Design, Analysis, and Optimization of Equitable and Value-based Baseline Testing Policies for Sports-Related Concussion

运动相关脑震荡公平且基于价值的基线测试政策的设计、分析和优化

• 批准号: 10649169
• 负责人: Gian-gabriel Garcia
• 金额: $ 4.87万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10649169
• 关键词: Design; Analysis; Optimization; Equitable; Value

Project Summary/Abstract This goal of this project is to design and optimize an equitable and value-based approach to baseline testing for sports-related concussion by synthesizing machine learning and systems science methods. Concussion, one of the most common types of traumatic brain injury, afflicts upwards of 3.6 million people annually and is a major public health issue. Timely and effective concussion management is considered a major factor in mitigating both short-term and long-term consequences of the injury. Baseline testing is a widely used practice that provides physicians and athletic trainers a reference point indicating someone’s “normal” performance across several concussion-specific functional domains. Baseline testing is also a resource-intensive process, requiring specific expertise in the time-consuming administration of a multi-dimensional concussion assessment battery; nevertheless, baseline testing is considered essential to the injury management process for those at elevated risk of concussion, including student-athletes and military personnel. Despite widespread use of baseline testing, there is a lack of evidence-based guidance on who should be prioritized for baseline testing in resource-limited environments. The multidisciplinary research team aims to address this knowledge gap by synthesizing machine learning and systems science methods with data from the Concussion Assessment, Research, and Education Consortium – one of the largest multi-site datasets available on sports-related concussion. Specifically, the project aims to first use interpretable machine learning methods and statistical modeling to estimate the diagnostic utility of baseline testing in a heterogeneous cohort of student-athletes. Next, the project aims to design a decision- analytic model that can optimally allocate baseline tests. This model will take into account: (1) personalized estimates for the diagnostic utility of baseline tests, (2) individualized risk for sport-related concussion, (3) resource constraints at a given institution, and (4) equity considerations in the allocation of baseline tests. This research can transform how clinicians, athletic trainers, and other trained medical staff approach baseline testing and concussion diagnosis for those who may be under-represented in the development of existing clinical guidelines, leading to more timely and accurate diagnosis of concussion. Moreover, resources saved through an efficient allocation of baseline tests can be reallocated to other valuable tasks performed by specialized medical personnel, including other tasks along the concussion care continuum, heat illnesses prevention, and COVID-19 screening.

项目概要/摘要 该项目的目标是设计和优化一种公平且基于价值的基线测试方法 通过综合机器学习和系统科学方法来治疗与运动相关的脑震荡， 最常见的创伤性脑损伤类型之一，每年影响超过 360 万人，是一种 及时有效的脑震荡管理被认为是重大公共卫生问题。 减轻伤害的短期和长期后果是一种广泛使用的做法。 为医生和运动教练提供一个参考点，表明某人的“正常”表现 跨多个特定脑震荡功能领域的基线测试也是一个资源密集型过程， 需要特定的专业知识来管理多维脑震荡的耗时过程 尽管如此，基线测试被认为对于伤害管理过程至关重要 对于那些脑震荡风险较高的人，包括学生运动员和军事人员，尽管这种情况很普遍。 使用基线测试，缺乏基于证据的指导来确定谁应该优先考虑基线 在资源有限的环境中进行测试。 多学科研究团队旨在通过综合机器学习和 系统科学方法以及来自脑震荡评估、研究和教育联盟的数据 – 具体而言，该项目旨在提供有关运动相关脑震荡的最大的多站点数据集之一。 首先使用可解释的机器学习方法和统计模型来估计诊断效用 接下来，该项目旨在设计一个决策- 可以优化分配基线测试的分析模型该模型将考虑：（1）个性化。 基线测试诊断效用的估计，(2) 运动相关脑震荡的个体化风险，(3) 特定机构的资源限制；(4) 基线测试分配中的公平考虑。 研究可以改变超级明星、运动教练和其他训练有素的医务人员接近基线的方式 为那些在现有的发展中可能代表性不足的人提供测试和脑震荡诊断 临床指南，可以更及时、更准确地诊断脑震荡，而且还可以节省资源。 通过有效分配基线测试，可以将其重新分配给执行的其他有价值的任务 专业医务人员，包括脑震荡护理连续过程中的其他任务、热病 预防和 COVID-19 筛查。