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A. Perdicchi, M. Iester, E. Capris, A. Siniscalco, A. Di Giulio, G. Scuderi, G. Calabria, S. M. Recupero; Comparison Between Discriminant Analysis Models and ‘‘Glaucoma Probability Score’’ for the Detection of Glaucomatous Optic Nerve Head Changes. Invest. Ophthalmol. Vis. Sci. 2008;49(13):3637. doi: https://doi.org/.
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© ARVO (1962-2015); The Authors (2016-present)
To evaluate and compare fourdifferent discriminant analysis formulas and the new GlaucomaProbability Score (GPS) for the detection of morphometric opticnerve head changes in chronic open-angle glaucoma.
This is a prospectively planned cross-sectional study.Two hundred and fourteen consecutive eyes were recruited intothis study. For each patient, the eyes were evaluated by a slitlamp examination, and the visual fields were assessed by aHumphrey Field Analyzer 750 (HFA, Humphrey Inc, SanLeandro, CA), using the standard full threshold 24-2 (SwedishInteractive Threshold Algorithm) program. The optic nerveheads were morphometrically evaluated using the HeidelbergRetina Tomograph 3 (HRT 3, Heidelberg Engineering, Heidelberg,Germany; software version 3.0). From the HRT data, 4discriminant analysis formulas and the GPS were considered.All data were analyzed by Student t test and Pearson rcoefficient. A linear regression model was also used to determinethe independent contribution of variables included in the model.Sensitivity, specificity, diagnostic precision, and receiver operatingcharacteristic curve areas were calculated for all the 5methods examined. k statistic was used to study the agreementamong, and between, the 5 different methods.
One hundred and nineteen normal eyes and 95 eyes withprimary open-angle glaucoma were included in the study. Nosignificant difference was found between the 2 study subgroupsin both age and refractive error. Significant (P<0.001)correlations were found between visual field indices and theHRT parameters. Sensitivity, specificity, and diagnostic precisionof the 4 formulas ranged between 50% and 99.16%. BathijaAQ1 et al’s formula had the highest diagnostic precision, followed byMikelberg’s formula. Using k statistics, k ranged from 0.177 to0.528 when comparing each single discriminant formula with theGPS.
The GPS showed similar sensitivity and specificityto the Mikelberg and Bathija formulas; this method is apromising one for differentiating between healthy and glaucomatouseyes, requiring no subjective user input.Key Words: glaucoma, optic nerve head, Glaucoma ProbabilityScore, topographic map, discriminant formula, diagnosis,confocal scanning laser tomography
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