Optimizing Concussion Care Seeking: Identification of Factors Predicting Previous Concussion Diagnosis Status.
Journal
Medicine and science in sports and exercise
ISSN: 1530-0315
Titre abrégé: Med Sci Sports Exerc
Pays: United States
ID NLM: 8005433
Informations de publication
Date de publication:
01 12 2022
01 12 2022
Historique:
pubmed:
27
7
2022
medline:
18
11
2022
entrez:
26
7
2022
Statut:
ppublish
Résumé
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. 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). 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. 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.
Identifiants
pubmed: 35881927
doi: 10.1249/MSS.0000000000003004
pii: 00005768-202212000-00011
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
2087-2098Investigateurs
April Hoy
(A)
Louise Kelly
(L)
Christina Master
(C)
Justus Ortega
(J)
Nicholas Port
(N)
Gerald McGinty
(G)
Kenneth L Cameron
(KL)
Adam James Susmarski
(AJ)
Christopher C Giza
(CC)
Holly Benjamin
(H)
Thomas Buckley
(T)
Thomas Kaminski
(T)
James R Clugston
(JR)
Patrick G O'Donnell
(PG)
Luis Feigenbaum
(L)
James T Eckner
(JT)
Jason P Mihalik
(JP)
Stefan M Duma
(SM)
Christopher Miles
(C)
Informations de copyright
Copyright © 2022 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.
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