Preinjury Measures Do Not Predict Future Concussion Among Collegiate Student-Athletes Findings From the CARE Consortium

被引:0
作者
Lempke, Landon M. [1 ,9 ]
Breedlove, Katherine B. [2 ,3 ]
Caccese, Jaclyn A. [4 ]
McCrea, Michael W. [5 ]
McAllister, Thomas P. [6 ]
Broglio, Steven D. [1 ]
Schmidt, Julianne C. [7 ]
Lynall, Robert A. [7 ]
Buckley, Thomas [8 ]
机构
[1] Univ Michigan, Michigan Concuss Ctr, Ann Arbor, MI USA
[2] Harvard Med Sch, Ctr Clin Spect, Boston, MA USA
[3] Harvard Med Sch, Brigham & Womens Hosp, Dept Radiol, Boston, MA USA
[4] Ohio State Univ, Sch Hlth & Rehabil Sci, Coll Med, Columbus, OH USA
[5] Med Coll Wisconsin, Ctr Neurotrauma Res, Dept Neurosurg, Milwaukee, WI USA
[6] Indiana Univ Sch Med, Dept Psychiat, Indianapolis, IN USA
[7] Univ Georgia, Dept Kinesiol, UGA Concuss Res Lab, Athens, GA USA
[8] Univ Delaware, Dept Kinesiol & Appl Physiol, Newark, DE USA
[9] Univ Michigan, Sch Kinesiol, Michigan Concuss Ctr, 830 N Univ Ave, Ann Arbor, MI 48109 USA
关键词
Mild Traumatic Brain Injury; Diagnosis; Sports Medicine; Prevention; Injury Risk;
D O I
10.1097/PHM.0000000000002302
中图分类号
R49 [康复医学];
学科分类号
100215 ;
摘要
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% & PLUSMN; 1.4, sensitivity = 1.1% & PLUSMN; 1.4, positive predictive value = 4.9% & PLUSMN; 6.5). The all-predictor multivariable model was the strongest (area under the curve = 68.3% & PLUSMN; 1.6, sensitivity = 20.7% & PLUSMN; 2.7, positive predictive value = 16.5% & PLUSMN; 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.
引用
收藏
页码:823 / 828
页数:6
相关论文
共 19 条
  • [1] Acute Sport Concussion Assessment Optimization: A Prospective Assessment from the CARE Consortium
    Broglio, Steven P.
    Harezlak, Jaroslaw
    Katz, Barry
    Zhao, Shi
    McAllister, Thomas
    McCrea, Michael
    Hazzard, Joseph
    Kelly, Louise
    Campbell, Darren
    Jackson, Jonathan
    McGinty, Gerald
    O'Donnell, Patrick
    Cameron, Kenneth
    Susmarski, Adam
    Goldman, Josh
    Giza, Christopher
    Buckley, Thomas
    Kaminski, Thomas
    Clugston, James
    Schmidt, Julianne
    Feigenbaum, Luis
    Eckner, James T.
    Anderson, Scott
    Master, Christina
    Kontos, Anthony
    Chrisman, Sara
    Brooks, Alison
    [J]. SPORTS MEDICINE, 2019, 49 (12) : 1977 - 1987
  • [2] Test-Retest Reliability and Interpretation of Common Concussion Assessment Tools: Findings from the NCAA-DoD CARE Consortium
    Broglio, Steven P.
    Katz, Barry P.
    Zhao, Shi
    McCrea, Michael
    McAllister, Thomas
    [J]. SPORTS MEDICINE, 2018, 48 (05) : 1255 - 1268
  • [3] A National Study on the Effects of Concussion in Collegiate Athletes and US Military Service Academy Members: The NCAA-DoD Concussion Assessment, Research and Education (CARE) Consortium Structure and Methods
    Broglio, Steven P.
    McCrea, Michael
    McAllister, Thomas
    Harezlak, Jaroslaw
    Katz, Barry
    Hack, Dallas
    Hainline, Brian
    [J]. SPORTS MEDICINE, 2017, 47 (07) : 1437 - 1451
  • [4] Does baseline concussion testing aid in identifying future concussion risk?
    Caccese, Jaclyn B.
    Johns, Kassandra E.
    Langdon, Jody L.
    Shaver, George W.
    Buckley, Thomas A.
    [J]. RESEARCH IN SPORTS MEDICINE, 2020, 28 (04) : 594 - 599
  • [5] Predicting Risk of Sport-Related Concussion in Collegiate Athletes and Military Cadets: A Machine Learning Approach Using Baseline Data from the CARE Consortium Study
    Castellanos, Joel
    Phoo, Cheng Perng
    Eckner, James T.
    Franco, Lea
    Broglio, Steven P.
    McCrea, Mike
    McAllister, Thomas
    Wiens, Jenna
    [J]. SPORTS MEDICINE, 2021, 51 (03) : 567 - 579
  • [6] Epidemiology of Concussions in National Collegiate Athletic Association (NCAA) Sports: 2014/15-2018/19
    Chandran, Avinash
    Boltz, Adrian J.
    Morris, Sarah N.
    Robison, Hannah J.
    Nedimyer, Aliza K.
    Collins, Christy L.
    Register-Mihalik, Johna K.
    [J]. AMERICAN JOURNAL OF SPORTS MEDICINE, 2022, 50 (02) : 526 - 536
  • [7] SMOTE: Synthetic minority over-sampling technique
    Chawla, Nitesh V.
    Bowyer, Kevin W.
    Hall, Lawrence O.
    Kegelmeyer, W. Philip
    [J]. 2002, American Association for Artificial Intelligence (16)
  • [8] Derogatis L.R., 2000, BSI 18 BRIEF SYMPTOM
  • [9] Quantifying the Value of Multidimensional Assessment Models for Acute Concussion: An Analysis of Data from the NCAA-DoD Care Consortium
    Garcia, Gian-Gabriel P.
    Broglio, Steven P.
    Lavieri, Mariel S.
    McCrea, Michael
    McAllister, Thomas
    [J]. SPORTS MEDICINE, 2018, 48 (07) : 1739 - 1749
  • [10] Gene selection for cancer classification using support vector machines
    Guyon, I
    Weston, J
    Barnhill, S
    Vapnik, V
    [J]. MACHINE LEARNING, 2002, 46 (1-3) : 389 - 422