Mending a Broken Heart Allocation System with Machine Learning

项目摘要 心脏移植是治疗终末期心力衰竭的一种挽救生命的方法，心力衰竭是一种致命的疾病。 每年25万美国人。不幸的是，已故供者心脏的供应不能满足需求，并且 超过三分之一的候选人将在不进行移植的情况下死亡或被摘牌。在这种稀缺的情况下，分配必须 通过从医学上最紧急的到最不紧急的顺序对候选人进行排序，充分利用稀有的已故捐赠者的心脏。 与其他器官移植系统相比，目前还没有用来对心脏移植进行排名的客观评分。 候补名单上的候选人。取而代之的是，每个候选人的移植优先考虑的是“状态”，a 名称由他们的移植中心开出的支持性治疗决定。我之前已经展示过 一些心脏移植中心似乎以更高的比率过度使用强化疗法 而不是其他中心。我的初步数据表明，这些做法对心脏有影响 分配效益。高生存福利中心为符合以下条件的候选人保留了密集的支持性治疗 如果不进行移植，预后很差，通过优先考虑病情最重的患者来挽救生命。相比之下，低存活率 福利中心列出了稳定的候选人，并升级了支持性治疗的使用。根据这些数据，有 显然需要一个新的系统来公平分配捐赠者的心脏。此K08应用程序的总体目标是 开发和模拟一种新的心脏分配分数(HAS)，旨在客观地识别符合以下条件的候选人 从心脏移植中获得最大的生存利益。以前尝试使用以下方法开发这样的分数 传统的统计方法已经不准确，但尖端的机器学习(ML)技术 在许多临床情况下，表现优于传统回归模型。此外，一个新的开源心脏 需要模拟分配模型(HSAM)来比较策略备选方案，因为可用的方案是 封闭源码、僵化、过时，并且在结构上无法模拟使用ML开发的分配分数。我的 总体假设是，随着ML的发展，将导致优化心脏分配的政策。我要测试一下 这一假设有三个目的。在目标1中，我将使用完整的国家移植登记数据集(N=109,315 成人候选人)预测等待名单生存，将ML预测模型与当前基于治疗的模型进行比较 系统。在目标2中，我将使用相同的注册中心来预测心脏移植受者的移植后存活率，比较 将传统的统计方法应用到ML。在目标3中，我将开发a)一个新的、开源的HSAM，我将使用它来 B)将当前的策略与根据目标1和2的最佳预测模型构建的策略的小说进行比较。我 职业生涯的总体目标是通过设计公平和有效地分配稀缺物资的交付系统来拯救生命 医疗资源。为了实现这一目标，我计划攻读卫生服务研究博士学位，重点是ML， 模拟建模和健康策略。实现这一提议的目标将导致一部小说的基础 心脏分配系统，有可能拯救生命，并为我配备未来R01所需的技能- 在稀缺医疗资源配置领域的应用水平。

PROJECT ABSTRACT Heart transplantation is a life-saving treatment for end-stage heart failure, a devastating disease which kills over 250,000 Americans each year. Unfortunately, the supply of deceased donor hearts cannot meet demand, and over a third of candidates will die or be delisted without transplant. In the context of such scarcity, allocation must make the best use of scarce deceased donor hearts by ranking candidates from most to least medically urgent. In contrast to other organ transplant systems, there is currently no objective score used to rank heart transplant candidates on the waitlist. Instead, each candidate’s priority for transplantation is based on “Status,” a designation determined by the supportive therapy prescribed by their transplant center. I have previously shown that some heart transplant centers appear to overtreat candidates with intensive therapies at far higher rates than other centers. My preliminary data demonstrates that these practices have consequences for heart allocation effectiveness. High survival benefit centers reserve intense supportive therapy for candidates who have poor prognoses without transplant, saving lives by prioritizing the sickest patients. In contrast, low survival benefit centers list stable candidates and escalate the use of supportive therapies. Based on these data, there is a clear need for a new system to fairly allocate donor hearts. The overall objective of this K08 application is to develop and simulate a novel Heart Allocation Score (HAS) designed to objectively identify the candidates who gain the greatest survival benefit from heart transplantation. Previous attempts to develop such a score using conventional statistical methods have been inaccurate, but cutting-edge machine learning (ML) techniques outperform conventional regression models in many clinical contexts. In addition, a new open-source Heart Simulated Allocation Model (HSAM) is needed to compare policy alternatives because the available program is closed-source, inflexible, outdated, and structurally unable to simulate allocation scores developed with ML. My overall hypothesis is that a HAS developed with ML will lead to policy that optimizes heart allocation. I will test this hypothesis in three Aims. In Aim 1, I will use the complete national transplant registry dataset (N = 109,315 adult candidates) to predict waitlist survival, comparing ML prediction models to the current therapy-based system. In Aim 2, I will use the same registry to predict post-transplant survival for heart recipients, comparing conventional statistical methods to ML. In Aim 3, I will develop a) a new, open-source HSAM which I will use to b) compare current policy to a novel HAS policy constructed from the best prediction models from Aim 1 & 2. My overall career goal is to save lives by designing delivery systems that fairly and efficiently distribute scarce medical resources. To accomplish this, I plan to earn a PhD in Health Services Research focused on ML, simulation modeling, and health policy. Achieving the goals of this proposal will lead to the foundation of a novel heart allocation system that has the potential to save lives and equip me with the skills needed for future R01- level applications in the field of scarce healthcare resource allocation.