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Published on: January 21, 2017
Properties of perimetric threshold estimates from full threshold, ZEST, and SITA-like strategies, as determined by
Andrew Turpin1, Allison M McKendrick, Chris A Johnson
1Department of Computing, Curtin University of Technology, Perth, Western Australia, Australia.
Purpose:
To investigate the accuracy and precision of threshold estimates returned by two Bayesian perimetric strategies, staircase-QUEST or SQ (a Swedish interactive threshold algorithm [SITA]-like strategy) and ZEST (zippy estimation by sequential testing), and to compare these measures with those of the full-threshold (FT) algorithm.
Methods:
A computerized visual field simulation model was developed to compare the performance (accuracy, precision, and number of presentations) of the three algorithms. SQ implemented aspects of the SITA algorithm that are in the public domain. The simulation was tested by using standard automated perimetry (SAP) visual field data from 265 normal subjects and 163 observers with glaucomatous visual field loss and by exploring the effect of response variability and response errors on algorithm performance.
Results:
SQ was faster than FT or ZEST, with a comparable mean error when simulating field tests on patients. Point-wise analysis revealed similar error and standard deviation of error as a function of threshold for FT and SQ. If the initial estimate of threshold for either procedure was incorrect, the means and standard deviations of the error increased markedly. ZEST produced more accurate thresholds than did the other two strategies when the initial estimate was removed from the true threshold.
Conclusions:
When simulated patients made errors, the accuracy and precision of sensitivity estimates were poor when the initial estimate of threshold either overestimated or underestimated the true threshold. This was particularly so for FT and SQ. ZEST demonstrated more consistent error properties than the other two measures.
