Prediction of glaucoma severity using parameters from the electroretinogram

Area of Science:

• Ophthalmology
• Neuroscience
• Medical Imaging

Background:

• Glaucoma is an optic neuropathy characterized by progressive retinal ganglion cell (RGC) loss.
• Functional changes in RGCs precede detectable cell loss.
• The electroretinogram (ERG), particularly the photopic negative response (PhNR), is used to assess RGC function.

Purpose of the Study:

• To investigate if additional ERG parameters, non-linear modeling (MARS), and achromatic stimuli improve glaucoma severity prediction.
• To compare the predictive power of PhNR alone versus comprehensive ERG markers.
• To evaluate the utility of achromatic versus chromatic stimuli in ERG-based glaucoma assessment.

Main Methods:

• Recorded ERGs, including PhNR, a-wave, b-wave, i-wave, and b- to i-wave trough, from 103 eyes of 55 glaucoma patients.
• Utilized both chromatic (red-on-blue) and achromatic (white-on-white) stimuli of equal luminance.
• Applied linear and multivariate adaptive regression splines (MARS) models to predict glaucoma severity determined by perimetry and OCT.

Main Results:

• All ERG markers collectively predicted glaucoma severity significantly better than PhNR alone (P ≤ 0.02).
• MARS models outperformed linear models when using all ERG markers (P = 0.01).
• No significant difference was found between achromatic and chromatic stimulus models.

Conclusions:

• The photopic ERG contains substantial information beyond the conventional PhNR for characterizing RGC function and predicting glaucoma severity.
• Comprehensive analysis of ERG parameters, particularly with advanced modeling techniques like MARS, offers a more robust assessment of glaucoma.
• Achromatic stimuli are as effective as chromatic stimuli for this purpose.