BACKGROUND & AIMS: 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.
BACKGROUND & AIMS: 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: Woon Geon Shin; Sang Hoon Park; Sun-Young Jun; Jae One Jung; Joon Ho Moon; Jong Pyo Kim; Kyoung Oh Kim; Cheol Hee Park; Tai Ho Hahn; Kyo-Sang Yoo; Jong Hyeok Kim; Choong Kee Park Journal: Gut Liver Date: 2007-12-31 Impact factor: 4.519
Authors: Christian Mölleken; Barbara Sitek; Corinna Henkel; Gereon Poschmann; Bence Sipos; Sebastian Wiese; Bettina Warscheid; Christoph Broelsch; Markus Reiser; Scott L Friedman; Ida Tornøe; Anders Schlosser; Günter Klöppel; Wolff Schmiegel; Helmut E Meyer; Uffe Holmskov; Kai Stühler Journal: Hepatology Date: 2009-04 Impact factor: 17.425
Authors: Keyur Patel; Katja S Remlinger; Terence G Walker; Peter Leitner; Joseph E Lucas; Stephen D Gardner; John G McHutchison; Will Irving; Indra Neil Guha Journal: Clin Gastroenterol Hepatol Date: 2014-05-09 Impact factor: 11.382
Authors: Dalia Omran; Ayman Yosry; Samar K Darweesh; Mohammed M Nabeel; Mohammed El-Beshlawey; Sameh Saif; Azza Fared; Mohamed Hassany; Rania A Zayed Journal: Clin Exp Med Date: 2017-05-31 Impact factor: 3.984