title: Serum markers detect the presence of liver fibrosis: a cohort study.
abstract: OBJECTIVE
  Histologic examination of a liver biopsy specimen is regarded as the reference standard for detecting liver fibrosis. Biopsy can be painful and hazardous, and assessment is subjective and prone to sampling error. We developed a panel of sensitive automated immunoassays to detect matrix constituents and mediators of matrix remodeling in serum to evaluate their performance in the detection of liver fibrosis.
  
  
  METHODS
  In an international multicenter cohort study, serum levels of 9 surrogate markers of liver fibrosis were compared with fibrosis stage in liver biopsy specimens obtained from 1021 subjects with chronic liver disease. Discriminant analysis of a test set of samples was used to identify an algorithm combining age, hyaluronic acid, amino-terminal propeptide of type III collagen, and tissue inhibitor of matrix metalloproteinase 1 that was subsequently evaluated using a validation set of biopsy specimens and serum samples.
  
  
  RESULTS
  The algorithm detected fibrosis (sensitivity, 90%) and accurately detected the absence of fibrosis (negative predictive value for significant fibrosis, 92%; area under the curve of a receiver operating characteristic plot, .804; standard error, .02; P < .0001; 95% confidence interval, .758-.851). Performance was excellent for alcoholic liver disease and nonalcoholic fatty liver disease. The algorithm performed equally well in comparison with each of the pathologists. In contrast, pathologists' agreement over histologic scores ranged from very good to moderate (kappa = .97-.46).
  
  
  CONCLUSIONS
  Assessment of liver fibrosis with multiple serum markers used in combination is sensitive, specific, and reproducible, suggesting they may be used in conjunction with liver biopsy to assess a range of chronic liver diseases.
authors.affiliation: Liver Group, Division of Infection, Inflammation and Repair, University of Southampton, England SO16 6YD. wmr@soton.ac.uk
authors.forename: William M C
authors.initials: WM
authors.lastname: Rosenberg
authors.forename: Michael
authors.initials: M
authors.lastname: Voelker
authors.forename: Robert
authors.initials: R
authors.lastname: Thiel
authors.forename: Michael
authors.initials: M
authors.lastname: Becka
authors.forename: Alastair
authors.initials: A
authors.lastname: Burt
authors.forename: Detlef
authors.initials: D
authors.lastname: Schuppan
authors.forename: Stefan
authors.initials: S
authors.lastname: Hubscher
authors.forename: Tania
authors.initials: T
authors.lastname: Roskams
authors.forename: Massimo
authors.initials: M
authors.lastname: Pinzani
authors.forename: Michael J P
authors.initials: MJ
authors.lastname: Arthur
mesh_terms: D000328:Adult; D000465:Algorithms; D001331:Automation; D015415:Biomarkers / Q000032:analysis*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007118:Immunoassay; D008103:Liver Cirrhosis / Q000175:diagnosis* / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers