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

综合数据分析可识别持续性脑震荡后缺陷和随后的肌肉骨骼损伤风险

• 批准号: 10373428
• 负责人: Thomas A. Buckley
• 金额: $ 19.01万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-03 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10373428
• 关键词: Acute; Address; Affect; Athletic; Athletic Injuries; Brain Concussion; Cardiovascular Diseases; Centers for Disease Control and Prevention (U.S.); Chronic; Clinical; Common Data Element; Computerized Medical Record; Consensus; Data; Data Analyses; Databases; Development; Diabetes Mellitus; Diagnosis; Dimensions; Economics; Elderly; Female; Gait; Goals; Health; Health Care Costs; Impairment; Incidence; Individual; Injury; Interdisciplinary Study; Intervention; Literature; Measures; Methods; Modeling; Musculoskeletal; Musculoskeletal Equilibrium; National Institute of Neurological Disorders and Stroke; Participant; Personal Satisfaction; Physical activity; Positioning Attribute; Prevention Protocols; Prevention program; Problem Solving; Protocols documentation; Public Health; Recording of previous events; Recovery; Recurrence; Rehabilitation therapy; Research; Risk; Risk Assessment; Role; Schools; Sensitivity and Specificity; Statistical Models; Syndrome; Time; United States; United States National Institutes of Health; cost; disability; economic cost; effectiveness evaluation; evidence base; gait examination; high dimensionality; high risk; improved; injury prevention; instrument; longitudinal analysis; longitudinal dataset; male; musculoskeletal injury; neuroimaging marker; neuropathology; neurophysiology; novel; portability; premature; programs; recruit; retention rate; social; societal costs; student athlete; tool

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.

项目总结/摘要 据估计，美国每年发生160万至380万例脑震荡， 潜在严重并发症，包括二次撞击综合征相关死亡、复发性脑震荡或 其他后续损伤和以后的神经病理学。目前的研究结果成功地集中在 急性诊断脑震荡;然而，敏感和具体的确定脑震荡仍然是 阐明，因此，运动员可能会返回参与，尽管持续的神经生理 赤字此外，可能由于这些持续的赤字，最近的证据表明， 在脑震荡后一年内发生后续肌肉骨骼损伤的风险（↑1.5 - 3.0x）。因此第一 本研究的目的是利用NINDS的共同数据来量化脑震荡后的神经生理缺陷 元件（CDE）和仪器化的单任务和双任务步态协议。研究的第二个目的是 应用复杂的高维积分和删失充分降维（Hi-CSDR） 建模，以评估共同数据元素在制定综合风险评分方面的有效性， 随后的肌肉骨骼损伤。该模型将从现有的广泛的脑震荡发展而来 数据库，包括共同的数据元素和便携式步态分析结果。Hi-CSDR 建模具有在纵向分析的背景下操作的优势， 虽然脑震荡后肌肉骨骼损伤的风险升高， 在文献中建立，尚未确定潜在的机制，但初步结果表明， 可能涉及姿势控制的持续性损伤。为了实现这些目标，我们将招募约100名 脑震荡学生运动员，他们将在基线时接受NINDS CDE评估，通过临床恢复， 并且在脑震荡后六个月内每月进行一次，以确定神经生理学恢复。向一位少校发表演说 由于文献的限制，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）中，以制定综合风险评分 通过Hi-CSDR模型识别肌肉骨骼损伤风险。所有参与者都将通过 电子医疗记录，以确定脑震荡后一年内的肌肉骨骼损伤， 控制先前的脑震荡史和先前的肌肉骨骼损伤。这些发现如果客观地 记录，将确定脑震荡后神经生理损伤的作用，并阐明 后续肌肉骨骼损伤的潜在机制。