June 2013
Volume 54, Issue 15
ARVO Annual Meeting Abstract  |   June 2013
Temporal filtering of longitudinal visual field data
Author Affiliations & Notes
  • Chris Johnson
    Ophthal & Visual Sci, University of Iowa, Iowa City, IA
  • Carrie Doyle
    Ophthal & Visual Sci, University of Iowa, Iowa City, IA
    Neurology, University of Iowa, Iowa City, IA
  • Trina Eden
    Ophthal & Visual Sci, University of Iowa, Iowa City, IA
    Neurology, University of Iowa, Iowa City, IA
  • Michael Wall
    Neurology, University of Iowa, Iowa City, IA
    Neurology, Veterans Administration, Iowa City, IA
  • Footnotes
    Commercial Relationships Chris Johnson, Lundbeck (C), Ivantis (C), Jaeb Center (C), AEGIS (C), Acufocus (C), Haag Streit (C); Carrie Doyle, None; Trina Eden, None; Michael Wall, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3931. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Chris Johnson, Carrie Doyle, Trina Eden, Michael Wall; Temporal filtering of longitudinal visual field data. Invest. Ophthalmol. Vis. Sci. 2013;54(15):3931.

      Download citation file:

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

  • Supplements

Purpose: To determine whether low pass filtering (3 temporal bin smoothing) of longitudinal visual field data would influence the ability to detect progressive glaucomatous damage.

Methods: One eye of one hundred and eight patients with a clinical diagnosis of glaucoma (MD mean at baseline = -6.73, SD = 4.45, Max = -0.12, Min = -19.94) underwent automated perimetric testing with the Humphrey Field Analyzer 24-2 stimulus pattern, a size III target and the SITA standard threshold estimation strategy. Testing was performed every six months, with two initial baseline visual field measures. To be eligible for the study, participants were required to have at least five additional visual field tests beyond the initial baseline measurements. Mean Deviation (MD) and Pattern Standard Deviation (PSD) are often used as clinical methods of monitoring glaucomatous visual field progression, as is the visual field index or VFI which is based on MD. Linear regression was used to assess the filtered and unfiltered data sets.

Results: We found minimal differences in the slope of linear regression results for filtered versus unfiltered MD (Unfiltered mean = -0.446, SD = 0.703, max = 0.869, min = -2.45; Filtered mean = -0.316, SD = 0.597, Max = 0.919, Min = -2.17) or PSD (Unfiltered mean = 0.102, SD = 0.410, Max = 2.25, Min = -0.958; Filtered mean = 0.044, SD = 0.436, Max = 2.099, Min = -0.977) or in the goodness of fit for the regression evaluations.

Conclusions: Although previous investigations of procedures to minimize variability for longitudinal visual field data sets (e.g. low pass filtering and one-omitting and three-omitting analysis methods) have reported modest improvements in the ability to determine glaucomatous visual field changes, our findings revealed no difference between filtered and unfiltered data sets in assessing visual field change. This may be due to our use of global indices (MD and PSD) that provide a composite evaluation of all visual field locations tested (reducing variability) rather than individual points or small groups of points evaluated in prior studies, and the use of empirical visual field data rather than simulated results.

Keywords: 758 visual fields • 642 perimetry • 629 optic nerve  

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.