Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma

Abstract

• PURPOSE: 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. • DESIGN: Observational cross-sectional study. • METHODS: setting: Academic tertiary-care center. study population: One eye from 42 control subjects and 65 patients with open-angle glaucoma with visual acuity of ≥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. • RESULTS: 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%). • CONCLUSION: 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. © 2006 by Elsevier Inc. All rights reserved.