Hepatic macrosteatosis in the US pediatric deceased liver donor population.

INTRODUCTION: The pediatric obesity epidemic is associated with early development of hepatic macrosteatosis, a hallmark of non-alcoholic fatty LI disease, which is thought to be more rapidly progressive in children than adults. Macrosteatosis in adult allografts is associated with allograft loss, but this has not been examined in pediatric donors. METHODS: We studied all pediatric potential whole LI donors (2005-2018) who had a LI biopsy in the SRTR (n = 862) and whose LI was transplanted (n = 862). Macrosteatosis was abstracted from biopsy reports and compared to values in the SRTR standard analytic file. Recipients of macrosteatotic pediatric allografts were matched 1:1 to recipients of non-macrosteatotic pediatric allografts by propensity score matching on donor/recipient variables. All-cause allograft loss was estimated via Kaplan-Meier analysis and Cox proportional hazards model. RESULTS: From 2005 to 2018, the proportion of pediatric donors (age ≥2 years) with obesity increased (14.8% to 21.7%; p < .001), as did the proportion of pediatric deceased whole LI-only donor allografts with macrosteatosis (n = 10 648; 1.8% to 3.9%; p < .001). The median degree of macrosteatosis among macrosteatotic donors was 10% (IQR 5-30). There were no significant differences in all-cause allograft loss between recipients of pediatric LI allografts with and without macrosteatosis at 90 days (p = .11) or 1 year (p = .14) post-transplant in Kaplan-Meier analysis or a Cox proportional hazards model (p > .05). CONCLUSION: Obese pediatric LI donors have increased over time and were more likely to have hepatic macrosteatosis; however, pediatric macrosteatosis did not appear to adversely affect recipient outcomes.

Non-invasive evaluation of hepatic macrosteatosis in deceased donors.

INTRODUCTION: Liver 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%. METHODS: We 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. RESULTS: The 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. CONCLUSION: Commonly 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.