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Updated: Mar 14, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
Published on: May 25, 2020
Baseline Fourier-Domain Optical Coherence Tomography Structural Risk Factors for Visual Field Progression in the
Xinbo Zhang1, Anna Dastiridou2, Brian A Francis2
1Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Purpose:
To identify baseline structural parameters that predict the progression of visual field (VF) loss in patients with open-angle glaucoma.
Design:
Multicenter cohort study.
Methods:
Participants from the Advanced Imaging for Glaucoma (AIG) study were enrolled and followed up. VF progression is defined as either a confirmed progression event on Humphrey Progression Analysis or a significant (P < .05) negative slope for VF index (VFI). Fourier-domain optical coherence tomography (FDOCT) was used to measure optic disc, peripapillary retinal nerve fiber layer (NFL), and macular ganglion cell complex (GCC) thickness parameters.
Results:
A total of 277 eyes of 188 participants were followed up for 3.7 ± 2.1 years. VF progression was observed in 83 eyes (30%). Several baseline NFL and GCC parameters, but not disc parameters, were found to be significant predictors of progression on univariate Cox regression analysis. The most accurate single predictors were the GCC focal loss volume (FLV), followed closely by NFL-FLV. An abnormal GCC-FLV at baseline increased risk of progression by a hazard ratio of 3.1. Multivariate Cox analysis showed that combining age and central corneal thickness with GCC-FLV in a composite index called "Glaucoma Composite Progression Index" (GCPI) further improved the accuracy of progression prediction. GCC-FLV and GCPI were both found to be significantly correlated with the annual rate of change in VFI.
Conclusion:
Focal GCC and NFL loss as measured by FDOCT are the strongest predictors for VF progression among the measurements considered. Older age and thinner central corneal thickness can enhance the predictive power using the composite risk model.
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