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.

该智能互联健康 (SCH) 奖项将通过研究将称为“脱敏”的个性化抗体去除方案纳入肾脏配对捐赠 (KPD) 系统，为改善患者获得肾移植的机会做出贡献。肾移植是最终的金标准治疗方法，可为终末期肾病患者提供最佳的生活质量。然而，由于移植候选者与其肾脏捐赠者之间的血型或人类白细胞抗原组织类型不相容等限制，许多人无法获得这种治疗。为了克服这些不相容性，移植界设计了几种新颖的方案，包括 KPD 和脱敏。 KPD 允许有愿意但不相容的活体捐献者的患者将其不相容的捐献者更换为更相容的捐献者（也在 KPD 捐献者-患者库中），而脱敏程序会在手术前从移植受者的血流中去除抗体，以降低捐献肾脏潜在排斥的风险。目前，这两种方案都有局限性。为了克服这些限制，著名的移植专家一直主张将这两种方案结合起来。该项目旨在开发基于随机模拟和优化的算法，用于在采用脱敏疗法的 KPD 系统中匹配捐赠者和接受者。传统的 KPD 系统中，移植候选者只需将不相容的供体更换为系统中更相容的供体，与此相反，设想的 KPD 系统将为患者提供接受个性化脱敏治疗的额外选择，以及交换供体的选择，以显着增加匹配的可能性。研究目标是开发一个集成的动态随机模拟优化模型，包括：（i）优化策略，以确定最佳的个性化脱敏方案； (ii) 改进稳健/随机优化方法，将脱敏治疗整合到 KPD 匹配中； (iii) 一种决策支持工具，帮助患者决定是接受肾脏兼容性较差的脱敏方案，还是等待兼容性更好的方案。集成动态随机模拟优化模型的输出将包括来自组合和模拟优化算法的建议配对匹配、基于模拟患者行为实现的匹配以及关键性能系统指标的统计估计。在该项目的最后一年，该团队将为乔治华盛顿大学移植研究所 (GWTI) 和弗吉尼亚联邦大学 (VCU) 健康休姆-李移植中心定制算法，这两个中心有兴趣建立联合本地 KPD 交换中心。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.