Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
Diagnostic performance of a handheld electroretinography test for glaucoma
Author Affiliations & Notes
  • Anika Kumar
    Department of Ophthalmology, University of California San Francisco, San Francisco, California, United States
  • Nathan Sanchez
    Department of Ophthalmology, University of California San Francisco, San Francisco, California, United States
  • Alan W Kong
    Department of Ophthalmology, University of California San Francisco, San Francisco, California, United States
  • Benjamin Arnold
    Department of Ophthalmology, University of California San Francisco, San Francisco, California, United States
  • Yvonne Ou
    Department of Ophthalmology, University of California San Francisco, San Francisco, California, United States
  • Footnotes
    Commercial Relationships   Anika Kumar None; Nathan Sanchez None; Alan Kong None; Benjamin Arnold None; Yvonne Ou None
  • Footnotes
    Support  NIH-NEI EY028148, NIH-NEI P30 EY002162, Research to Prevent Blindness unrestricted grant
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 4781. doi:
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    • Get Citation

      Anika Kumar, Nathan Sanchez, Alan W Kong, Benjamin Arnold, Yvonne Ou; Diagnostic performance of a handheld electroretinography test for glaucoma. Invest. Ophthalmol. Vis. Sci. 2024;65(7):4781.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Static automated perimetry is ubiquitously utilized in visual function assessment for glaucoma diagnosis, but can be limited by subjectivity in patient responses, requirement of expensive, bulky equipment and a trained technician, and patient dislike for the testing paradigm. This study evaluated the diagnostic performance of a sinusoidal flicker stimulus test at various frequencies using a portable handheld electroretinography (ERG) device in glaucoma vs control participants. We hypothesized that higher frequency stimuli would have greater discriminatory ability because the OFF pathway, which may predominate at higher frequencies, is more susceptible in glaucoma.

Methods : In this cross-sectional study, participants with a glaucoma diagnosis and controls were recruited from ophthalmology and optometry clinics. The RETeval device (LKC Technologies), a handheld ERG recording system, was used to administer a sinusoidal flicker stimulus modulated at 14 frequencies from 1 to 50 Hz, and the first harmonic frequency response amplitudes were collected. Logistic regression models with glaucoma diagnosis as the outcome and amplitude responses at various frequencies as the explanatory variables were trained using data from 67% of participants; models were then tested on the remaining 33% of participants. Receiver operating characteristic curves demonstrating model performance on the testing set were generated and area under the curve (AUC) was calculated.

Results : 117 eyes from 72 participants (18 control, 54 glaucoma, mean age [SD]=70.4[12.2] years, 51.4% female) were included. Among glaucomatous eyes, average(SD) mean deviation was -4.61(5.55) dB. In a model using the combined effects of the amplitude responses measured at all frequencies, the AUC was 0.57 (95% CI:0.37-0.78). In a model using measurements from frequencies only 30Hz and greater, where the OFF pathway may predominate, the AUC was 0.81 (95% CI:0.66-0.97). In this higher frequency model, sensitivity was 80% and specificity was 74% at the Youden’s J cutoff.

Conclusions : These results demonstrate evidence supporting handheld electroretinography to diagnose glaucoma by measuring retinal amplitude responses to sinusoidal flicker stimuli at frequencies between 30 and 50Hz, supporting the hypothesis that the OFF pathway may be more susceptible in glaucoma.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

 

Receiver operating characteristic curve using trained model utilizing amplitude responses from frequencies >= 30 Hz

Receiver operating characteristic curve using trained model utilizing amplitude responses from frequencies >= 30 Hz

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