Evidence-based organ allocation

BACKGROUND: There are not enough cadaveric kidneys to meet the demands of t ransplant candidates. The equity and efficiency of alternative organ alloca tion strategies have not been rigorously compared. METHODS: We developed a five-compartment Monte Carlo simulation model to co mpare alternative organ allocation strategies, accommodating dynamic change s in recipient and donor characteristics, patient and graft survival rates, and quality of Life. The model simulated the operations of a single organ procurement organization and attempted to predict the evolution of the tran splant waiting list for 10 years. Four allocation strategies were compared: a first-come first-transplanted system; a point system currently utilized by the United Network of Organ Sharing; an efficiency-based algorithm that incorporated correlates of patient and graft survival; and a distributive e fficiency algorithm, which had an additional goal of promoting equitable al location among African-American and other candidates. RESULTS: A 10-year computer simulation vas performed. The distributive effi ciency policy was associated with a 3.5% +/- 0.8% (mean +/- SD) increase in quality-adjusted life expectancy (33.9 months vs 32.7 months), a decrease in the median waiting time to transplantation among those who were transpla nted (6.6 months vs 16.3 months), and an increase in the overall likelihood of transplantation (61% vs 45%), compared with the United Network of Organ Sharing algorithm. improved equity and efficiency were also seen by race ( African-American vs other), sex, and age (<50 or greater than or equal to 5 0 years). Sensitivity analyses did not appreciably change the qualitative r esults. CONCLUSION: Evidence-based organ allocation strategies in cadaveric kidney transplantation would yield improved equity and efficiency measures compare d with existing algorithms. (C) 1999 by Excerpta Medica, Inc.