Optimizing Concussion Care Seeking: Identification of Factors Predicting Previous Concussion Diagnosis Status. Issue 12 (26th December 2022)

• Record Type: Journal Article
• Authors: REGISTER-MIHALIK, JOHNA; LEEDS, DANIEL D.; KROSHUS, EMILY; KERR, ZACHARY YUKIO; KNIGHT, KRISTEN; D'LAURO, CHRISTOPHER; LYNALL, ROBERT C.; AHMED, TANVIR; HAGIWARA, YUTA; BROGLIO, STEVEN P.; MCCREA, MICHAEL A.; MCALLISTER, THOMAS W.; SCHMIDT, JULIANNE D.
• Other Names: Hoy April author non-byline.; Kelly Louise author non-byline.; Master Christina author non-byline.; Ortega Justus author non-byline.; Port Nicholas author non-byline.; McGinty Gerald author non-byline.; Cameron Kenneth L. author non-byline.; Susmarski Adam James author non-byline.; Giza Christopher C. author non-byline.; Benjamin Holly author non-byline.; Buckley Thomas author non-byline.; Kaminski Thomas author non-byline.; Clugston James R. author non-byline.; O'Donnell Patrick G. author non-byline.; Feigenbaum Luis author non-byline.; Eckner James T. author non-byline.; Mihalik Jason P. author non-byline.; Duma Stefan M. author non-byline.; Miles Christopher author non-byline.
• Is Part Of: Medicine and science in sports and exercise. Volume 54:Issue 12(2022)
• Page Start: 2087
• Page End: 2098
• Publication Date: 2022-12-26
• Subjects: MTBI -- EDUCATION -- DISCLOSURE -- CONCUSSION MANAGEMENT; Sports medicine -- Periodicals; Exercise -- Physiological aspects -- Periodicals; Exercise -- Health aspects -- Periodicals; 612.044
• DOI: 10.1249/MSS.0000000000003004
• Languages: English
• ISSNs: 0195-9131

Abstract

ABSTRACT: Purpose: There is limited understanding of factors affecting concussion diagnosis status using large sample sizes. The study objective was to identify factors that can accurately classify previous concussion diagnosis status among collegiate student-athletes and service academy cadets with concussion history. Methods: This retrospective study used support vector machine, Gaussian Naïve Bayes, and decision tree machine learning techniques to identify individual (e.g., sex) and institutional (e.g., academic caliber) factors that accurately classify previous concussion diagnosis status (all diagnosed vs 1+ undiagnosed) among Concussion Assessment, Research, and Education Consortium participants with concussion histories ( n = 7714). Results: Across all classifiers, the factors examined enable >50% classification between previous diagnosed and undiagnosed concussion histories. However, across 20-fold cross validation, ROC-AUC accuracy averaged between 56% and 65% using all factors. Similar performance is achieved considering individual risk factors alone. By contrast, classifications with institutional risk factors typically did not distinguish between those with all concussions diagnosed versus 1+ undiagnosed; average performances using only institutional risk factors were almost always <58%, including confidence intervals for many groups <50%. Participants with more extensive concussion histories were more commonly classified as having one or more of those previous concussions undiagnosed. Conclusions: Although the current study provides preliminary evidence about factors to help classify concussion diagnosis status, more work is needed given the tested models' accuracy. Future work should include a broader set of theoretically indicated factors, at levels ranging from individual behavioral determinants to features of the setting in which the individual was injured.