Towards Patient-Tailored Perimetry: Automated Perimetry Can Be Improved by Seeding Procedures With Patient-Specific Structural Information

Jonathan Denniss, Allison M McKendrick, Andrew Turpin

Research output: Contribution to journalArticlepeer-review

Abstract

PURPOSE: 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.

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

TRANSLATIONAL RELEVANCE: 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.

Original languageEnglish
Pages (from-to)3
JournalTranslational vision science & technology
Volume2
Issue number4
DOIs
Publication statusPublished - May 2013
Externally publishedYes

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