Scanning laser polarimetry with variable corneal compensation and optical coherence tomography in normal and glaucomatous eyes

Abstract

Purpose To evaluate the relationship between visual function and retinal nerve fiber layer (RNFL) measurements obtained with scanning laser polarimetry with variable corneal compensation (SLP-VCC) and optical coherence tomography (OCT). Design Cross-sectional analysis of normal and glaucomatous eyes in a tertiary care academic referral practice. Methods A commercial GDx nerve fiber analyzer was modified to enable the measurement of corneal polarization axis and magnitude so that compensation for corneal birefringence was eye specific. Complete examination, SLP with fixed corneal compensation (FCC) and variable corneal compensation (VCC), optical coherence tomography (OCT) imaging of the peripapillary RNFL, and automated achromatic perimetry were performed in all subjects. Exclusion criteria were visual acuity less than 20/40, diseases other than glaucoma, and unreliable perimetry. Results Fifty-nine patients (59 eyes; 29 normal, 30 glaucomatous) were enrolled (mean age, 56.7 ± 20.3 years, range, 20–91). All eyes with glaucoma had associated visual field loss (average mean defect, −8.4 ± 5.8 dB). Using SLP-FCC, nine of 12 retardation parameters (75%) were significantly less in glaucomatous eyes. Using SLP-VCC, 11of 12 retardation parameters (92%) were significantly less in glaucomatous eyes. Multiple regression models constructed for each retardation parameter with visual field demonstrated that the following VCC parameters were statistically significant whereas FCC parameters were not: ellipse average (FCC, P = .28, VCC, P = .001), superior average (FCC, P = .38, VCC, P < .001), inferior average (FCC, P = .10, VCC, P = .008), average thickness (FCC, P = .30, VCC, P = .031), and superior integral (FCC, P = .43, VCC, P = .001). Similar results were obtained for multiple regression models constructed with OCT-derived RNFL thickness: ellipse average (FCC, P = .99, VCC, P = .002), superior average (FCC, P = .90, VCC, P < .001), inferior average (FCC, P = .61, VCC, P = .007), and superior integral (FCC, P = .92, VCC, P < .001). Conclusions Compared with fixed compensation, mean-based SLP parameters generated with SLP-VCC have greater correlation with visual function and RNFL thickness assessments obtained with OCT.