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Comparison of regression models for serial visual field analysis.

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The decay exponential regression model best fits and predicts visual field progression in glaucoma patients. This model offers superior performance over linear models for tracking disease severity.

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Area of Science:

  • Ophthalmology
  • Medical Statistics
  • Glaucoma Research

Background:

  • Open-angle glaucoma is a leading cause of irreversible blindness.
  • Accurate measurement of visual field (VF) decay rate is crucial for monitoring glaucoma progression.
  • Traditional linear models may not fully capture the complex patterns of VF loss.

Purpose of the Study:

  • To compare the fit and predictive performance of four pointwise regression models for measuring VF decay in open-angle glaucoma.
  • To identify the most effective model for tracking glaucoma progression.

Main Methods:

  • Analysis of Humphrey VF data from 588 open-angle glaucoma patients with at least 6 years of follow-up.
  • Application of four first-order regression models: ordinary least-squares linear, non-decay exponential, decay exponential, and Tobit-censored linear regression.
  • Regression of threshold sensitivity against follow-up time at each VF test location.

Main Results:

  • The decay exponential regression model demonstrated the best fit in 42.7% of locations and the best forecasting ability in 65.5% of locations.
  • This model outperformed others across all glaucoma severity levels.
  • Average baseline VF mean deviation was -8.2 dB, with a mean follow-up of 8.7 years.

Conclusions:

  • The pointwise decay exponential regression (PER) model is superior to the ordinary least-squares linear regression model for fitting and forecasting VF data in glaucoma patients.
  • The PER model provides accurate predictions across a spectrum of glaucoma severity.
  • The PER model is clinically understandable and recommended for use by healthcare professionals.