A multivariable model of clinical variables predicts advanced fibrosis in chronic hepatitis C

Background: A noninvasive method to identify advanced hepatic fibrosis (AHF) in chronic hepatitis C (CHC) could preclude the need for routine liver biopsy. Recent evidence suggests that obesity may contribute to hepatic fibrosis in hepatitis C virus infection. Goals: To determine whether clinical variables, including body mass index (BMI), can predict risk of AHF. Study: Retrospective review of untreated CHC patients evaluated between 1993 and 2002 without clinical or physical evidence of end-stage liver disease. Liver biopsies were scored for fibrosis, steatosis, and inflammation. Multivariable analysis was used to derive and internally validate a prediction equation. A clinical index was created from the equation by assigning points for each variable. The risk of AHF was measured for each risk category. Results: Two hundred eighty-six satisfied inclusion criteria, of which 86 (30%) had AHF. In the derivation subgroup (N = 190), 5 factors were independently associated with AHF: diabetes mellitus, platelets count < 150,000, aspartate aminotransferase >= 65 IU/mL, international normalized ratio >= 1.1, and bilirubin >= 0.85 mg/dL. The corresponding risk index contained 3 categories: low-risk (score of 0), intermediate risk (scores of 1 to 3), and high risk (scores of >= 4), in which the respective risks of AHF were 9%, 34%, and 92%. Inclusion of BMI did not improve model performance. Conclusions: A model for estimating AHF risk in CHC performed well in this population. BMI had no effect on the risk of AHF. If this model can be validated in other patient cohorts, it could preclude the need for liver biopsy in patients with scores of 0 or >= 4.