Detection of Functional Deterioration in Glaucoma by Trend Analysis Using Overlapping Clusters of Locations

PURPOSE: Cluster trend analysis detects glaucomatous deterioration within predefined subsets (clusters) of visual field locations. However, it may miss small defects straddling boundaries between the clusters. This study assesses whether simultaneously using a second set of clusters, overlapping the first, could improve progression detection. METHODS: Deterioration in eyes with or at risk of glaucomatous visual field loss was “detected” by mean deviation (MD) on the first visit at which the P value from linear regression over time was below the fifth percentile of its permutation distribution. Similarly, P values were calculated for each of 10 predefined nonoverlapping clusters of locations, or 21 overlapping clusters; deterioration was “detected” when the Nth-smallest P value was below the fifth percentile of its permutation distribution, for different N. Times to detect deterioration were compared using survival models. RESULTS: Biannual series of ≥5 visual fields (mean = 14) were available for 420 eyes of 213 participants. Deterioration of 33% of eyes was detected earliest using N = 1 overlapping cluster in 3.3 years (95% confidence interval 2.7–4.6 years); or N = 2 nonoverlapping clusters in 3.3 years (2.7–5.0) (comparison P = 0.654). There was also no significant difference in the probability that deterioration would be confirmed (92.8% vs. 94.4%, P = 0.289). Both overlapping and nonoverlapping clusters detected deterioration significantly sooner than MD (4.5 years, P ≤ 0.001). CONCLUSIONS: After equalizing specificity, overlapping clusters of locations did not significantly reduce the time to detect deterioration compared with nonoverlapping clusters. TRANSLATIONAL RELEVANCE: Cluster trend analyses detected deterioration sooner than global analyses even when defects straddled cluster borders.