TY - JOUR
T1 - A Data-Driven Approach to Unlikely, Possible, Probable, and Definite Acute Concussion Assessment
AU - Concussion Assessment, Research, and Education Consortium Investigators
AU - Garcia, Gian Gabriel P.
AU - Lavieri, Mariel S.
AU - Jiang, Ruiwei
AU - McAllister, Thomas W.
AU - McCrea, Michael A.
AU - Broglio, Steven P.
N1 - Funding Information:
The material is based on work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE 1256260. This publication was made possible, in part, with support from the Grand Alliance CARE Consortium, funded, in part, by the NCAA and the Department of Defense (DoD). The United States Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702-5014 is the awarding and administering acquisition office. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Psychological Health and Traumatic Brain Injury Program under Award No. W81XWH-14-2-0151. Opinions, interpretations, conclusions, and recommendations are those of the author(s) and are not necessarily endorsed by the Department of Defense (DoD) (Defense Health Program [DHP] funds). We thank April Marie (Reed) Hoy, MS, ATC (Azusa Pacific University), Joseph B. Hazzard Jr., EdD, ATC (Bloomsburg University), Louise A. Kelly, PhD (California Lutheran University), Justus D. Ortega, PhD (Humboldt State University), Nicholas Port, PhD (Indiana University), Margot Putukian, MD (Princeton University), Gerald McGinty, DPT and Jonathan C. Jackson, PhD (United States Air Force Academy), Kenneth L. Cameron, PhD, MPC, ATC (United States Military Academy), Christopher Giza, MD (University of California Los Angeles), Holly J. Benjamin, MD (University of Chicago), Thomas Buckley, EdD, ATC and Thomas W. Kaminski, PhD, ATC (University of Delaware), James R. Clugston, MD, MS (University of Florida), Julianne D. Schmidt, PhD, ATC (University of Georgia), Louis A. Feigenbaum, DPT, ATC (University of Miami), James T. Eckner, MD, MS (University of Michigan), Kevin M. Guskiewicz, PhD, ATC and Jason P. Mihalik, PhD, ATC (University of North Carolina), Jessica Dysart Miles, PhD, ATC (University of North Georgia), Scott Anderson, ATC (University of Oklahoma), Christina L. Master, MD (University of Pennsylvania), Anthony P. Kontos, PhD and Micky Collins, PhD (University of Pittsburgh), Sara P.D. Chrisman, MD, MPH (University of Washington), Alison Brooks, MD, MPH (University of Wisconsin), Steven Rowson, PhD (Virginia Tech), Christopher M. Miles, MD and Laura J. Lintner, DO (Wake Forest University), and Brian H. Dykhuizen, MS, ATC, LAT (Wilmington College).
Publisher Copyright:
Copyright © 2019, Mary Ann Liebert, Inc.
PY - 2019/5/15
Y1 - 2019/5/15
N2 - 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 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.
AB - 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 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.
KW - acute concussion assessment
KW - possible, probable, and definite concussion
KW - risk stratification
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U2 - 10.1089/neu.2018.6098
DO - 10.1089/neu.2018.6098
M3 - Article
C2 - 30484375
AN - SCOPUS:85065516208
VL - 36
SP - 1571
EP - 1583
JO - Central Nervous System Trauma
JF - Central Nervous System Trauma
SN - 0897-7151
IS - 10
ER -