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Eun Ji Lee, Tae-Woo Kim, Robert N. Weinreb, Ki Ho Park, Seok Hwan Kim, Dong Myung Kim; Trend-Based Analysis of Retinal Nerve Fiber Layer Thickness Measured by Optical Coherence Tomography in Eyes with Localized Nerve Fiber Layer Defects. Invest. Ophthalmol. Vis. Sci. 2011;52(2):1138-1144. doi: 10.1167/iovs.10-5975.
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To evaluate the rate of change in retinal nerve fiber layer (RNFL) thickness measured by optical coherence tomography (OCT) in eyes with stable and progressive localized RNFL defects and to investigate, in a trend-based approach, the diagnostic capability of OCT in the detection of progressive RNFL thinning.
The study included 153 glaucomatous eyes with localized RNFL defects. The patients were divided into nonprogressors (n = 77) and progressors (n = 76) on the basis of an evaluation of serial red-free photographs. The rates of progressive thinning in global, quadrant, and clock-hour OCT RNFL thicknesses were determined, by linear regression, and were compared between groups. Areas under receiver operating characteristic curves and sensitivities at fixed specificities were calculated for each parameter.
The rate of progressive RNFL thinning was significantly faster in progressors than in nonprogressors globally; in the inferior quadrant; in the 10, 11, 6, and 7 o'clock sectors; and in the affected quadrant and clock-hour sector thicknesses (all P ≤ 0.001). The rate of RNFL thinning in affected clock-hour sectors had the highest ability to discriminate between stable and progressive RNFL thinning with a sensitivity of 62% (95% confidence interval, 50%–73%) at a specificity ≥80%. Agreement between OCT and red-free photography was strongest when the criterion of −3.6 μm/year with P < 0.1 was used for each clock hour.
The rate of OCT RNFL thinning was significantly greater in patients with progressive localized RNFL defects than in those with stable localized defects. The data suggest that trend-based analysis of OCT RNFL thickness may be useful in glaucoma progression analysis and may complement other diagnostic tests.
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