IMPROVING THE PREDICTION OF VISUAL-FIELD PROGRESSION IN GLAUCOMA USING SPATIAL PROCESSING

Purpose: The authors show how the predictive performance of a method f or determining glaucomatous progression in a series of visual fields c an be improved by first subjecting the data to a spatial processing te chnique. Method: Thirty patients with normal-tension glaucoma, each wi th at least ten Humphrey fields and 3.5 years of follow-up, were inclu ded. A linear regression model of sensitivity against time of follow-u p determined rates of change at individual test locations over the fir st five fields (mean follow-up 1.46 years; standard deviation = 0.08) in each field series. Predictions of sensitivity at each location of t he field nearest to 1 and 2 years after the fifth field were generated using these rates of change. Predictive performance was evaluated by the difference between the predicted and measured sensitivity values. The analysis was repeated using the same field data subjected to a spa tial filtering technique used in image processing. Results: Using line ar modeling of the unprocessed field series, at 1 year after the fifth field, 72% of all predicted values were within +/-5 dB of the corresp onding measured threshold. This prediction precision improved to 83% u sing the processed data. At the 2-year follow-up field, the predictive performance improved from 56% to 73% with respect to the +/-5 dB crit erion. Conclusions: Predictions of visual field progression using a po intwise linear model can be improved by spatial processing without inc reased cost or patient time. These methods have clinical potential for accurately detecting and forecasting visual field deterioration in th e follow-up of glaucoma.