Detectability of Visual Field Defects in Glaucoma Using Moving Versus Static Stimuli for Perimetry

Transl Vis Sci Technol. 2023 Aug 1;12(8):12. doi: 10.1167/tvst.12.8.12.

Abstract

Purpose: We have previously shown that using moving, instead of static, stimuli extends the effective dynamic range of automated perimetry in glaucoma. In this study, we further investigate the effect of using moving stimuli on the detectability of functional loss.

Methods: We used two experimental perimetry paradigms to test 155 subjects with a diagnosis of glaucoma or glaucoma suspect, and 34 healthy control subjects. One test used stimuli moving parallel to the average nerve fiber bundle orientation at each location; the other used static stimuli. Algorithms were otherwise identical. Sensitivities to moving stimuli were transformed to the equivalent values for static stimuli based on a Bland-Altman plot. The proportions of locations outside age-corrected normative limits were compared, and test-retest variability was compared against defect depth for each stimulus type.

Results: More tested locations were below the fifth percentile of the normative range for that location using static stimuli. However, among locations abnormal according to standard clinical perimetry on the same day, 19.2% were abnormal using static stimuli, versus 20.5% using moving stimuli (P = 0.372). Test-retest variability was 44% lower for moving stimuli across the range of defect depths.

Conclusions: When compared with static automated perimetry and expressed on a common scale, moving stimuli extend the effective dynamic range and decrease variability, without decreasing the detectability of known functional defects.

Translational relevance: Moving stimuli provide a method to improve known problems of current clinical perimetry.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Glaucoma* / diagnosis
  • Humans
  • Ocular Hypertension*
  • Vision Disorders / diagnosis
  • Visual Field Tests / methods
  • Visual Fields