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Background: Staging hepatic fibrosis by liver biopsy guides prognosis and treatment of hepatitis C, but is invasive and expensive. We sought to create an algorithm of serum markers that accurately and reliably predict liver fibrosis stage among hepatitis C patients. Methods: Ten biochemical markers were measured at time of liver biopsy in 117 untreated hepatitis C patients (training set). Multivariate logistic regression and ROC curve analyses were used to create a predictive model for significant fibrosis (METAVIR F2, F3, and F4), advanced fibrosis (F3 and F4), and cirrhosis (F4). The model was validated in 104 patients from other institutions. Results: A model (Hepascore) of bilirubin, ␥-glutamyltransferase, hyaluronic acid, ␣ 2 -macroglobulin, age, and sex produced areas under the ROC curves (AUCs) of 0.85, 0.96, and 0.94 for significant fibrosis, advanced fibrosis, and cirrhosis, respectively. In the training set, a score >0.5 (range, 0.0 -1.0) was 92% specific and 67% sensitive for significant fibrosis, a score <0.5 was 81% specific and 95% sensitive for advanced fibrosis, and a score <0.84 was 84% specific and 71% sensitive for cirrhosis. Among the

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Background: Determining the stage of fibrosis by liver biopsy is important in managing patients with hepatitis C virus infection. We investigated the predictive value of the proprietary FibroTest score to accurately identify significant fibrosis in Australian hepatitis C patients. Methods: Serum obtained from 125 confirmed hepatitis C patients before antiviral therapy was analyzed for haptoglobin, α2-macroglobulin, apolipoprotein A1, bilirubin, and γ-glutamyltransferase activity, and the FibroTest score was computed. Liver fibrosis pathology was staged according to a defined system on a scale of F0 to F4. We used predictive values and a ROC curve to assess the accuracy of FibroTest scores. Results: The prevalence of significant fibrosis defined by liver biopsy was 0.38. The most useful single test for predicting significant fibrosis was serum α2-macroglobulin (cutoff value, 2.52 g/L; sensitivity, 75%; specificity, 67%). The negative predictive value of a FibroTest score &lt;0.1 was 85%, and the positive predictive value of a score &gt;0.6 was 78%. Although 33 of the 125 patients had FibroTest scores &lt;0.1 and were therefore deemed unlikely to have fibrosis, 6 (18%) had significant fibrosis. Conversely, of the 24 patients with scores &gt;0.6 who were likely to have significant fibrosis, 5 (21%) had mild fibrosis. Of the 125 patients in the cohort, 57 (46%) could have avoided liver biopsy, but discrepant results were recorded in 11 of those 57 (19%). Conclusion: The FibroTest score could not accurately predict the presence or absence of significant liver fibrosis.

In NAFLD subjects, non-invasive models have modest accuracy for determining significant fibrosis and have predictive values less than 90% in the majority of subjects. Complex models are more accurate than simple bedside models across a range of fibrosis.