Rescuing Kidneys at Risk of Discard

项目摘要。肾移植（KT）是一种上级替代透析的许多患者， 在寿命、生活质量、发病率和终末期肾病（ESRD）护理费用方面。然而，在这方面， 在2017年等待KT的95，000名患者中，只有不到20，000人接受了KT。同年，约 4，000名患者死亡，另有4，700名患者因被认为病情过重而被从等待名单中删除 移植尽管等待名单很长，但仍有超过3，500（19%）的已故捐赠者肾脏被丢弃。 “没有发现肾脏”是丢弃1,325个捐赠肾脏的原因。丢弃率 对于低质量的肾脏显著增加，对于最低质量的肾脏达到高达60% 十分位数。尽管许多被丢弃的肾脏，即使是质量最低的十分位数的肾脏，也会发生这种丢弃 相对于维持透析，对某些接受者具有显著的生存益处。 目前的肾脏分配系统不能有效地分配具有高丢弃风险的肾脏。的 患者优先化算法不能快速识别将从这种肾脏中获益最多的患者，并且 捐献的肾脏被浪费了需要改进肾脏分配系统，以减少 我们需要减少被丢弃的肾脏数量，同时将它们公平地分配给最受益的患者。然而，在这方面， 修改分配政策具有挑战性，因为患者和移植专业人员必须权衡风险， 接受低质量肾脏的好处与等待高质量肾脏的风险。的 移植社区和患者接受低质量肾脏的偏好尚不清楚。 这项研究的目的是开发肾脏分配算法，以拯救可行的死者。 捐赠的肾脏所开发的算法将（i）使用 诸如肾脏供体概况指数或新的肾脏丢弃风险评分的测量;（ii）识别患者 人群，如等待移植时间较长或功能状态较低的人群， 大部分来自存活的肾脏移植临床医生（外科医生和肾病学家）和患者的偏好 (on透析和移植受者）将使用离散选择实验来引出， 分配效率和公平性的权衡对于有丢弃风险的肾脏。离散事件仿真软件 引擎将被开发。模拟引擎将整合移植中心和患者特定肾脏 接受行为，以及开发的分配算法。不同分配算法的好处 将在模拟中进行评估。 器官共享联合网络、器官采购协会代表 组织和美国肾脏病患者协会将担任科学顾问委员会。的 开发的分配算法将通过减少肾脏丢弃，每年帮助挽救约1 000人的生命。 研究结果将广泛传播给移植界，以促进快速实施。

Project Summary. Kidney transplantation (KT) is a superior alternative to dialysis for many patients with respect to longevity, quality of life, morbidity, and the cost of End-Stage Renal Disease (ESRD) care. However, of the 95,000 patients waitlisted for a KT in 2017, less than 20,000 received a KT. In the same year, about 4,000 patients died, while another 4,700 were removed from the waitlist because they were considered too sick to transplant. Despite the long waitlist, more than 3,500 (19%) of the deceased donor kidneys were discarded. “No Recipient Found” was the stated reason for discarding 1,325 of the donated kidneys. The discard rate increases significantly for the low-quality kidneys, reaching as high as 60% for the kidneys in the lowest quality decile. This discard occurs even though many discarded kidneys, even those in the lowest quality decile, would have conferred significant survival benefits to some recipients relative to remaining on dialysis. The current kidney allocation system does not efficiently allocate kidneys at high risk of discard. The patient prioritization algorithm does not quickly identify patients who will benefit most from such kidneys, and the donated kidneys are wasted. Improvements to the kidney allocation system are needed to reduce the number of discarded kidneys while allocating them equitably to patients who would most benefit. However, revising allocation policy is challenging because patients and transplant professionals must weigh the risks and benefits of accepting a low-quality kidney against the risks of waiting for a much higher quality kidney. The transplant community and patient preferences in accepting low-quality kidneys are unknown. The proposed study aims to develop kidney allocation algorithms that rescue viable deceased donor kidneys from discard. The developed algorithms will (i) identify kidneys at risk of discard using measures such as Kidney Donor Profile Index, or a novel Kidney Discard Risk Score; (ii) identify patient populations, such as those with longer waiting times to transplant or low functional status, who will benefit the most from viable kidneys. Preferences of the transplant clinicians (Surgeons and Nephrologists) and patients (on dialysis and transplant recipients) will be elicited using discrete-choice experiments that will quantify allocation efficiency and fairness tradeoffs for kidneys at risk of discard. A discrete event simulation software engine will be developed. The simulation engine will incorporate transplant center and patient-specific kidney acceptance behaviors, and the developed allocation algorithms. The benefits of different allocation algorithms will be evaluated in the simulation. Representatives from the United Network of Organ Sharing, Association of Organ Procurement Organizations, and the American Association of Kidney Patients will serve on the scientific advisory board. The developed allocation algorithms will help save approximately 1,000 lives yearly by reducing kidney discards. The research outcome will be widely disseminated to the transplant community to foster rapid implementation.