Validation of serum non-invasive tests of liver fibrosis as prognostic markers of clinical outcomes in people with fatty liver disease in Australia.

Abstract

Background and aim: The validity of non-invasive tests (NITs) of liver fibrosis for the prediction of liver and mortality outcomes in an Australian cohort is unknown. We aimed to verify the utility of available NITs to predict overall and cause-specific mortality and major adverse liver outcome (MALO).

Methods: This was an analysis from the Crossroads 1 clinic sub-study of a randomly sampled adult cohort from regional Australia between 2001 and 2003. Baseline variables included demographic details, anthropometry, health and lifestyle data, and laboratory tests. Non-alcoholic fatty liver disease (NAFLD) and metabolic-(dysfunction) associated fatty liver disease (MAFLD) were defined by fatty liver index ≥ 60 and other accepted criteria. Outcomes were defined by the International Statistical Classification of Diseases and Related Health Problems 10th Revision codes for linked hospitalization and death registry data. Available serum-based NITs were analyzed as predictors of overall, cardiovascular disease-related, and cancer-related mortality and MALO in those with fatty liver disease (FLD).

Results: In total, 1324 and 1444 participants were included for NAFLD and MAFLD analysis (prevalence 35.4% and 40.7%, respectively). There were 298 deaths (89 cardiovascular disease-related and 98 cancer-related) and 24 MALO over a median 19.7 years of follow-up time. In both forms of FLD, fibrosis-4 index, Steatosis-Associated Fibrosis Estimator score, and Forns fibrosis score consistently had the highest area under the receiver operating characteristic curve (AUROC) for overall and cause-specific mortality, with AUROC > 0.70 for each outcome. However, all had poor discriminatory ability for determining MALO in each FLD.

Conclusions: Several liver fibrosis NITs perform similarly reasonably well in predicting the risk of mortality outcomes in those with FLD but are poorly discriminatory for MALO prediction.