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

这项智能互联健康（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，为改善患者接受肾脏移植做出贡献。肾移植是为终末期肾病患者提供最佳生活质量的确定性金标准治疗。然而，由于移植候选人与其肾脏供体之间的血型或人类白细胞抗原组织类型不相容等限制，许多人无法获得治疗。为了克服这些不相容性，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其不相容的供体与更相容的供体交换，也是在KPD供体-患者池中，而脱敏程序在手术前从移植受体的血流中去除抗体，以降低潜在的排斥反应的风险。目前，这两种方案都有局限性。为了克服这些局限性，著名的移植专家一直主张将这两种方案结合起来。本计画旨在发展随机模拟与最佳化的演算法，以配合KPD系统中的供者与受者进行脱敏治疗。与传统的KPD系统相比，在传统的KPD系统中，移植候选者简单地将其不相容的供体交换为系统中更相容的供体，所设想的KPD系统将为患者提供经历个性化脱敏治疗的额外选择，沿着交换供体的选择，以显著增加他们匹配的可能性。本研究的目的是发展一个整合的动态随机模拟-最佳化模型，包括：（i）一个最佳化策略，以找出最佳的个人化脱敏治疗方案;（ii）改良的强健/随机最佳化方法，以整合脱敏治疗与KPD匹配;以及（iii）决策支持工具，用于帮助患者决定是接受具有较不相容的肾脏的脱敏方案，还是等待更相容的肾脏。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、基于模拟患者行为的实现匹配以及关键性能系统度量的统计估计。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-李移植中心定制算法，这两所大学有兴趣开发一个联合的本地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。

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.