A - 20 Identifying Concussion in College-Aged Individuals Using a Multimodal Assessment of Vestibular and Oculomotor Function

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2024-07-06 DOI:10.1093/arclin/acae052.20

Lilian A. Klein, A. J. Zynda, M. Loftin, A. J. Tracey, A. Pollard-McGrandy, Haley Clark, E. R. Davis, T. Covassin

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引用次数: 0

Abstract

Vestibular and oculomotor assessments are fundamental tools to identify concussions. However, research is unclear on which vestibular and oculomotor assessment or combination of assessments best detect concussions. The purpose of this study was to assess the discriminant ability of the Vestibular/Ocular Motor Screening (VOMS), Balance Error Scoring System (BESS), modified BESS (mBESS), and High-Level Mobility Assessment Tool (HiMAT) to identify college-aged individuals with concussion from controls. A prospective study of college-aged individuals (18–30 years) diagnosed with concussion within 5 days of enrollment was conducted. Demographics, injury information, VOMS, BESS/mBESS, and HiMAT were completed at the initial visit. Logistic regressions (LR) and receiver operating characteristic (ROC) analyses of the area-under-the-curve (AUC) determined the ability of the VOMS, BESS/mBESS, and HiMAT to identify concussion from control. A total of 214 participants (mean age = 20.4¬ ± 2.5 years, 52.8% female) were enrolled, with 137 (64.0%) concussions and 77 (36.0%) controls. The VOMS total (AUC = 0.93, 95%CI = 0.90–0.97, p < 0.001) and HiMAT total (AUC = 0.79, 95%CI = 0.65–0.93, p < 0.001) significantly identified concussion from control, while the BESS (AUC = 0.58, 95%CI = 0.50–0.67, p = 0.05) and mBESS (AUC = 0.55, 95%CI = 0.47–0.64, p = 0.23) did not. A 2-factor model with combined VOMS and HiMAT totals did not improve identification (AUC = 0.92, 95%CI = 0.85–1.00, p < 0.001). The VOMS total score demonstrated outstanding discriminant ability, HiMAT total score demonstrated adequate discriminant ability, and BESS/mBESS total errors demonstrated unacceptable discriminant ability in identifying concussions from controls. Our findings suggest that the VOMS remains a preferred vestibular and oculomotor functioning assessment for identifying concussion. Furthermore, incorporating objective assessments with the VOMS, such as HiMAT, does not improve diagnostic yield.

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A - 20 通过对前庭和眼球运动功能的多模态评估识别大学生脑震荡

前庭和眼球运动评估是识别脑震荡的基本工具。然而，关于哪种前庭和眼球运动评估或评估组合能最好地检测脑震荡的研究尚不明确。本研究的目的是评估前庭/眼球运动筛查（VOMS）、平衡失误评分系统（BESS）、改良平衡失误评分系统（mBESS）和高水平移动能力评估工具（HiMAT）在从对照组中识别患有脑震荡的大学适龄学生方面的鉴别能力。我们对入学后 5 天内被诊断为脑震荡的大学年龄段人群（18-30 岁）进行了一项前瞻性研究。首次就诊时填写了人口统计学、受伤信息、VOMS、BESS/mBESS 和 HiMAT。逻辑回归（LR）和曲线下面积（AUC）的接收者操作特征（ROC）分析确定了VOMS、BESS/mBESS和HiMAT从对照组中识别脑震荡的能力。共有 214 名参与者（平均年龄 = 20.4±2.5 岁，52.8% 为女性）参加了该研究，其中脑震荡患者 137 人（64.0%），对照组 77 人（36.0%）。 VOMS 总分（AUC = 0.93，95%CI = 0.90-0.97，p < 0.001）和 HiMAT 总分（AUC = 0.79，95%CI = 0.65-0.93，p < 0.001）能显著识别脑震荡和对照组，而 BESS（AUC = 0.58，95%CI = 0.50-0.67，p = 0.05）和 mBESS（AUC = 0.55，95%CI = 0.47-0.64，p = 0.23）则不能。综合 VOMS 和 HiMAT 总分的双因素模型并未提高识别率（AUC = 0.92，95%CI = 0.85-1.00，p <0.001）。在鉴别脑震荡与对照组方面，VOMS 总分表现出突出的鉴别能力，HiMAT 总分表现出足够的鉴别能力，而 BESS/mBESS 总误差表现出不可接受的鉴别能力。我们的研究结果表明，VOMS 仍是识别脑震荡的首选前庭和眼球运动功能评估方法。此外，将客观评估与 VOMS（如 HiMAT）相结合并不能提高诊断率。

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来源期刊

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464

期刊介绍： ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.