June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Area and volume analysis of the superior and inferior retinal nerve fiber layer (rNFL) bundles from SD-OCT thickness maps: correlation with visual field mean deviation (VFMD) and optic nerve head measurements
Author Affiliations & Notes
  • Andrew Camp
    Bascom Palmer Eye Institute, Miami, FL
  • William J Feuer
    Bascom Palmer Eye Institute, Miami, FL
  • Jean-Claude Mwanza
    University of North Carolina, Chapel Hill, NC
  • Donald L Budenz
    University of North Carolina, Chapel Hill, NC
  • Giacinto Triolo
    University Scientific Institute San Raffaele, Milan, Italy
    Bascom Palmer Eye Institute, Miami, FL
  • Pedro Monsalve
    Bascom Palmer Eye Institute, Miami, FL
  • Richard K Parrish
    Bascom Palmer Eye Institute, Miami, FL
  • Steven Gedde
    Bascom Palmer Eye Institute, Miami, FL
  • Luis E Vazquez
    Bascom Palmer Eye Institute, Miami, FL
  • Footnotes
    Commercial Relationships Andrew Camp, None; William Feuer, None; Jean-Claude Mwanza, None; Donald Budenz, None; Giacinto Triolo, None; Pedro Monsalve, None; Richard Parrish, None; Steven Gedde, None; Luis Vazquez, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 627. doi:
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      Andrew Camp, William J Feuer, Jean-Claude Mwanza, Donald L Budenz, Giacinto Triolo, Pedro Monsalve, Richard K Parrish, Steven Gedde, Luis E Vazquez; Area and volume analysis of the superior and inferior retinal nerve fiber layer (rNFL) bundles from SD-OCT thickness maps: correlation with visual field mean deviation (VFMD) and optic nerve head measurements. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):627.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: Peripapillary rNFL and macular ganglion cell-inner plexiform layer (GCIPL) thickness analyses are powerful diagnostic tools for early glaucoma but less effective for evaluation of moderate and severe glaucoma. We developed a novel method to measure area and volume of superior and inferior rNFL bundles. Here we compare the correlation of standard rNFL and GCIPL analyses and our new method to clinical parameters including VFMD and vertical cup:disc ratio (VCDR) in glaucomatous eyes.

Methods: Published data including VFMD, VCDR, rNFL and GCIPL thickness from 55 glaucoma subjects (26 early, 11 moderate and 14 severe) was provided by Budenz and colleagues (PMID: 21917932). Raw thickness maps of optic disc OCT scans were 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). Spearman coefficients (r) were calculated to compare the bundle area and volume, rNFL and GCIPL to VFMD and VCDR. We compared bundle, rNFL and GCIPL r values with a test for paired correlation coefficients.

Results: The S, I and S+I bundles correlated similarly with VFMD for area (r = 0.688, 0.552, and 0.704) and volume (r = 0.684, 0.560, 0.707) compared to the corresponding rNFL quadrant thickness (r = 0.631, 0.596, 0.648) (paired p>0.05 for all). Bundle area and volume correlation was greater than corresponding GCIPL parameters (r = 0.392, 0.342, 0.398) (paired p<0.05 for all). The bundles had greater correlation with VCDR for area (r = -0.583, -0.725, -0.696) and volume (r = -0.596, -0.729, -0.705) than corresponding rNFL (r = -0.483, -0.458, -0.544) (paired p<0.05) and GCIPL parameters (r = -0.431, -0.194, -0.343) (paired p<0.05 for I, S+I; p>0.05 for S). Subgroup analysis of moderate/severe glaucoma showed a greater correlation between VFMD and S+I bundle area and volume (r = 0.782, 0.785) than average rNFL and GCIPL (r = 0.579, 0.270).

Conclusions: rNFL bundle analysis correlates well with clinical parameters in early, moderate and severe glaucoma. Correlation is greater between VFMD and bundle analysis than current rNFL or GCIPL analyses in moderate/severe glaucoma. Further research is needed to determine whether this improved correlation is observed in larger data sets.

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