. Kidney transplantation (KT) is a superior alternative to dialysis for many patients with respect to longevity, quality of life, morbidity, and the cost of End-Stage Renal Disease (ESRD) care. However, of the 95,000 patients waitlisted for a KT in 2017, less than 20,000 received a KT. In the same year, about 4,000 patients died, while another 4,700 were removed from the waitlist because they were considered too sick to transplant. Despite the long waitlist, more than 3,500 (19%) of the deceased donor kidneys were discarded. ?No Recipient Found? was the stated reason for discarding 1,325 of the donated kidneys. The discard rate increases significantly for the low-quality kidneys, reaching as high as 60% for the kidneys in the lowest quality decile. This discard occurs even though many discarded kidneys, even those in the lowest quality decile, would have conferred significant survival benefits to some recipients relative to remaining on dialysis. The current kidney allocation system does not efficiently allocate kidneys at high risk of discard. The patient prioritization algorithm does not quickly identify patients who will benefit most from such kidneys, and the donated kidneys are wasted. Improvements to the kidney allocation system are needed to reduce the number of discarded kidneys while allocating them equitably to patients who would most benefit. However, revising allocation policy is challenging because patients and transplant professionals must weigh the risks and benefits of accepting a low-quality kidney against the risks of waiting for a much higher quality kidney. The transplant community and patient preferences in accepting low-quality kidneys are unknown. The proposed study aims to develop kidney allocation algorithms that rescue viable deceased donor kidneys from discard. The developed algorithms will (i) identify kidneys at risk of discard using measures such as Kidney Donor Profile Index, or a novel Kidney Discard Risk Score; (ii) identify patient populations, such as those with longer waiting times to transplant or low functional status, who will benefit the most from viable kidneys. Preferences of the transplant clinicians (Surgeons and Nephrologists) and patients (on dialysis and transplant recipients) will be elicited using discrete-choice experiments that will quantify allocation efficiency and fairness tradeoffs for kidneys at risk of discard. A discrete event simulation software engine will be developed. The simulation engine will incorporate transplant center and patient-specific kidney acceptance behaviors, and the developed allocation algorithms. The benefits of different allocation algorithms will be evaluated in the simulation. Representatives from the United Network of Organ Sharing, Association of Organ Procurement Organizations, and the American Association of Kidney Patients will serve on the scientific advisory board. The developed allocation algorithms will help save approximately 1,000 lives yearly by reducing kidney discards. The research outcome will be widely disseminated to the transplant community to foster rapid implementation.

Public Health Relevance

. Of deceased donor kidneys, more than 3,500 were discarded in 2017, as more than 4,000 patients awaiting transplantation died and another 4,700 were removed from the waitlist because they were too sick to transplant. The US Department of Health and Human Services Final Rule requires to devise algorithms that efficiently and equitably allocated donated kidneys. The proposed study aims to develop and evaluate such algorithms for allocating kidneys at high risk of discard eliciting preferences of patients and transplant professionals, within a data-driven approach.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Research Project (R01)
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Health Services Organization and Delivery Study Section (HSOD)
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Abbott, Kevin C
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Northwestern University at Chicago
Internal Medicine/Medicine
Schools of Medicine
United States
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