• Gian-Gabriel P Garcia, Mariel S Lavieri, Ruiwei Jiang, Thomas W McAllister, Michael A McCrea, and Steven P Broglio.
• 1 Department of Industrial and Operations Engineering and University of Michigan, Ann Arbor, Michigan.
• J. Neurotrauma. 2019 May 15; 36 (10): 1571-1583.

AbstractKutcher and Giza suggested incorporating levels of certainty in concussion diagnosis decisions. These guidelines were based on clinical experience rather than objective data. Therefore, we combined data-driven optimization with predictive modeling to identify which athletes are unlikely to have concussion and to classify remaining athletes as having possible, probable, or definite concussion with diagnostic certainty. We developed and validated our framework using data from the Concussion Assessment, Research, and Education (CARE) Consortium. Acute concussions had assessments at <6 h (n = 1085) and 24-48 h post-injury (n = 1413). Normal performances consisted of assessments at baseline (n = 1635) and the time of unrestricted return to play (n = 1345). We evaluated the distribution of acute concussions and normal performances across risk categories and identified inter-class and intra-class differences in demographics, time-of-injury characteristics, the Standard Assessment of Concussion (SAC), Sport Concussion Assessment Tool (SCAT) symptom assessments, and Balance Error Scoring System (BESS). Our algorithm accurately classified concussions as probable or definite (sensitivity = 91.07-97.40%). Definite and probable concussions had higher SCAT symptom scores than unlikely and possible concussions (p < 0.05). Definite concussions had lower SAC and higher BESS scores (p < 0.05). Baseline to post-injury change scores for the SAC, SCAT symptoms, and BESS were significantly different between acute possible and probable concussions and normal performances (p < 0.05). There were no consistent patterns in demographics across risk categories, although a greater proportion of concussions classified as unlikely were reported immediately compared with definite concussions (p < 0.05). Although clinical interpretation is still needed, our data-driven approach to concussion risk stratification provides a promising step toward evidence-based concussion assessment.

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