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

这项智能和互联健康（SCH）奖将通过研究将被称为“脱敏”的个性化抗体去除方案纳入肾脏配对捐赠（KPD）系统，有助于改善患者获得肾脏移植的机会。肾移植是终末期肾病患者提供最佳生活质量的决定性金标准治疗方法。然而，由于血型或人类白细胞抗原组织类型与移植候选人和肾脏供体之间的不相容等限制，许多人无法获得这种治疗。为了克服这些不兼容性，移植界设计了几种新的方案，包括KPD和脱敏。KPD允许有意愿但不相容的活体供体的患者将不相容的供体与更相容的供体交换，也在KPD供体-患者池中，同时脱敏程序在手术前从移植受体血液中去除抗体，以降低捐赠肾脏的潜在排斥风险。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在倡导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，为脱敏治疗的KPD系统匹配供体和受体。与传统的KPD系统相比，移植候选人只是简单地将不相容的供体交换为系统中更相容的供体，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，以显着增加他们匹配的可能性。研究目标是建立一个集成的动态随机模拟-优化模型，该模型包括：(i)确定最佳个性化脱敏方案的优化策略；（ii）改进鲁棒/随机优化方法，将脱敏治疗整合到KPD匹配中；（iii）一个决策支持工具，帮助患者决定是否接受不太匹配的肾脏的脱敏方案，或者等待更匹配的肾脏。集成动态随机模拟-优化模型的输出将包括组合和模拟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关键绩效系统指标的统计估计。在项目的最后一年，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-李移植中心量身定制算法，这两所大学对开发联合本地KPD交流感兴趣。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.