Geographic Variation in Non-HCC MELD Exceptions and Its Effect on Liver Transplant Waitlist Outcomes

总结：移植时终末期肝病（MELD）评分模型的地理差异 捐赠服务区引发了关于捐赠肝脏分配的重大政策辩论。一个重要的警告， 这种感知的差异是对分配MELD的关注，其中MELD评分较低的患者 如果他们计算的MELD被认为低估了等待名单的风险， mortality.移植时计算的MELD的分析表明区域之间的变化要小得多。一些 高分配MELD地区实际上相对于该国其他地区具有较低的MELD计算分数。的 通过分配与计算的MELD分数绘制的不同图片突出了MELD的关键作用 例外点在获得移植的地理差异中发挥作用。国家肝脏审查委员会 最近实施了MELD例外分数的国家标准化，以减轻 地理差异。这一策略的一个潜在缺陷是假设与死亡率相关的风险 给定的MELD评分或异常诊断在地理区域之间是相同的。我们知道， 然而，健康和获得保健的决定因素在全国各地差异很大，这些差异 对健康结果有重大影响。实施地理上统一的MELD例外评分 不考虑这些重要差异的政策可能会产生高估的意外效果。 在某些地区，例外患者等待名单死亡风险，而在其他地区， 加剧了地理差异，而不是减轻它。我假设，显著的地理差异 存在于县一级MELD例外分数的普遍性和适当性中，并且 根据候选人的地理位置对MELD例外分数进行适当调整， 肝移植等候名单死亡率的差异。尽管有大量关于肝脏地理差异的文献， 移植，很少有研究真正应用正式的地理空间分析，以地理为基础的问题，面临的 外地我建议利用这些技术来实现以下独特的目标：（1）确定 县一级MELD例外的普遍性和适当性，并诊断 这些例外情况为移植候选人提供了不适当的优势， MELD例外评分;（2）确定调整后的MELD例外评分，使等待名单死亡率相等 每个地理区域中例外和非例外患者之间的风险;以及（3）模拟等待名单结果 在根据地理位置分配MELD例外点的替代分配方案下， 目标2中确定的区域。这项研究将提供一个更细粒度和个性化的方法， 等待名单死亡率的估计。这个奖项所提供的指导和培训将促进我作为一个 通过让我专注于建立地理空间分析和高级模拟方面的专业知识， 作为一名外科医生科学家，我的长期职业目标是独立研究，并获得R 01资金。

Summary: Geographic variation in model for end stage liver disease (MELD) scores at transplant across donation service areas has fueled a major policy debate over allocation of donor livers. An important caveat to this perceived disparity is the focus on allocation MELD, in which patients with lower calculated MELD scores may be assigned a higher score if their calculated MELD is thought to underestimate the risk of waitlist mortality. Analysis of calculated MELD at transplant demonstrates much less variation between regions. Some high allocation MELD regions actually have low calculated MELD scores relative to the rest of the country. The different pictures painted by allocation versus calculated MELD scores highlight the critical role MELD exception points play in geographic differences in access to transplantation. A national liver review board was recently implemented to impose national standardization of MELD exception scores in an effort to mitigate geographic disparity. A potential flaw in this strategy is the assumption that the risk of mortality associated with a given MELD score or exception diagnosis is the same across geographic areas. We know that social determinants of health and access to care vary widely across the country, however, and that these differences have a significant impact on health outcomes. Imposing a geographically uniform MELD exception scoring policy that does not account for these important differences may have the unintended effect of overestimating the risk of waitlist mortality for exception patients in some regions while underestimating that risk in others, thus worsening geographic disparity rather than mitigating it. I hypothesize that significant geographic variation exists in the prevalence and appropriateness of MELD exceptions scores at the county level, and that appropriate adjustment to MELD exception scores based on a candidate’s location will mitigate geographic disparity in liver transplant waitlist mortality. Despite abundant literature on geographic disparity in liver transplantation, few studies actually apply formal geospatial analysis to geographically based questions facing the field. I propose to utilize such techniques to accomplish the following unique aims: (1) determine the prevalence and appropriateness of MELD exceptions on a county level and diagnose the presence of geographic clusters where these exceptions provide an inappropriate advantage to transplant candidates with MELD exception scores; (2) determine adjusted MELD exception scores which would equalize waitlist mortality risk between exception and non-exception patients in each geographic area; and (3) simulate waitlist outcomes under an alternative allocation scenario where MELD exception points are assigned according to geographic region as determined in Aim 2. This study will provide a more granular and personalized approach to the estimation of waitlist mortality. The mentoring and training afforded by this award will foster my growth as an investigator by allowing me to focus on building an expertise in geospatial analysis and advanced simulation, advancing my long-term career goal of research independence as a surgeon-scientist with R01 funding.