Faculty Bibliography

BACKGROUND:Since February 2020, exception points have been allocated equivalent to the median model for end-stage liver disease at transplant within 250 nautical miles of the transplant center (MMaT/250). We compared transplant rate and waitlist mortality for hepatocellular carcinoma (HCC) exception, non-HCC exception, and non-exception candidates to determine whether MMaT/250 advantages (or disadvantages) exception candidates. METHODS:Using Scientific Registry of Transplant Recipients data, we identified 23 686 adult, first-time, active, deceased donor liver transplant (DDLT) candidates between February 4, 2020, and February 3, 2022. We compared DDLT rates using Cox regression, and waitlist mortality/dropout using competing risks regression in non-exception versus HCC versus non-HCC candidates. RESULTS:Within 24 mo of study entry, 58.4% of non-exception candidates received DDLT, compared with 57.8% for HCC candidates and 70.5% for non-HCC candidates. After adjustment, HCC candidates had 27% lower DDLT rate (adjusted hazard ratio = 0.680.730.77) compared with non-exception candidates. However, waitlist mortality for HCC was comparable to non-exception candidates (adjusted subhazard ratio [asHR] = 0.931.031.15). Non-HCC candidates with pulmonary complications of cirrhosis or cholangiocarcinoma had substantially higher risk of waitlist mortality compared with non-exception candidates (asHR = 1.271.702.29 for pulmonary complications of cirrhosis, 1.352.043.07 for cholangiocarcinoma). The same was not true of non-HCC candidates with exceptions for other reasons (asHR = 0.540.881.44). CONCLUSIONS:Under MMaT/250, HCC, and non-exception candidates have comparable risks of dying before receiving liver transplant, despite lower transplant rates for HCC. However, non-HCC candidates with pulmonary complications of cirrhosis or cholangiocarcinoma have substantially higher risk of dying before receiving liver transplant; these candidates may merit increased allocation priority.

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.

BACKGROUND:In the context of the organ shortage, donation after circulatory death (DCD) provides an opportunity to expand the donor pool. Although deceased-donor liver transplantation from DCD donors has expanded, DCD livers continue to be discarded at elevated rates; the use of DCD livers from older donors, or donors with comorbidities, is controversial. METHODS:Using US registry data from 2009 to 2020, we identified 1564 candidates on whose behalf a DCD liver offer was accepted ("acceptors") and 16 981 candidates on whose behalf the same DCD offers were declined ("decliners"). We characterized outcomes of decliners using a competing risk framework and estimated the survival benefit (adjusted hazard ratio [95% confidence interval]) of accepting DCD livers using Cox regression. RESULTS:Within 10 y of DCD offer decline, 50.9% of candidates died or were removed from the waitlist before transplantation with any type of allograft. DCD acceptors had lower mortality compared with decliners at 10 y postoffer (35.4% versus 48.9%, P < 0.001). After adjustment for candidate covariates, DCD offer acceptance was associated with a 46% reduction in mortality (0.54 [0.49-0.61]). Acceptors of older (age ≥50), obese (body mass index ≥30), hypertensive, nonlocal, diabetic, and increased risk DCD livers had 44% (0.56 [0.42-0.73]), 40% (0.60 [0.49-0.74]), 48% (0.52 [0.41-0.66]), 46% (0.54 [0.45-0.65]), 32% (0.68 [0.43-1.05]), and 45% (0.55 [0.42-0.72]) lower mortality risk compared with DCD decliners, respectively. CONCLUSIONS:DCD offer acceptance is associated with considerable long-term survival benefits for liver transplant candidates, even with older DCD donors or donors with comorbidities. Increased recovery and utilization of DCD livers should be encouraged.

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/CONCLUSIONS: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.

Acuity circles (AC), the new liver allocation system, was implemented on February 4, 2020. Difference-in-differences analyses estimated the effect of AC on adjusted deceased donor transplant and offer rates across Pediatric End-Stage Liver Disease (PELD) and Model for End-Stage Liver Disease (MELD) categories and types of exception statuses. The offer rates were the number of first offers, top 5 offers, and top 10 offers on the match run per person-year. Each analysis adjusted for candidate characteristics and only used active candidate time on the waiting list. The before-AC period was February 4, 2019, to February 3, 2020, and the after-AC period was February 4, 2020, to February 3, 2021. Candidates with PELD/MELD scores 29 to 32 and PELD/MELD scores 33 to 36 had higher transplant rates than candidates with PELD/MELD scores 15 to 28 after AC compared with before AC (transplant rate ratios: PELD/MELD scores 29-32, _2.34 3.32_4.71 ; PELD/MELD scores 33-36, _1.70 2.51_3.71 ). Candidates with PELD/MELD scores 29 or higher had higher offer rates than candidates with PELD/MELD scores 15 to 28, and candidates with PELD/MELD scores 29 to 32 had the largest difference (offer rate ratios [ORR]: first offers, _2.77 3.95_5.63 ; top 5 offers, _3.90 4.39_4.95 ; top 10 offers, _4.85 5.30_5.80 ). Candidates with exceptions had lower offer rates than candidates without exceptions for offers in the top 5 (ORR: hepatocellular carcinoma [HCC], _0.68 0.77_0.88 ; non-HCC, _0.73 0.81_0.89 ) and top 10 (ORR: HCC, _0.59 0.65_0.71 ; non-HCC, _0.69 0.75_0.81 ). Recipients with PELD/MELD scores 15 to 28 and an HCC exception received a larger proportion of donation after circulatory death (DCD) donors after AC than before AC, although the differences in the liver donor risk index were comparatively small. Thus, candidates with PELD/MELD scores 29 to 34 and no exceptions had better access to transplant after AC, and donor quality did not notably change beyond the proportion of DCD donors.

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.