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

基本信息

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
项目类别：
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

项目摘要

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/摘要然而，2017 年美国对适合肝移植 (LT) 的肝脏的需求超过了供应。 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这些数字并未考虑到六分之五的患者患有终末期肝病符合 LT 标准的 (ESLD) 从未被列入候补名单，患者不可避免地会在候补名单上死亡，或者死亡。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) 优先考虑最贫困者；3) 效用——最大化“预期” 通过使用终末期肝病模型，为患者群体带来总体好处的净额。（MELD）评分来优先考虑患者，当前系统主要关注紧迫性，有利于目前病情最严重的人被称为“最差的人”。这最大限度地减少了候补名单上的死亡率。但是，这并没有考虑到公平的机会。（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，而候补名单上其他对 LT 有同样强烈道德要求的人则需要改善 LT 分配。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（用于等待名单优先顺序的分数）首先，为了估计没有 LT 的生存率，纵向稳健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接受 LT 其次，必须开发长期的 LT 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）第三，肝细胞癌（HCC）患者的分配政策必须考虑亲属关系。考虑到优先考虑 HCC 的意外后果，LT 相对于其他 HCC 治疗的长期益处这项工作对于未来的设计至关重要。我们将利用改进的分配系统来大幅提高 ESLD 患者的生存率。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，以及联合器官共享网络 (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) 开发随时间更新的长期模型；使用 HCC 患者的肿瘤、实验室数据和临床变量建立 LT 前生存模型；结合人口、临床和实验室变量的长期 LT 后风险模型 4) 建立分配；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。