Non-invasive evaluation of hepatic macrosteatosis in deceased donors

Abstract

IntroductionLiver allocation changes have led to increased travel and expenditures, highlighting the need to efficiently identify marginal livers suitable for transplant. We evaluated the validity of existing non-invasive liver quality tests and a novel machine learning-based model at predicting deceased donor macrosteatosis >30%. MethodsWe compared previously-validated non-invasive tests and a novel machine learning-based model to biopsies in predicting macrosteatosis >30%. We also tested them in populations enriched for macrosteatosis. ResultsThe Hepatic Steatosis Index area-under-the-curve (AUC) was 0.56. At the threshold identified by Youden's J statistic, sensitivity, specificity, positive, and negative predictive values were 49.6%, 58.9%, 14.0%, and 89.7%. Other tests demonstrated comparable results. Machine learning produced the highest AUC (0.71). Even in populations enriched for macrosteatosis, no test was sufficiently predictive. ConclusionCommonly used clinical scoring systems and a novel machine learning-based model were not clinically useful, highlighting the importance of pre-procurement biopsies to facilitate allocation.