OPTIMIZATION AND SIMULATION OF KIDNEY PAIRED DONATION PROGRAMS ABSTRACT An evolving strategy known as kidney paired donation (KPD) provides an approach to overcome the barriers faced by many patients with kidney failure who present with willing, but immunologically or blood type incompatible living donors. KPD programs use a computerized algorithm to match one incompatible donor/recipient pair to another pair with a complementary incompatibility, such that the donor of the first pair gives to the recipient of the second, and vice versa. More complex exchanges of organs involving three or more pairs are also considered as are altruistic or non-directed donors (NDD) who donate a kidney voluntarily and thereby have the potential to create a chain of kidney transplants. Such donors and chains have become increasingly important in KPD programs. Checking the viability of all potential transplants in a pool is not logistically possible, and so a fundamental problem in a KPD program is selecting an optimal subset of matches to consider among the many possibilities that exist. We have previously developed methods of selecting potential matches that take account of the uncertainty in the process; namely that potential transplants that are identified on a computer algorithm often fail when an attempt is made to put them into practice. We develop approaches to the problem that take account of this uncertainty and so provide new and better strategies for choosing potential matches with a view to presenting fall back options when potential transplants are found not to be viable. This approach has the potential to greatly increase the number and/or utility of transplants performed. In this renewal, we will build on initial successes and extend our methods to incorporate several important additional aspects of KPD. Our matching algorithms will be generalized to allow for nontraditional sources of donors, including donors from compatible pairs, deceased donors, and international KPD programs to unlock many potential transplants in existing KPDs. We will develop computational algorithms that will allow selection of larger subsets, which in turn will lead to a greater number of fallback options and increases in potential transplants. We will further develop our micro simulation model to include and examine the results of innovative sources of donors and to enhance the user interface and methods of visualization. We have developed and will refine calculators to predict outcomes such as graft survival based on donor and recipient characteristics and incorporate these calculations into our matching methods. This proposal aims to increase the number and quality of kidney transplants with associated benefits in patient quality of life and reduced medical costs.

Public Health Relevance

The USRDS reports that for the 2014 year, Medicare spent $30.9 billion to care for patients with End Stage Renal Disease (ESRD), or nearly 7.1% percent of all Medicare spending. When non?Medicare spending is factored in, total ESRD costs reached $35.3 billion or 1.6% of the $2.2 trillion the US spent on healthcare in 2007. Both in terms of patient outcomes and in terms of medical costs, transplantation, and especially living?donor transplantation is the preferred treatment of ESRD. Maintaining a patient who has received a kidney transplant is less expensive and, at the same time, the quality of life of the patient is improved. KPD provides an excellent opportunity to greatly increase the number of kidney transplants and is probably the area of research into treatment of ESRD with the greatest potential. This project would investigate ways in which the supply of suitable donors could be increased and further develop methods of matching to increase the quality and quantity of transplants done. The overarching aim is to improve patient outcomes while also reducing health care costs.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Abbott, Kevin C
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University of Michigan Ann Arbor
Schools of Public Health
Ann Arbor
United States
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