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 x age x prothrombin time x direct bilirubin) / (albumin x platelet). The sensitivity and specificity for significant hepatic fibrosis index-1 were 73.3% and 95.6%, respectively. Area under the curve of significant hepatic fibrosis index-1 was 0.895 (P< 0.001), which was higher 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 a new marker; 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.