title: Validity of administrative data for the diagnosis of primary sclerosing cholangitis: a population-based study.
abstract: OBJECTIVE
  Administrative databases could be useful in studying the epidemiology of primary sclerosing cholangitis (PSC); however, there is no information regarding the validity of the diagnostic code in administrative databases. The aims of this study were to determine the validity of administrative data for a diagnosis of PSC and generate algorithms for the identification of PSC patients.
  
  
  METHODS
  The sensitivity (Se) and positive predictive value (PPV) of a PSC diagnosis based on administrative data from 2000 to 2003 were determined through chart review data. Algorithms were developed by considering variables associated with PSC and coding details. A logistic regression model was constructed using covariates associated with PSC. Based on this model, each subject was assigned a probability of having PSC. A cutoff value was selected that maximized the Se and specificity (Sp) of correctly predicting PSC cases.
  
  
  RESULTS
  In the administrative data, the initial Se and PPV were 83.7 and 7.2% respectively. The optimal algorithm included one PSC code and one inflammatory bowel disease code and had Se 56% and PPV 59%. Overall, the algorithms yielded inadequate PPV and Se estimates to identify a cohort of true PSC cases. The predictive model was constructed using six covariates. For this model, the area under the receiver operating characteristic curve was 93.5%. A cutoff of 0.0729 was used, which maximized the Se 81.9% and Sp 90.7%; however, the PPV was 41.0%.
  
  
  CONCLUSIONS
  An algorithm for the identification of true PSC cases from administrative data was not possible. We recommend that PSC receives a distinct ICD code from ascending cholangitis.
authors: Division of Gastroenterology, University of Calgary, Calgary, AB, Canada.
authors: Natalie A
authors: NA
authors: Molodecky
authors: Robert P
authors: RP
authors: Myers
authors: Herman W
authors: HW
authors: Barkema
authors: Hude
authors: H
authors: Quan
authors: Gilaad G
authors: GG
authors: Kaplan
mesh_terms: D000328:Adult; D000368:Aged; D000416:Alberta / Q000453:epidemiology; D000465:Algorithms; D001706:Biopsy; D015209:Cholangitis, Sclerosing / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology; D057225:Data Mining; D019992:Databases as Topic; D005260:Female; D006801:Humans; D038801:International Classification of Diseases; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D013997:Time Factors