Objective: To build a mathematical model for diagnosing liver fibrosis progression by using conventional laboratory indicators, and to evaluate its clinical value of predicting hepatic fibrosis and hepatocirrhosis in chronic hepatitis B.
Methods: Liver biopsy and routine laboratory tests were performed in 391 patients with chronic hepatitis B. Using Multiple logistic regression to analyse evidently relevant indicators,then the models predicting for different stages of liver fibrosis were built and analyzed by receiver operating characteristic (ROC) curve.
Results: Age, platelet (PLT), international rate (INR), total bilirubin, albumin (ALB), aspartate aminotransferase, gamma-glutamyltranspeptidase (GGT), total bile acid and cholinesterase (CHE) were correlated with liver fibrosis stage. Multiple Logistic regression analysis showed that PLT, INR, ALB, GGT and CHE were independent predictors of three models ( S > or = 2, S > or = 3, S = 4). We finally built the predicting models and got Fibrosis scores (FS). ROC curve analysis revealed that the area under the curve was 0.784 in model-1 (S > or = 2), 0.768 in model-2 (S > or = 3) and 0.806 in model-3 (S = 4). A FS, cutoff point of 7.09 had 67.4% sensitivity, 79.3% specificity and 71.1% accuracy in Model-1. A FS2 cutoff point of 5.67 had 75.0% sensitivity, 67.7% specificity and 72.9% accuracy in Model-2. A FS3 cutoff point of 3.65 had 71.4% sensitivity, 78.5% specificity and 73.7% accuracy in Model-3.
Conclusion: The mathematical models using conventional laboratory indicators have fairly well value for predicting hepatic fibrosis progressing in chronic hepatitis B.