September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Sampling the visual field based on an individual’s Retinal Nerve Fiber Layer (RNFL) thickness
Author Affiliations & Notes
  • Shonraj Ballae Ganeshrao
    Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
    Optometry and Vision Sciences, The University of Melbourne, Melbourne, Victoria, Australia
  • Allison M McKendrick
    Optometry and Vision Sciences, The University of Melbourne, Melbourne, Victoria, Australia
  • Andrew Turpin
    Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
  • Footnotes
    Commercial Relationships   Shonraj Ballae Ganeshrao, None; Allison McKendrick, Haag-Streit AG (F), Haag-Streit AG (R), Heidelberg Engineering GmbH (F); Andrew Turpin, Haag-Streit AG (F), Haag-Streit AG (R), Heidelberg Engineering GmbH (F)
  • Footnotes
    Support  ARC LP100100250 (AT & AMM); ARC LP130100055 (AT&AM);ARC Future Fellowship FT0991326 (AT)
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 367. doi:
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    • Get Citation

      Shonraj Ballae Ganeshrao, Allison M McKendrick, Andrew Turpin; Sampling the visual field based on an individual’s Retinal Nerve Fiber Layer (RNFL) thickness. Invest. Ophthalmol. Vis. Sci. 2016;57(12):367.

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

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Abstract

Purpose : Current perimetric tests use fixed spatial sampling. Here we explore the utility of having unique spatial sampling based on an individual’s RNFL thickness profile, using an approach called “defect-based” sampling.

Methods : Baseline visual fields (VF) were measured on 23 glaucoma participants using a high spatial resolution 2 X 2 degree grid (386 locations in total) via the Open Perimetry Interface (OPI). A suprathreshold test procedure was used that required 2 “seen” or “not seen” to terminate at each location. Participants also underwent spectral domain optical coherence tomography (OCT) to measure peripapillary RNFL thickness. An individualised map was used to relate structure and function. Defect-based sampling begins with preselection of half of the VF locations (n=26) from the 24-2 pattern. The 24-2 VF locations were ranked according to their positive predictive value (Wang et al. IOVS,54:756-61,2013) to detect glaucoma, with the top 26 locations tested for every patient. The positioning of the remaining 26 locations was customised based on an individual’s RNFL thickness. The optic nerve head (ONH) was divided into 24 x 30 degree sectors (each sector overlapped with the 2 adjacent sectors by 15 degrees). ONH sectors were ranked in ascending order according to their sector average difference from normal RNFL thickness. VF locations were allocated to regions mapping back to the ONH sectors, beginning with the sector with most abnormal RNFL. All VF locations within the 2 X 2 degree grid that mapped back to the sector were chosen. The process repeated with the 2nd ranked abnormal ONH sector, then the 3rd, and so on until all VF locations were automatically allocated. To compare the performance between the defect-based sampling and the 24-2 pattern, the number of abnormal VF locations identified by each method using the suprathreshold VF as ground truth was calculated.

Results : The defect-based sampling method identified a higher number of abnormal VF locations in 17 out of 23 participants compared to the 24-2 test pattern. A pair wise t-test showed a significant difference in the mean number of abnormal VF locations [µ(defect-based)=19.39, µ(24-2)=16.22, t(22)=3.48, p=0.002].

Conclusions : Structural information can be used to customise sampling of VF defects for individual patients. The defect-based sampling method identified a higher number of abnormal VF locations compared to the 24-2 pattern.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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