Derek Orshan1, Andrew Tirsi2,3,4, Hosam Sheha5, Vasiliki Gliagias6, Joby Tsai6, Sung Chul Park5,6, Stephen A Obstbaum5,6, Celso Tello5,6,7. 1. Department of Ophthalmology, Larkin Community Hospital, Miami, FL, USA. 2. Department of Ophthalmology, Manhattan Eye, Ear, and Throat Hospital, Northwell Health, 210 E 64th St, 8th Floor,, New York, NY, 10065, USA. atirsi@northwell.edu. 3. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA. atirsi@northwell.edu. 4. Diopsys Inc, Pine Brook, NJ, USA. atirsi@northwell.edu. 5. Department of Ophthalmology, Manhattan Eye, Ear, and Throat Hospital, Northwell Health, 210 E 64th St, 8th Floor,, New York, NY, 10065, USA. 6. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA. 7. Diopsys Inc, Pine Brook, NJ, USA.
Abstract
PURPOSE: To derive and validate structure-function models for estimating retinal ganglion cell (RGC) count using optical coherence tomography (OCT) and steady-state pattern electroretinography (ssPERG) parameters in glaucoma suspects (GS) and preperimetric glaucoma (PPG). METHODS: In this prospective cross-sectional study, 25 subjects (50 eyes) were recruited at the Manhattan Eye, Ear, and Throat Hospital. Subjects underwent comprehensive eye examinations, OCT, standard automated perimetry (SAP), and ssPERG testing. Eyes were divided into three groups based on the Global Glaucoma Staging System: healthy (N = 30), GS (N = 10), and PPG (N = 10) eyes. The combined structure-function index (CSFI), which estimates retinal ganglion cell count (eRGCCSFI) from SAP and OCT parameters, was calculated in each study subject. Two prediction formulas were derived using a generalized linear mixed model (GLMM) to predict eRGCCSFI from ssPERG parameters, age, and average retinal nerve fiber layer thickness (ARNFLT) in 30 eyes selected at random (training group). GLMM predicted values were cross-validated with the remaining 20 eyes (validation group). RESULTS: The ARNFLT, ssPERG parameters magnitude (Mag) and magnitudeD (MagD), and eRGCCSFI were significantly different among study groups (ANOVA p ≤ 0.001). Pearson correlations demonstrated significant associations among ARNFLT, ssPERG parameters, and eRGCCSFI (r2 ≥ 0.31, p < 0.001). Two GLMMs predicted eRGCCSFI from Mag (eRGCMag) and MagD (eRGCMagD), respectively, with significant equations (F(3,18), F(3,19) ≥ 58.37, R2 = 0.90, p < 0.001). eRGCMag and eRGCMagD in the validation group (R2 = 0.89) correlated with eRGCCSFI similarly to the training group. Multivariate pairwise comparisons revealed that eRGCMag and eRGCMagD distinguished between healthy, GS, and PPG eyes (p ≤ 0.035), whereas independent Mag, MagD, and ARNFLT measures did not distinguish between GS and PPG eyes. CONCLUSION: This pilot study offers the first combined structure-function models for estimating RGC count using ssPERG parameters. RGC counts estimated with these models were generalizable, strongly associated with CSFI estimates, and performed better than individual ssPERG and OCT measures in distinguishing healthy, GS, and PPG eyes.
PURPOSE: To derive and validate structure-function models for estimating retinal ganglion cell (RGC) count using optical coherence tomography (OCT) and steady-state pattern electroretinography (ssPERG) parameters in glaucoma suspects (GS) and preperimetric glaucoma (PPG). METHODS: In this prospective cross-sectional study, 25 subjects (50 eyes) were recruited at the Manhattan Eye, Ear, and Throat Hospital. Subjects underwent comprehensive eye examinations, OCT, standard automated perimetry (SAP), and ssPERG testing. Eyes were divided into three groups based on the Global Glaucoma Staging System: healthy (N = 30), GS (N = 10), and PPG (N = 10) eyes. The combined structure-function index (CSFI), which estimates retinal ganglion cell count (eRGCCSFI) from SAP and OCT parameters, was calculated in each study subject. Two prediction formulas were derived using a generalized linear mixed model (GLMM) to predict eRGCCSFI from ssPERG parameters, age, and average retinal nerve fiber layer thickness (ARNFLT) in 30 eyes selected at random (training group). GLMM predicted values were cross-validated with the remaining 20 eyes (validation group). RESULTS: The ARNFLT, ssPERG parameters magnitude (Mag) and magnitudeD (MagD), and eRGCCSFI were significantly different among study groups (ANOVA p ≤ 0.001). Pearson correlations demonstrated significant associations among ARNFLT, ssPERG parameters, and eRGCCSFI (r2 ≥ 0.31, p < 0.001). Two GLMMs predicted eRGCCSFI from Mag (eRGCMag) and MagD (eRGCMagD), respectively, with significant equations (F(3,18), F(3,19) ≥ 58.37, R2 = 0.90, p < 0.001). eRGCMag and eRGCMagD in the validation group (R2 = 0.89) correlated with eRGCCSFI similarly to the training group. Multivariate pairwise comparisons revealed that eRGCMag and eRGCMagD distinguished between healthy, GS, and PPG eyes (p ≤ 0.035), whereas independent Mag, MagD, and ARNFLT measures did not distinguish between GS and PPG eyes. CONCLUSION: This pilot study offers the first combined structure-function models for estimating RGC count using ssPERG parameters. RGC counts estimated with these models were generalizable, strongly associated with CSFI estimates, and performed better than individual ssPERG and OCT measures in distinguishing healthy, GS, and PPG eyes.
Authors: Vittorio Porciatti; Brandon Bosse; Prashant K Parekh; Olga A Shif; William J Feuer; Lori M Ventura Journal: J Glaucoma Date: 2014 Oct-Nov Impact factor: 2.503
Authors: Christopher Bowd; Ali Tafreshi; Gianmarco Vizzeri; Linda M Zangwill; Pamela A Sample; Robert N Weinreb Journal: J Glaucoma Date: 2009-08 Impact factor: 2.503