Comparion of Retinal Nerve Fiber Layer Thickne and Optic Dik Algorithm with Optical Coherence Tomography to Detect Glaucoma

Purpoe To compare the performance of the retinal nerve fiber layer (RNFL) thickne and optic dik algorithm a determined by optical coherence tomography to detect glaucoma. Deign Obervational cro-ectional tudy. Method etting: Academic tertiary-care center. tudy population: One eye from 42 control ubject and 65 patient with open-angle glaucoma with viual acuity of ≥20/40, and no other ocular pathologic condition. obervation procedure: Two optical coherence tomography algorithm were ued: “fat RNFL thickne” and “fat optic dik.” main outcome meaure: Area under the receiver operating characteritic curve and enitivitie at fixed pecificitie were ued. Dicriminating ability of the average RNFL thickne and RNFL thickne in clock-hour ector and quadrant wa compared with the parameter that were derived from the fat optic dik algorithm. Claification and regreion tree were ued to determine the bet combination of parameter for the detection of glaucoma. Reult The average viual field mean deviation (±D) wa 0.0 ± 1.3 and −5.3 ± 5.0 dB in the control and glaucoma group, repectively. The RNFL thickne at the 7 o’clock ector, inferior quadrant, and the vertical C/D ratio had the highet area under the receiver operating characteritic curve (0.93 ± 0.02, 0.92 ± 0.03, and 0.90 ± 0.03, repectively). At 90% pecificity, the bet enitivitie (±E) from each algorithm were 86% ± 3% for RNFL thickne at the 7 o’clock ector and 79% ± 4% for horizontal integrated rim width (etimated rim area). The combination of inferior quadrant RNFL thickne and vertical C/D ratio achieved the bet claification (miclaification rate, 6.2%). Concluion The fat optic dik algorithm perform a well a the fat RNFL thickne algorithm for dicrimination of glaucoma from normal eye. A combination of the two algorithm may provide enhanced diagnotic performance.