Detection of Functional Change Using Cluster Trend Analysis in Glaucoma

Purpose Global analyses using mean deviation (MD) assess visual field progression, but can miss localized changes. Pointwise analyses are more sensitive to localized progression, but more variable so require confirmation. This study assessed whether cluster trend analysis, averaging information across subsets of locations, could improve progression detection. Methods A total of 133 test–retest eyes were tested 7 to 10 times. Rates of change and P values were calculated for possible re-orderings of these series to generate global analysis (“MD worsening faster than x dB/y with P < y”), pointwise and cluster analyses (“n locations [or clusters] worsening faster than x dB/y with P < y”) with specificity exactly 95%. These criteria were applied to 505 eyes tested over a mean of 10.5 years, to find how soon each detected “deterioration,” and compared using survival models. This was repeated including two subsequent visual fields to determine whether “deterioration” was confirmed. Results The best global criterion detected deterioration in 25% of eyes in 5.0 years (95% confidence interval [CI], 4.7–5.3 years), compared with 4.8 years (95% CI, 4.2–5.1) for the best cluster analysis criterion, and 4.1 years (95% CI, 4.0–4.5) for the best pointwise criterion. However, for pointwise analysis, only 38% of these changes were confirmed, compared with 61% for clusters and 76% for MD. The time until 25% of eyes showed subsequently confirmed deterioration was 6.3 years (95% CI, 6.0–7.2) for global, 6.3 years (95% CI, 6.0–7.0) for pointwise, and 6.0 years (95% CI, 5.3–6.6) for cluster analyses. Conclusions Although the specificity is still suboptimal, cluster trend analysis detects subsequently confirmed deterioration sooner than either global or pointwise analyses.