Development of a Novel Model for Predicting Postoperative Short-Term Outcome in Patients with Hepatitis B-Related Acute-on-Chronic Liver Failure Undergoing Liver Transplantation

BACKGROUND We aimed to create a novel predictive model through comparing the prognostic accuracy of the current mainstream scoring models in predicting the short-term outcome of patients with hepatitis B-related acute-on-chronic liver failure (HBACLF) undergoing liver transplantation (LT). MATERIAL AND METHODS Data on patients with HBACLF undergoing LT were retrospectively collected and analyzed. The area under the time-dependent receiver operating characteristic curve of 16 scoring models was calculated to evaluate their performance in predicting short-term survival after LT. Univariate analyses and LASSO regression were used to identify the independent variables, which were further selected by Cox stepwise regression. RESULTS A total of 135 patients were enrolled. Among the 16 scoring models, MELD-Na performed the best in predicting 3-month mortality after LT, with an AUC of 0.716. LASSO regression analysis revealed that only the MELD-Na was confirmed as an independent predictor (HR 1.0481, 95% C.I [1.0136, 1.0838], P<0.05). Cox stepwise regression identified 4 variables - MELD-Na, sex, systemic infection, and placement of T-tube during operation - which were used to construct a novel prognostic model with a C-index of 0.844 and a Brier score of 0.131 after internal validation and a C-index of 0.824 (95% C.I [0.658, 0.989]) and a Brier score of 0.119 in the external validation cohort at 3 months. CONCLUSIONS Compared with other scoring models, MELD-Na was an independent factor in predicting short-term outcome after LT. The constructed novel predictive model could exert clinical benefits on early prognostic assessment and case selection.