorithms; D059019:Clinical Coding; D057286:Electronic Health Records; D017722:Hospital Charges / Q000145:classification*; D006801:Humans; D007344:Insurance Claim Reporting; D038801:International Classification of Diseases; D016015:Logistic Models; D058729:Peripheral Arterial Disease / Q000145:classification / Q000175:diagnosis*; D012372:ROC Curve
keywords: billing codes; electronic medical record; informatics; peripheral artery disease