June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Identifying OCT Parameters to Predict Glaucoma Visual Field Progression
Author Affiliations & Notes
  • Lucy Cobbs
    Memorial Sloan Kettering Cancer Center, New York, New York, United States
  • María de los Angeles Ramos-Cadena
    Department of Ophthalmology, New York University School of Medicine, New York, New York, United States
  • Mengfei Wu
    Department of Ophthalmology, New York University School of Medicine, New York, New York, United States
  • Mengling Liu
    Department of Ophthalmology, New York University School of Medicine, New York, New York, United States
  • Hiroshi Ishikawa
    Department of Ophthalmology, New York University School of Medicine, New York, New York, United States
  • Gadi Wollstein
    Department of Ophthalmology, New York University School of Medicine, New York, New York, United States
  • Joel S Schuman
    Department of Ophthalmology, New York University School of Medicine, New York, New York, United States
  • Footnotes
    Commercial Relationships   Lucy Cobbs, None; María de los Angeles Ramos-Cadena, None; Mengfei Wu, None; Mengling Liu, None; Hiroshi Ishikawa, None; Gadi Wollstein, None; Joel Schuman, Zeiss (P)
  • Footnotes
    Support  NIH R01-EYE13178 and an unrestricted grant from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 1977. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Lucy Cobbs, María de los Angeles Ramos-Cadena, Mengfei Wu, Mengling Liu, Hiroshi Ishikawa, Gadi Wollstein, Joel S Schuman; Identifying OCT Parameters to Predict Glaucoma Visual Field Progression. Invest. Ophthalmol. Vis. Sci. 2020;61(7):1977.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Predicting progression of primary open angle glaucoma (POAG) continues to be a challenge. Recent studies have shown that macular parameters may predict glaucoma progression and disease onset equally as well or even better than the traditionally used optic nerve head (ONH) retinal nerve fiber layer (RNFL) thickness. We performed a retrospective longitudinal study to identify structural parameters which best predicted visual field (VF) loss.

Methods : Subjects with POAG with at least 5 qualified Cirrus OCT (Zeiss, Dublin, CA) macular and optic nerve head (ONH) scans and 5 qualified 24-2 Humphrey VF tests (Zeiss) were enrolled to this study. All parameters from the OCT’s report of the macula and ONH scans were used in the analysis. We identified subjects who were OCT ONH progressors or VF progressors using both event and trend based definitions of progression based on the Guided Progression Analyses. Students t-test was used to assess differences in baseline characteristics between groups, and mixed-effects model was used in longitudinal analysis to compare the rate of parameter change between groups.

Results : 263 eyes (180 subjects) with a mean follow-up time of 2.4±1.8 years were included in the study. Twenty-three eyes were identified as ONH progressors, 25 eyes were identified as VF progressors, and of these, 6 eyes were both VF and ONH progressors. At baseline, only macular average RNFL and macular inferior RNFL were significantly thinner in ONH progressors compared to non-progressors. Between the VF progressors and non-progressor groups, all OCT parameters were significantly thinner at baseline in the progressors except for four focal macular measurements and disc area. Mixed-effects modeling showed that both focal macular and ONH parameters thinned at a significantly greater rate in VF progressors compared to non-progressors (Table 1).

Conclusions : Focal macular retinal segmentations and focal ONH parameters thin significantly more per year in VF progressors, and VF progressors tend to have thinner baseline structural parameters.

This is a 2020 ARVO Annual Meeting abstract.

 

Table 1. Mixed-effects modeling shows each parameter which changed at a significantly greater rate in the VF progressor group than in the non-progressor group.

Table 1. Mixed-effects modeling shows each parameter which changed at a significantly greater rate in the VF progressor group than in the non-progressor group.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×