Optimizing Concussion Care Seeking: Using Machine Learning to Predict Delayed Concussion Reporting
Authors: Kroshus-Havril Emily , Leeds Daniel D. , McAllister Thomas W. , Kerr Zachary Yukio , Knight Kristen , Register-Mihalik Johna K. , Lynall Robert C. , D’Lauro Christopher , Ho Yuet , Rahman Muhibur , Broglio Steven P. , McCrea Michael A. , Schmidt Julianne D. ,   CARE Consortium Investigators included:, Port Nicholas , Campbell Darren , Putukian Margot , Chrisman Sara P.D. , Cameron Kenneth L. , Susmarski Adam James , Goldman Joshua T. , Benjamin Holly , Buckley Thomas , Kaminski Thomas , Clugston James R. , Feigenbaum Luis , Eckner James T. , Mihalik Jason P. , Kontos Anthony , McDevitt Jane , Brooks M. Alison , Rowson Steve , Miles Christopher , Lintner Laura , Kelly Louise , Master Christina 
Published: August 2024

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.