Newly developed sarcopenia after liver transplantation, determined by a fully automated 3D muscle volume estimation on abdominal CT, can predict post-transplant diabetes mellitus and poor survival outcomes
Sae‐Jin Park, Jeong Hee Yoon, Ijin Joo, Jeong Min Lee
Abstract
BACKGROUND: Loss of muscle mass is the most common complication of end-stage liver disease and negatively affects outcomes for liver transplantation (LT) recipients. We aimed to determine the prognostic value of a fully automated three-dimensional (3D) muscle volume estimation using deep learning algorithms on abdominal CT in patients who underwent liver transplantation (LT). METHODS: This retrospective study included 107 patients who underwent LT from 2014 to 2015. Serial CT scans, including pre-LT and 1- and 2-year follow-ups were performed. From the CT scans, deep learning-based automated body composition segmentation software was used to calculate muscle volumes in 3D. Sarcopenia was calculated by dividing average skeletal muscle area by height squared. Newly developed-(ND) sarcopenia was defined as the onset of sarcopenia 1 or 2 years after LT in patients without a history of sarcopenia before LT. Patients' clinical characteristics, including post-transplant diabetes mellitus (PTDM) and Model for end-stage liver disease score, were compared according to the presence or absence of sarcopenia after LT. A subgroup analysis was performed in the post-LT sarcopenic group. The Kaplan-Meier method was used for overall survival (OS). RESULTS: Patients with ND-sarcopenia had poorer OS than those who did not (P = 0.04, hazard ratio [HR], 3.34; 95% confidence interval [CI] 1.05 - 10.7). In the subgroup analysis for post-LT sarcopenia (n = 94), 34 patients (36.2%) had ND-sarcopenia. Patients with ND-sarcopenia had significantly worse OS (P = 0.002, HR 7.12; 95% CI 2.00 - 25.32) and higher PTDM occurrence rates (P = 0.02, HR 4.93; 95% CI 1.18 - 20.54) than those with sarcopenia prior to LT. CONCLUSION: ND-sarcopenia determined by muscle volume on abdominal CT can predict poor survival outcomes and the occurrence of PTDM for LT recipients.