Eye Tracking Metrics Differences among Uninjured Adolescents and Those with Acute or Persistent Post-Concussion Symptoms

Abstract

Significance: Eye tracking assessments that include pupil metrics can supplement current clinical assessments of vision and autonomic dysfunction in concussed adolescents.

Purpose: This study aimed to explore the utility of a 220-second eye tracking assessment in distinguishing eye position, saccadic movement, and pupillary dynamics among uninjured adolescents, those with acute post-concussion symptoms (≤28 days since concussion), or those with persistent post-concussion symptoms (>28 days since concussion).

Methods: Two hundred fifty-six eye tracking metrics across a prospective observational cohort of 180 uninjured adolescents recruited from a private suburban high school and 224 concussed adolescents, with acute or persistent symptoms, recruited from a tertiary care subspecialty concussion care program, 13 to 17 years old, from August 2017 to June 2021 were compared. Kruskal-Wallis tests were used, and Bonferroni corrections were applied to account for multiple comparisons and constructed receiver operating characteristic curves. Principal components analysis and regression models were applied to determine whether eye tracking metrics can augment clinical and demographic information in differentiating uninjured controls from concussed adolescents.

Results: Two metrics of eye position were worse in those with concussion than uninjured adolescents, and only one metric was significantly different between acute cases and persistent cases. Concussed adolescents had larger left and right mean, median, minimum, and maximum pupil size than uninjured controls. Concussed adolescents had greater differences in mean, median, and variance of left and right pupil size. Twelve metrics distinguished female concussed participants from uninjured; only four were associated with concussion status in males. A logistic regression model including clinical and demographics data and transformed eye tracking metrics performed better in predicting concussion status than clinical and demographics data alone.

Conclusions: Objective eye tracking technology is capable of quickly identifying vision and pupillary disturbances after concussion, augmenting traditional clinical concussion assessments. These metrics may add to existing clinical practice for monitoring recovery in a heterogeneous adolescent concussion population.

Appendix Figure A1.

Distribution of left.pupilsizemean for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A2.

Distribution of right.pupilsizemean for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A3.

Distribution of left.pupilsizemedian for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A4.

Distribution of right.pupilsizemedian for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A5.

Distribution of left.pupilsizeminabs for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A6.

Distribution of right.pupilsizeminabs for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A7.

Distribution of left.pupilsizemaxabs for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A8.

Distribution of right.pupilsizemaxabs for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A9.

Distribution of conj.pupilsizediffmean for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A10.

Distribution of conj.pupilsizediffmedian for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point.

Appendix Figure A11.

Distribution of conj.pupilsizediffvar for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point. Eight outliers were removed for this graph.

Appendix Figure A12.

Distribution of conj.varYbot for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point Twenty four outliers were removed for this graph.

Appendix Figure A13.

Distribution of conj.varYtopbotRatio for acute cases, persistent cases, and uninjured. Mean values for each group are indicated by the diamond point. Twenty nine outliers were removed for this graph.

Appendix Figure A14.

Similarity matrix calculated with Pearson’s R for the 13 eye tracking metrics.

Appendix Figure A15.

Scree plot showing the proportion of variance explained for the eye tracking metrics for each principal component.

Figure 1.

(A) Image of eye tracker with trained research staff and mock participant completing the assessment. (B) Image of screen that participant views during the assessment with the X and Y axes labelled. Some eye tracking metrics related to eye position and saccadic movement are measured in either the X and Y axis directions.