Mending a Broken Heart Allocation System with Machine Learning

用机器学习修复破碎的心分配系统

• 批准号: 10563177
• 负责人: William F Parker
• 金额: $ 12.34万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10563177
• 关键词: Adult; American; Charge; Clinical; Communities; Complex; Data; Data Set; Disease; Doctor of Philosophy; Effectiveness; Ensure; Ethics; Event; Foundations; Future; Goals; Health Policy; Health Resources; Health Services Research; Healthcare; Heart; Heart Transplantation; Heart failure; Life; Machine Learning; Medical; Minority; Modeling; Organ Procurements; Organ Transplantation; Outcome; Patients; Performance; Physicians; Policies; Policy Analysis; Prognosis; Publishing; Registries; Research; Resource Allocation; Resources; Scientist; Source; Statistical Methods; Statistical Models; Supportive care; System; Techniques; Testing; Training; Transplantation; United States Dept. of Health and Human Services; Update; Waiting Lists; candidate identification; career; clinical practice; design; experience; flexibility; health care delivery; high risk; improved; machine learning prediction; models and simulation; mortality risk; novel; open source; organ allocation; organ procurement transplantation network; post-transplant; predictive modeling; programs; risk stratification; simulation; simulation software; skills; tool; transplant centers; transplant registry

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.

项目摘要 心脏移植是一种挽救生命的终结阶段心力衰竭的治疗方法，这是一种毁灭性的疾病 每年25万美国人。不幸的是，已故捐助者心的供应无法满足需求，并且 超过三分之一的候选人将在没有移植的情况下死亡或被淘汰。在这种稀缺的背景下，分配必须 通过将大多数候选人从大多数到最紧迫的候选人进行排名，从而充分利用稀缺的死者捐助者心。 与其他器官移植系统相反，目前尚无客观评分来对心脏移植进行排名 候选人在候补名单上。相反，每个候选人的移植优先级基于“状态”，一个 由其移植中心规定的支持疗法确定的名称。我以前已经显示 一些心脏移植中心似乎以更高的速率过度疗法过度处理候选者 比其他中心。我的初步数据表明，这些实践对心脏产生后果 分配有效性。高生存福利中心为候选人保留强烈的支持疗法 没有移植的预后不佳，通过优先考虑最病的患者来挽救生命。相反，生存率低 福利中心列出了稳定的候选人，并升级使用支持疗法。基于这些数据， 显然需要一个新系统来分配捐助者的心。该K08应用程序的总体目标是 开发和模拟新颖的心脏分配评分（有）旨在客观地识别候选人 从心脏移植中获得最大的生存益处。以前尝试使用 传统的统计方法是不准确的，但是尖端的机器学习（ML）技术 在许多临床环境中，都超过常规回归模型。此外，新的开源心脏 需要模拟分配模型（HSAM）来比较策略替代方案，因为可用程序是 闭合源，僵化，过时的和结构上无法模拟用ML发展的分配得分。我的 总体假设是，与ML开发的A将导致优化心脏分配的政策。我会测试 这是三个目标的假设。在AIM 1中，我将使用完整的国家移植注册表数据集（n = 109,315 成人候选人）预测候补名单生存，将ML预测模型与当前基于治疗的基于治疗的模型进行比较 系统。在AIM 2中，我将使用相同的注册表来预测心脏接受者的移植后生存，并进行比较 ML的常规统计方法。在AIM 3中，我将开发a）一个新的开源HSAM b）将当前政策与小说的政策进行比较，这是根据AIM 1和2的最佳预测模型构建的政策。 总体职业目标是通过设计公平有效分发稀缺的递送系统来挽救生命 医疗资源。为此，我计划获得以ML为重点的卫生服务研究博士学位， 模拟建模和健康政策。实现这一提议的目标将导致小说的基础 心脏分配系统有可能挽救生命，并为我提供未来R01-的技能 - 稀缺医疗资源分配领域的水平应用。