Local Variability of Macular Thickness Measurements With SD-OCT and Influencing Factors

DOI: 10.1167/tvst.5.4.5 Article Local Variability of Macular Thickness Measurements With SD-OCT and Influencing Factors Arezoo Miraftabi 1,2 , Navid Amini 1 , Jeff Gornbein 1 , Sharon Henry 1 , Pablo Romero 1 , Anne L. Coleman 1 , Joseph Caprioli 1 , and Kouros Nouri-Mahdavi 1 Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA Eye Research Center, Rasoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran Correspondence: Kouros Nouri- Mahdavi, MD MSc, 100 Stein Plaza, Los Angeles, CA 90095, USA. e-mail: nouri-mahdavi@jsei.ucla.edu Received: 25 September 2015 Accepted: 9 May 2016 Published: 19 July 2016 Keywords: SD-OCT; macular imag- ing; glaucoma; variability Citation: Miraftabi A, Amini N, Gornbein J, et al. Local variability of macular thickness measurements with SD-OCT and influencing fac- tors. Trans Vis Sci Tech. 2016;5(4):5, doi:10.1167/tvst.5.4.5 Purpose: To compare the intrasession variability of spectral-domain optical coherence tomography (SD-OCT)-derived local macular thickness measures and explore influencing factors. Methods: One hundred two glaucomatous eyes (102 patients) and 21 healthy eyes (21 subjects) with three good quality macular images during the same session were enrolled. Thickness measurements were calculated for 38 superpixels for the inner plexiform (IPL), ganglion cell (GCL), or retinal nerve fiber layers (mRNFL), GC/IPL, ganglion cell complex, and full macular thickness. Spatial distribution and magnitude of measurement errors (ME; differences between the 3 individual superpixel values and their mean) and association between MEs and thickness, age, axial length, and image quality were explored. Results: MEs had a normal distribution with mostly random noise along with a small fraction of outliers (1.2%–6.6%; highest variability in mRNFL and on the nasal border) based on M-estimation. Boundaries of 95% prediction intervals for variability reached a maximum of 3 lm for all layers and diagnostic groups after exclusion of outliers. Correlation between proportion of outliers and thickness measures varied among various parameters. Age, axial length, or image quality did not influence MEs (P . 0.05 for both groups). Conclusions: Local variability of macular SD-OCT measurements is low and uniform across the macula. The relationship between superpixel thickness and outlier proportion varied as a function of the parameter of interest. Translational Relevance: Given the low and uniform variability within and across eyes, definition of an individualized ‘variability space’ seems unnecessary. The variability measurements from this study could be used for designing algorithms for detection of glaucoma progression. Introduction Glaucoma is characterized by progressive loss of retinal ganglion cells (RGCs). With the advent of spectral-domain optical coherence tomography (SD- OCT), there has been increased interest in measuring RGC loss or thinning of the inner retina in the macula. 1–5 Approximately 50% of the RGCs are located within 4.5 mm (168) of the foveal center. 6 The central macula is the only part of the retina where the RGC layer is more than one cell thick; the peak RGC density occurs approximately 750 to 1100 lm from the center of the fovea. 6 Regional inner retinal thickness measurements such as ganglion cell/inner plexiform layer (GC/IPL) thickness perform as well as regional retinal nerve fiber layer (RNFL) thickness measures for detection of early glaucoma. 3,4,7 Hood et al. 8 demonstrated that GC/IPL thinning could be demonstrated in a group of patients with preperimet- ric glaucoma, although other studies have not found macular thickness to perform as well as RNFL thickness for detection of glaucoma in such pa- tients. 5,9 The role of macular SD-OCT imaging for detec- tion of glaucoma progression is yet to be fully elucidated. Early results have indicated its potential use. 10–12 Reproducibility of any structural outcome measure is one of the main determinants of how TVST j 2016 j Vol. 5 j No. 4 j Article 5 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.