A comparison of algorithms for calculating glaucoma change probability confidence intervals

Purpose: To evaluate the ability to detect change in standard automated perimetry data using 4 different methods for calculating the glaucoma change probability (GCP). Methods: A database of stable visual fields, collected within I week from 35 glaucoma patients and within 6 months frorn 15 normal patients, was used to determine confidence intervals for GCP using 4 different methods. The methods classified visual field locations on the basis of either defect or mean threshold, and used test-retest data or baseline-less-follow-up data to determine values for the confidence intervals. The specificity of the 4 methods was measured using 3700 locations artificially generated to simulate stable visual field data. The sensitivity of the methods was measured using 3330 artificially generated locations that decreased in either a linear, curvilinear, or bi-linear fashion by 2, 3, or 4dB per year on average. Results: Using GCP with confidence intervals built using the methods described in the literature (on the basis of defect and test-retest differences) resulted in a higher specilicity than techniques based on mean threshold, However, the mean-based methods were mote sensitive at detecting a decrease in a location. Building confidence intervals using the difference between a baseline and the current measurement (baseline-less-follow-up), rather than test-retest differences, also improved the detection of visual field progression. Conclusions: Stratifying baseline visual field measurements based on defect and eccentricity as described in the literature results in an unusually high specificity: 98% accuracy in classifying the same stable data that generated the 95% confidence intervals, rather than the expected 95% accuracy. By stratifying measurements based on mean threshold, and using baseline-less-follow-up rather than test-retest differences to build 95% confidence intervals, sensitivity is increased by 14.1 %. This increase in sensitivity comes with a corresponding 2.2% decrease in specificity.