Prediction of glaucoma severity using parameters from the electroretinogram

Abstract

Glaucoma is an optic neuropathy that results in the progressive loss of retinal ganglion cells (RGCs), which are known to exhibit functional changes prior to cell loss. The electroretinogram (ERG) is a method that enables an objective assessment of retinal function, and the photopic negative response (PhNR) has conventionally been used to provide a measure of RGC function. This study sought to examine if additional parameters from the ERG (amplitudes of the a-, b-, i-wave, as well the trough between the b- and i-wave), a multivariate adaptive regression splines (MARS; a non-linear) model and achromatic stimuli could better predict glaucoma severity in 103 eyes of 55 individuals with glaucoma. Glaucoma severity was determined using standard automated perimetry and optical coherence tomography imaging. ERGs targeting the PhNR were recorded with a chromatic (red-on-blue) and achromatic (white-on-white) stimulus with the same luminance. Linear and MARS models were fitted to predict glaucoma severity using the PhNR only or all ERG markers, derived from chromatic and achromatic stimuli. Use of all ERG markers predicted glaucoma severity significantly better than the PhNR alone (P ≤ 0.02), and the MARS performed better than linear models when using all markers (P = 0.01), but there was no significant difference between the achromatic and chromatic stimulus models. This study shows that there is more information present in the photopic ERG beyond the conventional PhNR measure in characterizing RGC function.