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: 10088470
• 负责人: William F Parker
• 金额: $ 15.79万
• 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/10088470
• 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 Transplantation; Outcome; Patients; Performance; Physicians; Policies; Policy Analysis; Publishing; Registries; Research; Resource Allocation; Resources; Savings; Scientist; Source; Statistical Methods; Statistical Models; Structure; Supportive care; System; Techniques; Testing; Training; Transplantation; United States Dept. of Health and Human Services; Update; Waiting Lists; base; career; clinical practice; design; experience; flexibility; health care delivery; high risk; improved; 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 心脏移植是治疗终末期心力衰竭的一种挽救生命的方法， 每年25万美国人。不幸的是，已故捐赠心脏的供应无法满足需求， 超过三分之一的候选人将在没有移植的情况下死亡或被除名。在这种稀缺性的背景下，分配必须 通过从最紧急到最不紧急的顺序排列候选人，最大限度地利用稀缺的已故捐赠心脏。 与其他器官移植系统相比，目前没有客观评分用于对心脏移植进行排名 候选人在waitlist。相反，每个候选人的移植优先级是基于“状态”， 指定由他们的移植中心规定的支持治疗决定。我之前展示过 一些心脏移植中心似乎对候选人进行了过度治疗， 比其他中心。我的初步数据表明，这些做法对心脏有影响， 配置有效性。高生存获益中心为以下候选人保留了强烈的支持性治疗： 没有移植的情况下，他们的身体状况很差，通过优先考虑病情最严重的病人来挽救生命。相反，低生存率 福利中心列出稳定的候选人，并逐步增加支持疗法的使用。根据这些数据， 显然需要一个新的系统来公平地分配捐赠的心脏。本K 08应用程序的总体目标是 开发和模拟一种新的心脏分配评分（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益处。以前尝试开发这样的分数使用 传统的统计方法是不准确的，但尖端的机器学习（ML）技术 在许多临床环境中优于传统的回归模型。此外，一个新的开源心脏 需要模拟分配模型（HSAM）来比较政策备选方案，因为可用的方案是 封闭源代码，不灵活，过时，并且在结构上无法模拟使用ML开发的分配分数。我 总体假设是，用ML开发的HAS将导致优化心脏分配的政策。我将测试 这个假设有三个目的。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）来预测等待名单的生存率，将ML预测模型与当前基于治疗的 系统在目标2中，我将使用相同的注册表来预测心脏受者的移植后存活率， 传统的统计方法ML。在目标3中，我将开发a）一个新的开源HSAM，我将使用它来 B）将当前策略与根据目标1和2的最佳预测模型构建的新HAS策略进行比较。我 总体职业目标是通过设计公平有效地分配稀缺资源的交付系统来拯救生命 医疗资源为了实现这一目标，我计划获得一个专注于ML的健康服务研究博士学位， 仿真建模和健康政策。实现这一建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R 01所需的技能- 在稀缺的医疗资源配置领域的应用水平。