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Predicting Progressive Glaucomatous Optic Neuropathy Using Baseline Standard Automated Perimetry Data

¹From the Devers Eye Institute, Legacy Health System, Portland, Oregon; the ²Department of Mathematics and Statistics, San Diego State University, San Diego, California; and the ³Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa.

PURPOSE. To test the hypothesis that specific locations and patterns of threshold findings within the visual field have predictive value for progressive glaucomatous optic neuropathy (pGON).

METHODS. Age-adjusted standard automated perimetry thresholds, along with other clinical variables gathered at the initial examination of 168 individuals with high-risk ocular hypertension or early glaucoma, were used as predictors in a classification tree model. The classification variable was a determination of pGON, based on longitudinally gathered stereo optic nerve head photographs. Only data for the worse eye of each individual were included. Data from 100 normal subjects were used to test the specificity of the models.

RESULTS. Classification tree models suggest that patterns of baseline visual field findings are predictive of pGON with sensitivity 65% and specificity 87% on average. Average specificity when data from normal subjects were run on the models was 69%.

CONCLUSIONS. Classification trees can be used to determine which visual field locations are most predictive of poorer prognosis for pGON. Spatial patterns within the visual field convey useable predictive information, in most cases when thresholds are still well within the classically defined normal range.

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