Integrative Data Analysis to Identify Persistent Post-Concussion Deficits and Subsequent Musculoskeletal Injury Risk

项目摘要/摘要 据估计，美国每年有160-380万人发生脑震荡，他们 潜在的严重并发症，包括与二次冲击综合征相关的死亡、复发性脑震荡或 其他后续损伤，以及后来的神经病理。目前的发现已经成功地集中在 脑震荡的急性诊断；然而，脑震荡的敏感和特异性测定仍有待于 说明，因此运动员可能会重新参加比赛，尽管持续存在神经生理学 赤字。此外，可能由于这些持续的赤字，最近的证据表明， 脑震荡后一年发生后续肌肉骨骼损伤的风险(↑1.5-3.0倍)。因此，第一个 这项研究的目的是利用NINDS的共同数据来量化脑震荡后的神经生理缺陷 元素(CDE)和仪表化的单任务和双任务步态协议。这项研究的第二个目标是 应用复杂的高维综合和删失充分降维(Hi-CSDR) 评估通用数据元素在开发综合风险评分中的有效性的建模 随后的肌肉骨骼损伤。这一模型将从广泛存在的脑震荡发展而来 数据库，包括通用数据元素和便携式步态分析结果。Hi-CSDR 建模的优势在于，可以在纵向分析的环境中使用不同的分组 变量、审查和缺失数据，而脑震荡后肌肉骨骼损伤的风险增加是好的 在文献中建立的潜在机制尚未确定，然而初步结果表明 可能涉及姿势控制的持续性损害。为了实现这些目标，我们将招聘~100人 脑震荡的学生运动员将在基线时接受NINDS CDE评估，通过临床恢复， 并每月至6个月测定脑震荡后神经生理学恢复情况。要解决一个重大问题 由于文献上的限制，我们将招募大约偶数的男性和女性参与者。这些 参与者将被添加到现有数据库(N&>400)，用于制定综合风险评分 通过Hi-CSDR模型识别肌肉骨骼损伤风险。所有参与者都将被追踪到 用于识别脑震荡后一年肌肉骨骼损伤的电子医疗记录 控制既往脑震荡病史和既往肌肉骨骼损伤。这些发现，如果客观地说 将确定脑震荡后神经生理学损害的作用并阐明 随后肌肉骨骼损伤的潜在潜在机制。

PROJECT SUMMARY/ABSTRACT There are an estimated 1.6 – 3.8 million concussion which occur annually in the United States and they have potentially serious complications including second impact syndrome related fatalities, recurrent concussions or other subsequent injuries, and later life neuropathologies. Current findings have successfully focused on the acute diagnosis of concussions; however, sensitive and specific determination of concussion remains to be elucidated and therefore athletes may be returning to participation despite persistent neurophysiological deficits. Furthermore, potentially due to these persistent deficits, recent evidence is suggesting an elevated risk (↑1.5 – 3.0x) of subsequent musculoskeletal injury in the year following concussion. Therefore, the first aim of this study is to quantify post-concussion neurophysiological deficits utilizing the NINDS common data elements (CDEs) and an instrumented single and dual task gait protocol. The second aim of the study will apply a sophisticated high dimensional integrative and censored sufficient dimension reduction (Hi-CSDR) modeling to assess the effectiveness of common data elements in developing a composite risk score for subsequent musculoskeletal injury. This model will be developed from the extensive existing concussion database which includes both the common data elements and portable gait analysis results. The Hi-CSDR modeling has the advantage of operating in the context of longitudinal analysis with a diverse set of grouped variables, censored and missing data While the elevated risk of post-concussion musculoskeletal injury is well established in the literature, the underlying mechanism has not identified, however preliminary results suggest persistent impairments in postural control may be involved. To achieve these aims, we will recruit ~100 concussed student-athletes who will be assessed on the NINDS CDEs at baseline, through clinical recovery, and monthly up to six months post-concussion to determine neurophysiological recovery. To address a major limitation in the literature, we will recruit approximately an even number of male and female participants. These participants will be added to the existing database (N>400) for the developed of the composite risk score through the Hi-CSDR model to identify musculoskeletal injury risk. All participants will be tracked through electronic medical records to identify musculoskeletal injuries in the year following the concussion while controlling for prior concussion history and prior musculoskeletal injury. These findings, if objectively documented, would identify the role of post-concussion neurophysiological impairments and elucidate a potential underlying mechanism of subsequent musculoskeletal injury.