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Hepascore and Its Application to Liver Disease

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Biomarkers in Liver Disease

Abstract

Accurate quantification of the severity of liver fibrosis provides important prognostic information among patients with chronic liver disease and thus influences management. Histopathological staging of liver biopsy is considered the gold standard for assessment of liver fibrosis but is limited by its invasive nature, high cost, and sampling error. Hepascore is a serum model consisting of hyaluronic acid, bilirubin, alpha2-macroglobulin, gamma-glutamyl transpeptidase, age, and sex, originally developed to predict the severity of liver fibrosis in patients with chronic hepatitis C. Hepascore has been widely validated in chronic hepatitis C, chronic hepatitis B, alcoholic and nonalcoholic fatty liver disease, and other types of chronic liver disorders. Validation studies have confirmed good to excellent accuracy to predict or exclude different levels of hepatic fibrosis including cirrhosis. In addition, Hepascore is dynamic over time and has been demonstrated to predict adverse clinical outcomes in longitudinal cohorts of chronic hepatitis C and alcoholic liver disease patients. Thus, Hepascore is a valuable clinical tool to stage liver fibrosis and determine prognosis, need for treatment, screening, and surveillance strategies in patients with chronic liver disease.

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Abbreviations

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

AUC:

Area under ROC curves

CFLD:

Cystic fibrosis liver disease

CI:

Confidence interval

CV:

Coefficients of variation

GGT:

Gamma-glutamyl transpeptidase

HA:

Hyaluronic acid

HCC:

Hepatocellular carcinoma

HCV:

Hepatitis C virus

HH:

Hereditary hemochromatosis

MMP-2:

Matrix metalloproteinase-2

NAFLD:

Nonalcoholic fatty liver disease

NPV:

Negative predictive value

PPV:

Positive predictive value

SLFG:

Shanghai Liver Fibrosis Group’s index

TIMP-1:

Tissue inhibitor of matrix metalloproteinase-1

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Huang, Y., Jeffrey, G.P., Adams, L.A. (2016). Hepascore and Its Application to Liver Disease. In: Preedy, V. (eds) Biomarkers in Liver Disease. Biomarkers in Disease: Methods, Discoveries and Applications. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7742-2_29-1

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