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Published in final edited form as: Vision Res. 2021 Apr 7;184:30–36. doi: 10.1016/j.visres.2021.03.002

Vergence, Accommodation, and Visual Tracking in Children and Adolescents Evaluated in a Multidisciplinary Concussion Clinic

Emily K Wiecek 1,2, Tawna L Roberts 3, Ankoor S Shah 1,2, Aparna Raghuram 1,2
PMCID: PMC8145776  NIHMSID: NIHMS1691663  PMID: 33838503

Abstract

Many patients with concussion experience visual symptoms following injury that lead to a diagnosis of convergence insufficiency, accommodative insufficiency, or saccadic dysfunction. However, these diagnostic categories are based on aggregates of clinical tests developed from a non-concussed population and therefore may not accurately describe visual deficits in the concussed population. Thus, we sought to understand individual metrics of visual dysfunction in chronically symptomatic post-concussion patients. This retrospective cross-sectional study included patients examined at the multidisciplinary concussion clinic (MDCC) at Boston Children’s Hospital over four years. Patients aged 5–21 years who had a complete assessment of eye alignment, vergence, accommodation, and visual tracking, and had visual acuity better than or equal to 20/30 in each eye were included. Patients with history of amblyopia, strabismus, or ocular pathology were excluded. Chart review yielded 116 patients who met inclusion criteria (median age 15 years, 64% female). The majority of patients (52%) experienced a single concussion and most were sports-related (50%). Clinical data show vergence, accommodation, or visual tracking deficits in 95% of patients. A receded near point of convergence (NPC, 70/116) and reduced accommodative amplitude (63/116) were the most common deficits. Both NPC and accommodative amplitude were significantly correlated with one another (r = −0.5) and with measures of visual tracking (r = −0.34). Patients with chronic post-concussion symptoms show deficits in individual metrics of vergence, accommodation and visual tracking. The high incidence of these deficits, specifically NPC and accommodative amplitude, highlights the need for a detailed sensorimotor evaluation to guide personalized treatment following concussion.

Keywords: Concussion, Accommodation, Vergence, Visual tracking, Binocular vision

INTRODUCTION

Recent estimates indicate 2.8 million traumatic brain injury (TBI)-related emergency department visits, hospitalizations, and deaths occur in the United States per year. Young adults aged 15–24 years account for 17.9% of all TBI-related emergency department visits (Taylor et al., 2017). The most common cause of mild TBI in children is sports-related head injury, with an estimated 1.1 to 1.9 million sports-related concussions occurring annually (Bryan et al., 2016). The incidence of non-specific concussions in children presenting for emergency care has also increased over the past decade, with an estimated rate of 340.5 in 100,000 in 2003 to 601.3 in 100,000 in 2010 (Macpherson et al., 2014).

Many adolescents experience visual symptoms following concussion. They report symptoms such as blurred and double vision, visual fatigue, light sensitivity, and difficulty sustaining attention on a visual task (Ciuffreda & Ludlam, 2011; Gallaway et al., 2017; Laukkanen et al., 2017; Master et al., 2016). Mild traumatic brain injury results in widespread axonal damage across the neural architecture (Laskowski et al., 2015). Visual pathways involved in accommodation, vergence, and visual tracking are extensive throughout cortical and subcortical brain regions, so it is reasonable that these systems are affected post injury (Agarwal et al., 2016; Mays & Gamlin, 1995). Gallaway et al. found that over 80% of patients (6–72 years old) with a history of concussion and referred for a visual assessment fit the criteria for an abnormal visual diagnosis, with 47% having convergence insufficiency (CI) and 42% having accommodative insufficiency (Gallaway et al., 2017). In an adolescent population (11–17 years old), Master et al. found 69 of 100 participants with history of concussion had at least one oculomotor diagnosis, including CI (49%), accommodative disorders (51%), and saccadic dysfunction (29%). Additionally, the authors found that near point of convergence (NPC), accommodative amplitude, and the Developmental Eye Movement Test (DEM) ratio were best predictors of post-concussion symptoms as measured by the Convergence Insufficiency Symptom Survey (CISS) (Master et al., 2016).

Although vision diagnoses such as CI, accommodative disorders, and saccadic dysfunction have been used to define visual problems following concussion,(Gallaway et al., 2017; Master et al., 2016) individual clinical findings from a sensorimotor evaluation should also be considered (Desai et al., 2019; Matuseviciene et al., 2018; Raghuram et al., 2019). In a retrospective study, Master and colleagues showed that decreased accommodative amplitude and inducible symptoms during vestibular oculomotor testing predicted prolonged recovery of post-concussive symptoms (Master et al., 2018). Matuseviciene et al. also reported reduced accommodative amplitudes and receded NPC in concussed patients at within 14 days of injury (Matuseviciene et al., 2018). Their data showed accommodative amplitudes remained reduced in six of 13 post-concussion patients at follow-up assessments while NPC improved. Despite the improvement in NPC, the majority of patients remained symptomatic as determined by the CISS. Raghuram et al. retrospectively showed that NPC was abnormal in a population of chronically symptomatic post-concussion adolescents; however, a receded NPC did not necessarily indicate CI, as only 36.5% of patients met the strict 3-sign criteria for CI (exophoria greater at near than distance, receded NPC, reduced positive fusional vergence) (Convergence Insufficiency Treatment Trial (CITT) Study Group, 2008). Instead, patients with receded NPC met criteria for a plethora of other sensorimotor anomalies including convergence excess, accommodative insufficiency, and accommodative dysfunction (Raghuram et al., 2019).

Visual dysfunction occurs at higher rates in the post-concussion adolescents as compared to the normal population (Gallaway et al., 2017; Master et al., 2016), yet varying definitions of diagnostic categories may result in underdiagnosing symptomatic patients whose visual symptoms have yet to return to normal (Raghuram et. al, 2019). Given the heterogeneity in post-concussion etiology, presentation of symptoms, and recovery, we hypothesize that post-concussion vision dysfunction may not meet classical diagnostic definitions of visual dysfunction (Kenzie et al., 2017; Langdon et al., 2020). This retrospective analysis considers individual clinical findings rather than employing aggregate diagnostic criteria traditionally used to assign a specific diagnosis of CI, accommodative insufficiency, or saccadic dysfunction. The clinical data presented in this analysis further describe visual dysfunction in post-concussion adolescents and will serve as a foundation for determining which clinical assessments are most informative for assessing and treating visual dysfunction following concussion.

METHODS

The present study is a retrospective review of patients with a history of concussion examined in the Multidisciplinary Concussion Clinic (MDCC) at Boston Children’s Hospital between July 2014 and September 2018. Patients presented to the MDCC with prolonged (≥ 21 days), multisystem symptoms following concussion. All patients were evaluated by the authors (AR and AS) and identified using the MDCC database. Institutional Review Board approval was obtained to perform the chart review and all research adhered to the tenets of the Declaration of Helsinki. All patients had a comprehensive sensorimotor assessment administered by the author (AR), and visual acuity was better than or equal to 20/30 in each eye with their habitual correction. Exclusion criteria were history of amblyopia, manifest strabismus, vision therapy, malingering or conversion syndrome diagnosis from a board-certified psychologist, or the presence of ocular pathology diagnosis from a board-certified ophthalmologist (AS) that interfered with visual function. Patients ranged in age from 5–21 years old. The comprehensive sensorimotor assessment included a full evaluation of eye alignment and motility as well as a battery of vergence, accommodative, and visual tracking functions. All patients with hyperopia > +2.00 diopters (D), myopia > −0.75 D, astigmatism > 0.75 D, and anisometropia > 0.75 D wore habitual refractive error correction for assessment.

Vergence assessments included near point of convergence (NPC), positive and negative fusional vergence ranges at near (PFV, NFV), and vergence facility at near. Visual tracking assessment consisted of the Developmental Eye Movement Test (DEM) (Garzia et al., 1990). Accommodation assessments included monocular accommodative amplitude, accommodative accuracy, and accommodative facility (Raghuram et al., 2019; Scheiman & Wick, 2014). To avoid assumptions about which eye may be driving binocular function when comparing accommodation and vergence, we used accommodative measures from only the right eye. There was no difference in reported findings when using accommodative measures from the left eye. A detailed description of each measure is in the online supplementary material. Correlations between measures of vergence, accommodation, and visual tracking were examined to determine if expected relationships between vergence and accommodation were preserved in this population and how they relate to visual tracking measures.

Specified criteria were used to determine each clinical finding as either normal or abnormal. The criteria for each of the clinical measures is shown in Table 1 and were defined using previously established criteria from clinical studies (Convergence Insufficiency Treatment Trial (CITT) Study Group, 2008; Master et al., 2016; Raghuram et al., 2019; Scheiman & Wick, 2014). DEM results (vertical time, horizontal time, and errors) are reported as standard scores based on a normative age database (Garzia et al., 1990).

Table 1:

Normative criteria for clinical assessments of accommodation, vergence and visual tracking (Convergence Insufficiency Treatment Trial (CITT) Study Group, 2008; Master et al., 2016; Raghuram et al., 2019; Scheiman & Wick, 2014).

CLINICAL ASSESSMENT ABNORMAL CRITERIA
NEAR POINT OF CONVERGENCE (NPC) > 7 centimeters
EXOPHORIA AT NEAR ≥ 4△ than magnitude at distance
ESOPHORIA AT NEAR ≥ 3△
POSITIVE FUSIONAL VERGENCE (PFV) ≤ 15△ blur or break, or if Sheard’s criterion is not met (PFV measures less than twice the magnitude of the near phoria)
NEGATIVE FUSIONAL VERGENCE (NFV) < 8△ blur or break, or if Sheard’s criterion is not met (NFV measures less than twice the magnitude of the near phoria)
VERGENCE FACILITY ≤ 9 cycles per minute
AMPLITUDE OF ACCOMMODATION 2D less than (15- ¼ ×age)
MONOCULAR ACCOMMODATIVE FACILITY ≤ 6 cycles per minute
ACCOMMODATIVE RESPONSE ACCURACY Inadequate accommodation (lag) > 1.00 D; Excess accommodation (lead) ≤ −0.25D
DEM HORIZONTAL TIME, VERTICAL TIME, RATIO, ERROR SCORE A standard score of ≤ 85
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Each clinical finding outside the listed range was deemed as abnormal.

△

= prism diopters; D = diopters; DEM, Developmental Eye Movement Test. See Online Supplementary Material for further description of each clinical measure.

Statistical analysis was completed in R statistical software version 3.4.3 (The R Foundation for Statistical Computing, Vienna, Austria) and RStudio version 1.1.383 (RStudio, Boston, MA, USA; Kassambara, 2017; R Core Team, 2017; Team & Others, 2013). An analysis of variance (ANOVA) was used for comparisons across eye alignment and standardized t-tests were used for pair-wise comparisons. Pearson correlation coefficients were used to determine the associations between vergence, accommodation, visual tracking, and refractive error. The alpha value was set at p < 0.01 to address type I errors due to multiple outcome analyses and testing (Holm, 1979). Data were plotted using the ggpubr package (The R Foundation for Statistical Computing, Vienna, Austria; Kassambara, 2017).

RESULTS

The retrospective review yielded 136 charts of post-concussion patients. Twenty patients were removed based on inclusion and exclusion criteria resulting in a sample of 116 patients. Six out of the remaining 116 patients were not tested with the DEM and were excluded from all DEM analyses. Three patients had a significantly impaired score on the DEM, which resulted in a negative standard score. These scores were greater than 4 standard deviations from the mean of the distribution, deemed as outliers and also removed from DEM analysis. The median patient age at assessment was 15.7 years (range = 5–21, interquartile range (IQR) = 3.35). Vision evaluations were completed between 21 to 1192 days (median = 112.5 days, IQR = 143.5) after the most recent concussion. The majority of patients, 51.7% (60/116), reported a single concussion, with 19.8%, 15.5%, and 12.9% reporting two, three, and greater than or equal to four concussions, respectively. The causes of concussion included sport-related activities (58/116), motor vehicle accidents (17/116), and other causes such as falls (41/116).

Ocular Alignment and Vergence Measures

Approximately 30% of patients showed ocular misalignment with exophoria (24% (28/116)) presenting more commonly than esophoria (8.6% (10/116)). Vergence measures were abnormal in 78% (91/116) of patients. Receded NPC was the most common anomalous vergence finding, found in 60.3% (70/116) of patients. Nineteen percent of patients had abnormal PFV (22/116) or NFV (12/116), and 21.6% (25/116) had poor vergence facility. Figure 1A shows the distribution of vergence testing anomalies.

Figure 1. Incidence of abnormal findings.

Figure 1

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Incidence of abnormal findings depicted as a percentage of the 116 patients for each clinical measure (n=107 for DEM). Each finding was categorized as normal or abnormal based on criteria described in Table 1. NPC - near point of convergence, PFV - positive fusional vergence; NFV - negative fusional vergence; BI - difficulty with base in prism; BO - difficulty with base-out prism; Both - difficulty with both base-in and base-out prism; Amp - Amplitude of accommodation; MAF (−), MAF (+), MAF (+&-) - monocular accommodative facility and difficulty with minus lens, plus lens, or both, respectively; Lag of Acc- Lag (inadequate) of accommodation; Lead of Acc - lead (excess) of accommodation; DEM - Developmental Eye Movement Test

Patients with an exophoria had a more receded NPC compared to those patients who were orthophoric (p = 0.005, mean difference = 3.1 cm, 95% CI [0.97 5.28]) (Figure 2A). Patients with esophoria had higher PFV than patients with exophoria (p < 0.001, mean difference = 8.5 prism diopters (△), 95% CIs [4.89, 12.09]) or orthophoria (p=0.004, mean difference = 4.72△, 95% CI [1.87, 9.56]) (Figure 2B). A receded NPC was significantly correlated with decreased PFV at near (Figure 2C, p < 0.001, r = −0.35, 95% CI [−0.50, −0.18]) and modestly correlated with decreased vergence facility (Figure 2D, p = 0.004, r = −0.27, 95% CI [−0.43 −0.09]).

Figure 2. Relationships among ocular alignment and vergence measures.

Figure 2

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A, B Box plots showing the relationship between ocular alignment (phoria) with near point of convergence (NPC) and positive fusional vergence. P-values are listed above the graphs on brackets. C, D. Scatter plots between NPC and positive fusional vergence and between NPC and vergence facility. Regression lines are fit using total least squares (i.e. orthogonal) regression. Eso – esophoria; ortho – orthophoria; exo – exophoria; cm – centimeters; △ – prism diopters; cpm – cycle per minute.

Accommodation Measures

Approximately 74% (86/116) of patients had at least one abnormal accommodative finding. A reduced amplitude of accommodation was the most prevalent anomalous accommodation finding in 54.3% (63/116) of patients (Figure 1B). Fifty percent (58/116) of patients failed accommodative facility testing, and the majority of those who failed (46/58) had more difficulty clearing plus lenses (relaxing accommodation). The remaining patients who failed facility testing had difficulty clearing minus lenses (3/58) or both plus and minus lenses (9/58). Approximately 10% (12/116) of patients presented with abnormal accommodative accuracy, with three of 12 exhibiting a lead of accommodation and nine of 12 exhibiting a lag of accommodation.

There was no significant difference in accommodative amplitude between those patients who had difficulty relaxing (clearing plus two diopter lens), stimulating accommodation (clearing minus two diopter lenses), or both on facility testing (n = 58, p = 0.33). There was no correlation of accommodative amplitude with accommodative facility (p = 0.03, r = 0.2, 95% CI [0.22 0.38]) or accuracy (lag or lead of accommodation) (p = 0.03, r = −0.2, 95% CI [−0.37 −0.01]).

Vergence and Accommodation

The clinical data show quantifiable vergence and accommodative deficits in 91.4% (106/116) of the patients. Decreased accommodative amplitude was significantly correlated with receded NPC and decreased PFV at near (Figure 3A, p < 0.001, r = −0.57, 95% CI [−0.69, −0.44], Figure 3B, p < 0.001, r = 0.36, 95% CI [0.19, 0.51]), but not NFV nor vergence facility (r = 0.24, 95% CI [0.06, 0.40], p = 0.01; r = 0.20, 95% CI [0.19, 0.37], p = 0.03). There was no significant difference in amplitude, facility or accuracy of accommodation between patients with esophoria, orthophoria, or exophoria (p = 0.43; p = 0.26; p = 0.30, respectively). No vergence measures were correlated with accommodative facility or accuracy of accommodation (p > 0.05).

Figure 3. Relationship between accommodative amplitude and vergence measures.

Figure 3

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A. Correlation between accommodative amplitude and near point of convergence (NPC). B. Correlation between accommodative amplitude and positive fusional vergence (PFV). Regression lines are fit using total least squares (i.e. orthogonal) regression. P-values are listed above the graph. D – diopters; △ – prism diopters.

Developmental Eye Movement Test

Of the 107 patients included in the DEM analyses, 29% (31/107), 18.7% (20/107), 39.3% (42/107), 8.4% (9/107) fell 1 standard deviation below the mean for Horizontal, Vertical, Ratio, and Error standard scores, respectively (Figure 1C). The two most prevalent vergence and accommodative anomalies, receded NPC and reduced accommodative amplitude, were both mildly correlated with performance on the vertical component of the DEM (r = −0.34, 95% CI [−0.50, −0.16], p < 0.001; r = 0.26, 95% CI [0.08, 0.44], p = 0.007). Neither anomaly was correlated with the horizontal component (NPC: r = −0.10, p = 0.3; accommodative amplitude: r = 0.03, p = 0.80) nor the horizontal over vertical ratio (NPC: r = 0.15, p = 0.13; accommodative amplitude: r = −0.16, p = 0.10) (Figure 4).

Figure 4. Correlations of accommodative amplitude and near point of convergence (NPC) with Developmental Eye Movement (DEM) Test.

Figure 4

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The left (A, C) and right (B, D) panels depict vertical and horizontal DEM performance, respectively. R values displayed are the Pearson correlation coefficient. P-values are listed above the graphs. Regression lines are fit using total least squares (i.e. orthogonal) regression. cm – centimeters; D – diopters.

Binocular anomalies and refractive error

We also considered the relationship between refractive error with vergence and accommodation measures. The spherical equivalent from the right eye was used as a comparison in all binocular vergence measures. There was no correlation between refractive error and NPC, PFV, phoria, or vergence facility (all p values > 0.05). There was a mild correlation with refractive error and accommodative facility (r = −0.25, 95% CI [−0.41, −0.06], p = 0.01) and accuracy (r = 0.21, 95% CI [0.03, 0.38], p = 0.03), with hyperopic patients having lower cycles per minute and inadequate (lag) accommodation. There was no significant correlation with the refractive error and accommodative amplitude (r = −0.12, p = 0.2).

DISCUSSION

Visual symptoms are common after concussion in both adults and adolescents (Ciuffreda et al., 2007; Gallaway et al., 2017; Master et al., 2016; Thiagarajan et al., 2011). This retrospective study suggests vast visual dysfunction following concussion in children and adolescents that is quantifiable by clinical measures of vergence, accommodation, and visual tracking. While much of the current literature on the post-concussion visual system uses aggregates of multiple signs to define anomalies such as CI, accommodative insufficiency, or saccadic dysfunction (Ciuffreda et al., 2007; Ciuffreda & Ludlam, 2011; Gallaway et al., 2017; Master et al., 2016; Raghuram et al., 2019), our results add to the understanding of post-concussion vision disturbances by delineating individual clinical measures of vergence, accommodation, and visual tracking that are abnormal. The use of individual measures avoids discrepancy with varying definitions of diagnostic categories, which may result in underdiagnosing symptomatic patients whose visual symptoms have yet to return to normal. For example, the traditional diagnosis of naturally occurring CI in a general population includes exophoria greater at near than at far, decreased PFV ranges, and a receded NPC (Convergence Insufficiency Treatment Trial Study Group, 2008). Our data suggest that while many concussed patients have receded NPC (60.3%) or decreased PFV (19%), only 9% would meet the strict definition of naturally occurring CI used in clinical trials (Raghuram et al., 2019). Thus, the present study highlights the need to consider individual measures of visual function and to re-evaluate diagnostic criteria to be used in individuals with history of concussion. These findings further suggest that details of visual dysfunction are important in forming the basis for prescribing treatment paradigms that are personalized for each patient.

Our retrospective data show that clinical measures of vergence were associated with one another in many expected ways. Both near exophoria and reduced PFV ranges, were associated with receded NPC. It is well established that clinical measures of vergence correspond in a general population, and individuals with reduced convergence also exhibit deficits across other vergence measures including facility and phoria (Scheiman & Wick, 2014). These vergence relationships persist in patients with a history of concussion.

Unlike vergence measures, accommodative measures presented in seemingly contradictory ways following concussion. The two most common accommodative anomalies post-concussion are paradoxical; patients were unable to reach age-expected accommodative amplitudes but were also unable to relax accommodation (i.e., clear the plus lenses) on facility testing. In the real world, this may translate to the inability to focus sufficiently to a near object, such as reading material, as well as secondary limitation of relaxing the amount of focus that was generated to clear a distance target, such as the smart board in school. As such, knowing that accommodative dysfunction as a category exists is not enough; instead, we must evaluate these individual measures of the accommodative system to implement the most appropriate treatment (Master et al., 2016, 2018).

The extensive neural circuitry driving the accommodative system may also contribute to variability seen in our accommodative clinical measures (Green et al., 2010; Mays & Gamlin, 1995). Future work that considers objective measures of accommodation, such as an open-field autorefractor or photorefraction, is imperative to our understanding of the post-concussion accommodative system (Roberts et al., 2015; Roberts et al., 2109; Seidemann & Schaeffel, 2003).

In addition to vergence and accommodation, oculomotor function has also been examined in the concussion literature (Cobbs et al., 2017; Covassin et al., 2009; Galetta et al., 2011; Schatz et al., 2006; Subotic et al., 2017). The present study used the Developmental Eye Movement test because it includes a vertical test to account for automaticity naming issues that may confound the assessment of isolated oculomotor deficits (Garzia et al., 1990). Interestingly, there was a correlation with our two most common abnormal measures, NPC and accommodative amplitude, with vertical but not horizontal DEM time, indicating that accommodation and vergence deficits may relate to automaticity deficits in these rapid naming tasks. These relationships may translate to similar commonly used side-line assessments. An improved understanding of the interplay between visual dysfunction and rapid naming tasks is critical to the development of efficient and effective measures both acutely and in recovery following injury.

A complete assessment of visual dysfunction following concussion warrants above mentioned measures of vergence, accommodation, and visual tracking; however, the impact of refractive error must also be considered. Hyperopic spherical equivalent was mildly correlated with accommodative facility and accuracy, which may be due to the greater accommodative demand associated with uncorrected hyperopia. This information poses the hypothesis that correcting low refractive errors, particularly hyperopia, may be helpful in post-concussion patients who exhibit accommodative deficits. This may provide temporary relief to post-concussion patients as their visual system recovers. A significant correlation was not detected between refractive error and accommodative amplitude or any of the measures of vergence. The lack of association between refractive error and other measures of visual function suggests that refractive error alone cannot account for the full scope of visual dysfunction following concussion and a thorough sensorimotor assessment is warranted.

Our study is not without limitations. The population analyzed in this study was seen at the MDCC at Boston Children’s Hospital. Patients seek care at this clinic due to chronic post-concussion symptoms but not necessarily visual symptoms. The bias of chronic symptoms cannot be fully accounted for in this retrospective study; therefore, the findings may not apply to a broader population of post-concussion children and adolescents. A second limitation is the inability to control for other variables such as age, number of concussions, and time since concussion. Although time between injury and assessment varied in this retrospective data, there is little established research that shows distinct patterns in recovery of symptoms and visual sequelae post-concussion. The impressive incidence of abnormal findings in this heterogeneous population further emphasizes that these visual deficits may be long lasting and impactful in some patients regardless of the number of concussive events or the time since injury. The study is also limited in the scale of testing completed during a single clinic visit and future studies would benefit from other measures often found to be abnormal in patients with binocular vision deficits such as accommodative convergence/accommodation ratio. Additionally, future studies of binocular vision deficits in individuals with concussion should consider the inclusion of pupil dynamics as they may provide additional insight into the underlying mechanisms of the pathoneurophysiology of the deficits as pupil reactions, vergence, and accommodation are related in the near triad (Feil et al., 2017) and pupil dynamics have been shown to be abnormal post head injury (Ciuffreda et al., 2017; Master et al., 2020). Lastly, the results of this study are limited by the lack of a control group. While there is evidence that visual dysfunction occurs at a higher rate post-concussion as compared to the general population, a prospective, longitudinal study that includes a control cohort and within cohort pre- and post-concussion assessment is necessary to understand the incidence and recovery of visual dysfunction following injury.

To the authors’ knowledge, there are only three other studies that exclusively examine the visual sequelae of visual dysfunction in an adolescent population (Master et al., 2016, 2018; Storey et al., 2017). This study supplements these prior works by suggesting the breadth of visual dysfunction post-concussion may be more extensive and complex than previously described. This breadth indicates that aggregate diagnoses such as CI, accommodative insufficiency, and saccadic dysfunction are inadequate in explaining the persistent symptomatology in the 1.1–1.9 million concussions occurring annually in adolescents. Instead, these findings indicate that the approach to post-concussion visual symptoms should include evaluation of individual details in the vergence, accommodative, and visual tracking measures, and these details may be leveraged to personalize treatment for each symptomatic patient.

Supplementary Material

1

ACKNOWLEDGEMENTS

The authors thank Ryan Chinn at Boston Children’s Hospital for help with retrospective chart review.

Funding:

This work is supported by the Boston Children’s Hospital Ophthalmology Foundation Discovery Award (AR, EW, AS), National Eye Institute (NEI P30-EY026877 (TLR)), The Christ Family Fund (AS), Research to Prevent Blindness, Inc. (TLR).

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Financial Disclosures:

EW - No financial disclosures.

TR - No financial disclosures.

AS - Equity owner, Pfizer, Inc, (New York, NY, USA); Equity owner, Medtronic (Minneapolis, MN, USA).

AR - No financial disclosures.

REFERENCES

  1. Agarwal M, Ulmer JL, Chandra T, Klein AP, Mark LP, & Mohan S (2016). Imaging correlates of neural control of ocular movements. European Radiology, 26(7), 2193–2205. [DOI] [PubMed] [Google Scholar]
  2. Bryan MA, Rowhani-Rahbar A, Comstock RD, Rivara F, & Seattle Sports Concussion Research Collaborative. (2016). Sports- and Recreation-Related Concussions in US Youth. Pediatrics, 138(1). 10.1542/peds.2015-4635 [DOI] [PubMed] [Google Scholar]
  3. Ciuffreda KJ, Joshi NR, & Truong JQ (2017). Understanding the effects of mild traumatic brain injury on the pupillary light reflex. Concussion, CNC36. [DOI] [PMC free article] [PubMed]
  4. Ciuffreda KJ, Kapoor N, Rutner D, Suchoff IB, Han ME, & Craig S (2007). Occurrence of oculomotor dysfunctions in acquired brain injury: a retrospective analysis. Optometry, 78(4), 155–161. [DOI] [PubMed] [Google Scholar]
  5. Ciuffreda KJ, & Ludlam DP (2011). Conceptual model of optometric vision care in mild traumatic brain injury. J Behav Optom. https://www.researchgate.net/profile/Diana_Ludlam/publication/268172783_Conceptual_Model_of_Optometric_Vision_Care_in_Mild_Traumatic_Brain_Injury/links/54c184950cf2d03405c59c6b.pdf
  6. Cobbs L, Hasanaj L, Amorapanth P, Rizzo J-R, Nolan R, Serrano L, Raynowska J, Rucker JC, Jordan BD, Galetta SL, & Balcer LJ (2017). Mobile Universal Lexicon Evaluation System (MULES) test: A new measure of rapid picture naming for concussion. Journal of the Neurological Sciences, 372, 393–398. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Convergence Insufficiency Treatment Trial (CITT) Study Group. (2008). The convergence insufficiency treatment trial: design, methods, and baseline data. Ophthalmic Epidemiology, 15(1), 24–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Convergence Insufficiency Treatment Trial Study Group. (2008). Randomized clinical trial of treatments for symptomatic convergence insufficiency in children. Archives of Ophthalmology, 126(10), 1336–1349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Covassin T, Elbin RJ 3rd, Stiller-Ostrowski JL, & Kontos AP (2009). Immediate post-concussion assessment and cognitive testing (ImPACT) practices of sports medicine professionals. Journal of Athletic Training, 44(6), 639–644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Desai N, Wiebe DJ, Corwin DJ, Lockyer JE, Grady MF, & Master CL (2019). Factors Affecting Recovery Trajectories in Pediatric Female Concussion. Clinical Journal of Sport Medicine: Official Journal of the Canadian Academy of Sport Medicine, 29(5), 361–367. [DOI] [PubMed] [Google Scholar]
  11. Feil M, Moser B, & Abegg M (2017). The interaction of pupil response with the vergence system. Graefe’s Archive for Clinical and Experimental Ophthalmology = Albrecht von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie, 255(11), 2247–2253. [DOI] [PubMed] [Google Scholar]
  12. Galetta KM, Barrett J, Allen M, Madda F, Delicata D, Tennant AT, Branas CC, Maguire MG, Messner LV, Devick S, Galetta SL, & Balcer LJ (2011). The King-Devick test as a determinant of head trauma and concussion in boxers and MMA fighters. Neurology, 76(17), 1456–1462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gallaway M, Scheiman M, & Mitchell GL (2017). Vision Therapy for Post-Concussion Vision Disorders. Optometry and Vision Science: Official Publication of the American Academy of Optometry, 94(1), 68–73. [DOI] [PubMed] [Google Scholar]
  14. Garzia RP, Richman JE, Nicholson SB, & Gaines CS (1990). A new visual-verbal saccade test: The Developmental Eye Movement test (DEM). Journal of the American Optometric Association, 61(2), 124–135. [PubMed] [Google Scholar]
  15. Garzia RP, Richman JE, Nicholson SB, & Gaines CS (1990). A new visual-verbal saccade test: the development eye movement test (DEM). Journal of the American Optometric Association, 61(2), 124–135. [PubMed] [Google Scholar]
  16. Green W, Ciuffreda KJ, Thiagarajan P, Szymanowicz D, Ludlam DP, & Kapoor N (2010). Accommodation in mild traumatic brain injury. Journal of Rehabilitation Research and Development, 47(3), 183–199. [DOI] [PubMed] [Google Scholar]
  17. Holm S (1979). A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal of Statistics, Theory and Applications, 6(2), 65–70. [Google Scholar]
  18. Kassambara A (2017). ggpubr:“ggplot2” based publication ready plots (Version 0.1.6) [Computer software].
  19. Kenzie ES, Parks EL, Bigler ED, Lim MM, Chesnutt JC, Wakeland W. Concussion As a Multi-Scale Complex System: An Interdisciplinary Synthesis of Current Knowledge. Front Neurol. 2017;8:513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Langdon S, Königs M, Adang EAMC, Goedhart E, Oosterlaan J. Subtypes of Sport-Related Concussion: a Systematic Review and Meta-cluster Analysis. Sports Med. 2020;50(10):1829–1842. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Laskowski RA, Creed JA, & Raghupathi R (2015). Pathophysiology of Mild TBI: Implications for Altered Signaling Pathways. In Kobeissy FH (Ed.), Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. CRC Press/Taylor & Francis. [PubMed] [Google Scholar]
  22. Laukkanen H, Scheiman M, & Hayes JR (2017). Brain Injury Vision Symptom Survey (BIVSS) Questionnaire. Optometry and Vision Science: Official Publication of the American Academy of Optometry, 94(1), 43–50. [DOI] [PubMed] [Google Scholar]
  23. Macpherson A, Fridman L, Scolnik M, Corallo A, & Guttmann A (2014). A population-based study of paediatric emergency department and office visits for concussions from 2003 to 2010. Paediatrics & Child Health, 19(10), 543–546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Master CL, Master SR, Wiebe DJ, Storey EP, Lockyer JE, Podolak OE, & Grady MF (2018). Vision and Vestibular System Dysfunction Predicts Prolonged Concussion Recovery in Children. Clinical Journal of Sport Medicine: Official Journal of the Canadian Academy of Sport Medicine, 28(2), 139–145. [DOI] [PubMed] [Google Scholar]
  25. Master CL, Podolak OE, Ciuffreda KJ, Metzger KB, Joshi NR, McDonald CC, Margulies SS, Grady MF, & Arbogast KB (2020). Utility of Pupillary Light Reflex Metrics as a Physiologic Biomarker for Adolescent Sport-Related Concussion. JAMA Ophthalmology. 10.1001/jamaophthalmol.2020.3466 [DOI] [PMC free article] [PubMed]
  26. Master CL, Scheiman M, Gallaway M, Goodman A, Robinson RL, Master SR, & Grady MF (2016). Vision Diagnoses Are Common After Concussion in Adolescents. Clinical Pediatrics, 55(3), 260–267. [DOI] [PubMed] [Google Scholar]
  27. Matuseviciene G, Johansson J, Möller M, Godbolt AK, Pansell T, & Deboussard CN (2018). Longitudinal changes in oculomotor function in young adults with mild traumatic brain injury in Sweden: an exploratory prospective observational study. BMJ Open, 8(2), e018734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mays LE, & Gamlin PD (1995). Neuronal circuitry controlling the near response. Current Opinion in Neurobiology, 5(6), 763–768. [DOI] [PubMed] [Google Scholar]
  29. Raghuram A, Cotter S, Gowrisankaran S, Kanji J, Howell DR, Meehan WP 3rd, & Shah AS (2019). Post-Concussion: Receded Near Point of Convergence is Not Diagnostic of Convergence Insufficiency. American Journal of Ophthalmology. 10.1016/j.ajo.2019.04.008 [DOI] [PubMed]
  30. Roberts TL, Anderson HA, & Stuebing KK (2015). Accommodative Gain in Relation to Perceived Target Clarity. Optometry and Vision Science: Official Publication of the American Academy of Optometry, 92(11), 1092–1102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Roberts TL, Manny RE, & Anderson HA (2019). Impact of Visual Cues on the Magnitude and Variability of the Accommodative Response in Children With Emmetropia and Uncorrected Hyperopia and Adults. Investigative Ophthalmology & Visual Science, 60(5), 1527–1537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. R Core Team. (2017). R: A language and environment for statistical computing (Version 3.4.3) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/ [Google Scholar]
  33. Schatz P, Pardini JE, Lovell MR, Collins MW, & Podell K (2006). Sensitivity and specificity of the ImPACT Test Battery for concussion in athletes. Archives of Clinical Neuropsychology: The Official Journal of the National Academy of Neuropsychologists, 21(1), 91–99. [DOI] [PubMed] [Google Scholar]
  34. Scheiman M, & Wick B (2014). Clinical Management of Binocular Vision: Heterophoric, Accommodative, and Eye Movement Disorders. Lippincott Williams & Wilkins. [Google Scholar]
  35. Seidemann A, & Schaeffel F (2003). An evaluation of the lag of accommodation using photorefraction. Vision Research, 43(4), 419–430. [DOI] [PubMed] [Google Scholar]
  36. Storey EP, Master SR, Lockyer JE, Podolak OE, Grady MF, & Master CL (2017). Near Point of Convergence after Concussion in Children. Optometry and Vision Science: Official Publication of the American Academy of Optometry, 94(1), 96–100. [DOI] [PubMed] [Google Scholar]
  37. Subotic A, Ting WK-C, & Cusimano MD (2017). Characteristics of the King-Devick test in the assessment of concussed patients in the subacute and later stages after injury. PloS One, 12(8), e0183092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Taylor CA, Bell JM, Breiding MJ, & Xu L (2017). Traumatic Brain Injury-Related Emergency Department Visits, Hospitalizations, and Deaths - United States, 2007 and 2013. Morbidity and Mortality Weekly Report. Surveillance Summaries, 66(9), 1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Team, R. C., & Others. (2013). R: A language and environment for statistical computing. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.470.5851&rep=rep1&type=pdf
  40. Thiagarajan P, Ciuffreda KJ, & Ludlam DP (2011). Vergence dysfunction in mild traumatic brain injury (mTBI): a review: Vergence dysfunction in mild traumatic brain injury. Ophthalmic & Physiological Optics: The Journal of the British College of Ophthalmic Opticians, 31(5), 456–468. [DOI] [PubMed] [Google Scholar]

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