Novel prognostic scoring models for hepatitis B virus-related acute-on-chronic liver failure: A comparison with classical models.

Early diagnosis and accurate prognostic evaluation are important for guiding clinical treatment and reducing mortality in patients with hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF). The present study established novel prognostic scoring models to guide the clinical treatment of patients with HBV-ACLF. We performed a retrospective analysis of clinical data from two cohorts of patients diagnosed with HBV-ACLF. By comparing differences in baseline characteristics and clinical indicators between the survival ( = 102) and dead ( = 64) groups in the derivation cohort( = 166), four laboratory indicators (age, INR, TBIL, and HBeAg status) and three clinical signs (extrahepatic infection, ascites, and hepatic encephalopathy) were identified as independent risk factors. Logistic regression and nomogram models were used to construct three novel predictive models. By comparing the death and survival groups, we found that the three new models had higher predictions for AUROC (average of 0.856) than the three old models (average of 0.773). Model 1 had the strongest predictive power for the short-term survival rate of HBV-ACLF patients. Finally, we verified the predictive value of the new models for HBV-ACLF in a validation cohort ( = 42), and the Model 2 demonstrated good predictive accuracy for the 30-day survival rate of patients. The novel model based on seven predictors could accurately predict short-term mortality in patients with HBV-ACLF, which is promising for guiding clinical management and addressing the aetiological differences in Asian populations.