Chapter 9 The way forward
In Chapters 1, 2, and 6, we have placed the liver and kidney allocation systems in their historical context. A common thread throughout these chapters is that fairness mechanisms have become a central component of Eurotransplant’s allocation systems. In fact, Eurotransplant’s member countries have identified equality of opportunity as “the most important factor for allocation” [16].
This motivated the first goal of this thesis: the investigation of research questions relating to equality of opportunity. In Chapter 4, we assess whether, and why, female candidates for liver transplantation are more likely than male candidates to have an adverse waiting list outcome in Eurotransplant. In Chapter 7, we examine whether immunized candidates are adequately prioritized in ETKAS. The findings of these chapters highlight that both the liver and kidney allocation systems have room for improvement.
An important barrier to implementing such improvements has been that Eurotransplant did not have tools available to quantify the impact of allocation policy changes. To overcome this barrier, a second goal of this thesis was to develop tools that provide insight into the adequacy and the unintended consequences of allocation policy changes. In Chapters 5 and 8, we have provided detailed descriptions of the ELAS and ETKidney simulators. These simulators mimic the liver and kidney allocation processes in Eurotransplant, based on Eurotransplant allocation rules and Eurotransplant registry data. To build trust in these tools, we have developed these simulators in close collaboration with subject-matter experts. They have been validated through input-output validation and are publicly available online. The simulators have already become valuable tools for allocation policy development, as we illustrated through clinically motivated case studies in Chapters 5 and 8.
In this chapter, we reflect on the findings of this thesis, and what is needed to advance Eurotransplant’s liver and kidney allocation systems.
9.1 This thesis is a sharp look at familiar problems
The problems studied in this thesis were brought to our attention by clinicians affiliated with Eurotransplant’s advisory committees and the ETRL, who experience these issues in their daily work. The studied problems are therefore not new. In fact, sex disparity in liver transplantation has been a prominent topic for over a decade [38], [65], [66], and disadvantages for immunized candidates have previously been reported in both Germany [134], [135] and the United States [177].
This lack of novelty does not mean that these problems are not worth revisiting. One reason to re-examine these problems in this thesis is that consensus between Eurotransplant’s member countries is often required to change allocation policies. Investigating disparities using Eurotransplant-wide cohorts can help build such consensus. In some cases, Eurotransplant-wide cohorts are also needed to achieve sufficient statistical power. For example, the disadvantages faced by female candidates on the liver waiting list (Chapter 4) are likely too subtle to be detectable in single-center studies or even those conducted at the national level.
Eurotransplant’s expertise on the allocation systems can also be essential to contextualize any observed disparities. For instance, disparities may vary between countries due to heterogeneity in national allocation policies, or they may evolve over time as the allocation rules change. An important realization is also that disparities in waiting list outcomes need not be the result of allocation. For example, they can also arise through the offer acceptance behavior of transplant centers. Eurotransplant’s expertise on the allocation systems was critical in designing informative sensitivity checks relating to its allocation mechanisms (standard vs. non-standard allocation; center-driven vs. patient-driven offers), time periods, and countries (see Chapters 4 and 7).
A final motivation to revisit these existing problems is that the existing literature relies too heavily on the standard assumptions of the Cox proportional hazards model, which are implausible in the context of the transplantation waiting list. For example, in liver transplantation, dependent censoring due to transplantation is typically ignored, which introduces bias when modeling waiting list mortality (see Chapters 3 and 4). In kidney allocation, the modeling of access to transplantation is complicated by the relevance of two timescales: time since waiting list registration and time on dialysis. In our analyses, we have preferred to use dialysis as the timescale, as candidates are prioritized by dialysis time and not waiting time. However, care must then be taken to ensure that the adjustment variables are predetermined to the outcome (see Chapter 7).
9.2 We need to look beyond survival models for allocation
In transplantation research, efforts to improve allocation models often narrowly focus on the improvement of the statistical models that predict medical urgency, medical utility, or transplant benefit. In the literature on liver transplantation, for example, a plethora of refinements to MELD have been proposed, which include delta-MELD [39], integrated MELD [186], Updated MELD [40], ReFit MELD [41], MELD excluding INR [187], UKELD [188], MELD-Na [37], MELD-Plus [189], MELD lactate [190], ReMELD and ReMELD-Na [191], MELD 3.0 [20], and GEMA-Na [70].
Few of these models have been implemented for organ allocation, and those that were implemented have had a limited impact on the number of waiting list deaths. For example, in the United States, only MELD-Na and MELD 3.0 have been adopted for liver allocation. MELD-Na was introduced because LSAM simulations suggested that the score could prevent 40 to 60 waiting list deaths annually. MELD 3.0 was primarily introduced to rectify sex disparity, but was also projected to reduce waiting list mortality by up to 20 deaths per year [20]. These projections meant that MELD-Na could avert 2 to 3% of liver waiting list deaths in the United States, whereas MELD 3.0 was projected to reduce mortality by less than 1%. Although this is an improvement, it also shows that revising MELD is not a magic bullet in preventing liver waiting list deaths.
In Chapter 5, we quantified the impacts of introducing ReMELD-Na on liver waiting list outcomes in Eurotransplant. We find that ReMELD-Na could avert between 5 and 20 waiting list deaths per year, which corresponds to 1 to 4 percent of the waiting list deaths in Eurotransplant. Partially based on these findings, liver allocation in Eurotransplant has become based on ReMELD-Na since March 25, 2025. Eurotransplant is currently examining whether allocation can be improved further with MELD 3.0 or GEMA-Na. Although these efforts are worthwhile, it should be realized that further refinements to MELD may have diminishing returns, and could also reduce the number of waiting list deaths by less than one percent.
Other approaches are thus necessary to meaningfully reduce mortality on the liver waiting list. These approaches should look beyond the refinement of survival models for waiting list mortality. One approach – which is under-explored for liver allocation in Eurotransplant – is to reconsider the priority for candidates with exception points. In a second case study in Chapter 5, we show that modifying the exception point system in Belgium could reduce the number of Belgian waiting list deaths by up to 10%. Based on this information, BeLIAC asked Eurotransplant in February 2025 to cap all exception points in Belgium by 28 points. Similar revisions of the exception point systems should be explored with other national competent authorities. A different approach to reducing the number of waiting list deaths – which was not explored in this thesis – is to increase geographical sharing for candidates with extreme MELD scores, as is done in the United States for MELD scores exceeding 35 [102] and in Italy for MELD scores exceeding 30 [103]. A simulation study could be conducted that examines the effects of broader geographic sharing in Eurotransplant.
Liver allocation can also be improved by making it fairer. In Chapter 4 we describe that the smaller stature of females limits their access to transplantation, which indirectly increases the number of waiting list deaths in females. This finding suggests that waiting list outcomes between males and females cannot be equalized by only revising MELD using a Cox proportional hazards model with waiting list death as the outcome; such models cannot compensate for the disparities in waiting list outcomes that are indirectly due to access to transplantation. Instead, we suggest in Chapter 4 that a simulation study should be conducted to assess how many extra points small-statured candidates would need to rectify sex disparity.
While the inclusion of factors that are not directly related to survival may appear arbitrary, it is important to note that the current allocation system already includes such factors. For example, pediatric patients are already prioritized based on exception points, and blood group O candidates are protected by the restricted ABO blood group rules. Ultimately, we believe that there is no compelling reason for basing liver allocation solely on MELD. We note that this idea already appears to have been accepted by policymakers in the United States, where a policy-making process is underway to award points for factors other than pre-transplant mortality (quantified by MELD) [20]. Explicitly included among these factors is candidate height.
9.3 We should look beyond aggregate outcomes
ELIAC and national competent authorities have been hesitant to introduce MELD-Na for liver allocation because the literature indicates that this score has exacerbated sex disparity in liver allocation (e.g., [75]). Our results in Chapter 4 are compatible with this finding and suggest that females indeed have a slightly higher waiting list mortality rate than males when at the same MELD-Na score. This disadvantage corresponds to a 0.5 to 1 point difference on the MELD scale. The ReMELD-Na case study in Chapter 5 also shows that introducing ReMELD-Na would primarily prevent waiting list deaths among male candidates. Although this confirms concerns that ReMELD-Na would increase sex disparity, it should not be interpreted as an argument against ReMELD-Na per se; our analysis shows that ReMELD-Na would reduce the number of waiting list deaths for both sexes (albeit insignificantly for females). This illustrates that new allocation policies should not be introduced or rejected based on a single summary statistic. Instead, simulation studies should be conducted that quantify the impact of allocation policy changes on several subgroups, as we do in Chapter 5 for several vulnerable patient groups. Policymakers can then make rational decisions about whether the improvements for some patient groups can justify the unintended consequences these policy changes inevitably have on others. Not doing such a simulation can also harm certain patient groups. A cautionary example appears to have been the 2018 introduction of the Transplant Benefit Score (TBS) in the United Kingdom. While this scheme improved overall survival benefit [19], the policy has been controversial because it inadvertently reduced access to transplantation for young liver transplant candidates, as well as for those with hepatocellular carcinoma (HCC) [45], [46].
Policymakers should also be aware that using statistical models in allocation results in statistical discrimination. In a retrospective cohort of liver transplantation recipients from Eurotransplant (unpublished), we find that the sickest 3% of candidates for liver transplantation still have a graft survival probability two years after transplantation that exceeds 60%. These patients were on average 50 years old, had MELD scores exceeding 30 at listing, were admitted to the ICU before transplantation, and presented with grade 3 acute-on-chronic liver failure (ACLF), which alone is associated with a 28-day mortality exceeding 80% [22]. A benefits-based allocation could deny these patients access to liver transplantation. This would mean that the six out of ten patients who would survive more than two years with a functioning graft could be denied a liver transplantation, because four others would not survive. Whether such an allocation is ethically acceptable is a normative question that cannot be answered based on aggregate statistics.
9.4 Scientific evidence is rarely the bottleneck
The recommendations that are prepared by Eurotransplant’s advisory committees require approval from the Eurotransplant Board and the national competent authorities before they are implemented. A frequently mentioned barrier to obtaining approval from the national competent authorities is that any change to allocation has to be based on scientific evidence.
In our view, the availability of scientific evidence is rarely the bottleneck – at least not for the case studies included in this thesis. For example, there is broad consensus that kidneys from young donors should be preferentially allocated to young candidates [178], [179], [180], [181], [182], there is consensus that HLA-DR matching is more important than HLA-A or HLA-B matching in kidney allocation [129], [157], and ample evidence exists that hyponatremia is associated with an increased mortality on the liver waiting list [37], [191].
We think that the primary bottleneck lies in translating these findings into allocation policies that are acceptable to all stakeholders. Significant progress could be made if national competent authorities are willing to accept the results of the ELAS and ETKidney simulators as scientific evidence. In the United States, discrete-event simulation already plays such a role; tools such as LSAM and KPSAM have been instrumental in shaping the liver and kidney allocation policies in OPTN, the organ-sharing network of the United States.
Another issue has been that proposals to improve the allocation systems are sometimes too radical. The fact that ETKAS still gives equal priority to HLA matching at the A, B, and DR loci is not because emphasizing matching at the HLA-DR locus has not been explored. In fact, several policies have been proposed that emphasize matching on the HLA-DR locus. However, these proposals introduced new tiers for DR-matching (e.g., [130]), which represents a radical overhaul of the current points-based system. National competent authorities could not agree to this overhaul, for example because it strongly increased the number of international transplantations [131]. A more fruitful approach to improving the allocation system is to consider incremental changes that are supported by solid evidence and backed by a broad set of stakeholders. We see giving relatively more points to matching on the HLA-DR locus than the HLA-A locus – a policy explored in Chapter 8 – as an example of such an incremental approach.
9.5 The “chicken-and-egg” problem in allocation development
A persistent “chicken-and-egg” problem in Eurotransplant is that the transplant centers are reluctant to report information to Eurotransplant that is not required for allocation, while policymakers are reluctant to introduce new allocation policies that have not been validated in Eurotransplant. We encountered such a problem in the ReMELD-Na case study in Chapter 5; serum sodium is absent in the Eurotransplant database for most candidates, which complicates studying the impact of ReMELD-Na for liver allocation.
It is important to note that serum sodium is virtually always measured alongside the other MELD biomarkers, which makes missingness of serum sodium a reporting issue, not a data availability issue. The same issue exists for serum albumin, which is required for MELD 3.0, and serum urea, which is required for GEMA-Na. This severely limits the utility of the Eurotransplant database for developing and validating liver allocation scores, especially when the SRTR makes data from the United States available for research, where the reporting of these biomarkers has been mandatory for years. For example, centers in the United States have been required to report serum sodium with every MELD update since 2004, twelve years before MELD-Na was introduced for allocation [192]. To develop liver allocation scores specifically for Eurotransplant, more prospective data collection is needed in Eurotransplant.
In kidney allocation, a similar “chicken-and-egg” problem complicates the study the impact of epitope matching. Such epitope matching has been described as a promising alternative to HLA matching at the serological level because (i) epitopes provide a more precise assessment of immunological compatibility that could improve post-transplant outcomes [160], and (ii) epitope matching can be more equitable than HLA matching [193]. However, studying whether epitope matching would improve outcomes after kidney transplantation requires high-resolution HLA typings, and such typings are not yet routinely reported to Eurotransplant [194].
Transplant centers express valid concerns over the increase in workload associated with prospective data collection. A task for Eurotransplant is to minimize this workload by limiting any prospective data collection to information that is deemed necessary for allocation development. For liver transplantation, the burden can be reduced by mandating the collection of specific biomarkers only at listing, which would enable external validation of new liver allocation scores using a “from registration” approach (see Chapter 3). An ELIAC recommendation in this direction is currently awaiting approval from the national competent authorities.
The workload for the transplant centers can also be reduced through automated data reporting. A success story in this regard is the introduction of the virtual crossmatch in January 2023, after which donor procurement organizations no longer need to manually enter the HLA typings of their donors. As a result, HLA typings of donors are now routinely available at Eurotransplant at an intermediate resolution. Implementing a similar automated reporting system for candidate HLA data has been requested by HLA typing laboratories, and should be a priority for Eurotransplant.
Special attention should also be given to the collection of follow-up information. The simulation of listing for repeat transplantation in the ETKidney and ELAS simulators depends on this information. Such information is also required to quantify the impact of allocation policy changes on post-transplant outcomes, a matter that is regularly inquired about by the advisory committees. A growing concern is that several centers within Eurotransplant have stopped reporting follow-up information to the Eurotransplant registry. Long-term follow-up information is therefore not available for many transplant recipients.
9.6 We need more constructive dialogue
The core principles of Eurotransplant’s current liver and kidney allocation systems have changed little since their respective introductions in 2007 and the 1990s. This stagnation stands in contrast to other regions and is surprising given the demographic shifts of our patient and donor populations, and clinical advancements in the field. The work presented in this thesis has described several areas for improvement in kidney and liver allocation. The primary challenge lies in translating these findings into allocation policies acceptable to Eurotransplant, national competent authorities, transplant centers, and ultimately, the patients who wait for a transplant.
Over the past two decades, allocation development has become a slow and tedious process. This has, at times, strained the relationships of Eurotransplant with its stakeholders. Some member countries even question whether there is a role for Eurotransplant in allocation development. These stakeholders should recognize – as is highlighted by this thesis – that Eurotransplant allocation systems are highly complex and must balance multiple, competing objectives. Improving these systems is not as straightforward as proposing a new statistical or machine learning model to score medical urgency, medical utility, or transplant benefit, and solely focusing on such solutions can harm vulnerable patient groups. In my view, Eurotransplant’s expert knowledge on these allocation systems makes it deserving of a seat at the table when new allocation policies are discussed.
At the same time, it is important that Eurotransplant listens to the clinical experts who on a daily basis experience the limitations of the current allocation systems. The topics studied in this thesis were motivated by conversations with these experts; for example, the size mismatch hypothesis in Chapter 4 was raised (off-topic) by a BeLIAC representative, and several nephrologists have expressed frustrations about the fact that the immunized candidates who are ineligible for the AM program are falling through the cracks in ETKAS (which we confirm in Chapter 7). It is true that Eurotransplant’s allocation systems feature mechanisms to help address these disparities. For example, livers from donors weighing less than 46 kg are offered with priority to candidates weighing less than 55 kg, and immunized candidates receive some extra priority through mismatch probability points. It can simultaneously be true that these mechanisms fall short of realizing equality of opportunity, and Eurotransplant should be more open to recognizing this. Historically, the main forums to develop new allocation policies in Eurotransplant have been the organ advisory committees. With the introduction of legal frameworks in the 1990s, national competent authorities now also have a strong say. A source of frustration appears to be that Eurotransplant submits finalized recommendations to national competent authorities, sometimes without prior consultation. To avoid this, Eurotransplant should engage these and other stakeholders earlier in policy discussions. An obstacle to playing such a role is that Eurotransplant has limited capacity for allocation development, employing only two full-time biostatisticians and seven medical doctors who have to spend most of their time on operational duties.
This stands in stark contrast to other regions where dedicated research departments or organizations have been established that focus exclusively on allocation development. Notable is the SRTR in the United States, which was established in 1984 to support statistical analyses relating to solid organ donation and which has developed the SAM family of simulators. SRTR operates on an annual budget of 7 million USD. Because of this investment gap, it not surprising that the heart, lung, and liver allocation systems used in Eurotransplant were developed in the United States. If we want to develop allocation systems tailored to our European patients, Eurotransplant’s member countries should be open to providing long-term funding for allocation research and development.
In the end, meaningful progress on the organ allocation policies can only be achieved through more constructive dialogue among Eurotransplant, the national competent authorities, and subject-matter experts affiliated with the transplantation centers. Together, these stakeholders should carefully consider how to weigh the ethical trade-offs involved in the allocation of deceased-donor organs. The simulators presented in this thesis can contribute to these discussions by making the associated trade-offs explicit.