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Fibrosis prediction is an essential part of the assessment and management of patients with chronic liver disease. Blood-based biomarkers offer a number of advantages over the traditional standard of fibrosis assessment of liver biopsy, including safety, cost-savings and wide spread accessibility. Current biomarker algorithms include indirect surrogate measures of fibrosis, including aminotransaminases and platelet count, or direct measures of fibrinogenesis or fibrinolysis such as hyaluronic acid and tissue inhibitor of metalloproteinase-1. A number of algorithms have now been validated across a range of chronic liver disease including chronic viral hepatitis, alcoholic and non-alcoholic fatty liver disease. Furthermore, several models have been demonstrated to be dynamic to changes in fibrosis over time and are predictive of liver-related survival and overall survival to a greater degree than liver biopsy. Current limitations of biomarker models include a significant indeterminate range, and a predictive ability that is limited to only a few stages of fibrosis. Utilization of these biomarker models requires knowledge of patient co-morbidities which may produce false positive or negative results in a small proportion of individuals. Furthermore, knowledge of the underlying prevalence of fibrosis in the patient population is required for interpretation of the positive or negative predictive values of a test result. Novel proteins identified by proteomic technology and genetic polymorphisms from genome association studies offer the possibility for further refinement and individualization of biomarker fibrosis models in the future.
Leon A. Adams (Tue,) studied this question.