Multiple clinical evaluation tools exist for adolescent concussion, with various degrees of correlation, presenting challenges for clinicians in identifying which elements of these tools provide the greatest diagnostic utility.


To determine the combination of elements from four commonly-used clinical concussion batteries that maximize discrimination of concussed from non-concussed participants.


Cross-sectional study


Suburban school and concussion program of a tertiary care academic center


166 non-concussed (from a suburban school) and 231 concussed (from the school and concussion program) participants aged 13–19 years

Main Outcome Measures

Individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, 5th Edition (SCAT-5, including the modified balance error scoring system [mBESS]), King-Devick test (K-D), and Post-Concussion Symptom Inventory (PCSI) were evaluated. The sub-components of these tests were grouped into interpretable factors using sparse principal component analysis (sPCA). The 13 resultant factors were combined with clinical covariates into a logistic regression and ranked by frequency of inclusion into the ideal model, and the predictive performance of the ideal model was compared to each of the individual batteries using the area under the receiving operating characteristic curve (AUC).


A cluster of 4 factors (F1: VVE saccades and vestibulo-ocular reflex; F2: mBESS double leg stance; F3: SCAT-5/PCSI symptoms; F4: K-D completion time) emerged. A model fit with the top factors performed as well as each battery in predicting concussion status (AUC=0.816 [95% CI: 0.731–0.889]), compared to SCAT-5 (AUC=0.784 [95% CI: 0.692–0.866]), PCSI (AUC=0.776 [95% CI: 0.674–0.863]), VVE (AUC=0.711 [95% CI: 0.602–0.814]), and K-D (AUC=0.708 [95% CI: 0.590–0.819]).


A multifaceted assessment for concussed adolescents, comprised of symptoms, attention, balance, and the visio-vestibular system, is critical. It is likely that current diagnostic batteries measure overlapping domains, and our sPCA analysis demonstrated strategies for streamlining comprehensive concussion assessment across a variety of settings.

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Author notes

* Co-first authors