June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Smoothing Algorithms for Pointwise Visual Field Predictions in Glaucoma
Esteban Morales; Parham Azarbod; Abdelmonem Afifi; Fei Yu; Anne Coleman; Kouros Nouri-Mahdavi; Joseph Caprioli
Author Affiliations & Notes
  • Esteban Morales
    Ophthalmology, University of California, Los Angeles, Los Angeles, CA
  • Parham Azarbod
    Ophthalmology, University of California, Los Angeles, Los Angeles, CA
  • Abdelmonem Afifi
    Biostatistics, University of California, Los Angeles, Los Angeles, CA
  • Fei Yu
    Biostatistics, University of California, Los Angeles, Los Angeles, CA
  • Anne Coleman
    Epidemiology, University of California, Los Angeles, Los Angeles, CA
  • Kouros Nouri-Mahdavi
    Ophthalmology, University of California, Los Angeles, Los Angeles, CA
  • Joseph Caprioli
    Ophthalmology, University of California, Los Angeles, Los Angeles, CA
  • Footnotes
    Commercial Relationships Esteban Morales, None; Parham Azarbod, None; Abdelmonem Afifi, None; Fei Yu, None; Anne Coleman, None; Kouros Nouri-Mahdavi, Allergan (C); Joseph Caprioli, Allergan Inc. (F), Allergan Inc. (C), Allergan Inc. (R)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3928. doi:
  • Views
  • Share
  • Tools
    • Alerts
      User Alerts
      You are adding an alert for:
      Smoothing Algorithms for Pointwise Visual Field Predictions in Glaucoma
      You will receive an email whenever this article is corrected, updated, or cited in the literature. You can manage this and all other alerts in My Account
      The alert will be sent to:
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation
      Citation

      Esteban Morales, Parham Azarbod, Abdelmonem Afifi, Fei Yu, Anne Coleman, Kouros Nouri-Mahdavi, Joseph Caprioli; Smoothing Algorithms for Pointwise Visual Field Predictions in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2013;54(15):3928.

      Download citation file:

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

      ×
    • Get Permissions
  • Supplements
Abstract

Purpose: The study was conducted to compare four threshold smoothing techniques for predicting the rates of visual field (VF) worsening in glaucoma.

Methods: 798 patients with primary open-angle glaucoma and 6 or more years of follow-up were included. Thresholds at each VF location for the first four years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the Glaucoma Hemifield Test (GHT), the Garway-Heath clusters, nearest neighbor weighting (NN), and weighting by the correlation of rates of all other VF locations. Thresholds for smoothed locations were regressed with a pointwise exponential model. VF predictions derived from the exponential regression were compared with average of the actual VF thresholds at the final two follow-up visits. Since there is no formal statistical test to assess the statistical significance of the difference in root mean square (RMS) values among four approaches for model prediction, the approaches were simply ranked by the RMS values with smaller RMS value indicating better model prediction.

Results: The average (±SD) follow-up time and number of visual field exams used for the smoothing and prediction were 4.1 (±1.3) years and 8.1 (±2.4), respectively. The average (±SD) follow-up time for the final two visits was 8.3 (±2.2) years. The RMS values of the differences between the observed and the predicted thresholds at last follow up for each VF location was 11.8 dB for the GHT clusters, 12.4 dB for the correlation weighting, 12.4 dB for the Garway-Heath clusters, 12.5 dB for the NN weighting, and 12.6 for the raw (unsmoothed) values.

Conclusions: Although the differences among the pointwise VF predictions with the four smoothing techniques and the unsmoothed predictions is not large, the GHT method improved the predictions of final threshold values more than other methods, while the use of the raw values provided the worst predictions. It is worth considering using GHT clustering to improve the accuracy of pointwise predictions of VF outcomes in glaucoma.

Keywords: 758 visual fields  
© 2013, The Association for Research in Vision and Ophthalmology, Inc., all rights reserved. Permission to republish any abstract or part of an abstract in any form must be obtained in writing from the ARVO Office prior to publication.
Copyright © 2025 Association for Research in Vision and Ophthalmology.
×
×

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.

×
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy.Accept