Diagnosis of
liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV)
infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of
liver cirrhosis based on patient characteristics and
biomarkers of
liver fibrosis, including a panel of non-
cholesterol sterols reflecting
cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for
liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV
infection. A stepwise multivariate logistic model selection was performed with
liver cirrhosis, defined as Ishak
fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI) in the paper, was based on the model: Log-odds (predicting
cirrhosis) = -12.17+ (age × 0.11) + (BMI (kg/m(2)) × 0.23) + (D7-
lathosterol (μg/100 mg
cholesterol)×(-0.013)) + (Platelet count (x10(9)/L) × (-0.018)) + (
Prothrombin-INR × 3.69). The area under the ROC curve (AUROC) for prediction of
cirrhosis was 0.91 (95% CI 0.86-0.96). The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98). In conclusion, the new index may
complement other methods in diagnosing
cirrhosis in patients with chronic HCV
infection.