Serum levels of YKL‐40 and hyaluronic acid as noninvasive markers of liver fibrosis in haemodialysis patients with chronic hepatitis C virus infection
- 11 August 2008
- journal article
- research article
- Published by Wiley in Journal of Viral Hepatitis
- Vol. 15 (9) , 666-674
- https://doi.org/10.1111/j.1365-2893.2008.00992.x
Abstract
Summary. Hepatitis C virus (HCV) infection is highly prevalent among end‐stage renal disease (ESRD) patients undergoing haemodialysis and it is an important cause of morbidity and mortality in this population. The aim of this study was to evaluate the diagnostic value of YKL‐40 and hyaluronic acid (HA) as noninvasive markers of liver fibrosis in 185 ESRD HCV‐infected patients. Significant liver fibrosis was defined as METAVIR F2, F3 or F4 stages. Significant fibrosis was observed in 45 patients (24%). By univariate analysis, higher levels of YKL‐40, HA, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and gamma‐glutamyltransferase (GGT) as well as reduced platelet count were associated with fibrosis. However, by multivariate analysis, only AST (P = 0.001), platelet count (P = 0.004) and HA (P = 0.042) were independently associated with significant fibrosis. For the prediction of significant fibrosis, the areas under receiver operating characterictic curve (AUROC) of the regression model (0.798) was significantly higher than the AUROC of YKL‐40 (0.607) and HA (0.650). No difference was noted between the AUROC of the regression model and AST to platelet ratio index (APRI) (0.787). Values <8.38 of the regression model showed a negative predictive value of 94% and scores ≥9.6 exhibited a positive predictive value of 65%. If biopsy indication was restricted to scores in the intermediate range of the regression model, it could have been correctly avoided in 61% of the cases. In conclusion, APRI and a model based on AST, platelet count and HA showed better accuracy than YKL‐40 and HA (when used solely) for the prediction of significant fibrosis in ESRD HCV‐infected patients.Keywords
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