A comprehensive validation of HBV-related acute-on-chronic liver failure models to assist decision-making in targeted therapeutics

This research utilized an external longitudinal dataset of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) to compare and validate various predictive models that support the current recommendations to select the most effective predictive risk models to estimate short- and long-term mortality and facilitate decision-making about preferable therapeutics for HBV-ACLF patients. Twelve ACLF prognostic models were developed after a systematic literature search using the longitudinal data of 232 HBV-ACLF patients on the waiting list for liver transplantation (LT). Four statistical measures, the constant (A) and slope (B) of the fitted line, the area under the curve (C) and the net benefit (D), were calculated to assess and compare the calibration, discrimination and clinical usefulness of the 12 predictive models. According to the model calibration and discrimination, the logistic regression models (LRM2) and the United Kingdom model of end-stage liver disease(UKELD) were selected as the best predictive models for both 3-month and 5-year outcomes. The decision curve summarizes the benefits of intervention relative to the costs of unnecessary treatment. After the comprehensive validation and comparison of the currently used models, LRM2 was confirmed as a markedly effective prognostic model for LT-free HBV-ACLF patients for assisting targeted and standardized therapeutic decisions.