Concussion Assessment Across Several Clinical Batteries: Identifying the Components That Best Discriminate Injured Adolescents From Controls. (4th January 2022)

• Record Type: Journal Article
• Authors: Arbogast, Kristy; Mandel, Francesca; Corwin, Daniel; Mohammed, Fairuz; McDonald, Catherine; Barnett, Ian; Master, Christina
• Is Part Of: Neurology. Volume 98:Number 1(2022)Supplement 1
• Publication Date: 2022-01-04
• Subjects: Neurology -- Periodicals; Neurology -- Periodicals; Neurologie -- Périodiques; 616.8
• DOI: 10.1212/01.wnl.0000801836.17663.2d
• Languages: English
• ISSNs: 0028-3878

Abstract

Abstract : 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.