@article{ref1,
title="A data-driven approach to unlikely, possible, probable, and definite acute concussion assessment",
journal="Journal of neurotrauma",
year="2019",
author="Broglio, Steven P. and McCrea, Michael and McAllister, Thomas and Jiang, Ruiwei and Lavieri, Mariel S. and Garcia, Gian-Gabriel Palaci",
volume="36",
number="10",
pages="1571-1583",
abstract="Kutcher 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 classify remaining athletes as Possible, Probable, or Definite concussion by diagnostic certainty. We developed and validated our framework using data from the Concussion Assessment, Research, and Education (CARE) Consortium. Acute concussions consisted of assessments at <6 hours (n=1085) and 24-48 hours 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 interclass and intraclass 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 compared to 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 or 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 to Definite concussions (p<0.05). While clinical interpretation is still needed, our data-driven approach to concussion risk stratification provides a promising step towards evidence-based concussion assessment.<p /> <p>Language: en</p>",
language="en",
issn="0897-7151",
doi="10.1089/neu.2018.6098",
url="http://dx.doi.org/10.1089/neu.2018.6098"
}