Concussion Assessment Across Several Clinical Batteries: Identifying the Components That Best Discriminate Injured Adolescents From Controls

Objective To identify which sub-components of 4 clinical assessments optimize concussion diagnosis.

Background Multiple assessments are part of the clinical toolbox for diagnosing concussions in youth, including the Post-Concussion Symptom Inventory (PCSI), the visio-vestibular exam (VVE), the King-Devick (KD) assessment, and the Sport Concussion Assessment Tool (SCAT-5). Most of these assessments have sub-components that likely overlap in aspects of brain function they assess. Discerning the combination of sub-components that best discriminate concussed adolescents (cases) from uninjured controls would streamline concussion assessment.

Design/Methods Participants, 12–18 years, were prospectively enrolled from August 1, 2017 to April 29, 2020 Controls (n = 189, 53% female) were recruited from a suburban high school with PCSI, VVE, KD and SCAT-5 assessments associated with their sport seasons. Cases (n = 213, 52% female) were recruited from a specialty care concussion program, with the same assessments performed ≤28 days from injury. We implemented a forward-selection sparse principal component (PC) regression procedure to group sub-components into interpretable PCs and identify the PCs best able to discriminate cases from controls while accounting for age, sex, and concussion history.

Results The AUC of the baseline model with age, sex, and concussion history was 62%. The PC that combined all 5 sub-components of PCSI and SCAT-5 symptom count and symptom severity provided the largest AUC increase (+10.6%) relative to baseline. Other PC factors representing (1) KD completion time, (2) Errors in BESS tandem and double-leg stances, and (C) horizontal/vertical saccades and vestibular-ocular reflex also improved model AUC relative to baseline by 5.6%, 4.7%, and 4.5%, respectively. In contrast, the SCAT5 immediate recall test and right/left monocular accommodation did little to uniquely contribute to discrimination (<1% gain in AUC). Overall, the best model included 5 PCs (AUC = 77%).

Conclusions These data show overlapping features of clinical batteries, with symptoms providing the strongest discrimination, but unique features obtained from neurocognitive, vision, and vestibular testing.