Literature DB >> 27676319

Risk Assessment in High- and Low-MELD Liver Transplantation.

A Schlegel1, M Linecker1, P Kron1, G Györi1, M L De Oliveira1, B Müllhaupt2, P-A Clavien1, P Dutkowski1.   

Abstract

Allocation of liver grafts triggers emotional debates, as those patients, not receiving an organ, are prone to death. We analyzed a high-Model of End-stage Liver Disease (MELD) cohort (laboratory MELD score ≥30, n = 100, median laboratory MELD score of 35; interquartile range 31-37) of liver transplant recipients at our center during the past 10 years and compared results with a low-MELD group, matched by propensity scoring for donor age, recipient age, and cold ischemia time. End points of our study were cumulative posttransplantation morbidity, cost, and survival. Six different prediction models, including donor age x recipient MELD (D-MELD), Difference between listing MELD and MELD at transplant (Delta MELD), donor-risk index (DRI), Survival Outcomes Following Liver Transplant (SOFT), balance-of-risk (BAR), and University of California Los Angeles-Futility Risk Score (UCLA-FRS), were applied in both cohorts to identify risk for poor outcome and high cost. All score models were compared with a clinical-oriented decision, based on the combination of hemofiltration plus ventilation. Median intensive care unit and hospital stays were 8 and 26 days, respectively, after liver transplantation of high-MELD patients, with a significantly increased morbidity compared with low-MELD patients (median comprehensive complication index 56 vs. 36 points [maximum points 100] and double cost [median US$179 631 vs. US$80 229]). Five-year survival, however, was only 8% less than that of low-MELD patients (70% vs. 78%). Most prediction scores showed disappointing low positive predictive values for posttransplantation mortality, such as mortality above thresholds, despite good specificity. The clinical observation of hemofiltration plus ventilation in high-MELD patients was even superior in this respect compared with D-MELD, DRI, Delta MELD, and UCLA-FRS but inferior to SOFT and BAR models. Of all models tested, only the BAR score was linearly associated with complications. In conclusion, the BAR score was most useful for risk classification in liver transplantation, based on expected posttransplantation mortality and morbidity. Difficult decisions to accept liver grafts in high-risk recipients may thus be guided by additional BAR score calculation, to increase the safe use of scarce organs.
© 2016 The American Society of Transplantation and the American Society of Transplant Surgeons.

Entities:  

Keywords:  clinical research/practice; donors and donation: donor evaluation; donors and donation: donor followup; liver allograft function/dysfunction; liver transplantation/hepatology

Mesh:

Year:  2016        PMID: 27676319     DOI: 10.1111/ajt.14065

Source DB:  PubMed          Journal:  Am J Transplant        ISSN: 1600-6135            Impact factor:   8.086


  22 in total

1.  Exosome-derived galectin-9 may be a novel predictor of rejection and prognosis after liver transplantation.

Authors:  Ai-Bin Zhang; Yi-Fan Peng; Jun-Jun Jia; Yu Nie; Shi-Yu Zhang; Hai-Yang Xie; Lin Zhou; Shu-Sen Zheng
Journal:  J Zhejiang Univ Sci B       Date:  2019-07       Impact factor: 3.066

2.  Race and Gender Disparity in the Surgical Management of Hepatocellular Cancer: Analysis of the Surveillance, Epidemiology, and End Results (SEER) Program Registry.

Authors:  Michael Darden; Geoffrey Parker; Dominique Monlezun; Edward Anderson; Joseph F Buell
Journal:  World J Surg       Date:  2021-04-23       Impact factor: 3.352

3.  Improved posttransplant mortality after share 35 for liver transplantation.

Authors:  Allison J Kwong; Aparna Goel; Ajitha Mannalithara; W Ray Kim
Journal:  Hepatology       Date:  2017-11-13       Impact factor: 17.425

4.  The liver transplant risk score prognosticates the outcomes of liver transplant recipients at listing.

Authors:  Christof Kaltenmeier; Dana Jorgensen; Stalin Dharmayan; Subhashini Ayloo; Vikrant Rachakonda; David A Geller; Samer Tohme; Michele Molinari
Journal:  HPB (Oxford)       Date:  2020-11-11       Impact factor: 3.647

5.  Retransplantation in Late Hepatic Artery Thrombosis: Graft Access and Transplant Outcome.

Authors:  Bettina M Buchholz; Shakeeb Khan; Miruna D David; Bridget K Gunson; John R Isaac; Keith J Roberts; Paolo Muiesan; Darius F Mirza; Dhiraj Tripathi; M Thamara P R Perera
Journal:  Transplant Direct       Date:  2017-07-05

6.  Early Acute Kidney Injury Associated with Liver Transplantation: A Retrospective Case-Control Study.

Authors:  Mengzhuo Guo; Yuanchao Gao; Linlin Wang; Haijing Zhang; Xian Liu; Huan Zhang
Journal:  Med Sci Monit       Date:  2020-07-18

7.  "Real-time" risk models of postoperative morbidity and mortality for liver transplants.

Authors:  Shigeru Marubashi; Naoaki Ichihara; Yoshihiro Kakeji; Hiroaki Miyata; Akinobu Taketomi; Hiroto Egawa; Yasutsugu Takada; Koji Umeshita; Yasuyuki Seto; Mitsukazu Gotoh
Journal:  Ann Gastroenterol Surg       Date:  2018-11-02

8.  Predictive Capacity of Risk Models in Liver Transplantation.

Authors:  Jacob D de Boer; Hein Putter; Joris J Blok; Ian P J Alwayn; Bart van Hoek; Andries E Braat
Journal:  Transplant Direct       Date:  2019-05-22

9.  Early Allograft Dysfunction Increases Hospital Associated Costs After Liver Transplantation-A Propensity Score-Matched Analysis.

Authors:  Simon Moosburner; Igor M Sauer; Frank Förster; Thomas Winklmann; Joseph Maria George Vernon Gassner; Paul V Ritschl; Robert Öllinger; Johann Pratschke; Nathanael Raschzok
Journal:  Hepatol Commun       Date:  2020-12-05

10.  Preoperative Stratification of Liver Transplant Recipients: Validation of the LTRS.

Authors:  Michele Molinari; Dana Jorgensen; Subhashini Ayloo; Stalin Dharmayan; Christof Kaltenmeier; Rajil B Mehta; Naudia Jonassaint
Journal:  Transplantation       Date:  2020-12       Impact factor: 5.385

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.