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Jean-Claude Mwanza, Joshua L. Warren, Donald L. Budenz; Combining Spectral Domain Optical Coherence Tomography Structural Parameters for the Diagnosis of Glaucoma With Early Visual Field Loss. Invest. Ophthalmol. Vis. Sci. 2013;54(13):8393-8400. doi: 10.1167/iovs.13-12749.
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© ARVO (1962-2015); The Authors (2016-present)
To create a multivariable predictive model for glaucoma with early visual field loss using a combination of spectral-domain optical coherence tomography (SD-OCT) parameters, and to compare the results with single variable models.
Two hundred fifty-three subjects (149 healthy controls and 104 with early glaucoma) underwent optic disc and macular scanning using SD-OCT in one randomly selected eye per subject. Sixteen parameters (rim area, cup-to-disc area ratio, vertical cup-to-disc diameter ratio, average and quadrant RNFL thicknesses, average, minimum, and sectoral ganglion cell inner-plexiform layer [GCIPL] thicknesses) were collected and submitted to an exploratory factor analysis (EFA) followed by logistic regression with the backward elimination variable selection technique. Area under the curve (AUC) of the receiver operating characteristic (ROC), sensitivity, specificity, Akaike's information criterion (AIC), predicted probability, prediction interval length (PIL), and classification rates were used to determine the performances of the univariable and multivariable models.
The multivariable model had an AUC of 0.995 with 98.6% sensitivity, 96.0% specificity, and an AIC value of 43.29. Single variable models yielded AUCs of 0.943 to 0.987, sensitivities of 82.6% to 95.7%, specificities of 88.0% to 94.0%, and AICs of 113.16 to 59.64 (smaller is preferred). The EFA logistic regression model correctly classified 91.67% of cases with a median PIL of 0.050 in the validation set. Univariable models correctly classified 80.62% to 90.48% of cases with median PILs 1.9 to 3.0 times larger.
The multivariable model was successful in predicting glaucoma with early visual field loss and outperformed univariable models in terms of AUC, AIC, PILs, and classification rates.
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