Faculty Bibliography

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.

OBJECTIVE:The aim of the study was to investigate the impact of prioritizing infants, children, adolescents, and the sickest adults (Status 1) for deceased donor livers. We compared outcomes under two "SharePeds" allocation schema, which prioritize children and Status 1 adults for national sharing and enhanced access to pediatric donors or all donors younger than 35 years, to outcomes under the allocation plan approved by the Organ Procurement and Transplant Network in December 2017 (Organ Procurement and Transplantation Network [OPTN] 12-2017). METHODS:The 2017 Liver Simulated Allocation Model and Scientific Registry of Transplant Recipients data on all US liver transplant candidates and liver offers 7/2013 to 6/2016 were used to predict waitlist deaths, transplants, and post-transplant deaths under the OPTN 12-2017 and SharePeds schema. RESULTS:Prioritizing national sharing of pediatric donor livers with children (SharePeds 1) would decrease waitlist deaths for infants (<2 years, P = 0.0003) and children (2-11 years, P = 0.001), with no significant change for adults (P = 0.13). Prioritizing national sharing of all younger than 35-year-old deceased donor livers with children and Status 1A adults (SharePeds 2) would decrease waitlist deaths for infants, children, and all Status 1A/B patients (P < 0.0001 for each). SharePeds 1 and 2 would increase the number of liver transplants done in infants, children, and adolescents compared to the OPTN-2017 schema (P < 0.00005 for all age groups). Both SharePeds schema would increase the percentage of pediatric livers transplanted into pediatric recipients. CONCLUSIONS:Waitlist deaths could be significantly decreased, and liver transplants increased, for children and the sickest adults, by prioritizing children for pediatric livers and with broader national sharing of deceased donor livers.

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.

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 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.

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 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.