Background and Aims: It remains difficult to forecast the 180-day prognosis of patients with hepatitis B virus-acute-on-chronic liver failure (HBV-ACLF) using existing prognostic models. The present study aimed to derive novel-innovative models to enhance the predictive effectiveness of the 180-day mortality in HBV-ACLF. Methods: The present cohort study examined 171 HBV-ACLF patients (non-survivors, n=62; survivors, n=109). The 27 retrospectively collected parameters included the basic demographic characteristics, clinical comorbidities, and laboratory values. Backward step-wise logistic regression (LR) and the classification and regres-sion tree (CART) analysis were used to derive two predictive models. Meanwhile, a nomogram was created based on the LR analysis. The accuracy of the LR and CART model was detected through the area under the receiver operating char-acteristic curve (AUROC), compared with model of end-stage liver disease (MELD) scores. Results: Among 171 HBV-ACLF patients, the mean age was 45.17 years-old, and 11.7％ of the patients were female. The LR model was constructed with six independent factors, which included age, total bilirubin, prothrombin activity, lymphocytes, monocytes and hepatic encephalopathy. The following seven variables were the prog-nostic factors for HBV-ACLF in the CART model: age, total bilirubin, prothrombin time, lymphocytes, neutrophils, mono-cytes, and blood urea nitrogen. The AUROC for the CART model (0.878) was similar to that for the LR model (0.878, p=0.898), and this exceeded that for the MELD scores (0.728, p<0.0001). Conclusions: The LR and CART model are both superior to the MELD scores in predicting the 180-day mortal-ity of patients with HBV-ACLF. Both the LR and CART model can be used as medical decision-making tools by clinicians.