Using ethics, epidemiology and high-quality data to optimize the allocation of livers for transplantation

7.项目摘要/摘要 在美国，适合肝移植(LT)的肝脏供不应求。2017年，尽管 7500名患者接受了LT，超过2500名患者在死亡或病情严重后被从等待名单中删除 去移植。“这些数字不能解释这样一个事实，即6名终末期肝病患者中有5名 符合LT标准的(ESLD)从不会被列入等待名单。因此，患者不可避免地会在等待名单上死去，或者死亡 从来没有被列入过等待名单。一个最佳的移植系统应该通过实现三个价值来分配器官：1) 平等--所有人都能公平地获得移植；2)优先考虑最贫穷的人；以及3)效用--最大限度地提高“预期” 患者群体的总体良好净额。运用终末期肝病模型 (MELD)评分以确定患者的优先顺序，当前的系统主要关注紧迫性，有利于 目前病情最重的人是“最糟糕的人”这将等待名单上的死亡率降至最低。然而，这并不考虑公平访问。 (平等)针对不同的患者亚群(即，传统上处于不利地位的群体)，或效用(例如，最大化 LT的生存或净收益)。因此，许多长期移植被分配给预期寿命有限的患者 而在等待名单上的其他人也同样强烈地要求LT死亡。改善LT分配需要 新数据和创新模式，旨在优化稀缺资源的使用，通过以下方式延长寿命 &gt；15年。这需要的不仅仅是修订的终末期肝病模型(MELD)评分( 用于等待列表优先级排序的分数)。首先，为了在没有LT的情况下估计生存，稳健的纵向数据是 需要超出等候名单注册数据的范围，因为等待名单的中位数时间为180天，而等待名单的等待时间为50% 患者在上市后一年内接受LT。第二，必须开发长期的后LT风险模型，以 考虑对长期生存具有高度歧视性的因素(例如，年龄、急性肾脏与慢性肾脏 失败)。第三，肝细胞癌患者的分配政策必须考虑患者的亲属 考虑到优先考虑肝细胞癌的意外后果，肝移植与其他肝细胞癌治疗的长期益处 失代偿期肝硬变的患者。这项工作将是未来设计的基础 改善分配系统，推动ESLD患者存活率的大幅提高。我们将利用 来自美国最大的肝脏护理提供商退伍军人健康管理局的强大而详细的数据，以及 器官共享联合网络(UNOS)，以解决这些目标：1)制定及时更新的长期预 考虑急慢性肾脏疾病的LT生存模型；2)制定及时更新的、长期的 使用肿瘤、实验室数据和临床变量为肝细胞癌患者建立肝移植前生存模型；3)发展 包含人口统计学、临床和实验室变量的长期肝移植后风险模型；4)建立配置 量化在几种不同分配下获得的寿命年差异大小的模拟 在一系列时间范围内的计划，重点是NIH指定的健康差距人口。

7. Project Summary/Abstract The demand for livers suitable for a liver transplant (LT) in the US outnumbers the supply. In 2017, although 7,500 patients received a LT, more than 2,500 were removed from the waitlist after dying or becoming “too sick to transplant.” These numbers don't account for the fact that 5 out of 6 patients with end-stage liver disease (ESLD) meeting LT criteria are never waitlisted. Consequently, patients inevitably die on a waiting list, or die never having been waitlisted. An optimal transplant system should allocate organs by realizing three values: 1) equality—fair access to transplant for all; 2) priority to the worst off, and 3) utility—maximizing the `expected net amount of overall good' for the population of patients. By using the Model for End Stage Liver Disease (MELD) score to prioritize patients, the current system focuses predominantly on urgency, favoring the currently sickest as the `worst off.' This minimizes waitlist mortality. However, this does not consider fair access (equality) for different patient subgroups (i.e., traditionally disadvantaged groups), or utility (e.g., maximizing survival or net benefit of LT). As a result, many LTs are allocated to patients with limited life expectancy after LT, while others on the waitlist with equally strong ethical claims to LT die. Improving LT allocation requires new data and an innovative paradigm that seeks to optimize use of a scarce resource that can extend life by >15 years. This requires more than simply a revised Model for End-Stage Liver Disease (MELD) score (the score used for waitlist prioritization). First, to estimate survival without a LT, robust longitudinal data are needed that extend beyond waitlist registry data, given that median waitlist time is <180 days and >50% patients receive a LT within one year of listing. Second, long-term post-LT risk models must be developed, to account for factors that are highly discriminatory for long-term survival (e.g., age, acute vs chronic kidney failure). Third, allocation policies for patients with hepatocellular carcinoma (HCC) must consider the relative long-term benefit of LT vs. other HCC treatments given the unintended consequences of prioritizing HCC patients over those with decompensated cirrhosis. This work will be fundamental to the future design of an improved allocation system that drives major increases in survival for patients with ESLD. We will leverage powerful and detailed data from the Veterans Health Administration, the largest US provider of liver care, and United Network for Organ Sharing (UNOS), to address these aims: 1) to develop time-updated, long-term pre- LT survival models accounting for acute and chronic kidney disease; 2) to develop a time-updated, long-term pre-LT survival model using tumor, laboratory data, and clinical variables for patients with HCC; 3) to develop a long-term post-LT risk model incorporating demographic, clinical, and laboratory variables; 4) to build allocation simulations that quantify the magnitude of difference in life-years gained under several different allocation schemes across a range of time horizons, with a focus on NIH-designated health disparity populations.