Collaborative Research: SCH: Optimal Desensitization Protocol in Support of a Kidney Paired Donation (KPD) System

合作研究：SCH：支持肾脏配对捐赠 (KPD) 系统的最佳脱敏方案

• 批准号: 2123684
• 负责人: Michael Fu
• 金额: $ 15.97万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123684&HistoricalAwards=false
• 关键词: Collaborative; Research; SCH; Optimal; Desensitization

This Smart and Connected Health (SCH) award will contribute to improved patient access to kidney transplantation by studying the inclusion of a personalized antibody removal regimen known as “desensitization” into a kidney paired donation (KPD) system. Kidney transplantation is the definitive, gold standard treatment that provides the best quality of life for end-stage renal disease patients. The treatment, however, is not accessible to many due to constraints such as blood type or human leukocyte antigen tissue type incompatibility between transplant candidates and their kidney donors. To overcome these incompatibilities, the transplant community has devised several novel schemes including KPD and desensitization. KPD allows patients with a willing - but incompatible - living donor to swap their incompatible donor with a more compatible donor, also in the KPD donor-patient pool, while the desensitization procedure removes antibodies from transplant recipients’ blood streams prior to surgery to reduce the risk of potential rejection of donated kidneys. Currently, both of these schemes have limitations. To overcome the limitations, prominent transplant experts have been advocating for combining the two schemes. This project aims to develop stochastic simulation and optimization-based algorithms for matching donors and recipients in a KPD system with desensitization therapy. In contrast to a conventional KPD system where transplant candidates simply swap their incompatible donors for more compatible donors in the system, the envisioned KPD systems would offer patients the additional option of undergoing a personalized desensitization therapy along with the option of swapping donors to significantly increase their likelihood of a match. The research objective is to develop an integrated dynamic stochastic simulation-optimization model comprised of: (i) an optimization strategy to identify the optimal personalized protocol for desensitization; (ii) improved robust/stochastic optimization methods to integrate the desensitization therapy into the KPD matching; and (iii) a decision-support tool to help patients decide whether to accept the desensitization regimen with a less compatible kidney, or wait for a more compatible one. The output of the integrated dynamic stochastic simulation-optimization model will include the suggested paired matchings from the combinatorial and simulation optimization algorithms, the realized matchings based on simulated patient behavior, and statistical estimates of key performance system metrics. In the last year of the project, the team will tailor the algorithms for the George Washington University Transplant Institute (GWTI) and Virginia Commonwealth University (VCU) Health Hume-Lee Transplant Center, which are interested in developing a joint local KPD exchange.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

通过研究将个性化抗体去除方案纳入肾配对捐赠（KPD）系统，这项智能和连接的健康奖（SCH）奖将有助于改善患者接受肾脏移植的机会。肾脏移植是确定的金标准治疗方法，可为终末期肾脏疾病患者提供最佳的生活质量。然而，由于诸如血型或人类白细胞抗原组织类型在移植候选者及其肾脏供体之间不兼容之类的限制，因此无法获得这种处理。为了克服这些不兼容，移植社区设计了几种新的方案，包括KPD和脱敏。 KPD允许有愿意但不兼容的活捐赠者的患者与更兼容的供体（同时在KPD供体）中交换不兼容的供体，而脱敏程序则消除了手术前血液中的抗体，从而减少了捐赠者的潜在捐助者的风险。目前，这两个方案都有局限性。为了克服局限性，突出的移植专家一直主张将这两个方案结合起来。该项目旨在开发随机模拟和基于优化的算法，以匹配具有脱敏治疗的KPD系统中的供体和受体。与传统的KPD系统相比，移植候选者只是将其不兼容的捐助者换成系统中更兼容的供体，设想的KPD系统将为患者提供另外的选择，即进行个性化的脱敏治疗，并选择交换捐助者，以显着提高竞赛的可能性。研究目标是开发一个综合动态随机仿真 - 优化模型，该模型由以下方式组成：（i）优化策略，以识别脱敏的最佳个性化协议； （ii）改善了将脱敏疗法整合到KPD匹配中的鲁棒/随机优化方法； （iii）一种决策支持工具，可帮助患者决定是否接受兼容肾脏较不兼容的脱敏方案，还是等待更兼容的肾脏。集成动态随机模拟模型的输出将包括组合和仿真优化算法的建议配对匹配，基于模拟患者行为的实现匹配以及对关键性能系统指标的统计估计。在该项目的最后一年，团队将定制乔治华盛顿大学移植研究所（GWTI）和弗吉尼亚州联邦大学（VCU）健康休ue-lee移植中心的算法，这些中心有兴趣开发当地的KPD交换。这对NSF的法定任务和审查均具有宽泛的影响，这表明了审查的范围。

项目成果

Stochastic control for organ donations: A review

器官捐赠的随机控制：综述

• DOI: 10.1016/j.sysconle.2023.105476
• 发表时间: 2023
• 期刊: Systems & Control Letters
• 影响因子: 2.6
• 作者: Ren, Xingyu;Fu, Michael C.;Marcus, Steven I.
• 通讯作者: Marcus, Steven I.