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.

项目摘要/摘要 该项目的这个目标是设计和优化基准测试的公平且基于价值的方法 通过综合机器学习和系统科学方法进行与运动有关的咨询。脑震荡， 脑损伤的最常见类型之一，每年折磨360万以上，是一个 主要公共卫生问题。及时有效的咨询管理被认为是 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 这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 在几个咨询特定的功能领域中。基线测试也是一个资源密集的过程， 在耗时的多维咨询时需要具体的专业知识 评估电池；然而，基线测试被认为是伤害管理过程至关重要的 对于咨询风险较高的人，包括学生运动员和军事人员。尽管宽度很大 使用基线测试，缺乏基于证据的指导，应该优先考虑基线 在资源有限环境中进行测试。 多学科研究团队旨在通过综合机器学习和 系统科学方法具有来自脑震荡评估，研究和教育联盟的数据 - 可在与体育有关的咨询中获得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 在异质的学生运动员队列中进行基线测试。接下来，该项目旨在设计一个决定 - 可以最佳分配基线测试的分析模型。该模型将考虑到：（1）个性化 基线测试诊断实用程序的估计，（2）与运动相关咨询的个性化风险，（3） 在给定机构的资源约束，以及（4）基线测试分配中的股权考虑因素。这 研究可以改变临床医生，体育培训师和其他训练有素的医务人员的方法基线 对于那些在现有的发展中可能不足的人的测试和咨询诊断 临床指南，导致更及时，准确地诊断咨询。此外，资源节省了 通过有效分配基线测试，可以真正分配给其他有价值的任务 专业医务人员，包括咨询护理连续性的其他任务，热病 预防和共同筛选。