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Deceased donors as nondirected donors in kidney paired donation.

Wen WangMichael A ReesAlan B LeichtmanPeter X-K SongMathieu BrayValarie B AshbyTempie ShearonAndrew WhitemanJohn D Kalbfleisch
Published in: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons (2020)
As proof of concept, we simulate a revised kidney allocation system that includes deceased donor (DD) kidneys as chain-initiating kidneys (DD-CIK) in a kidney paired donation pool (KPDP), and estimate potential increases in number of transplants. We consider chains of length 2 in which the DD-CIK gives to a candidate in the KPDP, and that candidate's incompatible donor donates to theDD waitlist. In simulations, we vary initial pool size, arrival rates of candidate/donor pairs and (living) nondirected donors (NDDs), and delay time from entry to the KPDP until a candidate is eligible to receive a DD-CIK. Using data on candidate/donor pairs and NDDs from the Alliance for Paired Kidney Donation, and the actual DDs from the Scientific Registry of Transplant Recipients (SRTR) data, simulations extend over 2 years. With an initial pool of 400, respective candidate and NDD arrival rates of 2 per day and 3 per month, and delay times for access to DD-CIK of 6 months or less, including DD-CIKs increases the number of transplants by at least 447 over 2 years, and greatly reduces waiting times of KPDP candidates. Potential effects on waitlist candidates are discussed as are policy and ethical issues.
Keyphrases
  • kidney transplantation
  • healthcare
  • electronic health record
  • public health
  • big data
  • mental health
  • human health
  • machine learning
  • artificial intelligence
  • deep learning