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Retesting Visual Fields: Utilizing Prior Information to Decrease Test–Retest Variability in Glaucoma

¹From the School of Computer Science and Information Technology, RMIT University, Melbourne, Australia; and the ²Department of Optometry and Vision Sciences, University of Melbourne, Carlton, Victoria, Australia.

PURPOSE. To determine whether sensitivity estimates from an individual’s previous visual field tests can be incorporated into perimetric procedures to improve accuracy and reduce test–retest variability at subsequent visits.

METHODS. Computer simulation was used to determine the error, distribution of errors and presentation count for a series of perimetric algorithms. Baseline procedures were Full Threshold and Zippy Estimation by Sequential Testing (ZEST). Retest strategies were (1) allowing ZEST to continue from the previous test without reinitializing the probability density function [pdf]; (2) running ZEST with a Gaussian pdf centered about the previous result; (3) retest minimizing uncertainty (REMU), a new procedure combining suprathreshold and ZEST procedures incorporating prior test information. Empiric visual field data of 265 control and 163 patients with glaucoma were input into the simulation. Four error conditions were modeled: patients who make no errors, 15% false-positive (FP) with 3% false-negative (FN) errors, 15% FN with 3% FP errors, and 20% FP with 20% FN errors.

RESULTS. If sensitivity was stable from test to retest, all the retest algorithms were faster than the baseline algorithms by, on average, one presentation per location and are significantly more accurate (P < 0.05). When visual fields changed from test to retest, REMU was faster and more accurate than the other retest approaches and the baseline procedures. Relative to the baseline procedures, REMU showed decreased test–retest variability in impaired regions of visual field.

CONCLUSIONS. The obvious approaches to retest, such as continuing the previous procedure or seeding with previous values, have limitations when sensitivity changes between tests. REMU, however, significantly improves both accuracy and precision of testing and displays minimal bias, even when fields change and patients make errors.