shfi: a novel noninvasive predictive model for significant fibrosis in patients with chronic hepatitis b

Background: this study aimed at creating a new predictive model of significant fibrosis in chronic hepatitis b using direct and indirect parameters and comparing this model with other noninvasive models for its validation in clinical settings. methods: patients (n = 81), according to the ishak score, were classified as mild and significant fibrosis. serum matrix metalloproteinase-2, tissue inhibitor of metalloproteinase-2, beta-nerve growth factor levels, and indirect parameters were analyzed. to evaluate the presence of significant hepatic fibrosis, well-known conventional models were also evaluated. the cut-off values of each model were determined using receiver operating characteristic curves to distinguish patients with mild and significant fibrosis. results: significant hepatic fibrosis index-1 was constructed using the following equation: (matrix metalloproteinase-2  age  prothrombin time x direct bilirubin) / (albumin x platelet). the sensitivity and specificity for significant hepatic fibrosis index-1 were 73.3% and95.6%, respectively. area under the curve of significant hepatic fibrosis index-1was0.895 (p< 0.001), whichwashigher than the other models. due to limitations of matrix metalloproteinase-2, significant hepatic fibrosis index-2 was constructed using a formula without matrix metalloproteinase-2. however, there were no significant differences between significant hepatic fibrosis index-1 and significant hepatic fibrosis index-2 or other models, except for 3 models. conclusions: significant hepatic fibrosis index-1 employs anewmarker; matrix metalloproteinase-2 along with routine parameters had the best diagnostic performance for significant fibrosis in patients with chronic hepatitis b. using significant hepatic fibrosis index-1 or even significant hepatic fibrosis index-2 might be an alternative approach in place of liver biopsy to predict significant fibrosis in chronic hepatitis b cohort.