Abstract

We recently developed a simple novel index called fibrosis 6 (FIB-6) using machine learning data analysis. We aimed to evaluate its performance in the diagnosis of liver fibrosis and cirrhosis in chronic hepatitis B (CHB). A retrospective observational analysis of data was obtained from seven countries (Egypt, Kingdom of Saudi Arabia (KSA), Turkey, Greece, Oman, Qatar, and Jordan) of CHB patients. The inclusion criteria were receiving an adequate liver biopsy and a complete biochemical and hematological data. The diagnostic performance analysis of the FIB-6 index was conducted and compared with other non-invasive scores. A total of 603 patients were included for the analysis; the area under the receiver operating characteristic curve (AUROC) of FIB-6 for the discrimination of patients with cirrhosis (F4), compensated advanced chronic liver disease (cACLD) (F3 and F4), and significant fibrosis (F2-F4) was 0.854, 0.812, and 0.745, respectively. The analysis using the optimal cut-offs of FIB-6 showed a sensitivity of 70.9%, specificity of 84.1%, positive predictive value (PPV) of 40.3%, and negative predictive value (NPV) of 95.0% for the diagnosis of cirrhosis. For the diagnosis of cACLD, the results were 71.5%, 69.3%, 40.8%, and 89.2%, respectively, while for the diagnosis of significant fibrosis, the results were 68.3%, 67.5%, 59.9%, and 75.0%, respectively. When compared to those of fibrosis 4 (FIB-4) index, aspartate aminotransferase (AST)-to-platelet ratio index (APRI), and AST-to-alanine aminotransferase (ALT) ratio (AAR), the AUROC for the performance of FIB-6 was higher than that of FIB-4, APRI, and AAR in all fibrosis stages. FIB-6 gave the highest sensitivity and NPV (89.1% and 92.4%) in ruling out cACLD and cirrhosis, as compared to FIB-4 (63.8% and 83.0%), APRI (53.9% and 86.6%), and AAR (47.5% and 82.3%), respectively. The FIB-6 index could be used in ruling out cACLD, fibrosis, and cirrhosis with good reliability.

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