Literature DB >> 22042468

Are there better guidelines for allocation in liver transplantation? A novel score targeting justice and utility in the model for end-stage liver disease era.

Philipp Dutkowski1, Christian E Oberkofler, Ksenija Slankamenac, Milo A Puhan, Erik Schadde, Beat Müllhaupt, Andreas Geier, Pierre A Clavien.   

Abstract

OBJECTIVES: To design a new score on risk assessment for orthotopic liver transplantation (OLT) based on both donor and recipient parameters.
BACKGROUND: The balance of waiting list mortality and posttransplant outcome remains a difficult task in the era of the model for end-stage liver disease (MELD).
METHODS: Using the United Network for Organ Sharing database, a risk analysis was performed in adult recipients of OLT in the United States of America between 2002 and 2010 (n = 37,255). Living donor-, partial-, or combined-, and donation after cardiac death liver transplants were excluded. Next, a risk score was calculated (balance of risk score, BAR score) on the basis of logistic regression factors, and validated using our own OLT database (n = 233). Finally, the new score was compared with other prediction systems including donor risk index, survival outcome following liver transplantation, donor-age combined with MELD, and MELD score alone.
RESULTS: Six strongest predictors of posttransplant survival were identified: recipient MELD score, cold ischemia time, recipient age, donor age, previous OLT, and life support dependence prior to transplant. The new balance of risk score stratified recipients best in terms of patient survival in the United Network for Organ Sharing data, as in our European population.
CONCLUSIONS: The BAR system provides a new, simple and reliable tool to detect unfavorable combinations of donor and recipient factors, and is readily available before decision making of accepting or not an organ for a specific recipient. This score may offer great potential for better justice and utility, as it revealed to be superior to recent developed other prediction scores.

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Year:  2011        PMID: 22042468     DOI: 10.1097/SLA.0b013e3182365081

Source DB:  PubMed          Journal:  Ann Surg        ISSN: 0003-4932            Impact factor:   12.969


  87 in total

Review 1.  Strategies to optimize the use of marginal donors in liver transplantation.

Authors:  Daniele Pezzati; Davide Ghinolfi; Paolo De Simone; Emanuele Balzano; Franco Filipponi
Journal:  World J Hepatol       Date:  2015-11-18

2.  Declining predictive performance of the MELD: Cause for concern or reflection of changes in clinical practice?

Authors:  Nadim Mahmud; David S Goldberg
Journal:  Am J Transplant       Date:  2019-10-23       Impact factor: 8.086

Review 3.  How important is donor age in liver transplantation?

Authors:  Alberto Lué; Estela Solanas; Pedro Baptista; Sara Lorente; Juan J Araiz; Agustin Garcia-Gil; M Trinidad Serrano
Journal:  World J Gastroenterol       Date:  2016-06-07       Impact factor: 5.742

4.  Barriers to transplantation in adults with inborn errors of metabolism.

Authors:  S M Sirrs; H Faghfoury; E M Yoshida; T Geberhiwot
Journal:  JIMD Rep       Date:  2012-08-22

5.  Use of BAR score as predictor of short and long-term survival of liver transplantation patients.

Authors:  Chung-Mau Lo
Journal:  Hepatol Int       Date:  2014-10-31       Impact factor: 6.047

6.  Graft Reconditioning before Liver Transplantation.

Authors:  Dieter P Hoyer; Thomas Minor
Journal:  Visc Med       Date:  2016-07-29

7.  Outcomes of liver transplantation for end-stage biliary disease: A comparative study with end-stage liver disease.

Authors:  Yan-Hua Lai; Wei-Dong Duan; Qiang Yu; Sheng Ye; Nian-Jun Xiao; Dong-Xin Zhang; Zhi-Qiang Huang; Zhan-Yu Yang; Jia-Hong Dong
Journal:  World J Gastroenterol       Date:  2015-05-28       Impact factor: 5.742

Review 8.  [Deceased donor liver transplantation].

Authors:  D Seehofer; W Schöning; P Neuhaus
Journal:  Chirurg       Date:  2013-05       Impact factor: 0.955

9.  Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.

Authors:  Lawrence Lau; Yamuna Kankanige; Benjamin Rubinstein; Robert Jones; Christopher Christophi; Vijayaragavan Muralidharan; James Bailey
Journal:  Transplantation       Date:  2017-04       Impact factor: 4.939

Review 10.  Predictive factors of short term outcome after liver transplantation: A review.

Authors:  Giuliano Bolondi; Federico Mocchegiani; Roberto Montalti; Daniele Nicolini; Marco Vivarelli; Lesley De Pietri
Journal:  World J Gastroenterol       Date:  2016-07-14       Impact factor: 5.742

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