Calculating Life Years from Transplant (LYFT): Methods for Kidney and Kidney‐Pancreas Candidates

• Published in: American journal of transplantation Vol. 8; no. 4p2; pp. 997 - 1011
• Main Authors: Wolfe, R. A., McCullough, K. P., Schaubel, D. E., Kalbfleisch, J. D., Murray, S., Stegall, M. D., Leichtman, A. B.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.04.2008
• Subjects: Cadaver; Cox regression; deceased donor kidneys; diabetes mellitus; Graft Survival; Humans; kidney allocation; Kidney Failure, Chronic - surgery; Kidney Failure, Chronic - therapy; kidney transplant; Kidney Transplantation - physiology; Life Expectancy; Liver Transplantation - physiology; Models, Statistical; Models, Theoretical; OPTN; Renal Replacement Therapy - statistics & numerical data; simultaneous kidney‐pancreas transplant; SRTR; survival; Time Factors; Tissue and Organ Procurement - statistics & numerical data; Tissue Donors; United States; waiting list; waiting list mortality; United States
• ISSN: 1600-6135; 1600-6143; 1600-6143
• DOI: 10.1111/j.1600-6143.2008.02177.x

The Organ Procurement and Transplantation Network (OPTN) Kidney Committee is considering a proposal for a new deceased donor kidney allocation system. Among the components under consideration is a strategy to rank candidates in part by the estimated incremental years of life that are expected to be achieved with a transplant from a specific available deceased donor, computed as the difference in expected median lifespan with that transplant compared with remaining on dialysis. This concept has been termed life years from transplant or LYFT. Median lifespans could be calculated, based on objective medical criteria, for each candidate when a deceased donor kidney becomes available, based on Cox regression models using current candidate and donor medical information. The distribution of the calculated LYFT scores for an average nonexpanded criteria donor kidney is similar across candidate sex, race/ethnicity, insurance status and, with the exception of diabetes, diagnosis. LYFT scores tend to be higher for younger candidates and lower for diabetics receiving a kidney‐alone rather than a simultaneous kidney‐pancreas transplant. Prioritizing candidates with higher LYFT scores for each available kidney could substantially increase total years of life among both transplant candidates and recipients. LYFT is also a powerful metric for assessing trends in allocation outcomes and for comparing alternative allocation systems.