A comparison of algorithms for calculating glaucoma change probability confidence intervals.
OBJECTIVE
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 1 week from 35 glaucoma patients and within 6 months from 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 4 dB 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 specificity than techniques based on mean threshold. However, the mean-based methods were more 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.
Meng
Shuanghui
S
Department of Computing, Curtin University of Technology, Perth 6845, Western Australia.
Turpin
Andrew
A
Lazarescu
Mihai
M
Ivins
Jim
J
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016001:Confidence Intervals; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D008875:Middle Aged; D011336:Probability; D012680:Sensitivity and Specificity; D014786:Vision Disorders / Q000175:diagnosis*; D014794:Visual Fields