title: Improved prediction of fibrosis in chronic hepatitis C using measures of insulin resistance in a probability index.
abstract: We sought to develop a clinically useful index comprising standard and physiologically relevant variables to predict the probability of significant hepatic fibrosis in subjects with chronic hepatitis C virus (HCV) infection. Fibrosis was graded as mild (stages F0 or F1) or significant (stages F2-F4). Thirty-five clinical and laboratory parameters were analyzed initially in 176 patients with detectable HCV RNA to derive a fibrosis probability index (FPI) to predict significant fibrosis. This index then was validated in a second group of 126 subjects. Among 18 variables associated with severe fibrosis on univariate analysis, multiple logistic regression analysis identified age, aspartate aminotransferase (AST), total cholesterol level, insulin resistance (by homeostasis model), and past alcohol intake as independent predictors of significant fibrosis. The area under the receiver operating characteristic (ROC) curves was 0.84 for the initial cohort and 0.77 for the validation cohort. In the initial cohort, the sensitivity of the FPI based on these five predictors was 96%, and the negative predictive value was 93% at a score of >/=0.2. At scores >/=0.8, the FPI was 94% specific and had a positive predictive value of 87%. In conclusion, an FPI using routinely assessed markers and incorporating a measure of insulin resistance can reliably predict the probability of significant hepatic fibrosis in most patients with chronic HCV infection. Such an index should prove useful to guide decision making regarding the need for liver biopsy, and potentially for avoiding or deferring biopsy in a large proportion of patients with mild liver disease.
authors.affiliation: Storr Liver Unit, Westmead Millennium Institute, University of Sydney, Westmead, New South Wales, Australia.
authors.forename: Archana
authors.initials: A
authors.lastname: Sud
authors.forename: Jason M
authors.initials: JM
authors.lastname: Hui
authors.forename: Geoffrey C
authors.initials: GC
authors.lastname: Farrell
authors.forename: Priyanka
authors.initials: P
authors.lastname: Bandara
authors.forename: James G
authors.initials: JG
authors.lastname: Kench
authors.forename: Carolyn
authors.initials: C
authors.lastname: Fung
authors.forename: Rita
authors.initials: R
authors.lastname: Lin
authors.forename: Dev
authors.initials: D
authors.lastname: Samarasinghe
authors.forename: Christopher
authors.initials: C
authors.lastname: Liddle
authors.forename: Geoffrey W
authors.initials: GW
authors.lastname: McCaughan
authors.forename: Jacob
authors.initials: J
authors.lastname: George
mesh_terms: D000328:Adult; D015415:Biomarkers; D015331:Cohort Studies; D005260:Female; D019698:Hepatitis C, Chronic / Q000175:diagnosis* / Q000453:epidemiology; D006801:Humans; D007333:Insulin Resistance; D008103:Liver Cirrhosis / Q000175:diagnosis* / Q000453:epidemiology / Q000821:virology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D015995:Prevalence; D011336:Probability; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index
chemical_list: D015415:Biomarkers