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Updated: Jul 26, 2025
A Neuroscientific Approach to the Examination of Concussions in Student-Athletes
Published on: December 8, 2014
Preinjury Measures Do Not Predict Future Concussion Among Collegiate Student-Athletes: Findings From the CARE
Landon B Lempke1, Katherine M Breedlove, Jaclyn B Caccese
1From the Michigan Concussion Center, University of Michigan, Ann Arbor, Michigan (LBL, SPB); Center for Clinical Spectroscopy and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts (KMB); School of Health and Rehabilitation Sciences, The Ohio State University College of Medicine, Columbus, Ohio (JBC); Center for Neurotrauma Research, Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin (MAM); Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana (TWM); UGA Concussion Research Laboratory, Department of Kinesiology, University of Georgia, Athens, Georgia (JDS, RCL); and Department of Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware (TAB).
Abstract:
This prospective cohort study aimed to determine whether preinjury characteristics and performance on baseline concussion assessments predicted future concussions among collegiate student-athletes. Participant cases (concussed = 2529; control = 30,905) completed preinjury: demographic forms (sport, concussion history, sex), Immediate Post-Concussion Assessment and Cognitive Test, Balance Error Scoring System, Sport Concussion Assessment Tool symptom checklist, Standardized Assessment of Concussion, Brief Symptom Inventory-18 item, Wechsler Test of Adult Reading, and Brief Sensation Seeking Scale. We used machine-learning logistic regressions with area under the curve, sensitivity, and positive predictive values statistics for univariable and multivariable analyses. Primary sport was determined to be the strongest univariable predictor (area under the curve = 64.3% ± 1.4, sensitivity = 1.1% ± 1.4, positive predictive value = 4.9% ± 6.5). The all-predictor multivariable model was the strongest (area under the curve = 68.3% ± 1.6, sensitivity = 20.7% ± 2.7, positive predictive value = 16.5% ± 2.0). Despite a robust sample size and novel analytical approaches, accurate concussion prediction was not achieved regardless of modeling complexity. The strongest positive predictive value (16.5%) indicated only 17 of every 100 individuals flagged would experience a concussion. These findings suggest preinjury characteristics or baseline assessments have negligible utility for predicting subsequent concussion. Researchers, healthcare providers, and sporting organizations therefore should not use preinjury characteristics or baseline assessments for future concussion risk identification at this time.
