Targeted spatial sampling using GOANNA improves detection of visual field progression

Understanding and identifying visual field progression

Detecting deterioration of visual field sensitivity measurements is important for the diagnosis and management of glaucoma. This review surveys the current methods for assessing progression that are implemented in clinical devices, which have been used in clinical trials, alongside more recent advances proposed in the literature. Advice is also offered to clinicians on what they can do to improve the collection of perimetric data to help analytical progression methods more accurately predict change. This advice includes a discussion of how frequently visual field testing should be undertaken, with a view towards future developments, such as digital healthcare outside the standard clinical setting and more personalised approaches to perimetry.

Comparison of Methods to Detect and Measure Glaucomatous Visual Field Progression

PURPOSE

To compare methods to assess visual field (VF) progression in glaucoma.

METHODS

4,950 VFs of 253 primary open angle-glaucoma patients were evaluated for progression with the following methods: clinical evaluation, guided progression analysis (GPA), mean deviation (MD), and visual field index (VFI) rates, Advanced Glaucoma Intervention Study (AGIS) and Collaborative Initial Glaucoma Treatment Study (CIGTS) scores, pointwise linear regression (PLR), permutation of PLR (PoPLR), and glaucoma rate index (GRI). A separate simulated series of longitudinal VFs was assessed with all methods except for GPA and clinical evaluation.

RESULTS

The average (±SD) age of the patients at baseline was 65.4 (±11.5) years. The average (±SD) follow-up was 11.8 (±4.6) years, and the mean (±SD) number of VFs was 16.8 (±7.0). Proportion of series detected as progressing was 65% for PoPLR, 58% for GRI, 41% for GPA, 40% for PLR, 36% for CIGTS, 35% for clinicians, 31% for MD rate, 29% for AGIS, and 22% for VFI rate. Median times to detection of progression were 7.3 years for PoPLR, 7.5 years for GRI, 11 years for clinicians, 14 years for GPA, 16 years for PLR, 17 years for CIGTS, 19 years for AGIS, and more than 20 years for MD and VFI rates. In simulated VF series, GRI had the highest partial area under the receiver operator characteristic curve (0.040) to distinguish between glaucoma progression and aging/cataract decay, followed by VFI rate (0.028), MD rate (0.024), and PoPLR (0.006).

CONCLUSIONS

GRI and PoPLR showed the highest proportion of series detected as progressing and shortest times to progression detection. GRI exhibited the best ability to detect progression in the simulated VF series.

TRANSLATIONAL RELEVANCE

Knowledge of the properties of every method would allow tailoring application in both clinical and research settings.

Performance of a Defect-Mapping Microperimetry Approach for Characterizing Progressive Changes in Deep Scotomas

Purpose

To examine whether a microperimetry testing strategy based on quantifying the spatial extent of functional abnormalities (termed “defect-mapping” strategy) could improve the detection of progressive changes in deep scotomas compared to the conventional thresholding strategy.

Methods

A total of 30 healthy participants underwent two microperimetry examinations, each using the defect-mapping and thresholding strategies at the first visit to examine the test–retest variability of each method. Testing was performed using an isotropic stimulus pattern centered on the optic nerve head (ONH), which acted as a model of a deep scotoma. These tests were repeated at a second visit, except using a smaller stimulus pattern and thereby increasing the proportion of test locations falling within the ONH (to simulate the progressive enlargement of a deep scotoma). The extent of change detected between visits relative to measurement variability was compared between the two strategies.

Results

Relative to their effective dynamic ranges, the test–retest variability of the defect-mapping strategy (1.8%) was significantly lower compared to the thresholding strategy (3.3%; P < 0.001). The defect-mapping strategy also captured a significantly greater extent of change between visits relative to variability (−4.70 t⁻¹) compared to the thresholding strategy (2.74 t⁻¹; P < 0.001).

Conclusions

A defect-mapping microperimetry testing strategy shows promise for capturing the progressive enlargement of deep scotomas more effectively than the conventional thresholding strategy.

Translational Relevance

Microperimetry testing with the defect-mapping strategy could provide a more accurate clinical trial outcome measure for capturing progressive changes in deep scotomas in eyes with atrophic retinal diseases, warranting further investigations.

Improving Spatial Resolution and Test Times of Visual Field Testing Using ARREST

Purpose

Correctly classifying progression in moderate to advanced glaucoma is difficult. Pointwise visual field test–retest variability is high for sensitivities below approximately 20 dB; hence, reliably detecting progression requires many test repeats. We developed a testing approach that does not attempt to threshold accurately in areas with high variability, but instead expends presentations increasing spatial fidelity.

Methods

Our visual field procedure Australian Reduced Range Extended Spatial Test (ARREST; a variant of the Bayesian procedure Zippy Estimation by Sequential Testing [ZEST]) applies the following approach: once a location has an estimated sensitivity of <17 dB (a “defect”), it is checked that it is not an absolute defect (<0 dB, “blind”). Saved presentations are used to test extra locations that are located near the defect. Visual field deterioration events are either: (1) decreasing in the range of 40 to 17 dB, (2) decreasing from >17 dB to “defect”, or (3) “defect” to blind. To test this approach we used an empirical database of progressing moderate-advanced 24-2 visual fields (121 eyes) that we “reverse engineered” to create visual field series that progressed from normal to the end observed field. ARREST and ZEST were run on these fields with test accuracy, presentation time, and ability to detect progression compared.

Results

With specificity for detecting progression matched at 95%, ZEST and ARREST showed similar sensitivity for detecting progression. However, ARREST used approximately 25% to 40% fewer test presentations to achieve this result in advanced visual field damage. ARREST spatially defined the visual field deficit with greater precision than ZEST due to the addition of non–24-2 locations.

Conclusions

Spending time trying to accurately measure visual field locations that have high variability is not productive. Our simulations indicate that giving up attempting to quantify size III white-on-white sensitivities below 17 dB and using the presentations saved to test extra locations should better describe progression in moderate-to-advanced glaucoma in shorter time.

Translational Relevance

ARREST is a new visual field test algorithm that provides better spatial definition of visual field defects in faster test time than current procedures. This outcome is achieved by substituting inaccurate quantification of sensitivities <17 dB with new spatial locations.

The value of visual field testing in the era of advanced imaging: clinical and psychophysical perspectives

White‐on‐white standard automated perimetry (SAP) is widely used in clinical and research settings for assessment of contrast sensitivity using incremental light stimuli across the visual field. It is one of the main functional measures of the effect of disease upon the visual system. SAP has evolved over the last 40 years to become an indispensable tool for comprehensive assessment of visual function. In modern clinical practice, a range of objective measurements of ocular structure, such as optical coherence tomography, have also become invaluable additions to the arsenal of the ophthalmic examination. Although structure‐function correlation is a highly desirable determinant of an unambiguous clinical picture for a patient, in practice, clinicians are often faced with discordance of structural and functional results, which presents them with a challenge. The construction principles behind the development of SAP are used to discuss the interpretation of visual fields, as well as the problem of structure‐function discordance. Through illustrative clinical examples, we provide useful insights to assist clinicians in combining a range of clinical results obtained from SAP and from advanced imaging techniques into a coherent picture that can help direct clinical management.

Assessing the GOANNA Visual Field Algorithm Using Artificial Scotoma Generation on Human Observers

PURPOSE

To validate the performance of a new perimetric algorithm (Gradient-Oriented Automated Natural Neighbor Approach; GOANNA) in humans using a novel combination of computer simulation and human testing, which we call Artificial Scotoma Generation (ASG).

METHODS

Fifteen healthy observers were recruited. Baseline conventional automated perimetry was performed on the Octopus 900. Visual field sensitivity was measured using two different procedures: GOANNA and Zippy Estimation by Sequential Testing (ZEST). Four different scotoma types were induced in each observer by implementing a novel technique that inserts a step between the algorithm and the perimeter, which in turn alters presentation levels to simulate scotomata in human observers. Accuracy, precision, and unique number of locations tested were measured, with the maximum difference between a location and its neighbors (Max_d) used to stratify results.

RESULTS

GOANNA sampled significantly more locations than ZEST (paired t-test, P < 0.001), while maintaining comparable test times. Difference plots showed that GOANNA displayed greater accuracy than ZEST when Max_d was in the 10 to 30 dB range (with the exception of Max_d = 20 dB; Wilcoxon, P < 0.001). Similarly, GOANNA demonstrated greater precision than ZEST when Max_d was in the 20 to 30 dB range (Wilcoxon, P < 0.001).

CONCLUSIONS

We have introduced a novel method for assessing accuracy of perimetric algorithms in human observers. Results observed in the current study agreed with the results seen in earlier simulation studies, and thus provide support for performing larger scale clinical trials with GOANNA in the future.

TRANSLATIONAL RELEVANCE

The GOANNA perimetric testing algorithm offers a new paradigm for visual field testing where locations for testing are chosen that target scotoma borders. Further, the ASG methodology used in this paper to assess GOANNA shows promise as a hybrid between computer simulation and patient testing, which may allow more rapid development of new perimetric approaches.

What rates of glaucoma progression are clinically significant?

Clinically important rates of glaucoma progression (worsening) are ones that put a patient at risk of future functional impairment or reduction of vision-related quality of life. Rates of progression can be evaluated through measuring structural or functional changes of the optic nerve. Most treated eyes do not progress at rates that will lead to future visual impairment, but there are a significant proportion (3-17%) of eyes, that are at risk of impairment even under clinical care. While very fast rates of progression (e.g. MD progression of -1.5 dB/year) are generally problematic, much slower rates also may be deleterious for young patients, particularly those diagnosed with late disease. As a result, it is important to consider life expectancy, disease severity and vision-related quality of life based treatment targets to estimate future prognosis when evaluating whether a rate of glaucoma progression can be clinically relevant.

Comparison of nonparametric methods for static visual field interpolation

Visual field testing with standard automated perimetry produces a sparse representation of a sensitivity map, sometimes called the hill of vision (HOV), for the retina. Interpolation or resampling of these data is important for visual display, clinical interpretation, and quantitative analysis. Our objective was to compare several popular interpolation methods in terms of their utility to visual field testing. We evaluated nine nonparametric scattered data interpolation algorithms and compared their performances in normal subjects and patients with retinal degeneration. Interpolator performance was assessed by leave-one-out cross-validation accuracy and high-density interpolated HOV surface smoothness. Radial basis function (RBF) interpolation with a linear kernel yielded the best accuracy, with an overall mean absolute error (MAE) of 2.01 dB and root-mean-square error (RMSE) of 3.20 dB that were significantly better than all other methods (p ≤ 0.003). Thin-plate spline RBF interpolation yielded the best smoothness results (p < 0.001) and scored well for accuracy with overall MAE and RMSE values of 2.08 and 3.28 dB, respectively. Natural neighbor interpolation, which may be a more readily accessible method to some practitioners, also performed well. While no interpolator will be universally optimal, these interpolators are good choices among nonparametric methods.