Objective: Concussion is the most common type of brain injury in both pediatric and adult populations and can potentially result in persistent postconcussion symptoms. Objective assessment of physiologic "mild" traumatic brain injury in concussion patients remains challenging. This study evaluates an automated eye-tracking algorithm as a biomarker for concussion as defined by its symptoms and the clinical signs of convergence insufficiency and accommodation dysfunction in a pediatric population.
Design: Cross-sectional case-control study.
Setting: Primary care.
Patients: Concussed children (N = 56; mean age = 13 years), evaluated at a mean of 22-week post-injury, compared with 83 uninjured controls.
Independent variables: Metrics comparing velocity and conjugacy of eye movements over time were obtained and were compared with the correlation between Acute Concussion Evaluation (ACE) scores, convergence, and accommodation dysfunction.
Main outcome measures: Subjects' eye movements recorded with an automated eye tracker while they watched a 220-second cartoon film clip played continuously while moving within an aperture.
Results: Twelve eye-tracking metrics were significantly different between concussed and nonconcussed children. A model to classify concussion as diagnosed by its symptoms assessed using the ACE achieved an area under the curve (AUC) = 0.854 (71.9% sensitivity, 84.4% specificity, a cross-validated AUC = 0.789). An eye-tracking model built to identify near point of convergence (NPC) disability achieved 95.8% specificity and 57.1% sensitivity for an AUC = 0.810. Reduced binocular amplitude of accommodation had a Spearman correlation of 0.752(P value <0.001) with NPC.
Conclusion: Eye tracking correlated with concussion symptoms and detected convergence and accommodative abnormalities associated with concussion in the pediatric population. It demonstrates utility as a rapid, objective, noninvasive aid in the diagnosis of concussion.