*Department of Ophthalmology and Vision Science, University of Arizona College of Medicine, Tucson, AZ †Doheny Eye Institute and the Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA ‡University of Pittsburgh Medical Center, Pittsburgh, PA §Bascom Palmer Eye Institute, University of Miami, Miami, FL ∥Casey Eye Institute, Oregon Health & Science University, Portland, OR.
J Glaucoma. 2014 Mar;23(3):129-35. doi: 10.1097/IJG.0b013e318264b941.
To improve the diagnosis of glaucoma by combining time-domain optical coherence tomography (TD-OCT) measurements of the optic disc, circumpapillary retinal nerve fiber layer (RNFL), and macular retinal thickness.
Ninety-six age-matched normal and 96 perimetric glaucoma participants were included in this observational, cross-sectional study. Or-logic, support vector machine, relevance vector machine, and linear discrimination function were used to analyze the performances of combined TD-OCT diagnostic variables.
The area under the receiver-operating curve (AROC) was used to evaluate the diagnostic accuracy and to compare the diagnostic performance of single and combined anatomic variables. The best RNFL thickness variables were the inferior (AROC=0.900), overall (AROC=0.892), and superior quadrants (AROC=0.850). The best optic disc variables were horizontal integrated rim width (AROC=0.909), vertical integrated rim area (AROC=0.908), and cup/disc vertical ratio (AROC=0.890). All macular retinal thickness variables had AROCs of 0.829 or less. Combining the top 3 RNFL and optic disc variables in optimizing glaucoma diagnosis, support vector machine had the highest AROC, 0.954, followed by or-logic (AROC=0.946), linear discrimination function (AROC=0.946), and relevance vector machine (AROC=0.943). All combination diagnostic variables had significantly larger AROCs than any single diagnostic variable. There are no significant differences among the combination diagnostic indices.
With TD-OCT, RNFL and optic disc variables had better diagnostic accuracy than macular retinal variables. Combining top RNFL and optic disc variables significantly improved diagnostic performance. Clinically, or-logic classification was the most practical analytical tool with sufficient accuracy to diagnose early glaucoma.
通过联合时域光相干断层扫描（TD-OCT）对视盘、周边视网膜神经纤维层（RNFL）和黄斑视网膜厚度的测量，提高青光眼的诊断水平。
本观察性、横断面研究纳入了 96 名年龄匹配的正常人和 96 名青光眼患者。使用或逻辑、支持向量机、相关向量机和线性判别函数分析联合 TD-OCT 诊断变量的性能。
使用受试者工作特征曲线下面积（AROC）评估诊断准确性，并比较单变量和组合变量的诊断性能。最佳的 RNFL 厚度变量是下方（AROC=0.900）、整体（AROC=0.892）和上方象限（AROC=0.850）。最佳视盘变量是水平整合边缘宽度（AROC=0.909）、垂直整合边缘面积（AROC=0.908）和杯盘垂直比（AROC=0.890）。所有黄斑视网膜厚度变量的 AROC 均为 0.829 或更低。在优化青光眼诊断方面，组合前 3 个 RNFL 和视盘变量时，支持向量机的 AROC 最高，为 0.954，其次是或逻辑（AROC=0.946）、线性判别函数（AROC=0.946）和相关向量机（AROC=0.943）。所有组合诊断变量的 AROC 均显著大于任何单一诊断变量。组合诊断指标之间没有显著差异。
使用 TD-OCT，RNFL 和视盘变量的诊断准确性优于黄斑视网膜变量。组合最佳的 RNFL 和视盘变量可显著提高诊断性能。临床上，或逻辑分类是最实用的分析工具，具有足够的准确性来诊断早期青光眼。
