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BACKGROUND Acute-on-chronic liver failure (ACLF) patients have a high short-term mortality rate, and the severity evaluation of ACLF is necessary for prognostication. Therefore, it was meaningful to evaluate the association between type 2 diabetic mellitus (DM) and ACLF and further explore the feasibility of using DM as a prognostic indicator in ACLF patients. The association between type 2 DM and the prognosis of patients with severe liver disease remains unclear. AIM To examine the effect of type 2 DM on the prognosis of patients with ACLF. METHODS Clinical data from 222 ACLF patients were collected and analyzed. The patients were categorized into two groups depending on whether they had DM or not, and the clinical data of ACLF patients were measured within 48 h after admission. Complications of ACLF were documented during treatment, such as hepatic encephalopathy, hepatorenal syndrome, acute upper gastrointestinal hemorrhage, and spontaneous peritonitis (SBP). Values of laboratory parameters, complication rates, and hospital mortality rates were compared between two groups. RESULTS Among 222 ACLF patients, 38 cases were categorized into DM groups, the mean age was 56.32 years and 73.68% were male. The prognosis of ACLF patients was significantly correlated with DM in univariate [hazard ratio (HR) = 2.4, 95% confidence interval (CI) =1.5-3.7, P < 0.001] and multivariable analysis (HR = 3.17, 95%CI =1.82-5.523, P < 0.001). The incident of SBP (34.21% vs 13.59%, P = 0.038) and other infections like lung, urinary, blood, and cholecyst (44.74% vs 28.26%, P = 0.046) were higher in DM patients than non-DM counterparts. In addition, the ACLF patients with DM tended to have a high mortality rate ( P < 0.001). Cumulative survival time was also significantly shorter in the ACLF patients with DM than non-DM. CONCLUSION A significant association between DM and the prognosis of ACLF patients was found in China. The ACLF patients with DM had higher incidence of hospital mortality and infection than those without DM.

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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 treatmentnaive 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

Background and objective Though there are many advantages of pegylated interferon‐α (PegIFN‐α) treatment to chronic hepatitis B (CHB) patients, the response rate of PegIFN‐α is only 30 ~ 40%. Therefore, it is important to explore predictors at baseline and establish models to improve the response rate of PegIFN‐α. Methods We randomly divided 260 HBeAg‐positive CHB patients who were not previously treated and received PegIFN‐α monotherapy (180 μg/week) into a training dataset (70%) and testing dataset (30%). The intersect features were extracted from 50 routine laboratory variables using the recursive feature elimination method algorithm, Boruta algorithm, and Least Absolute Shrinkage and Selection Operator Regression algorithm in the training dataset. After that, based on the intersect features, eight machine learning models including Logistic Regression, k‐Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Naïve Bayes were applied to evaluate HBeAg seroconversion in HBeAg‐positive CHB patients receiving PegIFN‐α monotherapy in the training dataset and testing dataset. Results XGBoost model showed the best performance, which had largest AUROC (0.900, 95% CI: 0.85–0.95 and 0.910, 95% CI: 0.84–0.98, in training dataset and testing dataset, respectively), and the best calibration curve performance to predict HBeAg seroconversion. The importance of XGBoost model indicated that treatment time contributed greatest to HBeAg seroconversion, followed by HBV DNA(log), HBeAg, HBeAb, HBcAb, ALT, triglyceride, and ALP. Conclusions XGBoost model based on common laboratory variables had good performance in predicting HBeAg seroconversion in HBeAg‐positive CHB patients receiving PegIFN‐α monotherapy.