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Predicting Global Test-Retest Variability of Visual Fields in Glaucoma.

Eun Young Choi1, Dian Li2, Yuying Fan2

  • 1Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts; Department of Ophthalmology, Duke University, Durham, North Carolina.

Ophthalmology. Glaucoma
|December 14, 2020
PubMed
Summary

Predicting visual field test-retest variability in glaucoma is improved by including archetype loss patterns and total deviation values. This enhances accuracy over traditional methods for better glaucoma management.

Keywords:
GlaucomaMachine learningTest–retest variabilityVisual field

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Data Science

Background:

  • Glaucoma is a progressive optic neuropathy characterized by visual field (VF) loss.
  • Accurate modeling of VF test-retest variability is crucial for monitoring glaucoma progression.
  • Current methods may not fully capture the complexity of VF changes.

Purpose of the Study:

  • To develop and validate models for predicting global test-retest variability in glaucoma visual fields.
  • To compare the predictive performance of different modeling approaches.

Main Methods:

  • Retrospective analysis of 4044 eyes with reliable Humphrey Field Analyzer (Swedish interactive threshold algorithm 24-2) test-retest data.
  • Stepwise linear regression to build models using base parameters, total deviation (TD) values, and archetype VF loss patterns.
  • Global test-retest variability defined as root mean square deviation (RMSD) of TD values.

Main Results:

  • The mean RMSD was 4.39 ± 2.55 dB.
  • Models incorporating TD values (adjusted R² = 0.53) and archetype patterns (adjusted R² = 0.53) significantly improved prediction accuracy compared to base parameters alone (adjusted R² = 0.45).
  • Specific VF defects, particularly peripheral ones, were associated with greater variability.

Conclusions:

  • Including archetype visual field loss patterns and total deviation values from the initial VF test enhances the prediction of global test-retest variability.
  • These advanced modeling approaches offer improved accuracy over traditional global indices for glaucoma assessment.