Improving the prediction of visual field progression in glaucoma using spatial processing.

PURPOSE

The authors show how the predictive performance of a method for determining glaucomatous progression in a series of visual fields can be improved by first subjecting the data to a spatial processing technique.

METHOD

Thirty patients with normal-tension glaucoma, each with at least ten Humphrey fields and 3.5 years of follow-up, were included. A linear regression model of sensitivity against time of follow-up determined rates of change at individual test locations over the first five fields (mean follow-up 1.46 years; standard deviation = 0.08) in each field series. Predictions of sensitivity at each location of the 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 spatial filtering technique used in image processing.

RESULTS

Using linear modeling of the unprocessed field series, at 1 year after the fifth field, 72% of all predicted values were within +/- 5 dB of the corresponding measured threshold. This prediction precision improved to 83% using the processed data. At the 2-year follow-up field, the predictive performance improved from 56% to 73% with respect to the +/- 5 dB criterion.

CONCLUSIONS

Predictions of visual field progression using a pointwise linear model can be improved by spatial processing without increased cost or patient time. These methods have clinical potential for accurately detecting and forecasting visual field deterioration in the follow-up of glaucoma.