Improving our understanding, and detection, of glaucomatous damage: An approach based upon optical coherence tomography (OCT)

Prog Retin Eye Res. 2017 Mar;57:46-75. doi: 10.1016/j.preteyeres.2016.12.002. Epub 2016 Dec 22.

Abstract

Although ophthalmologists are becoming increasingly reliant upon optical coherence tomography (OCT), clinicians who care for glaucoma patients are not taking full advantage of the potential of this powerful technology. First, we ask, how would one describe the nature of glaucomatous damage if only OCT scans were available? In particular, a schematic model of glaucomatous damage is developed in section 2, and the nature of glaucomatous damage seen on OCT scans described in the context of this model in section 3. In particular, we illustrate that local thinning of the circumpapillary retinal nerve fiber layer (cpRNFL) around the optic disc can vary in location, depth, and/or width, as well as homogeneity of damage. Second, we seek to better understand the relationship between the thinning of the cpRNFL and the various patterns of sensitivity loss seen on visual fields obtained with standard automated perimetry. In sections 4 and 5, we illustrate why one should expect a wide range of visual field patterns, and iilustrate why they should not be placed into discrete categories. Finally, section 6 describes how the clinician can take better advantage of the information in OCT scans. The approach is summarized in a single-page report, which can be generated from a single wide-field scan. The superiority of this approach, as opposed to the typical reliance on summary metrics, is described.

Keywords: Glaucoma; Macula; OCT; Optical coherence tomography; Retinal ganglion cell; Retinal nerve fiber layer; Visual field.

Publication types

  • Review
  • Research Support, N.I.H., Extramural

MeSH terms

  • Glaucoma / diagnosis*
  • Humans
  • Intraocular Pressure*
  • Nerve Fibers / pathology
  • Optic Disk / pathology
  • Retinal Ganglion Cells / pathology*
  • Tomography, Optical Coherence / methods*
  • Visual Field Tests
  • Visual Fields