Heterogeneous donor circles for fair liver transplant allocation

The United States (U.S.) Department of Health and Human Services is interested in increasing geographical equity in access to liver transplant. The geographical disparity in the U.S. is fundamentally an outcome of variation in the organ supply to patient demand (s/d) ratios across the country (which cannot be treated as a single unit due to its size). To design a fairer system, we develop a nonlinear integer programming model that allocates the organ supply in order to maximize the minimum s/d ratios across all transplant centers. We design circular donation regions that are able to address the issues raised in legal challenges to earlier organ distribution frameworks. This allows us to reformulate our model as a set-partitioning problem. Our policy can be viewed as a heterogeneous donor circle policy, where the integer program optimizes the radius of the circle around each donation location. Compared to the current policy, which has fixed radius circles around donation locations, the heterogeneous donor circle policy greatly improves both the worst s/d ratio and the range between the maximum and minimum s/d ratios. We found that with the fixed radius policy of 500 nautical miles (NM), the s/d ratio ranges from 0.37 to 0.84 at transplant centers, while with the heterogeneous circle policy capped at a maximum radius of 500 NM, the s/d ratio ranges from 0.55 to 0.60, closely matching the national s/d ratio average of 0.5983. Our model matches the supply and demand in a more equitable fashion than existing policies and has a significant potential to improve the liver transplantation landscape.

Removing geographic boundaries from liver allocation: A method for designing continuous distribution scores

BACKGROUND

The Organ Procurement and Transplantation Network (OPTN) is eliminating geographic boundaries in liver allocation, in favor of continuous distribution. Continuous distribution allocates organs via a composite allocation score (CAS): a weighted sum of attributes like medical urgency, candidate biology, and placement efficiency. The opportunity this change represents, to include new variables and features for prioritizing candidates, will require lengthy and contentious discussions to establish community consensus. Continuous distribution could instead be implemented rapidly by computationally translating the allocation priorities for pediatric, status 1, and O/B blood type liver candidates that are presently implemented via geographic boundaries into points and weights in a CAS.

METHODS

Using simulation with optimization, we designed a CAS that is minimally disruptive to existing prioritizations, and that eliminates geographic boundaries and minimizes waitlist deaths without harming vulnerable populations.

RESULTS

Compared with Acuity Circles (AC) in a 3-year simulation, our optimized CAS decreased deaths from 7771.2 to 7678.8 while decreasing average (272.66 NM vs. 264.30 NM) and median (201.14 NM vs. 186.49 NM) travel distances. Our CAS increased travel only for high MELD and status 1 candidates (423.24 NM vs. 298.74 NM), and reduced travel for other candidates (198.98 NM vs. 250.09 NM); overall travel burden decreased.

CONCLUSION

Our CAS reduced waitlist deaths by sending livers for high-MELD and status 1 candidates farther, while keeping livers for lower MELD candidates nearby. This advanced computational method can be applied again after wider discussions of adding new priorities conclude; our method designs score weightings to achieve any specified feasible allocation outcomes.

Logistical burden of offers and allocation inefficiency in circle-based liver allocation

Recent changes to liver allocation replaced donor service areas with circles as the geographic unit of allocation. Circle-based allocation might increase the number of transplantation centers and candidates required to place a liver, thereby increasing the logistical burden of making and responding to offers on organ procurement organizations and transplantation centers. Circle-based allocation might also increase distribution time and cold ischemia time (CIT), particularly in densely populated areas of the country, thereby decreasing allocation efficiency. Using Scientific Registry of Transplant Recipient data from 2019 to 2021, we evaluated the number of transplantation centers and candidates required to place livers in the precircles and postcircles eras, nationally and by donor region. Compared with the precircles era, livers were offered to more candidates (5 vs. 9; p  < 0.001) and centers (3 vs. 5; p  < 0.001) before being accepted; more centers were involved in the match run by offer number 50 (9 vs. 14; p  < 0.001); CIT increased by 0.2 h (5.9 h vs. 6.1 h; p  < 0.001); and distribution time increased by 2.0 h (30.6 h vs. 32.6 h; p  < 0.001). Increased burden varied geographically by donor region; livers recovered in Region 9 were offered to many more candidates (4 vs. 12; p  < 0.001) and centers (3 vs. 8; p  < 0.001) before being accepted, resulting in the largest increase in CIT (5.4 h vs. 6.0 h; p  < 0.001). Circle-based allocation is associated with increased logistical burdens that are geographically heterogeneous. Continuous distribution systems will have to be carefully designed to avoid exacerbating this problem.

Life expectancy without a transplant for status 1A liver transplant candidates

Status 1A liver transplant candidates are given the highest medical priority for the allocation of deceased donor livers. Organ Procurement and Transplantation Network (OPTN) policy requires physicians to certify that a candidate has a life expectancy without a transplant of less than 7 days for that candidate to be given status 1A. Additionally, candidates receiving status 1A must have one of six medical conditions listed in policy. Using Scientific Registry of Transplant Recipients data from all prevalent liver transplant candidates from 2010 to 2020, we used a bias-corrected Kaplan-Meier model to calculate the survival of status 1A candidates and to determine their life expectancy without a transplant. We found that status 1A candidates have a life expectancy without a transplant of 24 (95% CI 20-46) days-over three times longer than what policy requires for status 1A designation. We repeated the analysis for subgroups of status 1A candidates based on the medical conditions that grant status 1A. We found that none of these subgroups met the life expectancy requirement. Harmonizing OPTN policy with observed data would sustain the integrity of the allocation process.

MELD 3.0: The Model for End-Stage Liver Disease Updated for the Modern Era

BACKGROUND & AIMS

The Model for End-Stage Liver Disease (MELD) has been established as a reliable indicator of short-term survival in patients with end-stage liver disease. The current version (MELDNa), consisting of the international normalized ratio and serum bilirubin, creatinine, and sodium, has been used to determine organ allocation priorities for liver transplantation in the United States. The objective was to optimize MELD further by taking into account additional variables and updating coefficients with contemporary data.

METHODS

All candidates registered on the liver transplant wait list in the US national registry from January 2016 through December 2018 were included. Uni- and multivariable Cox models were developed to predict survival up to 90 days after wait list registration. Model fit was tested using the concordance statistic (C-statistic) and reclassification, and the Liver Simulated Allocation Model was used to estimate the impact of replacing MELDNa with the new model.

RESULTS

The final multivariable model was characterized by (1) additional variables of female sex and serum albumin, (2) interactions between bilirubin and sodium and between albumin and creatinine, and (3) an upper bound for creatinine at 3.0 mg/dL. The final model (MELD 3.0) had better discrimination than MELDNa (C-statistic, 0.869 vs 0.862; P < .01). Importantly, MELD 3.0 correctly reclassified a net of 8.8% of decedents to a higher MELD tier, affording them a meaningfully higher chance of transplantation, particularly in women. In the Liver Simulated Allocation Model analysis, MELD 3.0 resulted in fewer wait list deaths compared to MELDNa (7788 vs 7850; P = .02).

CONCLUSION

MELD 3.0 affords more accurate mortality prediction in general than MELDNa and addresses determinants of wait list outcomes, including the sex disparity.

Correcting the sex disparity in MELD-Na

MELD-Na appears to disadvantage women awaiting liver transplant by underestimating their mortality rate. Fixing this problem involves: (1) estimating the magnitude of this disadvantage separately for each MELD-Na, (2) designing a correction for each MELD-Na, and (3) evaluating corrections to MELD-Na using simulated allocation. Using Kaplan-Meier modeling, we calculated 90-day without-transplant survival for men and women, separately at each MELD-Na. For most scores between 15 and 35, without-transplant survival was higher for men by 0-5 percentage points. We tested two proposed corrections to MELD-Na (MELD-Na-MDRD and MELD-GRAIL-Na), and one correction we developed (MELD-Na-Shift) to target the differences we quantified in survival across the MELD-Na spectrum. In terms of without-transplant survival, MELD-Na-MDRD overcorrected sex differences while MELD-GRAIL-Na and MELD-Na-Shift eliminated them. Estimating the impact of implementing these corrections with the liver simulated allocation model, we found that MELD-Na-Shift alone eliminated sex disparity in transplant rates (p = 0.4044) and mortality rates (p = 0.7070); transplant rates and mortality rates were overcorrected by MELD-Na-MDRD (p = 0.0025, p = 0.0006) and MELD-GRAIL-Na (p = 0.0079, p = 0.0005). We designed a corrected MELD-Na that eliminates sex disparities in without-transplant survival, but allocation changes directing smaller livers to shorter candidates may also be needed to equalize women's access to liver transplant.

MELD is MELD is MELD? Transplant center-level variation in waitlist mortality for candidates with the same biological MELD

Recently, model for end-stage liver disease (MELD)-based liver allocation in the United States has been questioned based on concerns that waitlist mortality for a given biologic MELD (bMELD), calculated using laboratory values alone, might be higher at certain centers in certain locations across the country. Therefore, we aimed to quantify the center-level variation in bMELD-predicted mortality risk. Using Scientific Registry of Transplant Recipients (SRTR) data from January 2015 to December 2019, we modeled mortality risk in 33 260 adult, first-time waitlisted candidates from 120 centers using multilevel Poisson regression, adjusting for sex, and time-varying age and bMELD. We calculated a "MELD correction factor" using each center's random intercept and bMELD coefficient. A MELD correction factor of +1 means that center's candidates have a higher-than-average bMELD-predicted mortality risk equivalent to 1 bMELD point. We found that the "MELD correction factor" median (IQR) was 0.03 (-0.47, 0.52), indicating almost no center-level variation. The number of centers with "MELD correction factors" within ±0.5 points, and between ±0.5-± 1, ±1.0-±1.5, and ±1.5-±2.0 points was 62, 41, 13, and 4, respectively. No centers had waitlisted candidates with a higher-than-average bMELD-predicted mortality risk beyond ±2 bMELD points. Given that bMELD similarly predicts waitlist mortality at centers across the country, our results support continued MELD-based prioritization of waitlisted candidates irrespective of center.

Liver simulated allocation model does not effectively predict organ offer decisions for pediatric liver transplant candidates

The SRTR maintains the liver-simulated allocation model (LSAM), a tool for estimating the impact of changes to liver allocation policy. Integral to LSAM is a model that predicts the decision to accept or decline a liver for transplant. LSAM implicitly assumes these decisions are made identically for adult and pediatric liver transplant (LT) candidates, which has not been previously validated. We applied LSAM's decision-making models to SRTR offer data from 2013 to 2016 to determine its efficacy for adult (≥18) and pediatric (<18) LT candidates, and pediatric subpopulations-teenagers (≥12 to <18), children (≥2 to <12), and infants (<2)-using the area under the receiver operating characteristic (ROC) curve (AUC). For nonstatus 1A candidates, all pediatric subgroups had higher rates of offer acceptance than adults. For non-1A candidates, LSAM's model performed substantially worse for pediatric candidates than adults (AUC 0.815 vs. 0.922); model performance decreased with age (AUC 0.898, 0.806, 0.783 for teenagers, children, and infants, respectively). For status 1A candidates, LSAM also performed worse for pediatric than adult candidates (AUC 0.711 vs. 0.779), especially for infants (AUC 0.618). To ensure pediatric candidates are not unpredictably or negatively impacted by allocation policy changes, we must explicitly account for pediatric-specific decision making in LSAM.

The Precise Relationship Between Model for End-Stage Liver Disease and Survival Without a Liver Transplant

BACKGROUND AND AIMS

Scores from the Model for End-Stage Liver Disease (MELD), which are used to prioritize candidates for deceased donor livers, are widely acknowledged to be negatively correlated with the 90-day survival rate without a liver transplant. However, inconsistent and outdated estimates of survival probabilities by MELD preclude useful applications of the MELD score.

APPROACH AND RESULTS

Using data from all prevalent liver waitlist candidates from 2016 to 2019, we estimated 3-day, 7-day, 14-day, 30-day, and 90-day without-transplant survival probabilities (with confidence intervals) for each MELD score and status 1A. We used an adjusted Kaplan-Meier model to avoid unrealistic assumptions and multiple observations per person instead of just the observation at listing. We found that 90-day without-transplant survival has improved over the last decade, with survival rates increasing >10% (in absolute terms) for some MELD scores. We demonstrated that MELD correctly prioritizes candidates in terms of without-transplant survival probability but that status 1A candidates' short-term without-transplant survival is higher than that of MELD 40 candidates and lower than that of MELD 39 candidates. Our primary result is the updated survival functions themselves.

CONCLUSIONS

We calculated without-transplant survival probabilities for each MELD score (and status 1A). The survival function is an invaluable tool for many applications in liver transplantation: awarding of exception points, calculating the relative demand for deceased donor livers in different geographic areas, calibrating the pediatric end-stage liver disease score, and deciding whether to accept an offered liver.

Heterogeneous Circles for Liver Allocation

BACKGROUND AND AIMS

In February 2020, the Organ Procurement and Transplantation Network replaced donor service area-based allocation of livers with acuity circles, a system based on three homogeneous circles around each donor hospital. This system has been criticized for neglecting to consider varying population density and proximity to coast and national borders.

APPROACH AND RESULTS

Using Scientific Registry of Transplant Recipients data from July 2013 to June 2017, we designed heterogeneous circles to reduce both circle size and variation in liver supply/demand ratios across transplant centers. We weighted liver demand by Model for End-Stage Liver Disease (MELD)/Pediatric End-Stage Liver Disease (PELD) because higher MELD/PELD candidates are more likely to be transplanted. Transplant centers in the West had the largest circles; transplant centers in the Midwest and South had the smallest circles. Supply/demand ratios ranged from 0.471 to 0.655 livers per MELD-weighted incident candidate. Our heterogeneous circles had lower variation in supply/demand ratios than homogeneous circles of any radius between 150 and 1,000 nautical miles (nm). Homogeneous circles of 500 nm, the largest circle used in the acuity circles allocation system, had a variance in supply/demand ratios 16 times higher than our heterogeneous circles (0.0156 vs. 0.0009) and a range of supply/demand ratios 2.3 times higher than our heterogeneous circles (0.421 vs. 0.184). Our heterogeneous circles had a median (interquartile range) radius of only 326 (275-470) nm but reduced disparities in supply/demand ratios significantly by accounting for population density, national borders, and geographic variation of supply and demand.

CONCLUSIONS

Large homogeneous circles create logistical burdens on transplant centers that do not need them, whereas small homogeneous circles increase geographic disparity. Using carefully designed heterogeneous circles can reduce geographic disparity in liver supply/demand ratios compared with homogeneous circles of radius ranging from 150 to 1,000 nm.

Allocating kidneys in optimized heterogeneous circles

Recently, the Organ Procurement and Transplant Network approved a plan to allocate kidneys within 250-nm circles around donor hospitals. These homogeneous circles might not substantially reduce geographic differences in transplant rates because deceased donor kidney supply and demand differ across the country. Using Scientific Registry of Transplant Recipients data from 2016-2019, we used an integer program to design unique, heterogeneous circles with sizes between 100 and 500 nm that reduced supply/demand ratio variation across transplant centers. We weighted demand according to wait time because candidates who have waited longer have higher priority. We compared supply/demand ratios and average travel distance of kidneys, using heterogeneous circles and 250 and 500-nm fixed-distance homogeneous circles. We found that 40% of circles could be 250 nm or smaller, while reducing supply/demand ratio variation more than homogeneous circles. Supply/demand ratios across centers for heterogeneous circles ranged from 0.06 to 0.13 kidneys per wait-year, compared to 0.04 to 0.47 and 0.05 to 0.15 kidneys per wait-year for 250-nm and 500-nm homogeneous circles, respectively. The average travel distance for kidneys using heterogeneous, and 250-nm and 500-nm fixed-distance circles was 173 nm, 134 nm, and 269 nm, respectively. Heterogeneous circles reduce geographic disparity compared to homogeneous circles, while maintaining reasonable travel distances.

Accelerating kidney allocation: Simultaneously expiring offers

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.

Geographic Disparity in Deceased Donor Liver Transplant Rates Following Share 35

BACKGROUND

The Organ Procurement and Transplantation Network implemented Share 35 on June 18, 2013, to broaden deceased donor liver sharing within regional boundaries. We investigated whether increased sharing under Share 35 impacted geographic disparity in deceased donor liver transplantation (DDLT) across donation service areas (DSAs).

METHODS

Using Scientific Registry of Transplant Recipients June 2009 to June 2017, we identified 86 083 adult liver transplant candidates and retrospectively estimated Model for End-Stage Liver Disease (MELD)-adjusted DDLT rates using nested multilevel Poisson regression with random intercepts for DSA and transplant program. From the variance in DDLT rates across 49 DSAs and 102 programs, we derived the DSA-level median incidence rate ratio (MIRR) of DDLT rates. MIRR is a robust metric of heterogeneity across each hierarchical level; larger MIRR indicates greater disparity.

RESULTS

MIRR was 2.18 pre-Share 35 and 2.16 post-Share 35. Thus, 2 candidates with the same MELD in 2 different DSAs were expected to have a 2.2-fold difference in DDLT rate driven by geography alone. After accounting for program-level heterogeneity, MIRR was attenuated to 2.10 pre-Share 35 and 1.96 post-Share 35. For candidates with MELD 15-34, MIRR decreased from 2.51 pre- to 2.27 post-Share 35, and for candidates with MELD 35-40, MIRR increased from 1.46 pre- to 1.51 post-Share 35, independent of program-level heterogeneity in DDLT. DSA-level heterogeneity in DDLT rates was greater than program-level heterogeneity pre- and post-Share 35.

CONCLUSIONS

Geographic disparity substantially impacted DDLT rates before and after Share 35, independent of program-level heterogeneity and particularly for candidates with MELD 35-40. Despite broader sharing, geography remains a major determinant of access to DDLT.

National Variation in Increased Infectious Risk Kidney Offer Acceptance

BACKGROUND

Despite providing survival benefit, increased risk for infectious disease (IRD) kidney offers are declined at 1.5 times the rate of non-IRD kidneys. Elucidating sources of variation in IRD kidney offer acceptance may highlight opportunities to expand use of these life-saving organs.

METHODS

To explore center-level variation in offer acceptance, we studied 6765 transplanted IRD kidneys offered to 187 transplant centers between 2009 and 2017 using Scientific Registry of Transplant Recipients data. We used multilevel logistic regression to determine characteristics associated with offer acceptance and to calculate the median odds ratio (MOR) of acceptance (higher MOR indicates greater heterogeneity).

RESULTS

Higher quality kidneys (per 10 units kidney donor profile index; adjusted odds ratio [aOR], 0.94; 95% confidence interval [CI], 0.92-0.95), higher yearly volume (per 10 deceased donor kidney transplants; aOR, 1.08, 95% CI, 1.06-1.10), smaller waitlist size (per 100 candidates; aOR, 0.97; 95% CI, 0.95-0.98), and fewer transplant centers in the donor service area (per center; aOR, 0.88; 95% CI, 0.85-0.91) were associated with greater odds of IRD acceptance. Adjusting for donor and center characteristics, we found wide heterogeneity in IRD offer acceptance (MOR, 1.96). In other words, if listed at a center with more aggressive acceptance practices, a candidate could be 2 times more likely to have an IRD kidney offer accepted.

CONCLUSIONS

Wide national variation in IRD kidney offer acceptance limits access to life-saving kidneys for many transplant candidates.

Restructuring the Organ Procurement and Transplantation Network contract to achieve policy coherence and infrastructure excellence

The Organ Procurement and Transplantation Network (OPTN) went up for competitive bid again this year, yet this contract has been held by only 1 entity since its inception. The OPTN's scope has grown steadily, and it now embraces several disparate missions: to operate the computing and coordination infrastructure that maintains waitlists and makes organ offers in priority order, to regulate transplant centers and organ procurement organizations, to follow and protect living donors, and to decide organ allocation policy in concert with the many voices of the transplant community. The contracting process and performance work statement continue to discourage both innovative approaches to the OPTN and competitive bids outside of United Network for Organ Sharing (UNOS), with evaluation criteria that either disqualify or strongly disadvantage new applicants. The performance work statement also emphasizes bureaucratic tasks while obligating the OPTN contractor to the specific committee structure that has impeded decision-making and tended to preserve the status quo in controversial matters. Finally, the UNOS computing infrastructure is antiquated and requires months to years to implement small changes. Restructuring the OPTN contract to separate the information technology requirements from the policy/regulatory responsibilities might allow more nimble and effective specialty contractors to offer their capabilities in service of the national transplant enterprise.

Geographic disparities in lung transplant rates

In November 2017, in response to a lawsuit from a New York City lung transplant candidate, an emergency change to the lung allocation policy eliminated the donation service area (DSA) as the first geographic tier of allocation. The lawsuit claimed that DSA borders are arbitrary and that allocation should be based on medical priority. We investigated whether deceased-donor lung transplant (LT) rates differed substantially between DSAs in the United States before the policy change. We estimated LT rates per active person-year using multilevel Poisson regression and empirical Bayes methods. We found that the median incidence rate ratio (MIRR) of transplant rates between DSAs was 2.05, meaning a candidate could be expected to double their LT rate by changing their DSA. This can be compared directly to a 1.54-fold increase in LT rate that we found associated with an increase in lung allocation score (LAS) category from 38-42 to 42-50. Changing a candidate's DSA would have had a greater impact on the candidate's LT rate than changing LAS categories from 38-42 to 42-50. In summary, we found that the DSA of listing was a major determinant of LT rate for candidates across the country before the emergency lung allocation change.

Temporal changes in the composition of a large multicenter kidney exchange clearinghouse: Do the hard-to-match accumulate?

One criticism of kidney paired donation (KPD) is that easy-to-match candidates leave the registry quickly, thus concentrating the pool with hard-to-match sensitized and blood type O candidates. We studied candidate/donor pairs who registered with the National Kidney Registry (NKR), the largest US KPD clearinghouse, from January 2012-June 2016. There were no changes in age, gender, BMI, race, ABO blood type, or panel-reactive antibody (PRA) of newly registering candidates over time, with consistent registration of hard-to-match candidates (59% type O and 38% PRA ≥97%). However, there was no accumulation of type O candidates over time, presumably due to increasing numbers of nondirected type O donors. Although there was an initial accumulation of candidates with PRA ≥97% (from 33% of the pool in 2012% to 43% in 2014, P = .03), the proportion decreased to 17% by June 2016 (P < .001). Some of this is explained by an increase in the proportion of candidates with PRA ≥97% who underwent a deceased donor kidney transplantation (DDKT) after the implementation of the Kidney Allocation System (KAS), from 8% of 2012 registrants to 17% of 2015 registrants (P = .02). In this large KPD clearinghouse, increasing participation of nondirected donors and the KAS have lessened the accumulation of hard-to-match candidates, but highly sensitized candidates remain hard-to-match.

Geographic disparity in kidney transplantation under KAS

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 1.81_1.86 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 1.40_1.45 , P < .01) and a three-year dialysis vintage was associated with a 1.57-fold increase (IRR = _1.56 1.57_1.59 , P < .001) in transplant rate. For pediatric candidates, MIRR was even more pronounced, at _1.66 1.92_2.27 . 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.

Kidney exchange match rates in a large multicenter clearinghouse

Kidney paired donation (KPD) can facilitate living donor transplantation for candidates with an incompatible donor, but requires waiting for a match while experiencing the morbidity of dialysis. The balance between waiting for KPD vs desensitization or deceased donor transplantation relies on the ability to estimate KPD wait times. We studied donor/candidate pairs in the National Kidney Registry (NKR), a large multicenter KPD clearinghouse, between October 2011 and September 2015 using a competing-risk framework. Among 1894 candidates, 52% were male, median age was 50 years, 66% were white, 59% had blood type O, 42% had panel reactive antibody (PRA)>80, and 50% obtained KPD through NKR. Median times to KPD ranged from 2 months for candidates with ABO-A and PRA 0, to over a year for candidates with ABO-O or PRA 98+. Candidates with PRA 80-97 and 98+ were 23% (95% confidence interval , 6%-37%) and 83% (78%-87%) less likely to be matched than PRA 0 candidates. ABO-O candidates were 67% (61%-73%) less likely to be matched than ABO-A candidates. Candidates with ABO-B or ABO-O donors were 31% (10%-56%) and 118% (82%-162%) more likely to match than those with ABO-A donors. Providers should counsel candidates about realistic, individualized expectations for KPD, especially in the context of their alternative treatment options.

MELD as a metric for survival benefit of liver transplantation

Currently, there is debate among the liver transplant community regarding the most appropriate mechanism for organ allocation: urgency-based (MELD) versus utility-based (survival benefit). We hypothesize that MELD and survival benefit are closely associated, and therefore, our current MELD-based allocation already reflects utility-based allocation. We used generalized gamma parametric models to quantify survival benefit of LT across MELD categories among 74 196 adult liver-only active candidates between 2006 and 2016 in the United States. We calculated time ratios (TR) of relative life expectancy with transplantation versus without and calculated expected life years gained after LT. LT extended life expectancy (TR > 1) for patients with MELD > 10. The highest MELD was associated with the longest relative life expectancy (TR = _1.05 1.20_1.37 for MELD 11-15, _2.29 2.49_2.70 for MELD 16-20, _5.30 5.72_6.16 for MELD 21-25, _15.12 16.35_17.67 for MELD 26-30; _39.26 43.21_47.55 for MELD 31-34; _120.04 128.25_137.02 for MELD 35-40). As a result, candidates with the highest MELD gained the most life years after LT: 0.2, 1.5, 3.5, 5.8, 6.9, 7.2 years for MELD 11-15, 16-20, 21-25, 26-30, 31-34, 35-40, respectively. Therefore, prioritizing candidates by MELD remains a simple, effective strategy for prioritizing candidates with a higher transplant survival benefit over those with lower survival benefit.

Offer acceptance practices and geographic variability in allocation model for end-stage liver disease at transplant

Offer acceptance practices may cause geographic variability in allocation Model for End-Stage Liver Disease (aMELD) score at transplant and could magnify the effect of donor supply and demand on aMELD variability. To evaluate these issues, offer acceptance practices of liver transplant programs and donation service areas (DSAs) were estimated using offers of livers from donors recovered between January 1, 2016, and December 31, 2016. Offer acceptance practices were compared with liver yield, local placement of transplanted livers, donor supply and demand, and aMELD at transplant. Offer acceptance was associated with liver yield (odds ratio, 1.32; P < 0.001), local placement of transplanted livers (odds ratio, 1.34; P < 0.001), and aMELD at transplant (average aMELD difference, -1.62; P < 0.001). However, the ratio of donated livers to listed candidates in a DSA (ie, donor-to-candidate ratio) was associated with median aMELD at transplant (r = -0.45; P < 0.001), but not with offer acceptance (r = 0.09; P = 0.50). Additionally, the association between DSA-level donor-to-candidate ratios and aMELD at transplant did not change after adjustment for offer acceptance. The average squared difference in median aMELD at transplant across DSAs was 24.6; removing the effect of donor-to-candidate ratios reduced the average squared differences more than removing the effect of program-level offer acceptance (33% and 15% reduction, respectively). Offer acceptance practices and donor-to-candidate ratios independently contributed to geographic variability in aMELD at transplant. Thus, neither offer acceptance nor donor-to-candidate ratios can explain all of the geographic variability in aMELD at transplant. Liver Transplantation 24 478-487 2018 AASLD.

Turn down for what? Patient outcomes associated with declining increased infectious risk kidneys

Transplant candidates who accept a kidney labeled increased risk for disease transmission (IRD) accept a low risk of window period infection, yet those who decline must wait for another offer that might harbor other risks or never even come. To characterize survival benefit of accepting IRD kidneys, we used 2010-2014 Scientific Registry of Transplant Recipients data to identify 104 998 adult transplant candidates who were offered IRD kidneys that were eventually accepted by someone; the median (interquartile range) Kidney Donor Profile Index (KDPI) of these kidneys was 30 (16-49). We followed patients from the offer decision until death or end-of-study. After 5 years, only 31.0% of candidates who declined IRDs later received non-IRD deceased donor kidney transplants; the median KDPI of these non-IRD kidneys was 52, compared to 21 of the IRDs they had declined. After a brief risk period in the first 30 days following IRD acceptance (adjusted hazard ratio [aHR] accept vs decline: _1.22 2.06_3.49 , P = .008) (absolute mortality 0.8% vs. 0.4%), those who accepted IRDs were at 33% lower risk of death 1-6 months postdecision (aHR _0.50 0.67_0.90 , P = .006), and at 48% lower risk of death beyond 6 months postdecision (aHR _0.46 0.52_0.58 , P < .001). Accepting an IRD kidney was associated with substantial long-term survival benefit; providers should consider this benefit when counseling patients on IRD offer acceptance.

Waitlist Outcomes of Liver Transplant Candidates Who Were Reprioritized Under Share 35

Under Share 35, deceased donor (DD) livers are offered regionally to candidates with Model for End-Stage Liver Disease (MELD) scores ≥35 before being offered locally to candidates with MELD scores <35. Using Scientific Registry of Transplant Recipients data from June 2013 to June 2015, we identified 1768 DD livers exported to regional candidates with MELD scores ≥35 who were transplanted at a median MELD score of 39 (interquartile range [IQR] 37-40) with 30-day posttransplant survival of 96%. In total, 1764 (99.8%) exports had an ABO-compatible candidate in the recovering organ procurement organization (OPO), representing 1219 unique reprioritized candidates who would have had priority over the regional candidate under pre-Share 35 allocation. Reprioritized candidates had a median waitlist MELD score of 31 (IQR 27-34) when the liver was exported. Overall, 291 (24%) reprioritized candidates had a comparable MELD score (within 3 points of the regional recipient), and 209 (72%) were eventually transplanted in 11 days (IQR 3-38 days) using a local (50%), regional (50%) or national (<1%) liver; 60 (21%) died, 13 (4.5%) remained on the waitlist and nine (3.1%) were removed for other reasons. Of those eventually transplanted, MELD score did not increase in 57%; it increased by 1-3 points in 37% and by ≥4 points in 5.7% after the export. In three cases, OPOs exchanged regional exports within a 24-h window. The majority of comparable reprioritized candidates were not disadvantaged; however, 21% died after an export.

The Impact of Redistricting Proposals on Health Care Expenditures for Liver Transplant Candidates and Recipients

Redistricting, which means sharing organs in novel districts developed through mathematical optimization, has been proposed to reduce pervasive geographic disparities in access to liver transplantation. The economic impact of redistricting was evaluated with two distinct data sources, Medicare claims and the University HealthSystem Consortium (UHC). We estimated total Medicare payments under (i) the current allocation system (Share 35), (ii) full regional sharing, (iii) an eight-district plan, and (iv) a four-district plan for a simulated population of patients listed for liver transplant over 5 years, using the liver simulated allocation model. The model predicted 5-year transplant volumes (Share 35, 29,267; regional sharing, 29,005; eight districts, 29,034; four districts, 28,265) and a reduction in overall mortality, including listed and posttransplant patients, of up to 676 lives. Compared with current allocation, the eight-district plan was estimated to reduce payments for pretransplant care ($1638 million to $1506 million, p < 0.001), transplant episode ($5607 million to $5569 million, p < 0.03) and posttransplant care ($479 million to $488 million, p < 0.001). The eight-district plan was estimated to increase per-patient transportation costs for organs ($8988 to $11,874 per patient, p < 0.001) and UHC estimated hospital costs ($4699 per case). In summary, redistricting appears to be potentially cost saving for the health care system but will increase the cost of performing liver transplants for some transplant centers.

Liver sharing and organ procurement organization performance under redistricted allocation

Concerns have been raised that optimized redistricting of liver allocation areas might have the unintended result of shifting livers from better-performing to poorer-performing organ procurement organizations (OPOs). We used liver simulated allocation modeling to simulate a 5-year period of liver sharing within either 4 or 8 optimized districts. We investigated whether each OPO's net liver import under redistricting would be correlated with 2 OPO performance metrics (observed to expected liver yield and liver donor conversion ratio), along with 2 other potential correlates (eligible deaths and incident listings above a Model for End-Stage Liver Disease score of 15). We found no evidence that livers would flow from better-performing OPOs to poorer-performing OPOs in either redistricting scenario. Instead, under these optimized redistricting plans, our simulations suggest that livers would flow from OPOs with more-than-expected eligible deaths toward those with fewer-than-expected eligible deaths and that livers would flow from OPOs with fewer-than-expected incident listings to those with more-than-expected incident listings; the latter is a pattern that is already established in the current allocation system. Redistricting liver distribution to reduce geographic inequity is expected to align liver allocation across the country with the distribution of supply and demand rather than transferring livers from better-performing OPOs to poorer-performing OPOs.

Liver sharing and organ procurement organization performance

Whether the liver allocation system shifts organs from better performing organ procurement organizations (OPOs) to poorer performing OPOs has been debated for many years. Models of OPO performance from the Scientific Registry of Transplant Recipients make it possible to study this question in a data-driven manner. We investigated whether each OPO's net liver import was correlated with 2 performance metrics [observed to expected (O:E) liver yield and liver donor conversion ratio] as well as 2 alternative explanations [eligible deaths and incident listings above a Model for End-Stage Liver Disease (MELD) score of 15]. We found no evidence to support the hypothesis that the allocation system transfers livers from better performing OPOs to centers with poorer performing OPOs. Also, having fewer eligible deaths was not associated with a net import. However, having more incident listings was strongly correlated with the net import, both before and after Share 35. Most importantly, the magnitude of the variation in OPO performance was much lower than the variation in demand: although the poorest performing OPOs differed from the best ones by less than 2-fold in the O:E liver yield, incident listings above a MELD score of 15 varied nearly 14-fold. Although it is imperative that all OPOs achieve the best possible results, the flow of livers is not explained by OPO performance metrics, and instead, it appears to be strongly related to differences in demand.

Early changes in liver distribution following implementation of Share 35

In June 2013, a change to the liver waitlist priority algorithm was implemented. Under Share 35, regional candidates with MELD ≥ 35 receive higher priority than local candidates with MELD < 35. We compared liver distribution and mortality in the first 12 months of Share 35 to an equivalent time period before. Under Share 35, new listings with MELD ≥ 35 increased slightly from 752 (9.2% of listings) to 820 (9.7%, p = 0.3), but the proportion of deceased-donor liver transplants (DDLTs) allocated to recipients with MELD ≥ 35 increased from 23.1% to 30.1% (p < 0.001). The proportion of regional shares increased from 18.9% to 30.4% (p < 0.001). Sharing of exports was less clustered among a handful of centers (Gini coefficient decreased from 0.49 to 0.34), but there was no evidence of change in CIT (p = 0.8). Total adult DDLT volume increased from 4133 to 4369, and adjusted odds of discard decreased by 14% (p = 0.03). Waitlist mortality decreased by 30% among patients with baseline MELD > 30 (SHR = 0.70, p < 0.001) with no change for patients with lower baseline MELD (p = 0.9). Posttransplant length-of-stay (p = 0.2) and posttransplant mortality (p = 0.9) remained unchanged. In the first 12 months, Share 35 was associated with more transplants, fewer discards, and lower waitlist mortality, but not at the expense of CIT or early posttransplant outcomes.

Impact of broader sharing on the transport time for deceased donor livers

Recent allocation policy changes have increased the sharing of deceased donor livers across local boundaries, and sharing even broader than this has been proposed as a remedy for persistent geographic disparities in liver transplantation. It is possible that broader sharing may increase cold ischemia times (CITs) and thus harm recipients. We constructed a detailed model of transport modes (car, helicopter, and fixed-wing aircraft) and transport times between all hospitals, and we investigated the relationship between the transport time and the CIT for deceased donor liver transplants. The median estimated transport time was 2.0 hours for regionally shared livers and 1.0 hour for locally allocated livers. The model-predicted transport mode was flying for 90% of regionally shared livers but for only 22% of locally allocated livers. The median CIT was 7.0 hours for regionally shared livers and 6.0 hours for locally allocated livers. Variation in the transport time accounted for only 14.7% of the variation in the CIT, and the transport time on average composed only 21% of the CIT. In conclusion, nontransport factors play a substantially larger role in the CIT than the transport time. Broader sharing will have only a marginal impact on the CIT but will significantly increase the fraction of transplants that are transported by flying rather than driving.

Addressing geographic disparities in liver transplantation through redistricting

Severe geographic disparities exist in liver transplantation; for patients with comparable disease severity, 90-day transplant rates range from 18% to 86% and death rates range from 14% to 82% across donation service areas (DSAs). Broader sharing has been proposed to resolve geographic inequity; however, we hypothesized that the efficacy of broader sharing depends on the geographic partitions used. To determine the potential impact of redistricting on geographic disparity in disease severity at transplantation, we combined existing DSAs into novel regions using mathematical redistricting optimization. Optimized maps and current maps were evaluated using the Liver Simulated Allocation Model. Primary analysis was based on 6700 deceased donors, 28 063 liver transplant candidates, and 242 727 Model of End-Stage Liver Disease (MELD) changes in 2010. Fully regional sharing within the current regional map would paradoxically worsen geographic disparity (variance in MELD at transplantation increases from 11.2 to 13.5, p = 0.021), although it would decrease waitlist deaths (from 1368 to 1329, p = 0.002). In contrast, regional sharing within an optimized map would significantly reduce geographic disparity (to 7.0, p = 0.002) while achieving a larger decrease in waitlist deaths (to 1307, p = 0.002). Redistricting optimization, but not broader sharing alone, would reduce geographic disparity in allocation of livers for transplant across the United States.

Center-level utilization of kidney paired donation

With many multicenter consortia and a United Network for Organ Sharing program, participation in kidney paired donation (KPD) has become mainstream in the United States and should be feasible for any center that performs live donor kidney transplantation (LDKT). Lack of participation in KPD may significantly disadvantage patients with incompatible donors. To explore utilization of this modality, we analyzed adjusted center-specific KPD rates based on casemix of adult LDKT-eligible patients at 207 centers between 2006 and 2011 using SRTR data. From 2006 to 2008, KPD transplants became more evenly distributed across centers, but from 2008 to 2011 the distribution remained unchanged (Gini coefficient = 0.91 for 2006, 0.76 for 2008 and 0.77 for 2011), showing an unfortunate stall in dissemination. At the 10% of centers with the highest KPD rates, 9.9-38.5% of LDKTs occurred through KPD during 2009-2011; if all centers adopted KPD at rates observed in the very high-KPD centers, the number of KPD transplants per year would increase by a factor of 3.2 (from 494 to 1593). Broader implementation of KPD across a wide number of centers is crucial to properly serve transplant candidates with healthy but incompatible live donors.

Dynamic challenges inhibiting optimal adoption of kidney paired donation: findings of a consensus conference

While kidney paired donation (KPD) enables the utilization of living donor kidneys from healthy and willing donors incompatible with their intended recipients, the strategy poses complex challenges that have limited its adoption in United States and Canada. A consensus conference was convened March 29-30, 2012 to address the dynamic challenges and complexities of KPD that inhibit optimal implementation. Stakeholders considered donor evaluation and care, histocompatibility testing, allocation algorithms, financing, geographic challenges and implementation strategies with the goal to safely maximize KPD at every transplant center. Best practices, knowledge gaps and research goals were identified and summarized in this document.

Equal Opportunity Supplemented by Fair Innings: equity and efficiency in allocating deceased donor kidneys

For 7 years, the Kidney Transplantation Committee of the United Network for Organ Sharing/Organ Procurement Transplantation Network has attempted to revise the kidney allocation algorithm for adults (≥18 years) in end-stage renal disease awaiting deceased donor kidney transplants. Changes to the kidney allocation system must conform to the 1984 National Organ Transplant Act (NOTA) which clearly states that allocation must take into account both efficiency (graft and person survival) and equity (fair distribution). In this article, we evaluate three allocation models: the current system, age-matching and a two-step model that we call "Equal Opportunity Supplemented by Fair Innings (EOFI)". We discuss the different conceptions of efficiency and equity employed by each model and evaluate whether EOFI could actually achieve the NOTA criteria of balancing equity and efficiency given current conditions of growing scarcity and donor-candidate age mismatch.

Controversies in kidney paired donation

Kidney paired donation represented 10% of living kidney donation in the United States in 2011. National registries around the world and several separate registries in the United States arrange paired donations, although with significant variations in their practices. Concerns about ethical considerations, clinical advisability, and the quantitative effectiveness of these approaches in paired donation result in these variations. For instance, although donor travel can be burdensome and might discourage paired donation, it was nearly universal until convincing analysis showed that living donor kidneys can sustain many hours of cold ischemia time without adverse consequences. Opinions also differ about whether the last donor in a chain of paired donation transplants initiated by a nondirected donor should donate immediately to someone on the deceased donor wait-list (a domino or closed chain) or should be asked to wait some length of time and donate to start another sequence of paired donations later (an open chain); some argue that asking the donor to donate later may be coercive, and others focus on balancing the probability that the waiting donor withdraws versus the number of additional transplants if the chain can be continued. Other controversies in paired donation include simultaneous versus nonsimultaneous donor operations, whether to enroll compatible pairs, and interactions with desensitization protocols. Efforts to expand public awareness of and participation in paired donation are needed to generate more transplant opportunities.

The interaction among donor characteristics, severity of liver disease, and the cost of liver transplantation

Accurate assessment of the impact of donor quality on liver transplant (LT) costs has been limited by the lack of a large, multicenter study of detailed clinical and economic data. A novel, retrospective database linking information from the University HealthSystem Consortium and the Organ Procurement and Transplantation Network registry was analyzed using multivariate regression to determine the relationship between donor quality (assessed through the Donor Risk Index [DRI]), recipient illness severity, and total inpatient costs (transplant and all readmissions) for 1 year following LT. Cost data were available for 9059 LT recipients. Increasing MELD score, higher DRI, simultaneous liver-kidney transplant, female sex, and prior liver transplant were associated with increasing cost of LT (P < 0.05). MELD and DRI interact to synergistically increase the cost of LT (P < 0.05). Donors in the highest DRI quartile added close to $12,000 to the cost of transplantation and nearly $22,000 to posttransplant costs in comparison to the lowest risk donors. Among the individual components of the DRI, donation after cardiac death (increased costs by $20,769 versus brain dead donors) had the greatest impact on transplant costs. Overall, 1-year costs were increased in older donors, minority donors, nationally shared organs, and those with cold ischemic times of 7-13 hours (P < 0.05 for all). In conclusion, donor quality, as measured by the DRI, is an independent predictor of LT costs in the perioperative and postoperative periods. Centers in highly competitive regions that perform transplantation on higher MELD patients with high DRI livers may be particularly affected by the synergistic impact of these factors.

Kidney paired donation: fundamentals, limitations, and expansions

Incompatibility between the candidate recipient and the prospective donor is a major obstacle to living donor kidney transplant. Kidney paired donation (KPD) can circumvent the incompatibility by matching them to another candidate and living donor for an exchange of transplants such that both transplants are compatible. KPD has faced legal, logistical, and ethical challenges since its inception in the 1980s. Although the full potential of this modality for facilitating transplant for individuals with incompatible donors is unrealized, great strides have been made. In this review article, we detail how several impediments to KPD have been overcome to the benefit of ever greater numbers of patients. Limitations and questions that have been addressed include blood group type O imbalance, reciprocal match requirements, simultaneous donor nephrectomy requirements, combining KPD with desensitization, the role of list-paired donation, geographic barriers, legal barriers, concerns regarding living donor safety, fragmented registries, and inefficient matching algorithms.

The interplay of socioeconomic status, distance to center, and interdonor service area travel on kidney transplant access and outcomes

BACKGROUND AND OBJECTIVES

Variation in kidney transplant access across the United States may motivate relocation of patients with ability to travel to better-supplied areas.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We examined national transplant registry and U.S. Census data for kidney transplant candidates listed in 1999 to 2009 with a reported residential zip code (n = 203,267). Cox's regression was used to assess associations of socioeconomic status (SES), distance from residence to transplant center, and relocation to a different donation service area (DSA) with transplant access and outcomes.

RESULTS

Patients in the highest SES quartile had increased access to transplant compared with those with lowest SES, driven strongly by 76% higher likelihood of living donor transplantation (adjusted hazard ratio [aHR] 1.76, 95% confidence interval [CI] 1.70 to 1.83). Waitlist death was reduced in high compared with low SES candidates (aHR 0.86, 95% CI 0.84 to 0.89). High SES patients also experienced lower mortality after living and deceased donor transplant. Patients living farther from the transplant center had reduced access to deceased donor transplant and increased risk of post-transplant death. Inter-DSA travel was associated with a dramatic increase in deceased donor transplant access (HR 1.94, 95% CI 1.88 to 2.00) and was predicted by high SES, white race, and longer deceased-donor allograft waiting time in initial DSA.

CONCLUSIONS

Ongoing disparities exist in kidney transplantation access and outcomes on the basis of geography and SES despite near-universal insurance coverage under Medicare. Inter-DSA travel improves access and is more common among high SES candidates.

The roles of dominos and nonsimultaneous chains in kidney paired donation

Efforts to expand kidney paired donation have included matching nondirected donors (NDDs) to incompatible pairs. In domino paired donation (DPD), an NDD gives to the recipient of an incompatible pair, beginning a string of simultaneous transplants that ends with a living donor giving to a recipient on the deceased donor waitlist. Recently, nonsimultaneous extended altruistic donor (NEAD) chains were introduced. In a NEAD chain, the last donor of the string of transplants initiated by an NDD is reserved to donate at a later time. Our aim was to project the impact of each of these strategies over 2 years of operation for paired donation programs that also allocate a given number of NDDs. Each NDD facilitated an average of 1.99 transplants using DPD versus 1.90 transplants using NEAD chains (p = 0.3), or 1.0 transplants donating directly to the waitlist (p < 0.001). NEAD chains did not yield more transplants compared with simultaneous DPD. Both DPD and NEAD chains relax reciprocality requirements and rebalance the blood-type distribution of donors. Because traditional paired donation will leave many incompatible pairs unmatched, novel approaches like DPD and NEAD chains must be explored if paired donation programs are to help a greater number of people.

The roles of dominos and nonsimultaneous chains in kidney paired donation

Efforts to expand kidney paired donation have included matching nondirected donors (NDDs) to incompatible pairs. In domino paired donation (DPD), an NDD gives to the recipient of an incompatible pair, beginning a string of simultaneous transplants that ends with a living donor giving to a recipient on the deceased donor waitlist. Recently, nonsimultaneous extended altruistic donor (NEAD) chains were introduced. In a NEAD chain, the last donor of the string of transplants initiated by an NDD is reserved to donate at a later time. Our aim was to project the impact of each of these strategies over 2 years of operation for paired donation programs that also allocate a given number of NDDs. Each NDD facilitated an average of 1.99 transplants using DPD versus 1.90 transplants using NEAD chains (p = 0.3), or 1.0 transplants donating directly to the waitlist (p < 0.001). NEAD chains did not yield more transplants compared with simultaneous DPD. Both DPD and NEAD chains relax reciprocality requirements and rebalance the blood-type distribution of donors. Because traditional paired donation will leave many incompatible pairs unmatched, novel approaches like DPD and NEAD chains must be explored if paired donation programs are to help a greater number of people.