Towards Patient-Tailored Perimetry: Automated Perimetry Can Be Improved by Seeding Procedures With Patient-Specific Structural Information.
OBJECTIVE
To explore the performance of patient-specific prior information, for example, from structural imaging, in improving perimetric procedures.


METHODS
Computer simulation was used to determine the error distribution and presentation count for Structure-Zippy Estimation by Sequential Testing (ZEST), a Bayesian procedure with prior distribution centered on a threshold prediction from structure. Structure-ZEST (SZEST) was trialled for single locations with combinations of true and predicted thresholds between 1 to 35 dB, and compared with a standard procedure with variability similar to Swedish Interactive Thresholding Algorithm (SITA) (Full-Threshold, FT). Clinical tests of glaucomatous visual fields (n= 163, median mean deviation -1.8 dB, 90% range +2.1 to -22.6 dB) were also compared between techniques.


RESULTS
For single locations, SZEST typically outperformed FT when structural predictions were within ± 9 dB of true sensitivity, depending on response errors. In damaged locations, mean absolute error was 0.5 to 1.8 dB lower, SD of threshold estimates was 1.2 to 1.5 dB lower, and 2 to 4 (29%-41%) fewer presentations were made for SZEST. Gains were smaller across whole visual fields (SZEST, mean absolute error: 0.5 to 1.2 dB lower, threshold estimate SD: 0.3 to 0.8 dB lower, 1 [17%] fewer presentation). The 90% retest limits of SZEST were median 1 to 3 dB narrower and more consistent (interquartile range 2-8 dB narrower) across the dynamic range than those for FT.


CONCLUSIONS
Seeding Bayesian perimetric procedures with structural measurements can reduce test variability of perimetry in glaucoma, despite imprecise structural predictions of threshold.


CONCLUSIONS
Structural data can reduce the variability of current perimetric techniques. A strong structure-function relationship is not necessary, however, structure must predict function within ±9 dB for gains to be realized.
Optometry and Vision Sciences, The University of Melbourne, Australia ; Computing and Information Systems, The University of Melbourne, Australia.
Jonathan
J
Denniss
Allison M
AM
McKendrick
Andrew
A
Turpin
automated perimetry; perimetry; static perimetry; structure–function; visual field