On May 31, 2018, the US Department of Health and Human Services receiv ed a critical comment raising concerns that the existing deceased donor liver allocation policy may not have been in compliance with the Organ Procurement and Transplantation Network (OPTN) Final Rule on deceased donor organ allocation. The Final Rule requires that OPTN organ allocation policies “shall not be on the basis of the candidate’s place of residence or place of listing,” except to the extent required by other components of the Final Rule (42 CFR 121.8(a)(8)). To better align with the Final Rule requirements, the OPTN Kidney and Pancreas Transplantation Committees explored alternative policies that did not use donor service area (DSA) as a unit of distribution. They also discussed the feasibility of national organ distribution. However, shorter cold ischemia time is associated with improved kidney allograft outcomes.1 Cold ischemia time may increase with increasing distance between a candidate’s transplant center and donor hospital. Thus, the desire to limit cold ischemia time was the legal basis for including a policy preference for candidates more local to donors creating local priority and to “avoid wasting organs… and to promote the efficient management of organ placement” (42 CFR 121.8(a)(2)). Therefore, the OPTN requested simulations from the Scientific Registry of Transplant Recipients(SRTR),2 with a circle of 150 nautical miles (NMs) and 250 NM as the first unit of distribution. The 250 NM size was selected because it represents the distance when organ procurement teams would consider changing the mode of transportation from driving to flying. The committee also requested simulations with a circle size of 500 NMs, which is larger than the median size of an OPTN region (11 regions cover the entire country) (see Supplemental Methods, Supplemental Figure 1, Supplemental Tables 1–3).
Implications of Simulation Results
Using the Kidney-Pancreas Simulated Allocation Model (KPSAM),3 SRTR estimated the size of the allocation circle(s) was a more important determinant of travel distance for kidneys than the number of proximity points, with the greatest travel distance observed in larger circles (500 NM) and smaller distance traveled in smaller circles (150 NM). Figure 1A shows the distribution of organ travel distance for kidney-alone transplants.
Circle size was a more important determinant of travel distance than proximity points for pancreas transplants, similar to kidney-alone transplants. Figure 1, B and C shows the simulated distribution of organ travel distance for kidney-pancreas and pancreas-alone transplants. Proximity points affected the travel distance within the circle but not outside it, because the area outside the circle encompasses the rest of the country.
On the basis of these results and feedback from the transplant community, the OPTN Board of Directors approved new kidney and pancreas allocation systems, both of which use a single circle with a radius of 250 miles around the donor hospital, with a maximum of two proximity points within the circle and four proximity points outside the circle. The four points were awarded outside the circle until the distance is ≥2500 NMs. Supplemental Figure 2 shows the variability in kidney-alone transplant rates across the OPTN region is reduced by this new policy. The characteristics of the kidney, kidney-pancreas, and pancreas-alone transplant recipients for that simulation were compared for the following: (1) actual transplants in 2017; (2) ten simulations using existing allocation policy; and (3) ten simulations using the approved future allocation policy (Supplemental Tables 4–6) The characteristics of observed transplants that occurred under the existing allocation policy were similar to those of candidates simulated to receive transplants in simulations of the existing allocation policy.
However, in the 250 NM scenario compared with existing allocation policy, kidney transplants increased for pediatric, female, Black, Latino, and highly sensitized calculated panel reactive antibody (cPRA) >80%–98% candidates, and for those on dialysis for ≥5 years. The transplants in candidates with cPRA >98% were unchanged. Transplants decreased slightly for nonmetropolitan candidates and those with an adult estimated post-transplant survival score of 0%–20%. Kidney-pancreas transplants were higher for female, Black, non-Latino, and highly sensitized candidates (cPRA ≥80%). Pancreas-alone transplants were lower globally under broader sharing. Pancreas-alone candidates aged ≥35 years or with cPRA ≥80% underwent slightly more transplants than those aged <35 years or with cPRA <80%.
Allocation systems attempt to balance to equity versus efficacy.4 The simulations show a decline in variability in kidney transplants across the OPTN region with the new policy. There was a declinein the standard deviation across DSAs of median time on dialysis for kidney-alone transplants from 433 days (range, 419–446) for the existing policy versus 417 (range, 397–438) for the new policy, in the simulations. Thus, allocation using 250 NM circles is likely to improve equity compared with the existing system, which uses DSAs and OPTN regions. The median size of a DSA in the country is 98 NMs, and the median size of the OPTN regions is 265 NMs. The proximity points are for increasing efficacy by minimizing the distance an organ travels, and potentially reducing cold ischemia time inside and outside of the circle.
The simulation models predicted an increase in kidney-pancreas transplants (existing policy, 815; new policy, 1056) and a corresponding decrease in kidney-alone transplants (existing policy, 13,080; new policy, 12,830) and pancreas-alone transplants (existing policy, 158; new policy, 94). Importantly, the increase in kidney-pancreas transplants largely explained the decrease in kidney-alone transplant. Annually, the number of kidney-pancreas transplants is increasing, whereas pancreas-alone transplants are decreasing.5,6
The simulations have several limitations. Most important, the KPSAM cannot account for changes in transplant programs’ organ-acceptance behaviors.7 Additionally, the KPSAM cannot model the number of transplants because accurate information on the kidney discard process does not exist (e.g., acceptance models cannot include offers for eventually discarded kidneys). The simulations for the new policy predicted 5261 waitlist deaths in a year (range 5247–5271). This range overlaps with the deaths with the existing policy, which was predicted at 5237 waitlist deaths in a year (range 5207–5268). The large demand for kidney allografts is one reason why it is unlikely number of kidney transplants are going to decline and waitlist deaths increase significantly.
The KPSAM cannot predict changes in the supply of kidney allografts, such as those created by the opioid epidemic, because it only has information about donors whose kidneys were transplanted. For example, because the opioid epidemic remains ongoing, it is unlikely the numbers of deceased donors and kidney transplants will decrease with the new allocation policy. Because the KPSAM only uses historical data, it is best interpreted as the effect of different allocation systems during, for example, 2017, rather than the effect in the years after policy implementation, which are not yet observed.
The KPSAM cannot accurately predict the number of transplants at each transplant program because it assumes no differences in offer acceptance across programs, which is not true in practice. Our modeling did not account for cost, particularly the cost of transporting the allograft from the donor hospital to the recipient’s transplant program. Given the relatively longer distances in the new policy, transportation costs could increase. The KPSAM does not predict cold ischemia time. Cold ischemia time is not necessarily well correlated with the distance an organ travels because the receiving center can often expedite the crossmatch testing by requesting blood sample from the donor before organ recovery. This is currently done for some kidneys that come from afar. The SRTR will monitor the effect of the new policy on cold ischemia time.
The new policy does create borders; therefore, OPTN has already started working toward a borderless allocation systems.8
Supplemental Concise Methods for the SRTR Simulations
The OPTN committees wanted to provide candidates listed closer to the donor hospital priority to achieve best use of organs, while mitigating the effects of further travel logistics and outcomes (Supplemental Table 1, Supplemental Figure 1). The new policy retained features on the existing kidney policy, including risk stratification of deceased kidney donors using the Kidney Donor Profile Index and candidates by estimated post-transplant survival, among others (Supplemental Table 2). As in the existing system, the same allocation points will be used to rank candidates in each classification listed in Supplemental Table 3, but with the inclusion of proximity points (Supplemental Table 1).7 The new policy also retained features of the existing pancreas, kidney-pancreas, and islet policy that risk stratifies deceased donor pancreata from deceased donors aged ≤50 years with a body mass index ≤30 kg/m2.9
Study Population for Simulations
The SRTR data used included all deceased kidney donors, waitlist candidates, and transplant recipients in the United States and has been described previously.2 This included all transplant candidates on the kidney, kidney-pancreas, and pancreas waiting lists from January 1, 2017 to December 31, 2017, and any offers for a pancreas or kidney for an eventually accepted organ from donors recovered during this period.
This study used the KPSAM, which is routinely used by OPTN committees to assess potential policy proposals.3 The KPSAM simulates both the arrival of donated organs and new candidates on the waiting list over a 1-year period using actual SRTR data. With each new organ arrival, it mimics the actual organ allocation by generating a match run according to the programmed allocation policy. Specifically, it checks compatibility of organs with candidates on the waiting list at the time an organ becomes available, creates ordered lists of compatible candidates (candidates with more priority points have priority for receiving the organs over candidates with fewer points in each ordered list), and simulates candidate acceptance or refusal of organ offers using a logistic regression model. This logistic regression uses donor factors only, and is on the basis of actual organ acceptance behavior in 2017. The logistic regression model did not include candidate factors because the pool of candidates in a concentric circle–based policy would look different than in the existing allocation policy. On the basis of these inputs, KPSAM calculates the number of transplants and organs discarded. This process is repeated ten times in KPSAM for both the existing allocation policy and for each proposed allocation policy, each time randomly permuting the order of donor arrivals and generating new random numbers to determine organ offer acceptance. Statistical tests of comparisons are not useful because randomness of the simulation modeling is the only source of variability. Thus, all comparisons would have “statistically significant” differences if the simulation is repeated enough times. Instead, the average and range of results for the ten iterations are described for the existing and proposed policies.
A.K. Israni reports receiving grant fundingfrom CSL Behring and has been a member of an Advisory Board to CSL Behring, outside of this work; and reports receiving research funding from Gilead outside of this work. A. Wey reports patents and inventions with University of Minnesota, clinical severity questionnaire for Sanfilippo Syndrome; and reports other interests/relationships as a biostatistician on the federal contract for the SRTR. B. Thompson reports receiving research funding from Atara Biotherapeutics. M. Pavlakis reports receiving research funding as principal investigator for the APOL1 Long-Term Kidney Transplantation Outcomes Network study and PI on a trial for the Trugraf Genomics study TruGraft Long-term Clinical Outcomes Study; and reports other interests/relationships with EBSCO Industries Inc. as content writer, and Transplant Solutions as a consultant. J. Snyder reports receiving research funding from Astellas, Atara Biotherapeutics, CSL Behring, and Novartis; reports receiving honoraria from CareDx; reports being a scientific advisor or member as Board Member of the Organ Donation and Transplantation Alliance, Board Member of Donate Life America, LifeSource Clinical Policy Board, Transplantation as Associate Editor, American Journal of Transplantation as Statistical Editor; and reports other interests/relationships as Director of SRTR. P. Stock reports being a scientific advisor or member of Encellin. R. Kandaswamy reports consultancy agreements with CareDX, Natera, TRACT Therapeutics (pending), and Vertex Pharmaceuticals; reports being scientific advisor or member with LifeSource as a Member of Clinical Policy Board, Senior Staff of Pancreas Transplantation; and reports speakers bureau with Natera and SRTR. S. Niederhaus reports receiving honoraria from the American Diabetes Association (ADA) as a speaker in 2019 on pancreas transplantation and honorarium donated to the ADA; reports being a scientific advisor or member of the National Kidney Foundation of Maryland/Delaware Board of Directors Member, OPTN Pancreas Committee Chair through July 1, 2020; and reports other interests/relationships with American Society of Transplantation, American Society of Transplant Surgeons, International Pancreas & Islet Transplant Association, National Kidney Foundation of Maryland/Delaware as above, and other academic societies.
This work was supported by the US Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation, the Hennepin Healthcare Research Institute, contractor for the SRTR, as a deliverable under contract HHSH75R60220C00011 and United Network for Organ Sharing, contractor for the OPTN, under contract 250-2019-00001C.
As a US government-sponsored work, there are no restrictions on the use of this study. The views expressed herein are those of the authors and not necessarily those of the OPTN and US government. The authors thank SRTR colleague Ms. Mary Van Beusekom ELS, for manuscript editing.
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2020121679/DCSupplemental.
Supplemental Figure 1. Scenario modeled for new kidney and pancreas allocation policy.
Supplemental Figure 2. Maps of transplant rate by OPTN region for kidney transplants alone.
Supplemental Table 1. Overview of scenarios modeled for new kidney and pancreas allocation policy, along with existing policy.
Supplemental Table 2. The new policy retained features of the existing kidney policy, including risk-stratified deceased donor kidney using kidney donor profile index and candidates by estimated post-transplant survival. Inner circle refers to candidates within the concentric circle around the donor hospital.
Supplemental Table 3. (A) Priority point system for new kidney allocation identical to existing policy. Proximity points, as described in Table 1, were added to these priority points. (B) Priority points awarded based on the calculated panel-reactive antibodies identical to existing policy.
Supplemental Table 4. Characteristics of actual kidney recipients in 2017 and those in simulations of existing policy and proposed policy, percentage (n). The new kidney and pancreas allocation policies, both of which use a concentric single circle with a radius of 250 nautical miles around the donor hospital and a maximum of 2 proximity points within the circle 4 proximity points outside the circle.
Supplemental Table 5. Characteristics of actual kidney-pancreas recipients in 2017 and of recipients in simulations of existing policy and the new policy, percentage (n). The new kidney and pancreas allocation policies, both of which uses a concentric single circle with a radius of 250 nautical miles around the donor hospital and a maximum of 2 proximity points within the circle and 4 proximity points outside the circle.
Supplemental Table 6. Characteristics of actual pancreas recipients in 2017 and of recipients in simulations of existing policy and the new policy, percentage (n). The new kidney and pancreas allocation policies, both of which uses a concentric single circle with a radius of 250 nautical miles around the donor hospital and a maximum of 2 proximity points within the circle and 4 proximity points outside the circle.
1. Wong G, Teixeira-Pinto A, Chapman JR, Craig JC, Pleass H, McDonald S, et al.: The impact of total ischemic time, donor age and the pathway of donor death on graft outcomes after deceased donor kidney transplantation
. Transplantation 101: 1152–1158, 2017
2. Leppke S, Leighton T, Zaun D, Chen SC, Skeans M, Israni AK, et al.: Scientific Registry of Transplant Recipients: Collecting, analyzing, and reporting data on transplantation in the United States. Transplant Rev (Orlando) 27: 50–56, 2013
3. Scientific Registry of Transplan t Recipients. Kidney-Pancreas Simulated Allocation Model. 2015. Available at: https://www.srtr.org/requesting-srtr-data/simulated-allocation-models/
. Accessed June 2, 2021
4. Caplan A: Bioethics of organ transplantation. Cold Spring Harb Perspect Med 4: a015685, 2014
5. Kasiske BL, London W, Ellison MD: Race and socioeconomic factors influencing early placement on the kidney transplant waiting list. J Am Soc Nephrol 9: 2142–2147, 1998
6. Kandaswamy R, Stock PG, Gustafson SK, Skeans MA, Urban R, Fox A, et al.: OPTN/SRTR 2018 Annual Data Report: Pancreas. Am J Transplant 20[Suppl s1]: 131–192, 2020
7. Israni AK, Salkowski N, Gustafson S, Snyder JJ, Friedewald JJ, Formica RN, et al.: New national allocation policy for deceased donor kidneys in the United States and possible effect on patient outcomes. J Am Soc Nephrol 25: 1842–1848, 2014
8. Snyder JJ, Salkowski N, Wey A, Pyke J, Israni AK, Kasiske BL: Organ distribution without geographic boundaries: A possible framework for organ allocation. Am J Transplant 18: 2635–2640, 2018
9. Smith JM, Biggins SW, Haselby DG, Kim WR, Wedd J, Lamb K, et al.: Kidney, pancreas and liver allocation and distribution in the United States. Am J Transplant 12: 3191–3212, 2012