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Title: Multi-Organ Exchange
Kidney exchange, where candidates with organ failure trade incompatible but willing donors, is a life-saving alternative to the deceased donor waitlist, which has inadequate supply to meet demand. While fielded kidney exchanges see huge benefit from altruistic kidney donors (who give an organ without a paired needy candidate), a significantly higher medical risk to the donor deters similar altruism with livers. In this paper, we begin by exploring the idea of large-scale liver exchange, and show on demographically accurate data that vetted kidney exchange algorithms can be adapted to clear such an exchange at the nationwide level. We then propose cross-organ donation where kidneys and livers can be bartered for each other. We show theoretically that this multi-organ exchange provides linearly more transplants than running separate kidney and liver exchanges. This linear gain is a product of altruistic kidney donors creating chains that thread through the liver pool; it exists even when only a small but constant portion of the donors on the kidney side of the pool are willing to donate a liver lobe. We support this result experimentally on demographically accurate multi-organ exchanges. We conclude with thoughts regarding the fielding of a nationwide liver or joint liver-kidney exchange from a legal and computational point of view.  more » « less
Award ID(s):
1733556 1718457
PAR ID:
10077420
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Journal of Artificial Intelligence Research
Volume:
60
ISSN:
1076-9757
Page Range / eLocation ID:
639 to 679
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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