Asman P, Heijl A, Olsson J, Rootzén H
Department of Ophthalmology, Malmö General Hospital, Sweden.
Acta Ophthalmol (Copenh). 1992 Oct;70(5):679-86. doi: 10.1111/j.1755-3768.1992.tb02152.x.
Interpretation of numeric automated threshold visual field results is often difficult. A large amount of data is obtained for every single field tested. Various approaches to summarize this data have been suggested, most commonly the mean and standard deviation of departures from age-corrected normal threshold values. These visual field indices differ substantially from subjective field interpretation where spatial relationships are important. We have previously devised two methods for automated field interpretation which take spatial information into account--regional up-down comparisons and arcuate cluster analysis. We now studied the merits of using these new spatial methods and compared them to traditional visual field indices for discrimination between normal and glaucomatous field results. Central static 30 degree field results in 101 eyes of 101 normal subjects and 101 eyes of 101 patients with glaucoma were discriminated using logistic regression analysis. The best field classification was obtained with a spatial visual field model combining up-down differences and arcuate clusters. The advantages of the spatial model were confirmed in an independent material of 163 eyes of 163 normal subjects and 76 eyes of 76 patients with glaucoma where eyes with large field defects had been removed. In this material the spatial model gave 87% sensitivity and 83% specificity while the best non-spatial model gave 82% sensitivity and 80% specificity. Visual field interpretation in glaucoma may be significantly enhanced if detection is focused on circumscribed field loss rather than on averages of differential light sensitivities and similar indices which do not take spatial relationships into consideration.
数字自动化阈值视野结果的解读往往颇具难度。每次测试视野都会获取大量数据。人们提出了各种汇总这些数据的方法，最常见的是偏离年龄校正后正常阈值的均值和标准差。这些视野指数与重视空间关系的主观视野解读有很大不同。我们之前设计了两种考虑空间信息的自动化视野解读方法——区域上下比较和弓形聚类分析。我们现在研究了使用这些新空间方法的优点，并将它们与传统视野指数进行比较，以区分正常和青光眼视野结果。通过逻辑回归分析对101名正常受试者的101只眼睛和101名青光眼患者的101只眼睛的中央静态30度视野结果进行区分。结合上下差异和弓形聚类的空间视野模型获得了最佳的视野分类。在一个独立的样本中，对163名正常受试者的163只眼睛和76名青光眼患者的76只眼睛（已排除有大视野缺损的眼睛）进行研究，证实了空间模型的优势。在这个样本中，空间模型的敏感度为87%，特异度为83%，而最佳的非空间模型的敏感度为82%，特异度为80%。如果青光眼视野解读的检测重点是局限性视野缺损，而非不考虑空间关系的差分光敏感度平均值及类似指数，那么视野解读可能会显著改善。
