A novel non-invasive model for the prediction of advanced liver fibrosis in chronic hepatitis B patients with NAFLD

There are still lack of non-invasive models to evaluate liver fibrosis in chronic hepatitis B (CHB) patients with nonalcoholic fatty liver disease (NAFLD). We aimed to establish a predictive model for advanced fibrosis in these patients. A total of 504 treatment-naive CHB patients with NAFLD who underwent liver biopsy were enrolled and randomly divided into a training set (n = 336) and a validation set (n = 168). Receiver operating characteristic (ROC) curve was used to compare predicting accuracy for the different models. One hundred fifty-six patients (31.0%) had advanced fibrosis. In the training set, platelet, prothrombin time, type 2 diabetes, HBeAg positivity and globulin were significantly associated with advanced fibrosis by multivariable analysis. A predictive model namely PPDHG for advanced fibrosis was developed based on these parameters. The areas under the ROC curve (AUROC) of PPDHG with an optimal cut-off value of -0.980 in predicting advanced fibrosis was 0.817 (95% confidence interval 0.772 to 0.862), with a sensitivity of 81.82% and a specificity of 66.81%. The predicting accuracy of PPDHG for advanced fibrosis was significantly superior to AST to platelet ratio index (APRI), fibrosis-4 score (FIB-4) and NAFLD fibrosis score (NFS). Further analysis revealed that the AUROC of PPDHG remained significantly higher than FIB-4 and NFS indexes, while it was comparable with APRI for predicting advanced fibrosis in the validation set. PPDHG had a better predicting performance than established models for advanced fibrosis in CHB patients with NAFLD. The application of PPDHG can reduce the necessary for liver biopsy in these patients.