Literature DB >> 32732838

Outcomes After Declining a Steatotic Donor Liver for Liver Transplant Candidates in the United States.

Kyle R Jackson1, Mary G Bowring1, Courtenay Holscher1, Christine E Haugen1, Jane J Long1, Luckmini Liyanage1, Allan B Massie1, Shane Ottmann1, Benjamin Philosophe1, Andrew M Cameron1, Dorry L Segev1,2,3, Jacqueline Garonzik-Wang1.   

Abstract

BACKGROUND: Steatotic donor livers (SDLs, ≥30% macrosteatosis on biopsy) are often declined, as they are associated with a higher risk of graft loss, even though candidates may wait an indefinite time for a subsequent organ offer. We sought to quantify outcomes for transplant candidates who declined or accepted an SDL offer.
METHODS: We used Scientific Registry of Transplant Recipients offer data from 2009 to 2015 to compare outcomes of 759 candidates who accepted an SDL to 13 362 matched controls who declined and followed candidates from the date of decision (decline or accept) until death or end of study period. We used a competing risk framework to understand the natural history of candidates who declined and Cox regression to compare postdecision survival after declining versus accepting (ie, what could have happened if candidates who declined had instead accepted).
RESULTS: Among those who declined an SDL, only 53.1% of candidates were subsequently transplanted, 23.8% died, and 19.4% were removed from the waitlist. Candidates who accepted had a brief perioperative risk period within the first month posttransplant (adjusted hazard ratio [aHR]: 2.493.494.89, P < 0.001), but a 62% lower mortality risk (aHR: 0.310.380.46, P < 0.001) beyond this. Although the long-term survival benefit of acceptance did not vary by candidate model for end-stage liver disease (MELD), the short-term risk period did. MELD 6-21 candidates who accepted an SDL had a 7.88-fold higher mortality risk (aHR: 4.807.8812.93, P < 0.001) in the first month posttransplant, whereas MELD 35-40 candidates had a 68% lower mortality risk (aHR: 0.110.320.90, P = 0.03).
CONCLUSIONS: Appropriately selected SDLs can decrease wait time and provide substantial long-term survival benefit for liver transplant candidates.

Entities:  

Mesh:

Year:  2020        PMID: 32732838     DOI: 10.1097/TP.0000000000003062

Source DB:  PubMed          Journal:  Transplantation        ISSN: 0041-1337            Impact factor:   4.939


  4 in total

1.  Decreasing Significance of Early Allograft Dysfunction with Rising Use of Nonconventional Donors.

Authors:  Stephanie Ohara; Elizabeth Macdonough; Lena Egbert; Abigail Brooks; Blanca Lizaola-Mayo; Amit K Mathur; Bashar Aqel; Kunam S Reddy; Caroline C Jadlowiec
Journal:  Medicina (Kaunas)       Date:  2022-06-17       Impact factor: 2.948

2.  The vexing triad of obesity, alcohol, and coagulopathy predicts the need for multiple operations in liver transplantation.

Authors:  Hunter B Moore; Yanik J Bababekov; James J Pomposelli; Megan A Adams; Cara Crouch; Dor Yoeli; Rashikh A Choudhury; Tanner Ferrell; James R Burton; Elizabeth A Pomfret; Trevor L Nydam
Journal:  Am J Surg       Date:  2022-02-19       Impact factor: 3.125

Review 3.  Normothermic Machine Perfusion-Improving the Supply of Transplantable Livers for High-Risk Recipients.

Authors:  Angus Hann; Anisa Nutu; George Clarke; Ishaan Patel; Dimitri Sneiders; Ye H Oo; Hermien Hartog; M Thamara P R Perera
Journal:  Transpl Int       Date:  2022-05-31       Impact factor: 3.842

4.  Machine learning to predict waitlist dropout among liver transplant candidates with hepatocellular carcinoma.

Authors:  Allison Kwong; Bilal Hameed; Shareef Syed; Ryan Ho; Hossein Mard; Sahar Arshad; Isaac Ho; Tashfeen Suleman; Francis Yao; Neil Mehta
Journal:  Cancer Med       Date:  2022-01-14       Impact factor: 4.452

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.