Waitlist Outcomes for Exception and Non-exception Liver Transplant Candidates in the United States Following Implementation of the Median MELD at Transplant (MMaT)/250-mile Policy.

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.

Transplant Candidate Outcomes After Declining a DCD Liver in the United States.

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.

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.

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.

The Effect of Acuity Circles on Deceased Donor Transplant and Offer Rates Across Model for End-Stage Liver Disease Scores and Exception Statuses.

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 3.324.71 ; PELD/MELD scores 33-36, 1.70 2.513.71 ). 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 3.955.63 ; top 5 offers, 3.90 4.394.95 ; top 10 offers, 4.85 5.305.80 ). Candidates with exceptions had lower offer rates than candidates without exceptions for offers in the top 5 (ORR: hepatocellular carcinoma [HCC], 0.68 0.770.88 ; non-HCC, 0.73 0.810.89 ) and top 10 (ORR: HCC, 0.59 0.650.71 ; non-HCC, 0.69 0.750.81 ). 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.

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.