The unwell patient with advanced chronic liver disease: when to use each score?

Abstract

Background: Prognostication in chronic liver disease and the implementation of appropriate scoring systems is difficult given the variety of clinical manifestations. It is important to understand the limitations of each scoring system as well as the context and patient group from which it was developed to allow appropriate application. This review seeks to explore the optimal clinical uses of different predictive scores developed for compensated and decompensated chronic liver disease, acute on chronic liver failure, and hepatocellular carcinoma. We will also review future areas of research for each score and current gaps in the literature.

Main body: The Child-Pugh score is the pre-eminent prediction score for liver disease that was developed through empiric selection of relevant variables. It is useful for selection of patients for surgical resection of hepatocellular carcinoma but is inferior to other scores for other clinically relevant endpoints such as survival in acute decompensations. The Model for End-Stage Liver Disease (MELD) score and subsequent variants (MELD-Na, MELD 3.0) were developed to predict mortality following elective transjugular intrahepatic portosystemic shunt (TIPS) insertion. An alternative is the Frieberg Index of Post-TIPS Survival (FIPS) score, which has been externally validated for TIPS populations. Organ allocation for liver transplantation is also currently prioritised using the MELD score, with the MELD 3.0 reducing waitlist gender discrepancies. The Chronic Liver Failure Consortium (CLIF-C) acute decompensation (AD) and acute-on-chronic liver failure (ACLF) scores are used for predicting mortality in cirrhotic patients with acute decompensation of liver disease and acute-on-chronic liver failure, respectively. Both scores were developed from retrospective analyses of an observational European cohort with external validation. Understanding of ACLF presentation of advanced liver disease remains in the preliminary stages. Improving collective understanding is important to optimise prognostication. The albumin-bilirubin score is a non-invasive predictor of survival in patients with hepatocellular carcinoma. Incorporating artificial intelligence to personalise predictive algorithms may provide the most effective prognostication for all clinical phenotypes.

Conclusion: We summarised key prognostic scores used in advanced liver disease and make recommendations for the optimal uses. Nuances in the development and implementation of each are discussed to help guide effective use.