Optimizing Concussion Care Seeking: Using Machine Learning to Predict Delayed Concussion Reporting

Author:

Kroshus-Havril Emily12ORCID,Leeds Daniel D.32,McAllister Thomas W.42,Kerr Zachary Yukio52ORCID,Knight Kristen62,Register-Mihalik Johna K.52,Lynall Robert C.72ORCID,D’Lauro Christopher82,Ho Yuet32,Rahman Muhibur32,Broglio Steven P.92,McCrea Michael A.102,Schmidt Julianne D.72ORCID, ,Port Nicholas112,Campbell Darren122,Putukian Margot132,Chrisman Sara P.D.142,Cameron Kenneth L.152,Susmarski Adam James162,Goldman Joshua T.172,Benjamin Holly182,Buckley Thomas192,Kaminski Thomas192,Clugston James R.202,Feigenbaum Luis212,Eckner James T.222,Mihalik Jason P.232,Kontos Anthony242,McDevitt Jane252,Brooks M. Alison262,Rowson Steve272,Miles Christopher282,Lintner Laura292,Kelly Louise302,Master Christina312

Affiliation:

1. Center for Child Health, Behavior, and Development, Seattle Children’s Research Institute & Department of Pediatrics, University of Washington, Seattle, Washington, USA

2. Investigation performed at the University of Georgia, Athens, Georgia, USA

3. Computer and Information Sciences, Fordham University, New York, New York, USA

4. Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA

5. Matthew Gfeller Center & Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

6. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA

7. UGA Concussion Research Laboratory, University of Georgia, Athens, Georgia, USA

8. Department of Behavioral Sciences and Leadership, US Air Force Academy, Colorado Springs, Colorado, USA

9. University of Michigan Concussion Center, University of Michigan, Ann Arbor, Michigan, USA

10. Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA

11. School of Optometry, Indiana University, Bloomington, Indiana, USA

12. Intermountain Sports Medicine, Ogden, Utah, USA

13. Athletic Medicine, Princeton University, Princeton, New Jersey, USA

14. Center for Child Health, Behavior and Development, Seattle Children’s Research Institute, Seattle, Washington, USA

15. Keller Army Hospital, US Military Academy, West Point, New York, USA; Annapolis, Maryland, USA

16. Department Head Brigade Orthopaedics and Sports Medicine, US Naval Academy

17. Departments of Family Medicine & Orthopaedic Surgery, University of California, Los Angeles, Los Angeles, California, USA

18. Department of Rehabilitation Medicine and Pediatrics, University of Chicago, Chicago, Illinois, USA

19. Department of Kinesiology & Applied Physiology, University of Delaware, Newark, Delaware, USA

20. Community Health and Family Medicine, University of Florida, Gainesville, Florida, USA

21. Department of Physical Therapy, Miller School of Medicine, University of Miami, Coral Gables, Florida, USA

22. Department of PM&R, University of Michigan, Ann Arbor, Michigan, USA

23. Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

24. Department of Orthopaedic Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

25. Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, Pennsylvania, USA

26. Department of Orthopedics, University of Wisconsin–Madison, Madison, Wisconsin, USA

27. Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia, USA

28. Department of Family and Community Medicine, Wake Forest University, Winston-Salem, North Carolina, USA

29. Wake Forest School of Medicine Family Medicine, Winston Salem State University, Winston-Salem, North Carolina, USA

30. Department of Exercise Science, California Lutheran University, Thousand Oaks, California, USA

31. Division of Orthopedics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA

Abstract

Background: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, meaning that early identification is often contingent on individuals reporting their injury to medical staff. A fundamental understanding of the types and levels of factors that explain when concussions are reported can help identify promising directions for intervention. Purpose: To identify individual and institutional factors that predict immediate (vs delayed) injury reporting. Study Design: Case-control study; Level of evidence, 3. Methods: This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported “immediately” or “at a delay”), including both individual athlete/cadet and institutional characteristics. Results: In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers. Conclusion: Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated.

Funder

Department of Defense

Publisher

SAGE Publications

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