期刊
OPTOMETRY AND VISION SCIENCE
卷 88, 期 1, 页码 124-129出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/OPX.0b013e3181fdef9c
关键词
glaucoma; scanning laser polarimetry; optical coherence tomography; retinal ganglion cells; retinal nerve fiber layer; preperimetric glaucoma
Purpose. This study was performed to compare the effectiveness of scanning laser polarimetry with variable corneal compensation (GDx VCC) and optical coherence tomography (Stratus OCT) for the detection of loss of the retinal nerve fiber layer (RNFL) in preperimetric glaucomatous eyes. Methods. Sixty subjects with preperimetric glaucoma (60 eyes) and 60 normal subjects (60 eyes) were included. We measured the RNFL thickness with GDx VCC and Stratus OCT and analyzed the results by 12 clock hour RNFL measurements. The area under the receiver-operating characteristic curve was calculated, and the data from all clock hour segments were compared using regression analyses. Results. The mean RNFL thickness for GDx VCC were 49.00 +/- 17.23 mu m and 59.4 +/- 8.38 mu m (p < 0.01), and for Stratus OCT, they were 86.43 +/- 20.49 mu m and 106.61 +/- 9.57 mu m (p < 0.01) in the patients with preperimetric glaucoma and normal group, respectively. The mean RNFL thickness for the clock hour evaluations were significantly different between the patients with preperimetric glaucoma and the normal group (p < 0.05). In preperimetric glaucoma, the area under the receiver-operating characteristic curve was the highest for the 12 clock hour RNFL thickness for GDx VCC (0.905) and the 7 clock hour RNFL thickness for Stratus OCT (0.903). GDx VCC and Stratus OCT RNFL measurements had significantly high correlations in the superior and inferior quadrants (r > 0.750) and low correlation at the nasal quadrant (r = 0.210). Conclusions. Both GDx VCC and Stratus OCT instruments had similar correlations at each clock hour segment, and both were useful in the early detection of patients with preperimetric glaucoma. (Optom Vis Sci 2011; 88: 124-129)
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据