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

利用伦理学、流行病学和高质量数据来优化移植肝脏的分配

• 批准号: 10356830
• 负责人: David Seth Goldberg
• 金额: $ 51.28万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10356830
• 关键词: Accounting; Acute; Acute Renal Failure with Renal Papillary Necrosis; Address; Age; Caring; Cessation of life; Chronic Kidney Failure; Cirrhosis; Clinical; Clinical Data; Data; Deterioration; Disadvantaged; Epidemiology; Ethics; Female; Future; Geography; Goals; Health; Injury to Kidney; Judgment; Laboratories; Life; Life Expectancy; Liver; Liver Failure; Liver diseases; Modeling; Organ; Organ Donor; Outcome; Patients; Phenotype; Policies; Primary carcinoma of the liver cells; Prognosis; Provider; Resources; Rest; Risk; Scheme; System; Time; Transplant Recipients; Transplantation; United Network for Organ Sharing; United States National Institutes of Health; Update; Veterans; Veterans Health Administration; Waiting Lists; Work; base; cohort; cost; data registry; data sharing; design; end stage liver disease; ethnic minority; follow-up; health care service utilization; health disparity populations; improved; innovation; liver cancer model; liver transplantation; meetings; mortality; patient population; patient subsets; racial and ethnic; response; risk prediction model; simulation; simulation software; transplant model; transplant registry; transplantation therapy; tumor

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.

7. 项目总结/摘要 在美国，适合进行肝移植 (LT) 的肝脏的需求量超过了供应量。 2017年，虽然 7,500 名患者接受了 LT，超过 2,500 名患者因死亡或“病重”而被从候补名单中删除 移植。”这些数字并未考虑到这样一个事实：每 6 名患者中就有 5 名患有终末期肝病 符合 LT 标准的 (ESLD) 绝不会列入候补名单。因此，患者不可避免地会在等待名单上死亡，或者死亡 从未被列入候补名单。最佳的移植系统应通过实现三个值来分配器官：1） 平等——所有人都能公平地获得移植； 2) 优先考虑最坏的情况，以及 3) 效用——最大化“预期” 患者群体的总体利益净额。使用末期肝病模型 （MELD）评分来优先考虑患者，当前系统主要关注紧急情况，有利于 目前病情最严重，被称为“最糟糕的情况”。这可以最大限度地减少候补死亡率。然而，这并没有考虑公平访问 （平等）针对不同的患者亚组（即传统上处于弱势的群体），或效用（例如，最大化 生存率或 LT 的净效益）。因此，许多 LT 被分配给术后预期寿命有限的患者。 LT，而候补名单上其他同样强烈主张 LT 道德的人则死去。改善LT分配需要 新数据和创新范式旨在优化稀缺资源的使用，从而延长寿命 >15 年。这需要的不仅仅是修改终末期肝病模型 (MELD) 评分（ 用于候补名单优先级的分数）。首先，为了估计没有 LT 的生存率，稳健的纵向数据是 鉴于等待名单时间中位数<180天且>50%，需要超出等待名单登记数据 患者在上市后一年内获得 LT。其次，必须开发长期后风险模型，以 考虑到对长期生存具有高度歧视性的因素（例如年龄、急性肾病与慢性肾病） 失败）。第三，肝细胞癌（HCC）患者的分配政策必须考虑相对 考虑到优先考虑 HCC 的意外后果，LT 相对于其他 HCC 治疗的长期益处 患者优于失代偿性肝硬化患者。这项工作对于未来的设计至关重要 改进的分配系统可显着提高 ESLD 患者的生存率。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)，旨在实现以下目标：1) 制定及时更新的长期预共享网络 解释急性和慢性肾脏疾病的 LT 生存模型； 2）制定一个及时更新的、长期的 使用 HCC 患者的肿瘤、实验室数据和临床变量的 LT 前生存模型； 3）开发一个 纳入人口、临床和实验室变量的长期 LT 后风险模型； 4）构建配置 模拟量化几种不同分配下获得的生命年差异的大小 跨不同时间范围的计划，重点关注 NIH 指定的健康差异人群。