In:
American Journal of Physical Medicine & Rehabilitation, Ovid Technologies (Wolters Kluwer Health), Vol. 102, No. 9 ( 2023-9), p. 823-828
Kurzfassung:
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% ± 1.4, sensitivity = 1.1% ± 1.4, positive predictive value = 4.9% ± 6.5). The all-predictor multivariable model was the strongest (area under the curve = 68.3% ± 1.6, sensitivity = 20.7% ± 2.7, positive predictive value = 16.5% ± 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.
Materialart:
Online-Ressource
ISSN:
1537-7385
,
0894-9115
DOI:
10.1097/PHM.0000000000002302
Sprache:
Englisch
Verlag:
Ovid Technologies (Wolters Kluwer Health)
Publikationsdatum:
2023
ZDB Id:
2272463-1
ZDB Id:
2049617-5
SSG:
31
