Manassakorn Anita, Nouri-Mahdavi Kouros, Caprioli Joseph
Glaucoma Division, Jules Stein Eye Institute, University of California-Los Angeles, 100 Stein Plaza, Los Angeles, CA 90095, USA.
Am J Ophthalmol. 2006 Jan;141(1):105-115. doi: 10.1016/j.ajo.2005.08.023.
To compare the performance of the retinal nerve fiber layer (RNFL) thickness and optic disk algorithms as determined by optical coherence tomography to detect glaucoma.
Observational cross-sectional study.
setting: Academic tertiary-care center. study population: One eye from 42 control subjects and 65 patients with open-angle glaucoma with visual acuity of > or =20/40, and no other ocular pathologic condition. observation procedures: Two optical coherence tomography algorithms were used: "fast RNFL thickness" and "fast optic disk." main outcome measures: Area under the receiver operating characteristic curves and sensitivities at fixed specificities were used. Discriminating ability of the average RNFL thickness and RNFL thickness in clock-hour sectors and quadrants was compared with the parameters that were derived from the fast optic disk algorithm. Classification and regression trees were used to determine the best combination of parameters for the detection of glaucoma.
The average visual field mean deviation (+/-SD) was 0.0 +/- 1.3 and -5.3 +/- 5.0 dB in the control and glaucoma groups, respectively. The RNFL thickness at the 7 o'clock sector, inferior quadrant, and the vertical C/D ratio had the highest area under the receiver operating characteristic curves (0.93 +/- 0.02, 0.92 +/- 0.03, and 0.90 +/- 0.03, respectively). At 90% specificity, the best sensitivities (+/-SE) from each algorithm were 86% +/- 3% for RNFL thickness at the 7 o'clock sector and 79% +/- 4% for horizontal integrated rim width (estimated rim area). The combination of inferior quadrant RNFL thickness and vertical C/D ratio achieved the best classification (misclassification rate, 6.2%).
The fast optic disk algorithm performs as well as the fast RNFL thickness algorithm for discrimination of glaucoma from normal eyes. A combination of the two algorithms may provide enhanced diagnostic performance.
比较光学相干断层扫描测定的视网膜神经纤维层(RNFL)厚度和视盘算法检测青光眼的性能。
观察性横断面研究。
地点:学术性三级医疗中心。研究人群:42名对照受试者和65名开角型青光眼患者的一只眼,视力≥20/40,且无其他眼部病理状况。观察程序:使用两种光学相干断层扫描算法:“快速RNFL厚度”和“快速视盘”。主要观察指标:使用受试者操作特征曲线下面积以及固定特异性下的敏感度。将平均RNFL厚度以及钟点扇形区和象限内的RNFL厚度的鉴别能力与从快速视盘算法得出的参数进行比较。使用分类与回归树来确定检测青光眼的最佳参数组合。
对照组和青光眼组的平均视野平均缺损(±标准差)分别为0.0±1.3和-5.3±5.0dB。7点钟扇形区、下象限的RNFL厚度以及垂直杯盘比在受试者操作特征曲线下面积最高(分别为0.93±0.02、0.92±0.03和0.90±0.03)。在90%特异性时,各算法的最佳敏感度(±标准误)对于7点钟扇形区的RNFL厚度为86%±3%,对于水平整合边缘宽度(估计边缘面积)为79%±4%。下象限RNFL厚度和垂直杯盘比的组合实现了最佳分类(错误分类率为6.2%)。
快速视盘算法在鉴别青光眼和正常眼方面与快速RNFL厚度算法表现相当。两种算法的组合可能会提高诊断性能。