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Abstract #15184 Published in IGR 8-4
A comparison of algorithms for calculating glaucoma change probability confidence intervals
Meng S; Turpin A; Lazarescu M; Ivins JJournal of Glaucoma 2006; 15: 405-413
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 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.
Dr. S. Meng, Department of Computing, Curtin University of Technology, Perth 6845, Australia
Classification:
6.6.2 Automated (Part of: 6 Clinical examination methods > 6.6 Visual field examination and other visual function tests)
