Accuracy of a predictive model for severe hepatic fibrosis or cirrhosis in chronic hepatitis C

Abstract

AIM: To assess the accuracy of a model in diagnosing severe fibrosis/cirrhosis in chronic hepatitis C virus (HCV) infection. METHODS: The model, based on the sequential combination of the Bonacini score (BS: ALT/AST ratio, platelet count and INR) and ultrasonography liver surface characteristics, was applied to 176 patients with chronic HCV infection. Assuming a pre-test probability of 35%, the model defined four levels of post-test probability of severe fibrosis/cirrhosis: 90% (almost absolute). The predicted probabilities were compared with the observed patients’ distribution according to the histology (METAVIR). RESULTS: Severe fibrosis/cirrhosis was found in 67 patients (38%). The model discriminated patients in three comparable groups: 34% with a very high (>90%) or low (75%) or low (<10%) probability of cirrhosis, leaving only 33% of the patients still requiring liver biopsy.