Ability of Macular Inner Retinal Layer Thickness Asymmetry Evaluated by Optical Coherence Tomography to Detect Preperimetric Glaucoma

Abstract

Citation: Takemoto D, Higashide T, Ohkubo S, Udagawa S, Sugiyama K. Ability of macular inner retinal layer thickness asymmetry evaluated by optical coherence tomography to detect preperimetric glaucoma. Trans Vis Sci Tech. 2020;9(5):8, https://doi.org/10.1167/tvst.9.5.8 Purpose:We assessed the ability to detect preperimetric glaucoma (PPG) based on asymmetry in the thickness of the macular inner retinal layers measured by spectraldomain optical coherence tomography. Methods:We studied 45 normal eyes and 50 PPG eyes retrospectively. Threedimensional optical coherence tomography macular area scans were used to obtain the thickness of the retinal nerve fiber layer (RNFL), ganglion cell layer/inner plexiform layer (GCL/IPL), and ganglion cell complex (GCC). We calculated the thickness differences between the upper and lower macular hemispheres for the corresponding superpixels, then evaluated the mean absolute value of the thickness differences and the number of superpixels in which the thickness difference was greater than X μm, where X is an integer number from 1 to 10. Areas under the receiver operating characteristic curves (AUCs) for their PPG diagnostic performances were compared to the average thickness measurements of the total and hemiretinal sectors. X was determined at the maximum AUC value. Results:The AUC for the mean absolute value of the difference in GCL/IPL thickness (0.923) was higher than the difference in RNFL and GCC thickness (0.710 and 0.905, respectively). The AUC for the number of superpixels in which the GCL/IPL thickness difference was greater than 8 μm (X = 8) was 0.914. The ability to diagnose PPG using these GCL/IPL parameters was statistically higher than for total or superior and inferior hemiretinal GCL/IPL thicknesses. Conclusions:Asymmetry in the thickness of the GCL/IPL shows good PPG diagnostic performance.Translational Relevance: This approach would be useful in the early detection of glaucoma.

Reference Database of Inner Retinal Layer Thickness and Thickness Asymmetry in Healthy Thai Adults as Measured by the Spectralis Spectral-Domain Optical Coherence Tomography

“…Due to the inter-eye and inter-individual variations, several authors investigated a role of intraocular macular thickness asymmetry between superior and inferior retinal hemispheres, as a structural biometric for detecting pathological conditions. Various methods of estimation including an absolute mean difference, an inferior to superior retinal thickness ratio, and asymmetric index (a logarithmic ratio of inferior/superior retinal thickness) have confirmed a discriminative ability of intraocular thickness asymmetry to define pathologies [29‒38]. In a report using the Spectralis posterior pole retinal thickness scan protocol, Sullivan-Mee et al [29] found that a global absolute mean intraocular macular thickness asymmetry was one of the robust OCT diagnostic parameters for differentiating healthy and early glaucoma subjects, with 77% sensitivity and 80% specificity using a 9-µm cut-off point.…”

Real-Time Risk Score for Glaucoma Mass Screening by Spectral Domain Optical Coherence Tomography: Development and Validation

“…This logic included (1) the absolute value of each layer thickness of the whole or separate regions, 36 (2) vertical difference in peripapillary RNFL thickness, 35 (3) vertical and lateral difference of each layer in the macular area, 34 (4) wedge-shaped localized retinal NFLD around the optic disc, 37 (5) difference in the double hump pattern of the peripapillary RNFL, 38 (6) absolute value of the low value of the double hump pattern of the peripapillary RNFL, 38 and (7) axial length estimated from the angle of the double hump pattern of the peripapillary RNFL. Furthermore, in the macula, three patterns were examined using (A) whole data (model 1); (B) exclusion of surrounding areas to avoid the influence of large vessels (model 2) 39 ; and (C) exclusion of the central area on the basis that this area is not affected in early stage glaucoma (model 3). On implementation of all three algorithms, the odds ratio of variables regarding the vertical and laterality difference of each layer was the strongest indicator of glaucoma.…”