Abstract
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.
Highlights
Caused by the acute exacerbation of chronic hepatitis B (CHB), hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a severe life-threatening disease in patients who have previously diagnosed or undiagnosed chronic liver disease[1,2]
Three recent reviews regarding this topic have described standard model of end-stage liver disease (MELD) validation in advanced cirrhosis or ACLF patients compared to other MELD-based models[21,22,23]
Our research consists of two parts: (a) a systematic review conducted to identify relevant existing models for predicting the future risk of ACLF patients and (b) various statistical measures adopted to validate and compare the prognostic performance of different models in external longitudinal data and to choose the best model to assist clinical decision making for HBV-ACLF patients
Summary
Caused by the acute exacerbation of chronic hepatitis B (CHB), hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a severe life-threatening disease in patients who have previously diagnosed or undiagnosed chronic liver disease[1,2]. Few studies have validated ACLF models externally, no more than two or three studies exist and almost all were conducted in short-term survival cohorts. The objective of this study is to employ an external longitudinal dataset of HBV-ACLF patients to compare and validate various predictive models supporting the current recommendations in order to select the most effective predictive risk models to estimate short- and long-term mortality risk and facilitate decision-making about preferable therapeutics for LT-free patients. Our research consists of two parts: (a) a systematic review conducted to identify relevant existing models for predicting the future risk of ACLF patients and (b) various statistical measures adopted to validate and compare the prognostic performance of different models in external longitudinal data and to choose the best model to assist clinical decision making for HBV-ACLF patients
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