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.

A Risk Index for Living Donor Kidney Transplantation.

Choosing between multiple living kidney donors, or evaluating offers in kidney paired donation, can be challenging because no metric currently exists for living donor quality. Furthermore, some deceased donor (DD) kidneys can result in better outcomes than some living donor kidneys, yet there is no way to compare them on the same scale. To better inform clinical decision-making, we created a living kidney donor profile index (LKDPI) on the same scale as the DD KDPI, using Cox regression and adjusting for recipient characteristics. Donor age over 50 (hazard ratio [HR] per 10 years = 1.15 1.241.33 ), elevated BMI (HR per 10 units = 1.01 1.091.16 ), African-American race (HR = 1.15 1.251.37 ), cigarette use (HR = 1.09 1.161.23 ), as well as ABO incompatibility (HR = 1.03 1.271.58 ), HLA B (HR = 1.03 1.081.14 ) mismatches, and DR (HR = 1.04 1.091.15 ) mismatches were associated with greater risk of graft loss after living donor transplantation (all p < 0.05). Median (interquartile range) LKDPI score was 13 (1-27); 24.2% of donors had LKDPI < 0 (less risk than any DD kidney), and 4.4% of donors had LKDPI > 50 (more risk than the median DD kidney). The LKDPI is a useful tool for comparing living donor kidneys to each other and to deceased donor kidneys.

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.

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.

Survival benefit of primary deceased donor transplantation with high-KDPI kidneys.

The Kidney Donor Profile Index (KDPI) has been introduced as an aid to evaluating deceased donor kidney offers, but the relative benefit of high-KDPI kidney transplantation (KT) versus the clinical alternative (remaining on the waitlist until receipt of a lower KDPI kidney) remains unknown. Using time-dependent Cox regression, we evaluated the mortality risk associated with high-KDPI KT (KDPI 71-80, 81-90 or 91-100) versus a conservative, lower KDPI approach (remain on waitlist until receipt of KT with KDPI 0-70, 0-80 or 0-90) in first-time adult registrants, adjusting for candidate characteristics. High-KDPI KT was associated with increased short-term but decreased long-term mortality risk. Recipients of KDPI 71-80 KT, KDPI 81-90 KT and KDPI 91-100 KT reached a "break-even point" of cumulative survival at 7.7, 18.0 and 19.8 months post-KT, respectively, and had a survival benefit thereafter. Cumulative survival at 5 years was better in all three high-KDPI groups than the conservative approach (p < 0.01 for each comparison). Benefit of high-KDPI KT was greatest in patients age >50 years and patients at centers with median wait time ≥33 months. Recipients of high-KDPI KT can enjoy better long-term survival; a high-KDPI score does not automatically constitute a reason to reject a deceased donor kidney.

Identifying appropriate recipients for CDC infectious risk donor kidneys.

Over 10% of deceased donors in 2011 met PHS/CDC criteria for infectious risk donor (IRD), and discard rates are significantly higher for kidneys from these donors. We hypothesized that patient phenotypes exist for whom the survival benefit outweighs the infectious risk associated with IRDs. A patient-oriented Markov decision process model was developed and validated, based on SRTR data and meta-analyses of window period risks among persons with IRD behaviors. The Markov model allows patients to see, for their phenotype, their estimated survival after accepting versus declining an IRD offer, graphed over a 5-year horizon. Estimated 5-year survival differences associated with accepting IRDs ranged from -6.4% to +67.3% for a variety of patient phenotypes. Factors most predictive of the survival difference with IRD transplantation were age, PRA, previous transplant, and the expected time until the next non-IRD deceased donor offer. This study suggests that survival benefit derived from IRD kidneys varies widely by patient phenotype. Furthermore, within the inherent limitations of model-based prediction, this study demonstrates that it is possible to identify those predicted to benefit from IRD kidneys, and illustrates how estimated survival curves based on a clinical decision can be presented to better inform patient and provider decision-making.