Literature DB >> 28059835

Donor Liver Small Droplet Macrovesicular Steatosis Is Associated With Increased Risk for Recipient Allograft Rejection.

Won-Tak Choi1, Kuang-Yu Jen, Dongliang Wang, Mehdi Tavakol, John P Roberts, Ryan M Gill.   

Abstract

Although donor livers with <30% large droplet macrovesicular steatosis (MaS) and/or small droplet MaS (irrespective of percentage) are considered safe to use, this consensus is based on variable definitions of MaS subtypes and/or without a reproducible scoring system. We analyzed 134 donor liver biopsies from allografts transplanted at University of California at San Francisco between 2000 and 2015 to determine whether large and/or small droplet MaS is a risk factor for poor outcomes. Large droplet MaS was defined as a fat droplet occupying greater than one half of an individual hepatocyte, with nuclear displacement, and scored as the percentage of total parenchymal area replaced by large fat droplets on ×40 magnification. Small droplet MaS was defined as 1 to several discrete fat droplets, each occupying less than one half of an individual hepatocyte, and scored as the percentage of remaining hepatocytes (ie, hepatocytes not occupied by large fat droplets) containing small fat droplets on ×200 magnification (ie, small droplet MaS is the percentage of "remaining hepatocytes" with small fat droplets, and "remaining hepatocytes" is defined as 100% minus percent large droplet MaS). Thus, total MaS equals the sum of large and small droplet MaS, which cannot exceed 100%. Electronic medical records were reviewed to determine outcomes. There was an increased risk for acute cellular rejection (hazard ratio=2.5, P=0.0108) and bile duct loss suggestive of chronic ductopenic rejection (hazard ratio=2.4, P=0.0130) in donor livers with ≥30% small droplet MaS. Large droplet MaS (up to 60%) was not associated with adverse outcomes. Patient survival was not adversely affected by steatosis. Excellent agreement on the estimation of large (weighted κ=0.682) and small droplet MaS (weighted κ=0.780) was achieved. Our approach to donor steatosis scoring can identify liver allograft recipients at increased risk for rejection and highlights the importance of distinguishing between small and large droplet MaS in this evaluation.

Entities:  

Mesh:

Year:  2017        PMID: 28059835     DOI: 10.1097/PAS.0000000000000802

Source DB:  PubMed          Journal:  Am J Surg Pathol        ISSN: 0147-5185            Impact factor:   6.394


  6 in total

Review 1.  The dawn of liver perfusion machines.

Authors:  Danielle Detelich; James F Markmann
Journal:  Curr Opin Organ Transplant       Date:  2018-04       Impact factor: 2.640

2.  Donor Small-Droplet Macrovesicular Steatosis Affects Liver Transplant Outcome in HCV-Negative Recipients.

Authors:  Flaminia Ferri; Quirino Lai; Antonio Molinaro; Edoardo Poli; Lucia Parlati; Barbara Lattanzi; Gianluca Mennini; Fabio Melandro; Francesco Pugliese; Federica Maldarelli; Alessandro Corsi; Mara Riminucci; Manuela Merli; Massimo Rossi; Stefano Ginanni Corradini
Journal:  Can J Gastroenterol Hepatol       Date:  2019-05-02

3.  Banff consensus recommendations for steatosis assessment in donor livers.

Authors:  Desley A H Neil; Marta Minervini; Maxwell L Smith; Stefan G Hubscher; Elizabeth M Brunt; A Jake Demetris
Journal:  Hepatology       Date:  2021-12-06       Impact factor: 17.298

4.  Noninvasive Quantification of Liver Fat Content by Different Gradient Echo Magnetic Resonance Imaging Sequences in Patients with Nonalcoholic Fatty Liver Disease.

Authors:  Mansour Zabihzadeh; Mohammad Momen Gharibvand; Azim Motamedfar; Morteza Tahmasebi; Amir Hossein Sina; Kavous Bahrami; Mozafar Naserpour
Journal:  J Med Signals Sens       Date:  2018 Oct-Dec

5.  Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.

Authors:  Lulu Sun; Jon N Marsh; Matthew K Matlock; Ling Chen; Joseph P Gaut; Elizabeth M Brunt; S Joshua Swamidass; Ta-Chiang Liu
Journal:  EBioMedicine       Date:  2020-09-24       Impact factor: 8.143

6.  Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation.

Authors:  Fernando Pérez-Sanz; Miriam Riquelme-Pérez; Enrique Martínez-Barba; Jesús de la Peña-Moral; Alejandro Salazar Nicolás; Marina Carpes-Ruiz; Angel Esteban-Gil; María Del Carmen Legaz-García; María Antonia Parreño-González; Pablo Ramírez; Carlos M Martínez
Journal:  Sensors (Basel)       Date:  2021-03-12       Impact factor: 3.576

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.