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

这一智能与互联健康(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，从而有助于改善患者获得肾脏移植的机会。肾移植是为终末期肾病患者提供最佳生活质量的权威、黄金标准治疗方法。然而，由于移植候选者和他们的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，移植社区设计了几种新的方案，包括KPD和脱敏。KPD允许有自愿但血型不合的活体捐赠者的患者将其血型不合的捐赠者与KPD捐赠者-患者池中的更相容的捐赠者交换，而脱敏程序在手术前从移植受者的血流中去除抗体，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。该项目旨在开发随机模拟和基于优化的算法，用于匹配脱敏治疗的KPD系统中的供者和受者。与传统的KPD系统不同，传统的KPD系统只需将不相容的捐赠者交换为系统中更相容的捐赠者，而设想中的KPD系统将为患者提供额外的选择，即接受个性化脱敏治疗，以及交换捐赠者的选项，以显著增加他们匹配的可能性。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)确定最佳个性化脱敏方案的优化策略；(Ii)改进的稳健/随机优化方法，将脱敏治疗整合到KPD配型中；以及(Iii)决策支持工具，帮助患者决定是接受配伍较差的肾脏的脱敏方案，还是等待更相容的方案。集成的动态随机模拟-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绩效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)健康休谟-李移植中心定制算法，这两个中心有兴趣开发一个当地的KPD联合交易所。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.