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Luis E Vazquez, William J Feuer, Jean-Claude Mwanza, Donald L Budenz, Giacinto Triolo, Pedro Monsalve, Steven Gedde, Richard K Parrish; Area and volume analysis of the superior and inferior retinal nerve fiber layer (rNFL) bundles from SD-OCT thickness maps: detection of early, moderate, and severe glaucoma. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):4576.
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© ARVO (1962-2015); The Authors (2016-present)
Peripapillary rNFL and macular ganglion cell-inner plexiform layer (GCIPL) thickness analyses are powerful diagnostic tools for early glaucoma. We developed a novel method to measure area and volume of superior and inferior rNFL bundles. Here we compare the ability of current rNFL or GCIPL and our new bundle analysis to discriminate normal eyes from early glaucoma, and early glaucoma from severe glaucoma.
Previously published OCT data from 98 control and 55 early, 11 moderate, and 14 severe glaucoma patients was provided by Budenz and colleagues (PMIDs: 22365056 and 21917932). Raw thickness map data from optic disc scans was provided by Carl Zeiss Meditec. The area and volume of the superior (S), inferior (I), and summed superior-inferior (S+I) rNFL bundles were measured as described separately (Burke et al, ARVO 2015). The diagnostic accuracy of bundle parameters to differentiate between normal and early glaucoma was determined by computing the area under the curve (AUC) of the receiver operating characteristic. Discrimination of glaucoma stage was determined using one-way ANOVA, and a stepwise logistic regression analysis to determine if any parameter could discriminate different stages of glaucoma.
The AUCs of S, I, and S+I bundle area (0.970, 0.927, and 0.970) were similar to the AUCs of bundle volume (0.970, 0.934, and 0.970). AUCs of I and S+I bundle area and volume was greater than those of corresponding GCIPL (0.918 and 0.935) and rNFL parameters (0.933 and 0.939) (p<0.05 for all). The best bundle parameter AUC (0.970) was better than that of rNFL (0.939), but not GCIPL (0.959) (p<0.05). S area and volume had the greatest one-way ANOVA p value (p<0.0001), and S area was the only parameter (current and new) in a stepwise logistic regression model able to discriminate between early vs. moderate/severe, and early/moderate vs. severe (p<0.001).
rNFL bundle area and volume analysis may be a promising tool for evaluation of glaucoma. AUCs of bundle analysis are as good, if not better, than those of current rNFL and GCIPL analysis, and may be better at discriminating later stages of disease. Further research is needed to determine the usefulness of bundle analysis to monitor not only early, but also moderate and severe glaucoma.
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