Detection of retinal nerve fibre layer progression: comparison of the fast and extended modes of GDx guided progression analysis

Abstract

Purpose To compare detection of retinal nerve fiber layer (RNFL) changes using GDx Guided Progression Analysis (GPA) fast mode (which assumes fixed variability of a reference population) and extended mode (which measures individual variability) and to determine how they compare to photography and visual field based conventional methods for identifying glaucoma progression. Methods 172 eyes from 117 participants in the Diagnostic Innovations in Glaucoma Study (DIGS) (12 healthy, 108 glaucoma suspect and 52 glaucoma eyes) with ≥4 GDx VCC visits and ≥3 good quality GDx VCC scans at each visit were included. Results: Agreement between the GDx GPA fast mode and GDx GPA extended mode was limited. The GDx fast mode and extended mode detected 15 and 18 eyes, respectively as “likely progression” but only 7 of them agreed. The conventional reference standard (stereophotograph based optic disc and/or visual field progression) identified 9 eyes as progressing of which 2 eyes were also identified by the GDx fast and 3 eyes by the extended mode. In the GDx fast mode, we found that the progression detection varies depending on which 2 scans are included in the baseline and follow-up images. Conclusion: There was limited agreement between the GDx fast mode and the GDx extended mode for progression detection, and between different scans included in the GDx fast mode progression analysis. Longer follow-up is needed to determine the proportion of eyes classified as “likely progression” by the GDx analysis that are early change and the proportion that are false positive results.

Detection of glaucoma progression by population and individual derived variability criteria

“…Most of the remaining five eyes that demonstrated VF progression without corresponding GDx GPA progression had advanced structural damage that might indicate that the structural changes precede the functional changes or that functional assessment is more sensitive to detect changes in advanced stages of glaucoma. These results agree with previous studies reporting limited agreement between structural and functional progression detection methods 2 10 15–17. In a study by Alencar et al ,9 the GDx GPA was only able to identify 50% of the eyes that were detected as progressing by VF and stereophotographs.…”

“…Similar to a previous study, we observed poor agreement between VF and GDx GPA regardless of the use of population- or individual-derived variability criteria 10. However, RNFL thickness slope was significantly different between VF defined progressors and non-progressors.…”

“…Additionally, all scans were included in EM while only one scan at each visit was used in FM. Kjaergaard et al ,10 have demonstrated that selection of the tests to be included in the FM analysis is affecting the outcome of the GPA analysis, resulting in further GPA differences observed in our study. The analysis method employed in the TSNIT Progression Graph and Summary Parameter Charts is also different for the FM compared with EM.…”

Glaucoma Diagnosis and Monitoring Using Advanced Imaging Technologies

“…76 The results of Fast Mode are more variable than Extended Mode, depending on which two scans are included in the baseline and follow-up images. 77 When judging structural change using GDx-GPA, it is important to understand the GPA printout and use in conjunction with clinical judgment for final decision.…”

Challenges in estimating the accuracy of imaging-based detection methods for glaucomatous progression

“…However, in most studies that assess the inter-instrument agreement on progression detection on proprietary change detection algorithms, this agreement is moderate at best 15. This holds true even for progression detection algorithms that operate on data from the same instrument, whether this is functional16 17 or structural data 18 19. The latter is further explored for GDx guided progression analysis in the paper by Folio et al ,20 elsewhere in this issue, where the authors evaluate progression detection based on population-derived reproducibilities (guided progression analysis fast mode) versus individual-derived reproducibilities (guided progression analysis extended mode).…”