title: Diagnosing clinical subsets of autoimmune liver diseases based on a multivariable model.
abstract: BACKGROUND
  Diagnosing autoimmune hepatitis (AIH), primary biliary cirrhosis (PBC), primary sclerosing cholangitis, and other autoimmune liver diseases remains an imperfect process. We need a more accurate, evidence-based diagnostic system.
  
  
  METHODS
  We conducted a national survey and identified 988 cases of liver disease which did not satisfy the inclusion criteria for any liver disease of known etiology. We expected these cases to include autoimmune liver disease (AILD) and its variant forms. We selected 269 prototype cases for which histological re-evaluation of liver biopsy by independent expert hepatopathologists and the original diagnosis coincided. We did a multiple logistic regression analysis to determine explanatory variables that would distinguish cases of AIH and PBC from those of non-AIH and non-PBC, respectively. We constructed a multivariable diagnostic formula that gave AIH and PBC disease probabilities and validated it in a study of an additional 371 cases (validation group).
  
  
  RESULTS
  Based on the results of the statistical analysis, we selected three laboratory tests and four histological features as independent variables correlated to the diagnosis of both AIH and PBC. For the validation group, assuming that the original diagnosis was correct, the sensitivity and specificity for AIH were 86.3% and 92.4%, respectively. For PBC the sensitivity and specificity were 82.5% and 63.7%, respectively. A detailed analysis of inconsistent cases showed that the diagnosis based on the formula had given the correct diagnosis, for either AIH or PBC, except for 5 cases (1.3%) in which disease probability was low for both.
  
  
  CONCLUSIONS
  A seven-variable formula based on three laboratory tests and four histological features gives significant information for the diagnosis of AILD.
authors.affiliation: Division of Gastroenterology and Hepatology, Department of Internal Medicine, Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Tokyo 105-8461, Japan.
authors.forename: Mikio
authors.initials: M
authors.lastname: Zeniya
authors.forename: Fumitoki
authors.initials: F
authors.lastname: Watanabe
authors.forename: Toshio
authors.initials: T
authors.lastname: Morizane
authors.forename: Minoru
authors.initials: M
authors.lastname: Shibata
authors.forename: Shiro
authors.initials: S
authors.lastname: Maeyama
authors.forename: Masayoshi
authors.initials: M
authors.lastname: Kage
authors.forename: Yasuni
authors.initials: Y
authors.lastname: Nakanuma
authors.forename: Gotaro
authors.initials: G
authors.lastname: Toda
mesh_terms: D000465:Algorithms; D001327:Autoimmune Diseases / Q000175:diagnosis* / Q000473:pathology; D015209:Cholangitis, Sclerosing / Q000175:diagnosis; D005260:Female; D019693:Hepatitis, Autoimmune / Q000175:diagnosis; D006801:Humans; D008105:Liver Cirrhosis, Biliary / Q000175:diagnosis; D008107:Liver Diseases / Q000175:diagnosis* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012044:Regression Analysis; D012680:Sensitivity and Specificity