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The Organ Procurement and Transplantation Network (OPTN) Kidney Allocation System provides a priority to sensitized candidates based on the calculated panel reactive antibody (CPRA) value. The human leukocyte antigen (HLA) haplotype reference panel used for calculation of the CPRA by the United Network for Organ Sharing (UNOS), the OPTN contractor, has limitations. We derived a novel panel from the National Marrow Donor Program HLA haplotype data set and compared the accuracy of CPRA values generated with this panel (NMDP-CPRA) to those generated from the UNOS panel (UNOS-CPRA), using predicted and actual deceased donor kidney offers for a cohort of 24 282 candidates. The overall accuracy for kidney offers was similar using NMDP-CPRA and UNOS-CPRA. Accuracy was slightly higher for NMDP-CPRA than UNOS-CPRA for candidates in several highly sensitized CPRA categories, with deviations in linkage disequilibrium for Caucasians and the smaller size of the UNOS panel as contributing factors. HLA data derived from stem cell donors yields CPRA values that are comparable to those derived from deceased kidney donors while improving upon several problems with the current reference panel. Consideration should be given to using stem cell donors as the reference panel for calculation of CPRA to improve equity in kidney transplant allocation.

Background Matching survival of a donor kidney with that of the recipient (longevity matching), is used in some kidney allocation systems to maximize graft-life years. It is not part of the allocation algorithm for Australia. Given the growing evidence of survival benefit due to longevity matching based allocation algorithms, development of a similar kidney allocation system for Australia is currently underway. The aim of this research is to estimate the impact that changes to costs and health outcomes arising from ‘longevity matching’ on the Australian healthcare system. Methods A decision analytic model to estimate cost-effectiveness was developed using a Markov process. Four plausible competing allocation options were compared to the current kidney allocation practice. Models were simulated in one-year cycles for a 20-year time horizon, with transitions through distinct health states relevant to the kidney recipient. Willingness to pay was considered as AUD 28000. Results Base case analysis indicated that allocating the worst 20% of Kidney Donor Risk Index (KDRI) donor kidneys to the worst 20% of estimated post-transplant survival (EPTS) recipients (option 2) and allocating the oldest 25% of donor kidneys to the oldest 25% of recipients are both cost saving and more effective compared to the current Australian allocation practice. Option 2, returned the lowest costs, greatest health benefits and largest gain to net monetary benefits (NMB). Allocating the best 20% of KDRI donor kidneys to the best 20% of EPTS recipients had the lowest expected incremental NMB. Conclusion Of the four longevity-based kidney allocation practices considered, transplanting the lowest quality kidneys to the worst kidney recipients (option 2), was estimated to return the best value for money for the Australian health system.

Stakeholders have expressed concerns regarding decreased deceased donor kidney transplant (DDKT) rates for pediatric candidates under the Kidney Allocation System (KAS). To better understand what might be driving this, we studied Scientific Registry of Transplant Recipients kidney offer data for 3642 pediatric (age

BACKGROUND AND OBJECTIVES: In December 2014, the Kidney Allocation System (KAS) was implemented to improve equity in access to transplantation, but preliminary studies in children show mixed results. Thus, we aimed to assess how the 2014 KAS policy change affected racial and ethnic disparities in pediatric kidney transplantation access and related outcomes. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed a retrospective cohort study of children

The EuroTransplant Kidney Allocation System (ETKAS) aims at allocating organs to patients on the waiting list fairly whilst optimizing HLA match grades. ETKAS currently considers the number of HLA-A, -B, -DR mismatches. Evidently, epitope matching is biologically and clinically more relevant. We here executed ETKAS-based computer simulations to evaluate the impact of epitope matching on allocation and compared the strategies. A virtual population of 400,000 individuals was generated using the National Marrow Donor Program (NMDP) haplotype frequency dataset of 2011. Using this population, a waiting list of 10,400 patients was constructed and maintained during simulation, matching the 2015 Eurotransplant Annual Report characteristics. Unacceptable antigens were assigned randomly relative to their frequency using HLAMatchmaker. Over 22,600 kidneys were allocated in 10 years in triplicate using Markov Chain Monte Carlo simulations on 32-CPU-core cloud-computing instances. T-cell epitopes were calculated using the www.pirche.com portal. Waiting list effects were evaluated against ETKAS for five epitope matching scenarios. Baseline simulations of ETKAS slightly overestimated reported average HLA match grades. The best balanced scenario maintained prioritisation of HLA A-B-DR fully matched donors while replacing the HLA match grade by PIRCHE-II score and exchanging the HLA mismatch probability (MMP) by epitope MMP. This setup showed no considerable impact on kidney exchange rates and waiting time. PIRCHE-II scores improved, whereas the average HLA match grade diminishes slightly, yet leading to an improved estimated graft survival. We conclude that epitope-based matching in deceased donor kidney allocation is feasible while maintaining equal balances on the waiting list., Author summary Kidney transplantation is the best treatment option for patients suffering permanent loss of kidney function. High degrees of histocompatibility between patients and organ donors improve long-term function of transplanted kidneys. In order to ensure fair access to transplantation whilst maximising utility of each donor kidney, organ allocation organizations established recipient waiting lists and well-balanced algorithms to allocate donors to patients. Changing the allocation algorithms requires careful consideration of side-effects to avoid disadvantages of certain groups of patients. In this study, we evaluated the feasibility of modifying the existing Eurotransplant Kidney Allocation System (ETKAS) to incorporate indirect T-cell epitope matching, a novel technique for assessing functional histocompatibility. Using Markov chain Monte Carlo simulations, we compared the modified allocation to the current algorithm and found an overall improvement of indirect T cell epitope compatibility. Simultaneously, we observed no negative impact on allocation fairness or waiting times. Our simulation framework may serve as a basis to evaluate further adjustments to ETKAS in the future. From our results, we conclude that epitope matching can be safely incorporated into ETKAS.

The OPTN's simultaneous liver-kidney (SLK) allocation policy, implemented August 10, 2017, established medical eligibility criteria for adult SLK candidates and created Safety Net kidney allocation priority for liver-alone recipients with new/continued renal impairment. OPTN SLK and kidney after liver (KAL) data were analyzed (registrations as of December 31, 2019, transplants pre-policy [March 20, 2015-August 9, 2017] vs. post-policy [August 10, 2017-December 31, 2019]). Ninety-four percent of SLK registrations met eligibility criteria (99% CKD: 50% dialysis, 50% eGFR). SLK transplant volume decreased from a record 740 (2017) to 676 (2018, -9%), with a subsequent increase to 728 (2019, 1.6% below 2017 volume). For KAL listings within 1 year of liver transplant, waitlist mortality rates declined post-policy versus pre-policy (27 [95% CI = 20.6-34.7] vs. 16 [11.7-20.5]) while transplant rates increased fourfold (46 [32.2-60.0] vs. 197 [171.6-224.7]). There were 234 KAL transplants post-policy (94% Safety Net priority eligible), and no significant difference in 1-year patient/graft survival vs. kidney-alone (patient: 95.9% KAL, 97.0% kidney-alone [p = .39]; graft: 94.2% KAL, 94.6% kidney-alone [p = .81]). From pre- to post-policy, the proportion of all deceased donor kidney and liver transplants that were SLK decreased (kidney: 5.1% to 4.3%; liver: 9.7% to 8.7%). SLK policy implementation interrupted the longstanding rise in SLK transplants, while Safety Net priority directed kidneys to liver recipients in need with thus far minimal impact to posttransplant outcomes.

The current kidney allocation system (KAS) preferentially allocates kidneys from blood type A2 or A2B (A/A2B) donors to blood type B candidates. We used national data to evaluate center-level performance of A2/A2B to B transplants, and organ procurement organization (OPO) reporting of type A or AB donor subtyping, in 5-year time periods prior to (2009-2014) and following (2015-2019) KAS implementation. The number of centers performing A2/A2B to B transplants increased from 17 pre-KAS to 76 post-KAS, though this still represents only a minority of centers (7.3% pre-KAS and 32.6% post-KAS). For high-performing centers, the median net increase in A2/A2B to B transplants was 19 cases (range -2-72) per center in the 5 years post-KAS. The median net increase in total B recipient transplants was 21 cases (range -17-119) per center. Despite requirements for performance of subtyping, in 2019 subtyping was reported on only 56.4% of A/AB donors. This translates into potential missed opportunities for B recipients, and even post-KAS up to 2322 A2/A2B donor kidneys may have been allocated for transplantation as A/AB. Further progress must be made both at center and OPO levels to broaden implementation of A2/A2B to B transplants for the benefit of underserved recipients.

BACKGROUND The Kidney Allocation System (KAS) includes a scoring system to match transplant candidate life expectancy with expected longevity of the donor kidney, and a backdating policy that gives waitlist time credit to patients waitlisted after starting dialysis treatment (post-dialysis). We estimated the effect of the KAS on employment among patient subgroups targeted by the policy. METHODS We used a sample selection model to compare employment after transplant before and after KAS implementation among patients on the kidney-only transplant waitlist between December 4, 2011 and December 31, 2017. RESULTS Post-dialysis transplant recipients aged 18-49 were significantly more likely to be employed 1-year post transplant in the post-KAS era compared to the pre-KAS era. Transplant recipients aged 35-64 with no dialysis treatment were significantly less likely to be employed 1 year after transplant in the post-KAS era compared to the pre-KAS era. CONCLUSIONS This study provides the first assessment of employment after DDKT under the KAS and provides important information about both the methods used to measure employment after transplant and the outcome under the KAS. Changes in employment after DDKT among various patient subgroups have important implications for assessing long-term patient and societal effects of the KAS and organ allocation policy.

Background Kidney allocation policy in Poland is strictly patient oriented. Kidneys are transplanted by the same center that made procurement. Sometimes kidneys are transferred for transplantation to another center due to medical or logistic reasons or according to allocation rules. Aim The aim of the study was to compare early graft and recipient survival rates in cases of kidneys exported for transplantation to another center and in cases of kidneys transplanted typically. Methods Data came from national transplant registry. Kidney transplants from the years 2008 to 2017 were divided into several groups. One-year graft and patient survival rates were calculated for each group, and statistical differences were cross-calculated between groups. Results Groups were compared by using the χ2 test. Statistical analysis showed significantly lower graft and recipient survival rates: in the group of kidney transplantations performed in another transplant centers vs group of kidneys not transferred, where kidney transplantations were performed in a regional transplant center (P = .005 and = .02); and in the group of kidneys transferred due to logistic reason and allocation rules vs group of kidneys not transferred, where kidney transplantations were performed in regional transplant center (P = .01 and = .04). Conclusion Early results of kidneys procured and transplanted by the same regional transplant center are significantly better than kidneys that have been transferred for transplantation to another center, but this result concerns only the subgroup of kidneys shifted due to logistic or allocation reasons. Early results of kidneys discarded by a regional team due to medical reasons and transplanted by another team are not different from results of transplantations performed by the same center that made the procurement.

Using nonideal kidneys for transplant quickly might reduce the discard rate of kidney transplants. We studied changing kidney allocation to eliminate sequential offers, instead making offers to multiple centers for all nonlocally allocated kidneys, so that multiple centers must accept or decline within the same 1 hour. If more than 1 center accepted an offer, the kidney would go to the highest-priority accepting candidate. Using 2010 Kidney-Pancreas Simulated Allocation Model-Scientific Registry for Transplant Recipients data, we simulated the allocation of 12 933 kidneys, excluding locally allocated and zero-mismatch kidneys. We assumed that each hour of delay decreased the probability of acceptance by 5% and that kidneys would be discarded after 20 hours of offers beyond the local level. We simulated offering kidneys simultaneously to small, medium-size, and large batches of centers. Increasing the batch size increased the percentage of kidneys accepted and shortened allocation times. Going from small to large batches increased the number of kidneys accepted from 10 085 (92%) to 10 802 (98%) for low-Kidney Donor Risk Index kidneys and from 1257 (65%) to 1737 (89%) for high-Kidney Donor Risk Index kidneys. The average number of offers that a center received each week was 10.1 for small batches and 16.8 for large batches. Simultaneously expiring offers might allow faster allocation and decrease the number of discards, while still maintaining an acceptable screening burden.

The kidney allocation system (KAS) is based on quality-based “longevity matching” strategies that provide only a momentary snapshot of expected outcomes at the time of transplantation. The purpose of our study was to define on a continuous timeline the relative and mutual interactions of donor and recipient characteristics on graft survival.Total 39,108 subjects who underwent kidney transplant between October 25, 1999 and January 1, 2007 were identified in the United Network for Organ Sharing dataset. Our primary outcome was graft survival. Time-dependent receiver operating characteristic (ROC) curves and area under time-dependent ROC curve (AUC) were used to compare the predictive ability of the two allocation systems.During the first year after transplantation, both donor and recipient models showed identical relevance. From the first to the sixth years, although the two ROC curves were nearly identical, the donor model outweighed the recipient model. Both models intersected again at the sixth year. From that time onward, the ROC curve for recipient characteristics model predominated over the ROC curve for donor characteristics model. The predictive value of the recipient model (AUC = 0.752) was greater than that of the donor model (AUC = 0.673)We hope that this model will provide additional guidance and risk stratification to further optimize organ allocation based on the dynamic interaction of both donor and recipient characteristics over time.

The Kidney Allocation System (KAS) has resulted in fewer pediatric kidneys being allocated to pediatric deceased donor kidney transplant (pDDKT) recipients. This had prompted concerns that post-pDDKT outcomes may worsen. To study this, we used SRTR data to compare the outcomes of 953 pre-KAS pDDKT (age

Introduction:The Kidney Allocation System in the United States prioritizes candidates with Estimated Post-Transplant Survival (EPTS) ≤20% to receive deceased donor kidneys with Kidney Donor Profile Index (KDPI) ≤20%.Research Question:We compared access to KDPI ≤ 20% kidneys for EPTS ≤ 20% candidates across the United States to determine whether geographic disparities in access to these low KDPI kidneys exist.Design:We identified all incident adult deceased donor kidney candidates wait-listed January 1, 2015, to March 31, 2018, using United Network for Organ Sharing data. We calculated the proportion of candidates transplanted, final EPTS, and KDPI of transplanted kidneys for candidates listed with EPTS ≤ 20% versus >20%. We compared the odds of receiving a KDPI ≤ 20% deceased donor kidney for EPTS ≤ 20% candidates across regions using logistic regression.Results:Among 121 069 deceased donor kidney candidates, 28.5% had listing EPTS ≤ 20%. Of these, 16.1% received deceased donor kidney transplants (candidates listed EPTS > 20%: 17.1% transplanted) and 12.3% lost EPTS ≤ 20% status. Only 49.4% of transplanted EPTS ≤ 20% candidates received a KDPI ≤ 20% kidney, and 48.3% of KDPI ≤ 20% kidneys went to recipients with EPTS > 20% at the time of transplantation. Odds of receiving a KDPI ≤ 20% kidney were highest in region 6 and lowest in region 9 (odds ratio 0.19 [0.13 to 0.28]). The ratio of KDPI ≤ 20% donors per EPTS ≤ 20% candidate and likelihood of KDPI ≤ 20% transplantation were strongly correlated ( r2= 0.84).Discussion:Marked geographic variation in the likelihood of receiving a KDPI ≤ 20% deceased donor kidney among transplanted EPTS ≤ 20% candidates exists and is related to differences in organ availability within allocation borders. Policy changes to improve organ sharing are needed to improve equity in access to low KDPI kidneys.

Purpose of review On 4 December 2014, the new kidney allocation system (KAS) went into effect. As part of this system, UNOS approved for the first time a national system with a specific mechanism affording priority to allocate kidneys across so-called 'minor ABO incompatibility' from blood group A2 donors into blood group B recipients. This significantly increased the number of such transplants done and the opportunities to learn about the specifics of such transplants. Recent findings A2 to B transplants have been demonstrated to be well tolerated, effective, and cost-effective ways of addressing disparities in the allocation system. Further data about the use of anti-A titers and the limits to successful transplant have better defined the bounds of who can benefit from such transplants. Summary The success thus far with A2 to B transplants should increase comfort and acceptance of the allocation policy changes and we should see further increases in centers willing to use such transplants to better address inequalities in the system.

The demand for kidney transplantation continues to exceed the availability of deceased donor kidneys. Balancing the overarching principles of the optimal use of (utility) and equal access to (equity) this scarce resource requires a sophisticated allocation system. This review will examine how various factors are addressed in allocation systems around the world to strike a balance between utility and equity.

The Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) policy regarding kidney allocation for liver transplantation (LT) patients was implemented in August 2017. This study evaluated the effects of the simultaneous liver-kidney transplantation (SLKT) policy on outcomes in LT alone (LTA) patients with kidney dysfunction. We analyzed adult primary LTA patients with kidney dysfunction at listing (estimated glomerular filtration rate [eGFR] less than 30 mL/minute or dialysis requirement) between January 2015 and March 2019 using the OPTN/UNOS registry. Waitlist practice and kidney transplantation (KT) listing after LTA were compared between prepolicy and postpolicy groups. There were 3821 LTA listings with eGFR

Rates of simultaneous liver kidney (SLK) transplantation in the United States have progressively risen. On 8/10/17, the Organ Procurement and Transplantation Network implemented a policy defining criteria for SLK, with a 'Safety Net' to prioritize kidney allocation to liver recipients with ongoing renal failure. We performed a retrospective review of the United Network for Organ Sharing (UNOS) database to evaluate policy impact on SLK, kidney after liver (KAL) and kidney transplant alone (KTA). Rates and outcomes of SLK and KAL transplants were compared, as was utilization of high-quality kidney allografts with Kidney Donor Profile Indices (KDPI) < 35%. Here, SLK transplants comprised 9.0% and 4.5% of total post-policy liver and kidney transplants compared to 10.2% and 5.5% prior. Policy enactment did not affect 1-year graft or patient survival for SLK and KAL populations. Less post-policy SLK transplants utilized high-quality kidney allografts; in all transplant settings, outcomes using high-quality grafts remained stable. These findings suggest that policy implementation has reduced kidney allograft use in SLK transplantation, although both SLK and KAL rates have recently increased. Despite decreased high-quality kidney allograft use, SLK and KAL outcomes have remained stable. Additional studies and long-term follow-up will ensure optimal organ access and sharing.

The new kidney allocation system (KAS) and the Estimated Post Transplant Survival (EPTS) score were introduced in the United States. The transplant rate among very high EPTS candidates (> 90%) may be impacted by lack of KAS priority and geographic factors based on the location of the listed Organ Procurement Organization (OPO).

The deceased donor kidney allocation system in the United States has undergone several rounds of iterative changes, but these changes were not explicitly designed to address the geographic variation in access to transplantation. The new allocation system, expected to start in December 2020, changes the definition of "local allocation" from the Donation Service Area to 250 nautical mile circles originating from the donor hospital. While other solid organs have adopted a similar approach, the larger number of both kidney transplant centers and transplant candidates is likely to have different consequences. Here, we discuss the incredible increase in complexity in allocation, discuss some of the likely intended and unintended consequences, and propose metrics to monitor the new system.

IntroductionThe Kidney Allocation System (KAS) implemented on December 4, 2014, was expected to improve kidney transplant list wait-time and allocate more kidney to high cPRA patients. This study aims to demonstrate outcomes after the implementation of the KAS in a Hispanic transplant center.MethodsRetrospective study from a prospectively maintained database from a single-transplant center. Included all DDKT from July 2013 to June 2016. Compare and analyze DDKT selection and outcomes before and after KAS implementation.ResultsThe overall number of kidney transplants performed during this period was 220. All of the patients were Hispanic. Included 50.5% Pre-KAS and 49.5% Post-KAS. Pre-KAS group had a significantly shorter waiting-time list than the post-KAS group, 900.05 vs 1126.75 respectively. EPTS less than 20% significantly improved in the Post-KAS group compared to the Pre-KAS group, 41.3% vs 20.9% respectively. No differences observed in KDPI, 1-year graft failure, or 1-year mortality rates.ConclusionThis might be the first Hispanic only cohort evaluating the effects of KAS on a moderate volume kidney transplant center. The new system increased the wait-time list by approximately 25%, and it did not improve graft quality, graft failure, or mortality rates.

Despite the shortage of kidneys for transplantation in the United States, approximately 18%-20% of deceased donor kidneys are discarded each year. These discarded kidneys can offer a survival benefit to suitable patients. Revisions to the current kidney allocation policy may be needed to reduce deceased donor kidney discard. We surveyed transplant physicians and patients to assess their perceived acceptability of policy proposals to reduce the discard of deceased donor kidneys. Members of professional societies (AST, ASTS) and a patient organization (AAKP) were invited to complete the survey. Responses were obtained from 97 physicians and 107 patients. The majority of physicians (73.4%) and patients (73.8%) "somewhat" or "completely" accepted a policy for fast-tracking kidneys at risk of discard. Physicians and patients also supported returning a proportion of waiting time to patients who accept KDPI >85 kidneys and experience graft failure within the first 12 months, with 36% of physicians and 50% of patients electing to return 100% of the waiting time. The majority of physicians (75%) "somewhat or completely" accepted a policy to skip less aggressive centers for KDPI 90 + offers. Physicians and patients provided insights into factors researchers, and policymakers should consider in the design and implementation of these policies.

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 the deceased donor (DD) waitlist. In simulations, we vary initial pool size, arrival rates of candidate/donor pairs and (living) non-directed 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 two years. With an initial pool of 400, respective candidate and NDD arrival rates of two 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 two years, and greatly reduces waiting times of KPDP candidates. Potential effects on waitlist candidates are discussed as are policy and ethical issues.

With a rising demand for kidney transplantation, reliable pre-transplant assessment of organ quality becomes top priority. In clinical practice, physicians are regularly in doubt whether suboptimal kidney offers from older donors should be accepted. Here, we externally validate existing prediction models in a European population of older deceased donors, and subsequently developed and externally validated an adverse outcome prediction tool. Recipients of kidney grafts from deceased donors 50 years of age and older were included from the Netherlands Organ Transplant Registry (NOTR) and United States organ transplant registry from 2006-2018. The predicted adverse outcome was a composite of graft failure, death or chronic kidney disease stage 4 plus within one year after transplantation, modelled using logistic regression. Discrimination and calibration were assessed in internal, temporal and external validation. Seven existing models were validated with the same cohorts. The NOTR development cohort contained 2510 patients and 823 events. The temporal validation within NOTR had 837 patients and the external validation used 31987 patients in the United States organ transplant registry. Discrimination of our full adverse outcome model was moderate in external validation (C-statistic 0.63), though somewhat better than discrimination of the seven existing prediction models (average C-statistic 0.57). The model's calibration was highly accurate. Thus, since existing adverse outcome kidney graft survival models performed poorly in a population of older deceased donors, novel models were developed and externally validated, with maximum achievable performance in a population of older deceased kidney donors. These models could assist transplant clinicians in deciding whether to accept a kidney from an older donor.

Background and Aims UK NHSBT kidney matching scheme changed in September 2019, aiming to better match graft and patient survival through stratification of donors and recipients into risk quartiles. We present data on two years of transplants aiming to highlight discrepancies between our unit and the model on which the scheme is based, and the potential implications on service provision of its’ introduction. Method We reviewed all deceased donor transplants in our centre in 2015 and 2016. Recipients and donors were re-classified into the risk index quartiles and endpoint data included inpatient days in first year, 1 year eGFR, survival, imaging, and infection episodes. Comparisons were made with NHSBT literature. Results 196 deceased donor transplants were performed. Distribution of D1-4 kidneys to R1-4 recipients in our cohort did not reflect those presented in the allocation scheme models, with our population skewed toward higher risk R4 category (73.4%), including 55 D4R4 (83% of D4 kidneys), see Figure 1. 2.0% had an age difference between donor and recipient of >25years, and 12.8% 15-25 years, compared with the NHSBT proposed targets of 8% and 20% respectively. Within the R4 group, recipients receiving a D4 graft were associated with a higher rate of DGF (41.7%, vs 23.2% D1-D3 grafts, p=0.009), longer index admission (median 11 days vs 8 days, p=0.038) and more readmissions within the first post-operative year (median 18 vs 11 days, p = 0.005) – Figure 2. D4 grafts demonstrated lower mean eGFR at one year (35.7, vs. 54.8 ml/min, p Day ward attendances, bacteraemia, and CT imaging events did not differ by R or D category; D4 was associated with higher rates of transplant ultrasound (5.6 vs R1-3 4.25, p=0.009), and R4 with higher rates of urinary tract infection (3.6 vs R1-3 1.5, p=0.03). Conclusion Firstly, our transplant population is weighted to higher risk R4 recipients; secondly, intended principals of the allocation scheme are already largely being observed. Thirdly, our data does suggest that increasing R4D4 transplants will have a significant impact on transplant centres, with resource burden primarily within the first year. But despite poorer graft function, patient survival appears to be equivalent and improved matching may in the longer term reduce need for re-implantation as the scheme intends.

a) Purpose of review: Allocation of deceased donor kidneys for transplantation has evolved since the first utilitarian approach in 1989. This review looks at how the schemes have evolved over the ensuing three decades. b) Recent findings: Four kidney offering schemes have been used in the last 30 years. Successive schemes have evolved from offering only one donor kidney for a nationally prioritised patient, to offering both kidneys; from addressing only kidneys from donors after brain death (DBD) to offering kidneys from both DBD and circulatory death donors (DCD); from prioritising purely on the basis of zero class 2 and minimising class 1 mismatches to a more relaxed approach for older donors and more difficult to match patients, while introducing the concept of longevity matching. c) Summary: UK kidney offering schemes have evolved through a series of evidence-based analyses to try to optimally address the principles of utility and fairness in kidney offering.

The Kidney Allocation System (KAS) was developed to improve equity and utility in organ allocation. We examine the effect of this change on kidney graft distribution and survival.UNOS data was used to identify first-time adult recipients of a deceased donor kidney-alone transplant pre-KAS (Jan 2012-Dec 2014, n = 26,612) and post-KAS (Jan 2015-Dec 2017, n = 30,701), as well as grafts recovered Jan 2012-Jun 2019.Post-KAS, kidneys were more likely to experience cold ischemia time24 h (20.0% vs. 18.8%, p 0.001) and experienced more delayed graft function, though competing risks modeling demonstrated a lower hazard of graft loss post-KAS, HR 0.90 (95% CI 0.84-0.97, p = 0.007). Post-policy, KDPI85% kidneys were more likely to be shared regionally (37% vs. 14%), and more likely to be discarded (60.6% vs. 54.9%) after the policy change. KDPI85% graft and patient survival did not change.Implementation of the KAS has increased sharing of high-KDPI kidneys and has decreased the hazard of graft loss without an impact on patient survival.

Introduction The Allocation System Changes for Equity in Kidney Transplantation (ASCENT) trial was a cluster-randomized pragmatic, effectiveness-implementation study designed to test whether a multicomponent educational intervention targeting leadership, clinic staff, and patients in dialysis facilities improved knowledge and awareness of the 2014 Kidney Allocation System (KAS) change. Methods Participants included 690 dialysis facility medical directors, nephrologists, social workers, and other staff within 655 US dialysis facilities, with 51% (n = 334) in the intervention group and 49% (n = 321) in the control group. Intervention activities included a webinar targeting medical directors and facility staff, an approximately 10-minute educational video targeting dialysis staff, an approximately 10-minute educational video targeting patients, and a facility-specific audit and feedback report of transplant performance. The control group received a standard United Network for Organ Sharing brochure. Provider knowledge was a secondary outcome of the ASCENT trial and the primary outcome of this study; knowledge was assessed as a cumulative score on a 5-point Likert scale (higher score = greater knowledge). Intention-to-treat analysis was used. Results At baseline, nonintervention providers had a higher mean knowledge score (mean ± SD, 2.45 ± 1.43) than intervention providers (mean ± SD, 2.31 ± 1.46). After 3 months, the average knowledge score was slightly higher in the intervention (mean ± SD, 3.14 ± 1.28) versus nonintervention providers (mean ± SD, 3.07 ± 1.24), and the estimated mean difference in knowledge scores between the groups at follow-up minus the mean difference at baseline was 0.25 (95% confidence interval [CI], 0.11–0.48; P = 0.039). The effect size (0.41) was low to moderate. Conclusion Dialysis facility provider education could help extend the impact of a national policy change in organ allocation., Graphical abstract

The impact of a new national kidney allocation system (KAS) on access to the national deceased-donor waiting list (waitlisting) and racial/ethnic disparities in waitlisting among US end-stage renal disease (ESRD) patients is unknown. We examined waitlisting pre- and post-KAS among incident (N = 1 253 100) and prevalent (N = 1 556 954) ESRD patients from the United States Renal Data System database (2005-2015) using multivariable time-dependent Cox and interrupted time-series models. The adjusted waitlisting rate among incident patients was 9% lower post-KAS (hazard ratio [HR]: 0.91; 95% confidence interval [CI], 0.90-0.93), although preemptive waitlisting increased from 30.2% to 35.1% (P < .0001). The waitlisting decrease is largely due to a decline in inactively waitlisted patients. Pre-KAS, blacks had a 19% lower waitlisting rate vs whites (HR: 0.81; 95% CI, 0.80-0.82); following KAS, disparity declined to 12% (HR: 0.88; 95% CI, 0.85-0.90). In adjusted time-series analyses of prevalent patients, waitlisting rates declined by 3.45/10 000 per month post-KAS (P < .001), resulting in ≈146 fewer waitlisting events/month. Shorter dialysis vintage was associated with greater decreases in waitlisting post-KAS (P < .001). Racial disparity reduction was due in part to a steeper decline in inactive waitlisting among minorities and a greater proportion of actively waitlisted minority patients. Waitlisting and racial disparity in waitlisting declined post-KAS; however, disparity remains.

The Kidney Allocation System (KAS) was implemented in December 2014 with unknown impact on the pediatric waitlist. To understand the effect of KAS on pediatric registrants, deceased donor kidney transplant (DDKT) rate was assessed using interrupted time series analysis and time-to-event analysis. Two allocation eras were defined with an intermediary washout period: Era 1 (01/01/2013-09/01/2014), Era 2 (09/01/2014-03/01/2015), and Era 3(03/01/2015-03/01/2017). When using Cox proportional hazards, there was no significant association between allocation era and DDKT likelihood as compared to Era 1 (Era 3: aHR: 1.07, 95% CI: 0.97-1.18, P = .17). However, this was not consistent across all subgroups. Specifically, while highly sensitized pediatric registrants were consistently less likely to be transplanted than their less sensitized counterparts, this disparity was attenuated in Era 3 (Era 1 aHR: 0.04, 95%CI: 0.01-0.14, P < .001; Era 3 aHR: 0.33, 95% CI: 0.21-0.53, P < .001) whereas the youngest registrants aged 0-6 experienced a 21% decrease in DDKT likelihood in Era 3 as compared to Era 1 (aHR: 0.79, 95% CI: 0.64-0.98, P = .03). Thus, while overall DDKT likelihood remained stable with the introduction of KAS, registrants ≤ 6 years of age were disadvantaged, warranting further study to ensure equitable access to transplantation.

The Kidney Allocation System fundamentally altered kidney allocation, causing a substantial increase in regional and national sharing that we hypothesized might impact geographic disparities. We measured geographic disparity in deceased donor kidney transplant (DDKT) rate under KAS (6/1/2015-12/1/2016), and compared that with pre-KAS (6/1/2013-12/3/2014). We modeled DSA-level DDKT rates with multilevel Poisson regression, adjusting for allocation factors under KAS. Using the model we calculated a novel, improved metric of geographic disparity: the median incidence rate ratio (MIRR) of transplant rate, a measure of DSA-level variation that accounts for patient casemix and is robust to outlier values. Under KAS, MIRR was 1.75 1.811.86 for adults, meaning that similar candidates across different DSAs have a median 1.81-fold difference in DDKT rate. The impact of geography was greater than the impact of factors emphasized by KAS: having an EPTS score ≤20% was associated with a 1.40-fold increase (IRR = 1.35 1.401.45 , P < .01) and a three-year dialysis vintage was associated with a 1.57-fold increase (IRR = 1.56 1.571.59 , P < .001) in transplant rate. For pediatric candidates, MIRR was even more pronounced, at 1.66 1.922.27 . There was no change in geographic disparities with KAS (P = .3). Despite extensive changes to kidney allocation under KAS, geography remains a primary determinant of access to DDKT.

In December 2014, the current kidney allocation system (KAS) was implemented after over 10 years of development by the OPTN Kidney Committee. The Kidney Committee noted several limitations with the prior kidney allocation schema and sought to improve many of these limitations. This review will discuss those goals and the available results at a 2-year review. Two-year results of the new kidney allocation system: This system increased the allocation of allografts with longer expected function as estimated by the Kidney Donor Profile Index (KDPI) to those patients with longer expected post-transplant survival as estimated by the recipients’ EPTS score or the estimated post-transplant survival score. A large increase in transplantation to those who are highly sensitized, calculated panel reactive antibody (CPRA) 98% or greater has also occurred as planned. Redefining the start of waiting time to the start of chronic dialysis time resulted in a great increase in minority transplantation and to those with a long time of dialysis. There was a small increase in transplantation to blood type B candidates. Challenges: Organ discards have still increased compared to before the implementation of the new KAS, likely due to factors not addressed by the KAS such as the regulatory concerns (performance metric flagging) and financial costs of transplanting higher KDPI organs and higher EPTS candidates. There was a large “bolus” effect in the first year, and possibly still ongoing, regarding increased access to very highly sensitized patients and those with very long dialysis waiting time. These last two patient populations, along with increased cold ischemic times due to 10% more of all transplants being non-local transplants, likely resulted in the slight decrease of 1-year outcomes and increase in DGF compared to pre-KAS, as well as decrease in the 0-ABDR mismatch transplants by 57% at the first year. Two-year results of the new KAS show that most of the intended goals of the policy are being realized, namely longevity matching, equitable access to transplantation for ethic groups and for those with equivalent dialysis time, and sensitized patients. Policymakers are still following the long-term outcomes from increased transplantation of the highly sensitized and outcomes from the higher DGF rates likely from increased cold ischemic time of non-local transplants and from transplant into patients with very long dialysis times. The hard decision-making process is still in play, balancing equity in access compared to utility: long-term successful graft and patient survival, particularly in a system that has much greater demand for deceased donor allografts than supply.

INTRODUCTION: To improve kidney transplant allocation equitability, a new Kidney Allocation System (KAS) was implemented December 4, 2014. The purpose of this study was to determine if the impact of KAS on peri-operative outcomes differed by recipient race/ethnicity. METHODS: This was a time series analysis using data aggregated in monthly intervals from October 2012 through September 2015 using the University HealthSystem Consortium (UHC). This includes national data aggregated at the center level of all US kidney transplant centers that participate in the UHC (416 centers). Segmented regression with interaction terms was used to determine the impact of KAS on outcomes and differences by race/ethnicity. RESULTS: A total of 28,809 deceased donor kidney transplants were included with 25 months of pre-KAS data and 10 months of post-KAS data. After KAS implementation, the estimated transplant rate per month decreased significantly for Caucasians by 17.6 cases per month (p = 0.0001), and increased significantly for AAs by 37.8 (p = 0.0001), Hispanics by 16. (p = 0.0001), and other races by 8.2 cases per month (p = 0.0001). Delayed graft function, 7- and 14-day readmissions significantly increased after KAS, which did not differ by race. Hispanics saw a 7.7% decrease in ICU admissions after KAS, which differed as compared to other racial/ethnic cohorts (p = 0.0026). Costs of kidney transplantation increased significantly after KAS in all groups except Hispanics. Mortality, length of stay, in-hospital complications, and 30-day readmissions were not significantly impacted by KAS, also not differing by race/ethnicity. CONCLUSION: KAS had substantial impact on transplant rates by race/ethnicity. KAS also led to increased costs, readmissions, and delayed graft function (DGF) across all racial/ethnic groups. The impact of KAS on ICU cases solely within Hispanics requires further investigation into potential etiologies.

Kidney Allocation System (KAS) was enacted in 2014 to improve graft utility, while facilitating transplantation of highly-sensitized patients and preserving pediatric access to high-quality kidneys. Central to this system is the Kidney Donor Profile Index (KDPI), a metric intended to predict transplant outcomes based on donor characteristics but derived using only adult donors. We posited that KAS had inadvertently altered the profile and quantity of kidneys made available to pediatric recipients. This question arose from our observation that most pediatric donors carry a KDPI over 35 and have therefore been rendered relatively inaccessible to pediatric recipients under KAS. Here we explore early trends in pediatric transplantation following KAS, including 1) use of pediatric donors, 2) use of Public Health System (PHS) high infectious risk donors, 3) wait time, and 4) living donor transplantation. We note some concerning preliminary changes following KAS implementation, including the allocation of fewer deceased donor pediatric kidneys to children and stagnation in pediatric wait times. Moreover, the poor predictive power of the KDPI for adult donors appears to be even worse when applied to pediatric donors. These early trends warrant further observation and consideration of changes in pediatric kidney allocation if they persist. This article is protected by copyright. All rights reserved.

The new kidney allocation system recommends local and regional sharing of deceased donor kidneys (DDK) with 86-100% Kidney Donor Profile Index (KDPI) to minimize discard. Regional sharing can increase cold ischemia time (CIT) which may negatively impact transplant outcomes. Using a same donor mate kidney model, we aimed to define a CIT that should be targeted to optimize outcomes. Using Organ Procurement and Transplant Network/United Network for Organ Sharing database, we identified recipients of DDK from 2000 to 2013 with ≥85% KDPI. From this cohort, three groups of mate kidney recipients were identified based on CIT: group 1 (≥24 vs. ≥12 to

It is unknown whether dialysis facility staff are aware of the new kidney allocation system implemented in December 2014, which changed how deceased donor kidneys are allocated and waiting time is calculated. U.S. dialysis facilities with low annual waitlisting (