Development of a Simple Noninvasive Model to Predict Significant Fibrosis in Patients with Chronic Hepatitis B: Combination of Ultrasound Elastography, Serum Biomarkers, and Individual Characteristics

OBJECTIVES: The accurate assessment of liver fibrosis is clinically important in patients with chronic hepatitis B (CHB). Blood tests and elastography are now widely used for the noninvasive diagnosis of liver fibrosis in CHB patients. The aim of this study was to develop a new and more accurate predictive model, which combines elastography data, serum biomarkers, and individual characteristics, to discriminate between CHB patients with and without significant liver fibrosis. METHODS: Two noninvasive methods, specifically, an ultrasound elastography technique termed acoustic radiation force impulse imaging (ARFI) and a blood test, were used to assess a cohort of 345 patients (estimation group, 218 patients; validation group, 127 patients) with CHB. Multivariate logistic regression analysis revealed that ARFI, the aspartate aminotransferase (AST) to platelet ratio, and age were significantly associated with fibrosis. Based on these results, we constructed and validated a model for the diagnosis of significant hepatic fibrosis. RESULTS: The area under the receiver operating characteristic (ROC) curve was 0.921 for the estimation group and 0.929 for the validation group, significantly higher than those for ARFI (0.887, 0.893) and for the AST-to-platelet ratio index (APRI; 0.811, 0.859). Using an optimal cutoff of 3.05 in the validation group, all the indices of the proposed model, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic odds ratio, were better than those for ARFI or APRI. CONCLUSIONS: We developed a simple noninvasive model that used ultrasound elastography, routine serum biomarkers, and individual characteristics to accurately differentiate significant fibrosis in patients with CHB. Compared with elastography or the biomarker index alone, this model was significantly more accurate and robust.