Concussion assessment potentially aided by use of an objective multimodal concussion index

Author:

Jacquin Arnaud E1ORCID,Bazarian Jeffrey J2,Casa Douglas J3,Elbin Robert J4,Hotz Gillian5,Schnyer David M6,Yeargin Susan7,Prichep Leslie S1,Covassin Tracey8

Affiliation:

1. BrainScope Company, Bethesda, MD, USA

2. Department of Emergency Medicine, University of Rochester, Rochester, NY, USA

3. Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA

4. Department of Health, Human Performance and Recreation, Office for Sport Concussion Research, University of Arkansas, Fayetteville, AR, USA

5. Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL, USA

6. Department of Psychology, University of Texas at Austin, Austin, TX, USA

7. Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA

8. Department of Kinesiology, Michigan State University, East Lansing, MI, USA

Abstract

Objective Prompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion index (CI) using EEG at its core, to identify concussion, and to assess change over time throughout recovery. Methods Male and female concussed ( N = 232) and control ( N = 206) subjects 13–25 years were enrolled at 12 US colleges and high schools. Evaluations occurred within 72 h of injury, 5 days post-injury, at return-to-play (RTP), 45 days after RTP (RTP + 45); and included EEG, neurocognitive performance, and standard concussion assessments. Concussed subjects had a witnessed head impact, were removed from play for ≥ 5 days using site guidelines, and were divided into those with RTP < 14 or ≥14 days. Part 1 describes the derivation and efficacy of the machine learning derived classifier as a marker of concussion. Part 2 describes significance of differences in CI between groups at each time point and within each group across time points. Results Sensitivity = 84.9%, specificity = 76.0%, and AUC = 0.89 were obtained on a test Hold-Out group representing 20% of the total dataset. EEG features reflecting connectivity between brain regions contributed most to the CI. CI was stable over time in controls. Significant differences in CI between controls and concussed subjects were found at time of injury, with no significant differences at RTP and RTP + 45. Within the concussed, differences in rate of recovery were seen. Conclusions The CI was shown to have high accuracy as a marker of likelihood of concussion. Stability of CI in controls supports reliable interpretation of CI change in concussed subjects. Objective identification of the presence of concussion and assessment of readiness to return to normal activity can be aided by use of the CI, a rapidly obtained, point of care assessment tool.

Funder

U.S. Navy

Publisher

SAGE Publications

Subject

Earth-Surface Processes

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