Gardiner Stuart K, Mansberger Steven L, Demirel Shaban
Devers Eye Institute, Legacy Research Institute, Portland, Oregon, United States.
Invest Ophthalmol Vis Sci. 2017 May 1;58(6):BIO180-BIO190. doi: 10.1167/iovs.17-21562.
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.
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.
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.
Although the specificity is still suboptimal, cluster trend analysis detects subsequently confirmed deterioration sooner than either global or pointwise analyses.
使用平均偏差（MD）进行的全局分析可评估视野进展情况，但可能会遗漏局部变化。逐点分析对局部进展更为敏感，但变异性更大，因此需要进行确认。本研究评估了聚类趋势分析（对各位置子集的信息进行平均）是否能改善进展检测。
对133只进行重测的眼睛进行了7至10次测试。针对这些系列可能的重新排序计算变化率和P值，以生成全局分析（“MD恶化速度快于x dB/年且P < y”）、逐点分析和聚类分析（“n个位置[或聚类]恶化速度快于x dB/年且P < y”），特异性精确为95%。将这些标准应用于平均测试10.5年的505只眼睛，以确定每种方法多快能检测到“恶化”，并使用生存模型进行比较。重复此过程，纳入后续两个视野以确定“恶化”是否得到确认。
最佳全局标准在5.0年时检测到25%的眼睛出现恶化（95%置信区间[CI]，4.7 - 5.3年），最佳聚类分析标准为4.8年（95% CI，4.2 - 5.1年），最佳逐点标准为4.1年（95% CI，4.0 - 4.5年）。然而，对于逐点分析，这些变化中只有38%得到确认，聚类分析为61%，MD为76%。直到25%的眼睛出现随后得到确认的恶化的时间，全局分析为6.3年（95% CI，6.0 - 7.2年），逐点分析为6.3年（95% CI，6.0 - 7.0年），聚类分析为6.0年（95% CI，5.3 - 6.6年）。
尽管特异性仍未达到最佳，但聚类趋势分析比全局分析或逐点分析能更快检测到随后得到确认的恶化。
