Functional assessment of glaucoma: Uncovering progression

Hu, Rongrong; Racette, Lyne; Chen, Kelly; Johnson, Chris A.

doi:10.1016/j.survophthal.2020.04.004

Cited by 34 publications

(34 citation statements)

References 194 publications

(278 reference statements)

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“…This is not new to clinicians as similar arguments have been made for perimetry in the past. 25 Second, and most importantly, before making a judgement about progression, clinicians should examine the circumpapillary b-scan images to look for signs of glaucomatous damage and to confirm that there are no artifacts (e.g., clipping), other pathologies (e.g., ERMs), or segmentation errors affecting the segmentation of the cpRNFL. 14 – 16 Liu et al 17 emphasized this point in 2015.…”

Section: Discussionmentioning

confidence: 99%

Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression

Eguia

Tsamis

Zemborain

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose To assess the effects of local defects, segmentation errors, and improper image alignment on the performance of the commonly used optical coherence tomography (OCT) measure of progression, that is the change in global (average) circumpapillary retinal nerve fiber layer (cpRNFL) thickness (ΔG). Methods One hundred fifty eyes suspected of, or with, early glaucoma had OCT circle and cube scans obtained using eye tracking on two occasions at least 1 year apart. Statistical progression was defined by fixed values of ΔG (3–8 um) and quantile regression. For a reference standard, four authors identified 30 eyes as “likely progressed,” and 61 eyes that “likely had not progressed” based on OCT reports from both baseline and follow-up tests. Results A ΔG criterion of 4 um had the best accuracy: 77%, with 5 false positive (8.2%) and 16 false negative (53%). A post hoc analysis of circular b-scans and OCT probability maps of these eyes indicated that segmentation errors and local progression accounted for most of these mistakes. Segmentation errors, although less common, were also present in true positives and true negatives. Conclusions Local defects and segmentation errors are the primary reasons for the poor performance of cpRNFL thickness G metric. Because these problems are difficult, if not impossible, to eliminate, the G metric should not be relied on in isolation for detecting glaucomatous progression. Translational Relevance Local defects and segmentation errors are easily identified by viewing OCT circumpapillary images, which should be part of the standard protocol for detecting glaucomatous progression.

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Section: Discussionmentioning

confidence: 99%

Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression

Eguia

Tsamis

Zemborain

et al. 2020

Trans. Vis. Sci. Tech.

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“…Other methods, such as exponential regression,29 pointwise linear regression (PLR) and cluster trend analysis (CTA) may be used for estimating the rate of VF deterioration in severe glaucoma. PLR provides estimates of the rate of change for each VF location 30–32. Although PLR has been reported to be more sensitive to focal glaucomatous progression than global indices, there are several shortcomings.…”

Section: Discussionmentioning

confidence: 99%

“…However, these methods may not perform equally well in severe glaucoma with many blind locations. Regression analyses of the mean sensitivity by sectors or clusters (CTA) have been reported to provide a better combination of sensitivity and specificity than global trend analyses or PLR 31,32,35. However, CTA also may cause underestimation of the rates of VF progression in severe glaucoma when many locations in each cluster are undetectable from baseline.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Visual Field Progression in Advanced Glaucoma Using Trend Analysis of Targeted Mean Total Deviation

Miki

Okazaki

Weinreb

et al. 2022

Journal of Glaucoma

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Purpose: Trend analysis of visual field (VF) global indices may underestimate the rate of progression in severe glaucoma because of the influence of test points without detectable sensitivity. To test this hypothesis, we compared the rates of change of VF global indices with and without exclusion of undetectable points at various disease stages. Materials and Methods: Six hundred and forty-eight eyes of 366 glaucoma patients with 8 or more reliable 30-2 standard automated perimetry over more than 2 years were enrolled. We calculated targeted mean total deviation (TMTD) by averaging total deviation except points which were consistently undetectable in 3 baseline tests. Eyes were classified as early (≥−6 dB), moderate (−6 dB to −12 dB), advanced (−12 dB to −20 dB), and severe (<−20 dB) based on baseline mean deviation (MD). The rates of change of MD and TMTD in each stage were statistically compared. Results: Mean age±SD at baseline was 56.9±11.9 years. The MD slope (−0.34 dB/y) in severe glaucoma was significantly slower than TMTD slope (−0.42 dB/y, P=0.028) and was slower than MD slopes in the other stages. Difference between MD slopes and TMTD slopes was most prominent in eyes with MD values less than −25 dB (P=0.002). Conclusions: Undetectable locations in eyes with severe glaucoma may underestimate the rates of VF progression. Trend analysis of TMTD rather than global indices offers a practical and simple approach for alleviating underestimation of VF progression in severe glaucoma.

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“…Visual field (VF) assessment with static automated perimetry has been the clinical standard for monitoring functional changes in glaucoma. 3 , 4 However, the results of VF tests, even when performed over short time intervals, can vary significantly. This presents a major challenge in monitoring glaucoma progression, because variability in VF data can either obscure or mimic deteriorating visual function.…”

Section: Introductionmentioning

confidence: 99%

The Usefulness of Assessing Glaucoma Progression With Postprocessed Visual Field Data

Abu

Poleon

Racette

2022

Trans. Vis. Sci. Tech.

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Purpose Data postprocessing with statistical techniques that are less sensitive to noise can be used to reduce variability in visual field (VF) series. We evaluated the detection of glaucoma progression with postprocessed VF data generated with the dynamic structure–function (DSF) model and MM-estimation robust regression (MRR). Method The study included 118 glaucoma eyes with at least 15 visits selected from the Rotterdam dataset. The DSF and MRR models were each applied to observed mean deviation (MD) values from the first three visits (V 1 – 3 ) to predict the MD at V 4. MD at V 5 was predicted with data from V 1 – 4 and so on until the MD at V 9 was predicted, creating two additional datasets: DSF-predicted and MRR-predicted. Simple linear regression was performed to assess progression at the ninth visit. Sensitivity was evaluated by adjusting for false-positive rates estimated from patients with stable glaucoma and by using longer follow-up series (12th and 15th visits) as a surrogate for progression. Results For specificities of 80% to 100%, the DSF-predicted dataset had greater sensitivity than the observed and MRR-predicted dataset when positive rates were normalized with corresponding false-positive estimates. The DSF-predicted and observed datasets had similar sensitivity when the surrogate reference standard was applied. Conclusions Without compromising specificity, the use of DSF-predicted measurements to identify progression resulted in a better or similar sensitivity compared to using existing VF data. Translational Relevance The DSF model could be applied to postprocess existing visual field data, which could then be evaluated to identify patients at risk of progression.

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Functional assessment of glaucoma: Uncovering progression

Abstract: s u r v e y o f o p h t h a l m o l o g y 6 5 (2 0 2 0) 6 3 9 e6 6 1

Cited by 34 publications

References 194 publications

Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression

Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression

Evaluating Visual Field Progression in Advanced Glaucoma Using Trend Analysis of Targeted Mean Total Deviation

The Usefulness of Assessing Glaucoma Progression With Postprocessed Visual Field Data

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