Novel Structure–Function Models for Estimating Retinal Ganglion Cell Count Using Pattern Electroretinography in Glaucoma Suspects

TL;DR

This study shows that a new method using pattern electroretinography better predicts early retinal cell changes in people at risk for glaucoma compared to existing methods.

Contribution

The study introduces new ssPERG-derived models that outperform existing methods in predicting early retinal ganglion cell structural changes in glaucoma suspects.

Findings

ssPERG-derived models outperformed CSFI in predicting AvRNFLT and AvGCL-IPLT.

eRGCCMagD showed the strongest association with AvGCL-IPLT.

Rim area was best predicted by eRGCCMag and eRGCCCSFI.

Abstract

Background/Objectives: The early detection of retinal ganglion cell (RGC) dysfunction is critical for timely intervention in glaucoma suspects (GSs). The combined structure–function index (CSFI), which uses visual field and optical coherence tomography (OCT) data to estimate RGC counts, may be of limited utility in GSs. This study evaluates whether steady-state pattern electroretinogram (ssPERG)-derived estimates better predict early structural changes in GSs. Methods: Fifty eyes from 25 glaucoma suspects underwent ssPERG and spectral-domain OCT. Estimated RGC counts (eRGCC) were calculated using three parameters: ssPERG-Magnitude (eRGCCMag), ssPERG-MagnitudeD (eRGCCMagD), and CSFI (eRGCCCSFI). Linear regression and multivariable models were used to assess each model’s ability to predict the average retinal nerve fiber layer thickness (AvRNFLT), average ganglion cell layer–inner plexiform layer thickness (AvGCL-IPLT), and rim area. Results: eRGCCMag and eRGCCMagD were significantly correlated with eRGCCCSFI. Both PERG-derived models outperformed eRGCCCSFI in predicting AvRNFLT and AvGCL-IPLT, with eRGCCMagD showing the strongest association with AvGCL-IPLT. Conversely, the rim area was best predicted by eRGCCMag and eRGCCCSFI. These findings support a linear relationship between ssPERG parameters and early RGC structural changes, while the logarithmic nature of visual field loss may limit eRGCCCSFI’s predictive accuracy in GSs. Conclusions: ssPERG-derived estimates, particularly eRGCCMagD, better predict early structural changes in GSs than eRGCCCSFI. eRGCCMagD’s superior performance in predicting GCL-IPLT highlights its potential utility as an early biomarker of glaucomatous damage. ssPERG-based models offer a simpler and more sensitive tool for early glaucoma risk stratification, and may provide a clinical benchmark for tracking recoverable RGC dysfunction and treatment response.

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