Effect of a variability-adjusted algorithm on the efficiency of perimetric testing.

PURPOSE: Variability in perimetry increases with the amount of damage, making it difficult for testing algorithms to efficiently converge to the true sensitivity. This study describes a variability-adjusted algorithm (VAA), in which step size increases with variability.
METHODS: Contrasts were transformed to a new scale wherein the SD of frequency-of-seeing curves remains 1 unit for any sensitivity. A Bayesian thresholding procedure based on the existing Zippy Estimation by Sequential Testing (ZEST) algorithm was simulated on this new scale, and results converted back to decibels. The root-mean-squared (RMS) error from true sensitivity based on these simulations was compared against that achieved by ZEST using the same number of presentations. The procedure was repeated after limiting sensitivities to 15 dB or higher, the lower limit of reliable sensitivities using standard white-on-white perimetry in glaucoma, for both algorithms.
RESULTS: When the true sensitivity was 35 dB, with starting estimate also 35 dB, RMS errors of the algorithms were similar, ranging from 1.39 dB to 1.60 dB. When true sensitivity was instead 20 dB, with starting estimate 35 dB, VAA reduced the RMS error from 7.43 dB to 3.66 dB. Limiting sensitivities at 15 dB or higher reduced RMS errors, except when true sensitivity was near 15 dB.
CONCLUSIONS: VAA reduces perimetric variability without increasing test duration in cases in which the starting estimate of sensitivity is too high; for example, due to a small scotoma. Limiting the range of possible sensitivities at 15 dB or higher made algorithms more efficient, unless the true sensitivity was near this limit. This framework provides a new family of test algorithms that may benefit patients.