Literature DB >> 15153578

Incorporating recipient choice in kidney transplantation.

Xuanming Su1, Stefanos A Zenios, Glenn M Chertow.   

Abstract

Despite the acute shortage of cadaveric organs for kidney transplantation, more than 10% of cadaveric kidneys are discarded each year because of marginal quality. Transplant recipients' access to these kidneys and to information about their quality is limited. A Monte Carlo model was developed to simulate the operations of an organ procurement organization over a 10-yr period. Donor and recipient characteristics were generated from the United States Renal Data System. Kidneys were assigned one of five possible grades, which were determined by calculating the relative risk of graft failure associated with donor characteristics and HLA matching for every donor-candidate pair. Modeled were recipient decisions to accept or reject a kidney on the basis of the relative change in quality-adjusted life years (QALY). Compared were the United Network of Organ Sharing (UNOS) policy, the UNOS expanded donor criteria policy, two benchmark policies (one equity driven and the other efficiency driven), and a hybrid policy that incorporated recipient choice into the UNOS algorithm. Sensitivity analyses for major input variables were performed. Compared with UNOS, an algorithm that incorporated recipient choice predicted a 6% increase in QALY, a 12% decrease in median waiting time, a 39% increase in the likelihood of transplantation, and a 56% reduction in the number of discarded kidneys. Benefits were observed across categories of age, gender, and race. Incorporating recipient choice in kidney transplantation would improve equity, efficiency, and QALY of the end-stage renal disease population.

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Year:  2004        PMID: 15153578     DOI: 10.1097/01.asn.0000127866.34592.60

Source DB:  PubMed          Journal:  J Am Soc Nephrol        ISSN: 1046-6673            Impact factor:   10.121


  6 in total

1.  Accept/decline decision module for the liver simulated allocation model.

Authors:  Sang-Phil Kim; Diwakar Gupta; Ajay K Israni; Bertram L Kasiske
Journal:  Health Care Manag Sci       Date:  2014-08-30

2.  A discrete-event simulation model of the kidney transplantation system in Rajasthan, India.

Authors:  Mohd Shoaib; Utkarsh Prabhakar; Sumit Mahlawat; Varun Ramamohan
Journal:  Health Syst (Basingstoke)       Date:  2020-11-28

3.  A Systematic Review of Kidney Transplantation Decision Modelling Studies.

Authors:  Mohsen Yaghoubi; Sonya Cressman; Louisa Edwards; Steven Shechter; Mary M Doyle-Waters; Paul Keown; Ruth Sapir-Pichhadze; Stirling Bryan
Journal:  Appl Health Econ Health Policy       Date:  2022-08-09       Impact factor: 3.686

4.  The prognostic value of kidney transplant center report cards.

Authors:  J D Schold; L D Buccini; E L G Heaphy; D A Goldfarb; A R Sehgal; J Fung; E D Poggio; M W Kattan
Journal:  Am J Transplant       Date:  2013-05-24       Impact factor: 8.086

Review 5.  Predicting joint replacement waiting times.

Authors:  Lauren E Cipriano; Bert M Chesworth; Chris K Anderson; Gregory S Zaric
Journal:  Health Care Manag Sci       Date:  2007-06

6.  Patient preferences for the allocation of deceased donor kidneys for transplantation: a mixed methods study.

Authors:  Allison Tong; Stephen Jan; Germaine Wong; Jonathan C Craig; Michelle Irving; Steve Chadban; Alan Cass; Niamh Marren; Kirsten Howard
Journal:  BMC Nephrol       Date:  2012-04-18       Impact factor: 2.388

  6 in total

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