Literature DB >> 31012528

Accelerating kidney allocation: Simultaneously expiring offers.

Michal A Mankowski1, Martin Kosztowski2,3, Subramanian Raghavan4, Jacqueline M Garonzik-Wang2, David Axelrod3, Dorry L Segev2,5,6, Sommer E Gentry2,6,7.   

Abstract

Using nonideal kidneys for transplant quickly might reduce the discard rate of kidney transplants. We studied changing kidney allocation to eliminate sequential offers, instead making offers to multiple centers for all nonlocally allocated kidneys, so that multiple centers must accept or decline within the same 1 hour. If more than 1 center accepted an offer, the kidney would go to the highest-priority accepting candidate. Using 2010 Kidney-Pancreas Simulated Allocation Model-Scientific Registry for Transplant Recipients data, we simulated the allocation of 12 933 kidneys, excluding locally allocated and zero-mismatch kidneys. We assumed that each hour of delay decreased the probability of acceptance by 5% and that kidneys would be discarded after 20 hours of offers beyond the local level. We simulated offering kidneys simultaneously to small, medium-size, and large batches of centers. Increasing the batch size increased the percentage of kidneys accepted and shortened allocation times. Going from small to large batches increased the number of kidneys accepted from 10 085 (92%) to 10 802 (98%) for low-Kidney Donor Risk Index kidneys and from 1257 (65%) to 1737 (89%) for high-Kidney Donor Risk Index kidneys. The average number of offers that a center received each week was 10.1 for small batches and 16.8 for large batches. Simultaneously expiring offers might allow faster allocation and decrease the number of discards, while still maintaining an acceptable screening burden.
© 2019 The American Society of Transplantation and the American Society of Transplant Surgeons.

Entities:  

Keywords:  Scientific Registry for Transplant Recipients (SRTR); clinical research/practice; delayed graft function (DGF); health services and outcomes research; kidney transplantation/nephrology; mathematical model; organ allocation; organ procurement and allocation

Year:  2019        PMID: 31012528      PMCID: PMC6812592          DOI: 10.1111/ajt.15396

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


  9 in total

1.  DonorNet and the potential effects on organ utilization.

Authors:  D A Gerber; C J Arrington; S E Taranto; T Baker; R S Sung
Journal:  Am J Transplant       Date:  2010-04       Impact factor: 8.086

2.  Simulating the allocation of organs for transplantation.

Authors:  David Thompson; Larry Waisanen; Robert Wolfe; Robert M Merion; Keith McCullough; Ann Rodgers
Journal:  Health Care Manag Sci       Date:  2004-11

3.  Impact of the new fast track kidney allocation scheme for declined kidneys in the United Kingdom.

Authors:  Alan D White; Heather Roberts; Clare Ecuyer; Kathryn Brady; Samir Pathak; Brendan Clark; Lutz H Hostert; Magdy S Attia; Matthew Wellberry-Smith; Alex Hudson; Niaz Ahmad
Journal:  Clin Transplant       Date:  2015-09-28       Impact factor: 2.863

4.  Center-level variation in the development of delayed graft function after deceased donor kidney transplantation.

Authors:  Babak J Orandi; Nathan T James; Erin C Hall; Kyle J Van Arendonk; Jacqueline M Garonzik-Wang; Natasha Gupta; Robert A Montgomery; Niraj M Desai; Dorry L Segev
Journal:  Transplantation       Date:  2015-05       Impact factor: 4.939

Review 5.  Big data in organ transplantation: registries and administrative claims.

Authors:  A B Massie; L M Kucirka; L M Kuricka; D L Segev
Journal:  Am J Transplant       Date:  2014-08       Impact factor: 8.086

6.  The aggressive phenotype: center-level patterns in the utilization of suboptimal kidneys.

Authors:  J M Garonzik-Wang; N T James; K C Weatherspoon; N A Deshpande; J A Berger; E C Hall; R A Montgomery; D L Segev
Journal:  Am J Transplant       Date:  2011-10-12       Impact factor: 8.086

7.  Early Outcomes of the New UK Deceased Donor Kidney Fast-Track Offering Scheme.

Authors:  Chris J Callaghan; Lisa Mumford; Laura Pankhurst; Richard J Baker; J Andrew Bradley; Christopher J E Watson
Journal:  Transplantation       Date:  2017-12       Impact factor: 4.939

8.  The Relationships Between Cold Ischemia Time, Kidney Transplant Length of Stay, and Transplant-related Costs.

Authors:  Oscar K Serrano; David M Vock; Srinath Chinnakotla; Ty B Dunn; Raja Kandaswamy; Timothy L Pruett; Roger Feldman; Arthur J Matas; Erik B Finger
Journal:  Transplantation       Date:  2019-02       Impact factor: 4.939

9.  Association of Cold Ischemia Time With Acute Renal Transplant Rejection.

Authors:  Merve Postalcioglu; Arnaud D Kaze; Benjamin C Byun; Andrew Siedlecki; Stefan G Tullius; Edgar L Milford; Julie M Paik; Reza Abdi
Journal:  Transplantation       Date:  2018-07       Impact factor: 4.939

  9 in total
  1 in total

1.  Number of Donor Renal Arteries and Early Outcomes after Deceased Donor Kidney Transplantation.

Authors:  S Ali Husain; Kristen L King; Shelief Robbins-Juarez; Joel T Adler; Kasi R McCune; Sumit Mohan
Journal:  Kidney360       Date:  2021-09-08
  1 in total

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