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.

项目总结/摘要 这个项目的目标是设计和优化一个公平的和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有关的脑震荡。脑震荡 最常见的创伤性脑损伤类型之一，每年折磨超过360万人， 重大公共卫生问题。及时有效的脑震荡管理被认为是一个主要因素， 减轻伤害的短期和长期后果。基线测试是一种广泛使用的实践 它为医生和运动教练提供了一个参考点，表明某人的“正常”表现， 几个脑震荡特有的功能域基线测试也是一个资源密集型过程， 需要在多维脑震荡的耗时管理中的特定专业知识 评估组合;然而，基线测试被认为是伤害管理过程中必不可少的 对于那些脑震荡风险较高的人，包括学生运动员和军人。尽管人们普遍 使用基线检测，缺乏关于谁应该优先进行基线检测的循证指南 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 系统科学方法与来自脑震荡评估，研究和教育联盟的数据- 这是关于运动相关脑震荡的最大的多站点数据集之一。具体而言，该项目旨在 首先使用可解释的机器学习方法和统计建模来估计 基线测试在异质队列的学生运动员。接下来，该项目旨在设计一个决策- 分析模型，可以最佳地分配基线测试。该模型将考虑：（1）个性化 估计基线测试的诊断效用，（2）运动相关脑震荡的个体化风险，（3） 在一个给定的机构的资源限制，和（4）在分配基线测试的公平考虑。这 研究可以改变临床医生、运动训练员和其他受过训练的医务人员接近基线的方式 测试和脑震荡诊断为那些谁可能是代表性不足的发展现有的 临床指南，导致更及时和准确的诊断脑震荡。此外，节省的资源 通过有效分配基线测试，可以将其重新分配给其他有价值的任务， 专门的医疗人员，包括其他任务沿着震荡护理连续体，热疾病 预防和COVID-19筛查