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1From the Department of Ophthalmology, School of Medicine, and the 2Department of Epidemiology and International Health, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama.
OBJECTIVE. To compare the ability of quantitative optic disc topography and subjective optic disc evaluation to discriminate early glaucomatous from normal eyes in African Americans and whites.
METHODS. Monocular data from eyes of 88 African-American patients and 63 eyes of white patients with glaucoma were included in the analysis. Sixty-three eyes of African American normal subjects and 42 eyes of white normal subjects were used as a control group. Racial groups were defined by self-description. All subjects underwent topographic imaging, stereophotography, and standard perimetry. Glaucoma was defined by visual field defect alone. Stereophotos were graded in a masked fashion by three independent graders. The areas under the receiver operator curve (aROCs) were calculated for the overall stereophoto grade, each confocal scanning laser ophthalmoscope (CSLO) parameter, and previously described discriminant functions. After adjustment for disc area and age, the aROC associated with each parameter, discriminant function, and subjective stereophoto grade were compared between African Americans and whites.
RESULTS. The aROC for masked stereophotographic disc evaluation and the best discriminatory CSLO parameter (cup-to-disc ratio, CDR) was similar in whites (0.869 stereophotographic, 0.858 CSLO CDR) and African Americans (0.865 stereophotographic, 0.850 CSLO CDR). No significant differences were found between the aROC with subjective stereophotographic assessment and the most discriminatory optic disc parameter in either racial group.
CONCLUSIONS. Previously described racial differences in optic disc structure have little impact on the relative ability of subjective or objective methods to discriminate between glaucomatous and nonglaucomatous optic discs; however, differences in normative values necessitate race-specific cutoffs, to optimize disease detection strategies.
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