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Detection of Functional Change Using Cluster Trend Analysis in Glaucoma.

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Summary

Cluster trend analysis offers a more sensitive method for detecting visual field progression sooner than global or pointwise analyses. This approach aids in earlier identification of confirmed deterioration, improving patient monitoring.

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Area of Science:

  • Ophthalmology
  • Medical Technology
  • Data Analysis

Background:

  • Global analyses like mean deviation (MD) assess visual field progression but may miss localized changes.
  • Pointwise analyses are sensitive to localized progression but are more variable and require confirmation.

Purpose of the Study:

  • To evaluate if cluster trend analysis, by averaging data from subsets of locations, can enhance the detection of visual field progression.
  • To compare the efficacy of cluster trend analysis with global and pointwise analyses in detecting confirmed visual field deterioration.

Main Methods:

  • Calculated rates of change and P values for global, pointwise, and cluster analyses with 95% specificity.
  • Applied these criteria to 505 eyes over a mean of 10.5 years to determine time to detected deterioration.
  • Re-analyzed data including two subsequent visual fields to confirm deterioration detection.

Main Results:

  • The best pointwise criterion detected deterioration earliest (4.1 years), but only 38% of changes were confirmed.
  • Cluster analysis detected confirmed deterioration in 6.0 years, outperforming global analysis (6.3 years) and pointwise analysis (6.3 years).
  • Mean deviation (MD) analysis had the highest confirmation rate (76%) but detected confirmed deterioration later (6.3 years).

Conclusions:

  • Cluster trend analysis detects subsequently confirmed visual field deterioration sooner than global or pointwise analyses.
  • While specificity remains suboptimal, cluster analysis offers an improved method for early detection of confirmed progression.
  • This method enhances the ability to monitor patients with visual field defects more effectively.