July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
The accuracy of spontaneous venous pulsation assessment in discriminating glaucoma from glaucoma suspects
Author Affiliations & Notes
  • Sahar Shariflou
    University of Technology Sydney, Sydney, New South Wales, Australia
  • Ashish Agar
    Ophthalmology, Prince of Wales Hospital, Sydney, New South Wales, Australia
    Ophthalmology, University of New South Wales, Sydney, New South Wales, Australia
  • Kathryn Ailsa Rose
    University of Technology Sydney, Sydney, New South Wales, Australia
  • Mojtaba Golzan
    University of Technology Sydney, Sydney, New South Wales, Australia
  • Footnotes
    Commercial Relationships   Sahar Shariflou, None; Ashish Agar, None; Kathryn Rose, None; Mojtaba Golzan, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 5563. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Sahar Shariflou, Ashish Agar, Kathryn Ailsa Rose, Mojtaba Golzan; The accuracy of spontaneous venous pulsation assessment in discriminating glaucoma from glaucoma suspects. Invest. Ophthalmol. Vis. Sci. 2019;60(9):5563.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Biomarkers that improve diagnostic accuracy are particularly important in glaucoma, as the early stages of the disease continuum are often undetectable using traditional assessments. Spontaneous retinal venous pulsations (SVPs) have been identified as a possible biomarker for glaucoma. Early diagnosis through assessment of SVPs may be beneficial in reducing the number who if untreated progress to glaucomatous optic neuropathy. This pilot study investigates the sensitivity and specificity of SVPs in discriminating glaucoma from glaucoma suspects, compared with gold standard markers of glaucoma such as retinal nerve fibre layer (RNFL) thickness, visual field (VF) defects and retinal ganglion cell (RGC) count estimates.

Methods : 41 Glaucoma patients and 14 glaucoma suspects were dilated for video recording of their retinal circulation using a tablet-based ophthalmoscope. At the same visit Optical Coherence Tomography RNFL and Humphrey VF (24-2 SITA standard) assessments were also performed. Glaucoma was diagnosed by an experienced glaucoma specialist on the basis of glaucomatous optic neuropathy and/or VF defects. Glaucoma suspects were defined as having suspicious optic nerve appearance and/or VF defects but no proven disease. SVP amplitudes were quantified using a custom written algorithm and RGC count estimates were derived based on equations developed by Harwerth et al. (Prog Ret Eye Res, 2010). The area under the curve (AUC) of a receiver operating characteristic graph was used to assess the sensitivity and specificity of SVPs, RNFL thickness, RGC and VF mean deviation in discriminating those with glaucoma from glaucoma suspects.

Results : SVPs were detected in 100% of participants. A summary of mean SVP percentile pulse, RNFL thickness, RGC count and VF mean deviation for glaucoma and glaucoma suspects are presented in Table 1. The highest AUC was observed on the SVP ROC curve (p<0.05. AUC=0.67) (Table 1).

Conclusions : SVPs demonstrated the highest sensitivity at the fixed specificity of 80% in discriminating glaucoma from glaucoma suspects compared to the other studied parameters suggesting that they may be beneficial in differentiating stages of glaucoma. However, a larger sample size and examination of a sample without suspected glaucoma is required to define the benefit of SVPs as an early diagnostic marker in glaucoma.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

×
×

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.

×