Developing High-Quality Tools to Characterize Allograft Quality, Predict Transplant Outcomes and Expand Access to Kidney and Liver Transplantation

开发高质量工具来表征同种异体移植质量、预测移植结果并扩大肾移植和肝移植的机会

• 批准号: 10413907
• 负责人: David Seth Goldberg
• 金额: $ 54.62万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10413907
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Allografting; Biliary; Biological Markers; Blood Circulation; Blood Pressure; Calibration; Cessation of life; Characteristics; Clinical; Clinical Data; Communities; Complication; Creatinine; Data; Decision Making; Discrimination; Elderly; Event; Failure; Grant; Hepatitis; Hospitalization; Impairment; Individual; Ischemia; Joints; Kidney; Kidney Transplantation; Knowledge; Laboratories; Life; Link; Liver; Measures; Medicare; Methodology; Methods; Modeling; Names; Organ; Organ Donor; Organ Survival; Organ Transplantation; Outcome; Output; Overdose; Patients; Perfusion; Pharmaceutical Preparations; Policy Developments; Population; Probability; Province; Public Health; Registries; Research Personnel; Risk; Risk Assessment; Savings; Serum; Statistical Methods; Techniques; Time; Transplantation; Urine; Waiting Lists; Work; base; comorbidity; demographics; graft failure; hemodynamics; improved; indexing; liver transplantation; neglect; novel; organ allocation; organ injury; population health; pressure; secondary analysis; statistics; tool; web-based tool

Project Summary/Abstract In the US, nearly 95,000 patients are waitlisted for a kidney transplant, yet in 2018, only 14,700 received a deceased donor kidney transplant, while nearly 8,500 died or became too sick. The organ shortage is equally intense for liver transplant; in 2018, among more than 14,000 waitlisted patients, only 7,700 received a deceased donor liver transplant while 2,500 died or became too sick. Unfortunately, more than 5,000 kidneys and 2,000 livers from deceased donors were offered for transplant in 2018, but never transplanted. Although a subset of these organs was unsuitable for transplant, data clearly demonstrate that the inability to accurately assess graft quality directly led to many discards and/or undermined our ability to guide organs to appropriate patients. Prior to their organs being offered for transplant, deceased donors are hospitalized for days, often with numerous longitudinal data points (e.g., laboratory values) available to assess organ function. Yet, existing models of graft quality have these major flaws: 1) a reliance on cross-sectional clinical and laboratory data directly prior to procurement; 2) neglect of biologically-relevant, longitudinal data from the donor terminal hospitalization such serial hemodynamics (kidney and liver) and urine output (kidney); and 3) failure to integrate interactions between donor and recipient characteristics. As a result, existing kidney and liver donor risk models have inadequate prediction accuracy (C-statistics of only 0.6-0.65). Our group proposes to advance the field by developing state-of-the art models that make use of extensive, longitudinal donor data during the donor's terminal hospitalization—laboratory biomarkers of organ injury, and measures of organ function and perfusion. Second, we will develop highly robust allograft risk models using the joint modeling approach, which can account for longitudinal donor exposure data and time-to-event outcomes such as graft failure, instead of standard techniques (e.g., Cox regression). Third, we will highlight the real-world impact of the results in terms of population health. We have these specific aims: 1) Develop kidney graft failure models using joint modeling to predict graft failure with higher discrimination and calibration relative to the current kidney donor risk index; 2) Develop liver graft failure risk models using joint modeling to predict graft failure with high discrimination and calibration; 3a) Simulate the change in allograft life years from better pairing organs to recipients based on alignment of projected organ and patient survival; and 3b) Simulate the change in the number of transplants and allograft life years for the transplant population by implementing improved organ quality metrics in organ allocation to decrease discards. The models will be constructed using comprehensive US transplant data and externally validated with data from two Canadian provinces. The grant will also include important exploratory analyses of transplant complications by linking to data from Medicare. We will finally develop a web-based tool to enable real-time predictions of organ outcomes to put the results in the hands of clinicians and other investigators.

项目概要/摘要 在美国，近 95,000 名患者被列入肾移植的候补名单，但 2018 年，只有 14,700 名患者接受了肾移植手术。 死者捐献肾移植，而近 8,500 人死亡或病重 器官短缺同样严重。 2018年，在14,000多名等待名单上的患者中，只有7,700人接受了肝脏移植手术 不幸的是，有 2,500 人死亡或病重，有 5,000 多个肾脏被移植。 2018 年，有 2,000 个已故捐赠者的肝脏被提供进行移植，但从未进行过移植。 这些器官的子集不适合移植，数据清楚地表明无法准确地 评估移植质量直接导致许多丢弃和/或削弱了我们引导器官适当移植的能力 在将器官用于移植之前，已故的捐献者通常会住院数天。 有许多纵向数据点（例如实验室值）可用于评估器官功能。 现有的移植物质量模型存在以下主要缺陷：1）依赖于横断面临床和实验室 采购前的数据；2) 忽略来自供体终端的生物学相关的纵向数据 住院治疗，如连续血流动力学（肾脏和肝脏）和尿量（肾脏）；以及 3) 未能 因此，现有的肾脏和肝脏捐赠者之间的相互作用。 风险模型的预测精度不足（C 统计量仅为 0.6-0.65）。 通过开发利用广泛的纵向捐助者数据的最先进的模型来推进该领域的发展 捐献者临终住院期间——器官损伤的实验室生物标志物以及器官的测量 其次，我们将使用联合建模开发高度稳健的同种异体移植风险模型。 方法，可以解释纵向供体暴露数据和事件发生时间结果，例如移植物 第三，我们将强调失败的现实世界影响。 我们有以下具体目标：1) 开发肾移植衰竭模型。 使用联合建模来预测移植失败，相对于当前模型具有更高的辨别力和校准能力 肾捐献者风险指数；2) 使用联合建模开发肝移植失败风险模型来预测移植失败 具有高度辨别力和校准能力；3a) 模拟更好配对带来的同种异体移植物寿命年数的变化 根据预计器官和患者生存情况向接受者提供器官；以及 3b) 模拟变化； 通过实施改进的移植手术数量和移植人群的同种异体移植生命年 将使用器官分配中的器官质量指标来减少丢弃。 综合美国移植数据并通过加拿大两个省的数据进行外部验证。 还将包括通过链接到医疗保险数据对移植并发症进行重要的探索性分析。 我们最终将开发一个基于网络的工具来实时预测器官结果，并将结果放入 主教和其他调查人员之手。