Detection of Functional Change Using Cluster Trend Analysis in Glaucoma

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.

Utility of targeted mean total deviation trend analysis for detecting progressive visual field changes in early-to-moderate stage glaucoma

OBJECTIVES

To evaluate the clinical utility of trend-based analysis of the targeted mean total deviation (TMTD) by comparing its rates of visual field (VF) change and sensitivities of detecting VF progression with those of the mean total deviation (mTD) in the global and hemifield VF area in early to-moderate glaucoma patients.

METHODS

A single eye from 139 open-angle glaucoma patients with hemifield VF defects and a minimum two year follow-up were retrospectively evaluated. The TMTD was estimated by averaging the total deviation (TD) values after excluding VF points that had a threshold sensitivity of <0 dB in three baseline tests, and the mTD by averaging the entire VF TD values. The study patients were classified as VF progressors vs. non-progressors using both event- and trend-based analysis. The rates of change and ratios of progression detection were compared between TMTD and mTD.

RESULTS

This study included 49 VF progressors and 90 non-progressors. Slopes for the global and VF-affected hemifield TMTD were significantly faster than those for the mTD in each subgroup and in the entire cohort (P < 0.001). Trend-based TMTD analysis detected VF progression in greater proportion than either trend-based mTD or event-based analysis (38.1% vs. 30.2% vs. 27.3%, respectively: VF affected hemifields).

CONCLUSIONS

The rates of change in the TMTD are significantly faster than those for the mTD globally and in the VF-affected hemifields. Trend-based TMTD analysis shows greater sensitivity for detecting VF progression than trend-based mTD or event-based analysis in early-to-moderate glaucoma patients with hemifield VF loss.

Detectability of Visual Field Defects in Glaucoma Using Moving Versus Static Stimuli for Perimetry

Purpose

We have previously shown that using moving, instead of static, stimuli extends the effective dynamic range of automated perimetry in glaucoma. In this study, we further investigate the effect of using moving stimuli on the detectability of functional loss.

Methods

We used two experimental perimetry paradigms to test 155 subjects with a diagnosis of glaucoma or glaucoma suspect, and 34 healthy control subjects. One test used stimuli moving parallel to the average nerve fiber bundle orientation at each location; the other used static stimuli. Algorithms were otherwise identical. Sensitivities to moving stimuli were transformed to the equivalent values for static stimuli based on a Bland-Altman plot. The proportions of locations outside age-corrected normative limits were compared, and test-retest variability was compared against defect depth for each stimulus type.

Results

More tested locations were below the fifth percentile of the normative range for that location using static stimuli. However, among locations abnormal according to standard clinical perimetry on the same day, 19.2% were abnormal using static stimuli, versus 20.5% using moving stimuli (P = 0.372). Test-retest variability was 44% lower for moving stimuli across the range of defect depths.

Conclusions

When compared with static automated perimetry and expressed on a common scale, moving stimuli extend the effective dynamic range and decrease variability, without decreasing the detectability of known functional defects.

Translational Relevance

Moving stimuli provide a method to improve known problems of current clinical perimetry.

Retinal Vessel Pulsatile Characteristics Associated With Vascular Stiffness Can Predict the Rate of Functional Progression in Glaucoma Suspects

Purpose

Tissue stiffening and alterations in retinal blood flow have both been suggested as causative mechanisms of glaucomatous damage. We tested the hypothesis that retinal blood vessels also stiffen, using laser speckle flowgraphy (LSFG) to characterize vascular resistance.

Methods

In the longitudinal Portland Progression Project, 231 eyes of 124 subjects received LSFG scans of the optic nerve head (ONH) and automated perimetry every 6 months for six visits. Eyes were classified as either "glaucoma suspect" or "glaucoma" eyes based on the presence of functional loss on the first visit. Vascular resistance was quantified using the mean values of several instrument-defined parameterizations of the pulsatile waveform measured by LSFG, either in major vessels within the ONH (serving the retina) or in capillaries within ONH tissue, and age-adjusted using a separate group of 127 healthy eyes of 63 individuals. Parameters were compared against the severity and rate of change of functional loss using mean deviation (MD) over the six visits, within the two groups.

Results

Among 118 "glaucoma suspect" eyes (average MD, -0.4 dB; rate, -0.45 dB/y), higher vascular resistance was related to faster functional loss, but not current severity of loss. Parameters measured in major vessels were stronger predictors of rate than parameters measured in tissue. Among 113 "glaucoma" eyes (average MD, -4.3 dB; rate, -0.53 dB/y), higher vascular resistance was related to more severe current loss but not rate of loss.

Conclusions

Higher retinal vascular resistance and, by likely implication, stiffer retinal vessels were associated with more rapid functional loss in eyes without significant existing loss at baseline.

A Data-Driven Model for Simulating Longitudinal Visual Field Tests in Glaucoma

Purpose

To develop a simulation model for glaucomatous longitudinal visual field (VF) tests with controlled progression rates.

Methods

Longitudinal VF tests of 1008 eyes from 755 patients with glaucoma were used to learn the statistical characteristics of VF progression. The learned statistics and known anatomic correlations between VF test points were used to automatically generate progression patterns for baseline fields of patients with glaucoma. VF sequences were constructed by adding spatially correlated noise templates to the generated progression patterns. The two one-sided test (TOST) procedure was used to analyze the equivalence between simulated data and data from patients with glaucoma. VF progression detection rates in the simulated VF data were compared to those in patients with glaucoma using mean deviation (MD), cluster, and pointwise trend analysis.

Results

VF indices (MD, pattern standard deviation), MD linear regression slopes, and progression detection rates for the simulated and patients' data were practically equivalent (TOST P < 0.01). In patients with glaucoma, the detection rates in 7 years using MD, cluster, and pointwise trend analysis were 24.4%, 26.2%, and 38.4%, respectively. In the simulated data, the mean detection rates (95% confidence interval) for MD, cluster, and pointwise trend analysis were 24.7% (24.1%-25.2%), 24.9% (24.2%-25.5%), and 35.7% (34.9%-36.5%), respectively.

Conclusions

A novel simulation model generates glaucomatous VF sequences that are practically equivalent to longitudinal VFs from patients with glaucoma.

Translational Relevance

Simulated VF sequences with controlled progression rates can support the evaluation and optimization of methods to detect VF progression and can provide guidance for the interpretation of longitudinal VFs.

[Static automated perimetry in the diagnosis of glaucoma. Assessment of disease progression]

There are several ways to assess glaucoma progression using standard automated perimetry. Most often, ophthalmologists evaluate the stability of visual functions manually when comparing several study protocols. The advantages of clinical assessment are ease of implementation and the ability to interpret data from any device. The main disadvantage of this method is its subjectivity. There are many available automated methods for assessing disease progression involving Humphrey Field Analyzer and Octopus perimeters. Event analysis allows determining glaucoma progression at the time of examination, with consideration of the possible physiological fluctuations in light sensitivity. Trend analysis of perimetric indices makes it possible to assess the rate of glaucoma progression and forecast the trend of changes in visual functions over the next five years. All these methods for assessing progression have certain advantages and disadvantages and cannot be considered ideal. Pointwise and cluster trend analysis are more sensitive in early glaucoma and are being actively researched and developed. These methods have great potential, although they are not yet sufficiently available in clinical practice.

Clinicians' Use of Quantitative Information while Assessing the Rate of Functional Progression in Glaucoma

PURPOSE

Clinicians use both global and point-wise information from visual fields to assess the rate of glaucomatous functional progression. We asked which objective, quantitative measures best correlated with subjective assessment by glaucoma experts. In particular, we aimed to determine how much that judgment was based on localized rates of change vs. on global indices reported by the perimeter.

DESIGN

Prospective cohort study.

PARTICIPANTS

Eleven academic, expert glaucoma specialists independently scored the rate of functional progression, from 1 (improvement) to 7 (very rapid progression), for a series of 5 biannual clinical printouts from 100 glaucoma or glaucoma suspect eyes of 51 participants, 20 of which were scored twice to assess repeatability.

METHODS

Regression models were used to predict the average of the 11 clinicians' scores based on objective rates of change of mean deviation (MD), visual field index (VFI), pattern standard deviation (PSD), the Nth fastest progressing location, and the Nth fastest progressing of 10 anatomically defined clusters of locations after weighting by eccentricity.

MAIN OUTCOME MEASURES

Correlation between the objective rates of change and the average of the 11 clinicians' scores.

RESULTS

The average MD of the study eyes was -2.4 dB (range, -16.8 to +2.8 dB). The mean clinician score was highly repeatable, with an intraclass correlation coefficient of 0.95. It correlated better with the rate of change of VFI (pseudo-R2 = 0.73, 95% confidence interval [CI, 0.60-0.83]) than with MD (pseudo-R2 = 0.63, 95% CI [0.45-0.76]) or PSD (pseudo-R2 = 0.41, 95% CI [0.26-0.55]). Using point-wise information, the highest correlations were found with the fifth-fastest progressing location (pseudo-R2 = 0.71, 95% CI [0.56-0.80]) and the fastest-progressing cluster after eccentricity weighting (pseudo-R2 = 0.61, 95% CI [0.48-0.72]). Among 25 eyes with an average VFI of > 99%, the highest observed pseudo-R2 value was 0.34 (95% CI [0.16-0.61]) for PSD.

CONCLUSIONS

Expert academic glaucoma specialists' assessment of the rate of change correlated best with VFI rates, except in eyes with a VFI near the ceiling of 100%. Sensitivities averaged within clusters of locations have been shown to detect change sooner, but the experts' opinions correlated more closely with global VFI. This could be because it is currently the only index for which the perimeter automatically provides a quantitative estimate of the rate of functional progression.

Clinicians' Use of Quantitative Information When Assessing the Rate of Structural Progression in Glaucoma

PURPOSE

OCT scans contain large amounts of information, but clinicians often rely on reported layer thicknesses when assessing the rate of glaucomatous progression. We sought to determine which of these quantifications most closely relate to the subjective assessment of glaucoma experts who had all the diagnostic information available.

DESIGN

Prospective cohort study.

PARTICIPANTS

Eleven glaucoma specialists independently scored the rate of structural progression from a series of 5 biannual clinical OCT printouts.

METHODS

A total of 100 glaucoma or glaucoma suspect eyes of 51 participants were included; 20 were scored twice to assess repeatability. Scores ranged from 1 (improvement) to 7 (very rapid progression). Generalized estimating equation linear models were used to predict the mean clinician score from the rates of change of retinal nerve fiber layer thickness (RNFLT) or minimum rim width (MRW) globally or in the most rapidly thinning of the 6 sectors.

MAIN OUTCOME MEASURES

The correlation between the objective rates of change and the average of the 11 clinicians' scores.

RESULTS

Average RNFLT within the series of study eyes was 79.3 μm (range, 41.4-126.6). Some 95% of individual clinician scores varied by ≤ 1 point when repeated. The mean clinician score was more strongly correlated with the rate of change of RNFLT in the most rapidly changing sector in %/year (pseudo-R2 = 0.657) than the rate of global RNFLT (0.372). The rate of MRW in the most rapidly changing sector had pseudo-R2 = 0.149.

CONCLUSIONS

The rate of change of RNFLT in the most rapidly changing sector predicted experts' assessment of the rate of structural progression better than global rates or MRW. Sectoral rates may be a useful addition to current clinical printouts.

A Prospective Longitudinal Study to Investigate Corneal Hysteresis as a Risk Factor of Central Visual Field Progression in Glaucoma

PURPOSE

To evaluate the role of corneal hysteresis (CH) as a risk factor of central visual field (VF) progression in a cohort of glaucoma suspect and glaucoma patients.

DESIGN

Prospective cohort study.

METHODS

Two hundred forty-eight eyes of 143 subjects who were followed for an average of 4.8 years with a minimum of 5 visits with 10-2 and 24-2 VF tests were included. Univariable and multivariable linear mixed-effects models were used to identify characteristics associated with the rate of change over time in 10-2 and 24-2 mean deviation (MD). Mixed-effects logistic regression was used to evaluate characteristics associated with an increased likelihood of event-based 10-2 VF progression based on the clustered pointwise linear regression criterion.

RESULTS

CH was significantly associated with 10-2 and 24-2 VF progression in the univariable trend-based analysis. In multivariable trend-based analyses, lower CH was associated with a faster rate of decline in 10-2 MD (0.07 dB/y per 1 mm Hg, P < .001) but not with 24-2 MD (P = .490). In multivariable event-based analysis, lower CH was associated with an increased likelihood of 10-2 VF progression (odds ratio = 1.35 per 1 mm Hg lower, P = .025). Similar results were found in eyes with early glaucomatous damage at the baseline (baseline: 24-2 MD ≥ -6 dB).

CONCLUSIONS

Lower CH was associated with a statistically significant, but relatively small, increased risk of central VF progression on the 10-2 test grid. Given the substantial influence of central VF impairment on the quality of life, clinicians should consider using CH to assess the risk of progression in patients with primary open-angle glaucoma including those with early disease.

An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence

OBJECTIVE

The objective of this study was to develop an objective and easy-to-use glaucoma staging system based on visual fields (VFs).

SUBJECTS AND PARTICIPANTS

A total of 13,231 VFs from 8077 subjects were used to develop models and 8024 VFs from 4445 subjects were used to validate models.

METHODS

We developed an unsupervised machine learning model to identify clusters with similar VF values. We annotated the clusters based on their respective mean deviation (MD). We computed optimal MD thresholds that discriminate clusters with the highest accuracy based on Bayes minimum error principle. We evaluated the accuracy of the staging system and validated findings based on an independent validation dataset.

RESULTS

The unsupervised k -means algorithm discovered 4 clusters with 6784, 4034, 1541, and 872 VFs and average MDs of 0.0 dB (±1.4: SD), -4.8 dB (±1.9), -12.2 dB (±2.9), and -23.0 dB (±3.8), respectively. The supervised Bayes minimum error classifier identified optimal MD thresholds of -2.2, -8.0, and -17.3 dB for discriminating normal eyes and eyes at the early, moderate, and advanced stages of glaucoma. The accuracy of the glaucoma staging system was 94%, based on identified MD thresholds with respect to the initial k -means clusters.

CONCLUSIONS

We discovered that 4 severity levels based on MD thresholds of -2.2, -8.0, and -17.3 dB, provides the optimal number of severity stages based on unsupervised and supervised machine learning. This glaucoma staging system is unbiased, objective, easy-to-use, and consistent, which makes it highly suitable for use in glaucoma research and for day-to-day clinical practice.

Long- and Short-Term Variability of Perimetry in Glaucoma

Purpose

Test–retest variability in perimetry consists of short-term and long-term components, both of which impede assessment of progression. By minimizing and quantifying the algorithm-dependent short-term variability, we can quantify the algorithm-independent long-term variability that reflects true fluctuations in sensitivity between visits. We do this at locations with sensitivity both < 28 dB (when the stimulus is smaller than Ricco's area and complete spatial summation can be assumed) and > 28 dB (when partial summation occurs).

Methods

Frequency-of-seeing curves were measured at four locations of 35 participants with glaucoma. The standard deviation of cumulative Gaussian fits to those curves was modeled for a given sensitivity and used to simulate the expected short-term variability of a 30-presentation algorithm. A separate group of 137 participants was tested twice with that algorithm, 6 months apart. Long-term variance at different sensitivities was calculated as the LOESS fit of observed test–retest variance minus the LOESS fit of simulated short-term variance.

Results

Below 28 dB, short-term variability increased approximately linearly with increasing loss. Long-term variability also increased with damage below this point, attaining a maximum standard deviation of 2.4 dB at sensitivity 21 dB, before decreasing due to the floor effect of the algorithm. Above 30 dB, the observed test–retest variance was slightly smaller than the simulated short-term variance.

Conclusions

Long-term and short-term variability both increase with damage for perimetric stimuli smaller than Ricco's area. Above 28 dB, long-term variability constitutes a negligible proportion of test–retest variability.

Translational Relevance

Fluctuations in true sensitivity increase in glaucoma, even after accounting for increased short-term variability. This long-term variability cannot be reduced by altering testing algorithms alone.

Structure-Function Relationship between Cluster Mean Defect and Sector Peripapillary Retinal Nerve Fiber Layer Thickness in Primary Open Angle Glaucoma

Purpose

To determine the structure–function relationship between cluster mean defect (MD) offered by standard automated perimetry and corresponding sector peripapillary retinal nerve fiber layer thickness (pRNFLT) measured with optical coherence tomography (OCT) in primary open angle glaucoma (POAG).

Method

39 healthy eyes (control group), 43 early POAG eyes (global MD ≤ 6 dB, early group), 30 moderate POAG eyes (global MD between 6 and 12 dB, moderate group), and 53 advanced POAG eyes (global MD > 12 dB, advanced group) underwent visual field (VF) examination with Octopus perimeter (dynamic strategy/G2 pattern) and peripapillary retinal nerve fiber layer thickness measurements with RTVue-100 FD-OCT. Spearman analysis was used to investigate the correlation between cluster MDs provided by Octopus perimeter and corresponding sector pRNFLT for the total sample and each subgroup, respectively. Then, linear (y = a+ bx) and curvilinear (quadratic, y = a+bx + cx²) regression analyses were employed to investigate the model for the cluster MD-sector pRNFLT pair with significant correlation. The strength of the relationship was characterized with correlation coefficient (ρ) and coefficient of determination (R²). For the cluster–sector pair that could be fitted by both models, Wilcoxon signed rank test of absolute residuals was used to compare the goodness of fit.

Results

Correlation between cluster MDs and corresponding sector pRNFLT was significant for all clusters in the total sample (ρ values: −0.572 to 0.832, P < 0.001) and in the POAG group (ρ values: −0.551 to −0.777, P < 0.001). The highest ρ values were found for cluster-sector pair 9 and pair 3, respectively. The curvilinear (quadratic) model provided better fit for all 10 cluster-sector pairs in the total sample (R² values: 0.431–0.687, P < 0.001) and in the POAG group (R² values: 0.364–0.594, P < 0.01). The highest R² values were found also for cluster–sector pair 9 and pair 3, respectively. In the control group, no significant correlation was found for any cluster–sector pair (P > 0.01). In the early group, correlation was significant for cluster–sector pairs 3, 8, and 9 (ρ values: −0.449, −0.627, and −0.815, resp., P < 0.01). In the moderate group, correlation was significant for pairs 2, 3, 8, and 9 (ρ values: −0.703, −0.556, −0.680, and −0.637, resp., P < 0.01). In the advanced group, correlation was significant (P < 0.01) for all 10 pairs (ρ values: −0.395 to −0.699, P < 0.001) except for pairs 2, 3, and 8, and the highest ρ value was found for pair 1. For all cluster–sector pairs with significant correlation in the early, moderate, and advanced groups, only linear model could be fitted (P < 0.01), except for pair 9 in the early group and pair 5 in the advanced group.

Conclusions

Cluster MD of the Octopus visual field showed significant moderate-to-strong negative correlation and curvilinear (quadratic) relationship with the corresponding sector pRNFLT for POAG. This type of regional structure–function relationship varied according to the severity of POAG, and at each stage, the significantly correlated cluster–sector pairs mainly showed linear relationship. The results could provide guidance for better utilization of this regional structure–function method in the management of different stages of POAG.

Pointwise and Region-Wise Course of Visual Field Loss in Patients With Glaucoma

Purpose

Accurate assessment of visual field (VF) trend may help clinicians devise the optimum treatment regimen. This study was conducted to investigate the behavior of VF sequences using pointwise and region-wise linear, exponential, and sigmoid regression models.

Materials and Methods

In a retrospective cohort study, 277 eyes of 139 patients with glaucoma who had been followed for at least 7 years were investigated. Linear, exponential, and sigmoid regression models were fitted for each VF test location and Glaucoma Hemifield Test (GHT) region to model the trend of VF loss. The model with the lowest root mean square error (RMSE) was selected as the best fit.

Results

The mean age (standard deviation [SD]) of the patients was 59.9 years (9.8) with a mean follow-up time of 9.3 (0.7) years. The exponential regression had the best fit based on pointwise and region-wise approaches in 39.3% and 38.1% of eyes, respectively. The results showed a better performance based on sigmoid regression in patients with initial VF sensitivity threshold greater than 22 dB (71.6% in pointwise and 62.2% in region-wise approaches). The overall RMSE of the region-wise regression model was lower than the overall RMSE of the pointwise model.

Conclusions

In the current study, nonlinear regression models showed a better fit compared to the linear regression models in tracking VF loss behavior. Moreover, findings suggest region-wise analysis may provide a more appropriate approach for assessing VF deterioration.

Translational Relevance

Findings may confirm a nonlinear progression of VF deterioration in patients with glaucoma.

Progression of Visual Field Damage within Clusters Depends on Preexisting Damage

Purpose To investigate, in a routine clinical setting, how global progression or progression within visual field clusters depends on preexisting damage.

Methods Glaucoma patients with a visual field damage of at least 3 dB at baseline and a series of at least 5 good quality examinations were scrutinized retrospectively. The change in visual field damage within 10 visual field clusters was assessed in a mixed linear effects model with age, baseline global mean deviation (MD), baseline cluster MD, and observation time as covariates. In addition, progression was tested for a global MD rate with age, baseline global MD, and observation time as covariates.

Results A total of 50 patients with a mean (± SD) age of 78 (± 13) years and a baseline global mean defect (MD) of 7.6 dB (± 4.4) fulfilling the selection criteria were identified between 2001 and 2019 out of 5019 patients in a visual field database of a tertiary ophthalmology center. Baseline visual field damage (global MD) correlated positively (p < 0.001) with the progression rate within clusters, but not with the global MD rate (p = 0.075). Higher age was a significant predictor for more rapid progression in both models (p < 0.001).

Conclusion In this retrospective study of patients in a routine clinical setting, who were not enrolled in studies, and simply receiving routine clinical care, analyzing progression within visual field clusters was more sensitive than assessing the global MD rate.

Characteristics of Central Visual Field Progression in Eyes with Optic Disc Hemorrhage

PURPOSE

To investigate the characteristics and rate of central visual field loss after optic disc hemorrhage (DH).

DESIGN

Prospective cohort study.

METHODS

Three hundred forty-three eyes of 220 subjects who had ≥3 years of follow-up with a minimum of 5 visits with 10-2 and 24-2 visual field (VF) were recruited. Rates of 10-2 mean deviation (MD) loss in each hemifield and predefined zones were compared using linear mixed-effects models in DH and non-DH eyes. Clustered pointwise regression analysis was also used to define central VF progressors and compared with 24-2 VF loss using guided progression analysis.

RESULTS

Thirty-nine eyes with DH and 304 eyes without DH had a mean follow-up of 5.2 years. Eyes with DH had rates of 10-2 MD loss that were 3 times faster than non-DH eyes (mean difference -0.36 dB/year [95% confidence interval 0.54-0.18]; P < .001) and were 3.7 times more likely to progress (P = .002). A larger proportion of glaucomatous eyes showed central VF progression rather than peripheral VF progression in the DH group (30.8% vs. 20.5%) compared with the non-DH group (10.9% vs. 9.2%). In early glaucoma, the rate of 10-2 MD loss was 5.5 times faster in DH eyes than in non-DH eyes (P < .001). Superonasal and superotemporal central VF regions progressed more rapidly than other regions, especially in DH eyes.

CONCLUSION

Central VF loss is accelerated in glaucoma eyes with DH and it corresponds topographically to the DH location. In patients with glaucoma with DH, one should consider supplementing 10-2 VFs with 24-2 VFS to monitor the disease.

Time Lag Between Functional Change and Loss of Retinal Nerve Fiber Layer in Glaucoma

Purpose

It is often suggested that structural change is detectable before functional change in glaucoma. However, this may be related to the lower variability and hence narrower normative limits of structural tests. In this study, we ask whether a time lag exists between the true rates of change in structure and function, regardless of clinical detectability of those changes.

Methods

Structural equation models were used to determine whether the rate of change in function (mean linearized total deviation, AveTDLin) or structure (retinal nerve fiber layer thickness [RNFLT]) was predicted by the concurrent or previous rate for the other modality, after adjusting for its own rate in the previous time interval. Rates were calculated over 1135 pairs of consecutive visits from 318 eyes of 164 participants in the Portland Progression Project, with mean 207 days between visits.

Results

The rate of change of AveTDLin was predicted by its own rate in the previous time interval, but not by rates of RNFLT change in either the concurrent or previous time interval (both P > 0.05). Similarly, the rate of RNFLT change was not predicted by concurrent AveTDLin change after adjusting for its own previous rate. However, the rate of AveTDLin change in the previous time interval did significantly improve prediction of the current rate for RNFLT, with P = 0.005, suggesting a time lag of around six months between changes in AveTDLin and RNFLT.

Conclusions

Although RNFL thinning may be detectable sooner, true functional change appears to predict and precede thinning of the RNFL in glaucoma.

Functional assessment of glaucoma: Uncovering progression

Clinicians who manage glaucoma patients carefully monitor the visual field to determine if treatments are effective or interventions are needed. Visual field tests may reflect disease progression or variability among examinations. We describe the approaches and perimetric tests used to evaluate glaucomatous visual field progression and factors that are important for identifying progression. These include stimulus size, which area of the visual field to assess (central versus peripheral), and the testing frequency, evaluating which is important to detect change early while minimizing patient testing burden. We also review the different statistical methods developed to identify change. These include trend- and event-based analyses, parametric and nonparametric tests, population-based versus individualized approaches, as well as pointwise and global analyses. We hope this information will prove useful and important to enhance the management of glaucoma patients. Overall, analysis procedures based on series of at least 5 to 6 examinations that require confirmation and persistence of changes, that are guided by the pattern and shape of the glaucomatous visual field deficits, and that are consistent with structural defects provide the best clinical performance.

Detection of functional deterioration in glaucoma by trend analysis using comprehensive overlapping clusters of locations

Detecting rapid visual field deterioration is crucial for individuals with glaucoma. Cluster trend analysis detects visual field deterioration with higher sensitivity than global analyses by using predefined non-overlapping subsets of visual field locations. However, it may miss small defects that straddle cluster borders. This study introduces a comprehensive set of overlapping clusters, and assesses whether this further improves progression detection. Clusters were defined as locations from where ganglion cell axons enter the optic nerve head within a θ° wide sector, centered at 1º intervals, for various θ. Deterioration in eyes with or at risk of glaucomatous visual field loss was “detected” if ≥ N_θ clusters had deteriorated with p < p_Cluster, chosen empirically to give 95% specificity based on permuting the series. N_θ was chosen to minimize the time to detect subsequently-confirmed deterioration in ≥ 1/3rd of eyes. Times to detect deterioration were compared using Cox survival models. Biannual series were available for 422 eyes of 214 participants. Predefined non-overlapping clusters detected subsequently-confirmed change in ≥ 1/3rd of eyes in 3.41 years (95% confidence interval 2.75–5.48 years). After equalizing specificity, no criteria based on comprehensive overlapping clusters detected deterioration significantly sooner. The quickest was 3.13 years (2.69–4.65) for θ° = 20° and N_θ = 25, but the comparison with non-overlapping clusters had p = 0.672. Any improvement in sensitivity for detecting deterioration when using a comprehensive set of overlapping clusters was negated by the need to maintain equal specificity. The existing cluster trend analysis using predefined non-overlapping clusters provides a useful tool for monitoring visual field progression.

Detection of Functional Deterioration in Glaucoma by Trend Analysis Using Overlapping Clusters of Locations

Purpose

Cluster trend analysis detects glaucomatous deterioration within predefined subsets (clusters) of visual field locations. However, it may miss small defects straddling boundaries between the clusters. This study assesses whether simultaneously using a second set of clusters, overlapping the first, could improve progression detection.

Methods

Deterioration in eyes with or at risk of glaucomatous visual field loss was "detected" by mean deviation (MD) on the first visit at which the P value from linear regression over time was below the fifth percentile of its permutation distribution. Similarly, P values were calculated for each of 10 predefined nonoverlapping clusters of locations, or 21 overlapping clusters; deterioration was "detected" when the Nth-smallest P value was below the fifth percentile of its permutation distribution, for different N. Times to detect deterioration were compared using survival models.

Results

Biannual series of ≥5 visual fields (mean = 14) were available for 420 eyes of 213 participants. Deterioration of 33% of eyes was detected earliest using N = 1 overlapping cluster in 3.3 years (95% confidence interval 2.7-4.6 years); or N = 2 nonoverlapping clusters in 3.3 years (2.7-5.0) (comparison P = 0.654). There was also no significant difference in the probability that deterioration would be confirmed (92.8% vs. 94.4%, P = 0.289). Both overlapping and nonoverlapping clusters detected deterioration significantly sooner than MD (4.5 years, P ≤ 0.001).

Conclusions

After equalizing specificity, overlapping clusters of locations did not significantly reduce the time to detect deterioration compared with nonoverlapping clusters.

Translational Relevance

Cluster trend analyses detected deterioration sooner than global analyses even when defects straddled cluster borders.

Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard

PURPOSE

To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.

DESIGN

Retrospective, cross-sectional, longitudinal cohort study.

PARTICIPANTS

Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models.

METHOD

We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma.

MAIN OUTCOME MEASURES

The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma.

RESULTS

After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%.

CONCLUSIONS

The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.

Increased Optic Nerve Head Capillary Blood Flow in Early Primary Open-Angle Glaucoma

Purpose

Blood flow in the optic nerve head (ONH) is known to be reduced in eyes with advanced glaucoma. However, experimental results from non-human primates suggest an initial increase in ONH blood flow at the earliest stages of damage. This study assesses flow and pulsatile hemodynamics across a range of severities to test the hypothesis that this also occurs in human glaucoma.

Methods

Laser speckle flowgraphy was used to measure average mean blur rate (MBR_ave) within ONH tissue (a correlate of capillary blood flow) and the pulsatile waveform in 93 eyes with functional loss and 74 glaucoma suspect/fellow eyes without functional loss. These were compared against results from 92 healthy control eyes. Parameters produced by the instrument's software were age-corrected, then compared between groups using generalized estimating equation models.

Results

The mean MBR_ave in the control eyes was 12.5 units. In glaucoma suspect/fellow eyes, the mean was 16.4 units, higher with P < 0.0001. In eyes with functional loss, the mean was 13.8 units, lower than eyes without functional loss with P < 0.0001, although still higher than control eyes with P = 0.0096. Analysis of the pulsatile waveform suggested that the deceleration in flow as it approaches its maximum across the cardiac cycle was delayed in glaucoma.

Conclusions

Blood flow within ONH capillaries was higher in glaucoma suspect eyes than in healthy controls. It was less elevated in eyes that had developed functional loss. The mechanisms causing these changes and their relation to concurrent changes in pulsatile hemodynamics remain under investigation.

Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder

In this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4th, 6th and 8th visits from the first three (e.g., visit eight: VAE8: 5.14 dB vs. PW: 8.07 dB; P < 0.001). A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage.

Risk Factors for Fast Visual Field Progression in Glaucoma

PURPOSE

To identify baseline and longitudinal risk factors for fast visual field (VF) decay in patients with open-angle glaucoma.

DESIGN

Retrospective cohort study.

METHODS

Patients with open-angle glaucoma with ≥6 VFs and ≥4 years of follow-up were included. VF decay rates were measured with the following methods: mean deviation (MD) rate, VF index (VFI) rate, and the Glaucoma Rate Index (GRI). The relationship between VF rates and clinical variables were investigated with linear mixed models. Logistic regression analysis was performed to determine which factors were associated with fast progression.

RESULTS

A total of 1317 eyes of 745 patients with a mean (± SD) age of 63.3 (±10.9) years and a median (interquartile range) MD -2.4 (-0.7 to -5.6) dB at baseline were analyzed. The median (interquartile range) number of VFs was 12 (9 to 16), and mean follow-up duration was 11.5 (±3.7) years. Older age (P < .001), higher peak intraocular pressure (IOP) (P < .001), and glaucoma surgery during the study period (P < .001) were associated with faster rates of progression regardless of the method used. Worse baseline MD was associated with MD rate (P = .02), but neither with VFI rate (P = .37) nor GRI (P = .31); whereas pseudoexfoliative glaucoma was associated with faster rates of progression with MD (P = .008) and VFI (P = .01) rates, but not with GRI. Higher peak IOPs (P = .005) was a significant predictor for fast progression.

CONCLUSION

In this cohort, older age, peak IOP, pseudoexfoliative glaucoma, and baseline MD were associated with the rate of glaucomatous VF worsening. Fast progressors had a higher peak IOP than non-fast progressors. The identification and appropriately aggressive treatment of fast progressors would reduce visual disability from glaucoma.

Cluster analysis of computerized visual field and optical coherence tomography–ganglion cell complex defects in high intraocular pressure patients or early stage glaucoma

Purpose:

The aim of the study is to evaluate the relationship between functional defects shown by cluster analysis of computerized visual field and anatomic defects from optical coherence tomography–ganglion cell complex examination in ocular hypertension or eyes affected by glaucoma.

Methods:

205 eyes affected by ocular hypertension (intraocular pressure > 22 mmHg) or early stage glaucoma were enrolled. The age of the patients ranged from 26 to 87 years (average: 61.83 ± 1.54 years). Computerized 30° visual field (Octopus G1x Dynamic strategy) and optical coherence tomography–ganglion cell complex (I-Vue Optovue) analyses were performed for each eye selected; 68 eyes were tested and retested from two to seven times for a total of 320 visual fields and 320 optical coherence tomography–ganglion cell complex examinations. The visual field was considered abnormal with a mean defect < –2 and loss variance > 6. The optical coherence tomography–ganglion cell complex was considered abnormal with a significant focal loss volume (p < 5%) and/or a significant thinning of total, superior, or inferior thickness (p < 5%). Four different groups of examinations were created according to the results of visual field and ganglion cell complex: normal visual field and normal ganglion cell complex (group 1), abnormal visual field and abnormal ganglion cell complex (group 2), normal visual field and abnormal ganglion cell complex (group 3), and abnormal visual field and normal ganglion cell complex (group 4). The cluster analysis of visual fields (EyeSuite software Interzeag CH) was performed only in the visual field of group 3, and the correlation between cluster values and topographical changes at optical coherence tomography–ganglion cell complex was analyzed.

Results:

The results of the ganglion cell complex and visual field examinations matched 247 (77.19%) times. In 143 cases, the examinations belonged to group 1, in 104 to group 2, in 23 to group 3, and, finally, in 50 to group 4. The visual field cluster analysis performed on group 3 showed that the correlation between optical coherence tomography–ganglion cell complex and visual field cluster analysis defects was 100% (both the exams altered). In 72% of them, there was also a topographical correspondence between the visual field and optical coherence tomography–ganglion cell complex defects.

Conclusion:

In the early stages of glaucoma, the visual field cluster analysis seems to be useful to detect some focal defects that can be otherwise underestimated when globally considering the visual field. In group 3, where the conventional analysis of visual field was normal while the optical coherence tomography–ganglion cell complex exam was abnormal, the visual field cluster analysis showed a topographical correlation with optical coherence tomography–ganglion cell complex defects in more than 70% of the examinations performed. In addition, the patients with abnormal visual field and normal optical coherence tomography–ganglion cell complex were older than those with normal visual field and abnormal optical coherence tomography–ganglion cell complex (66.44 ± 3.51 vs 57.04 ± 5.96 years, p < 0.001 (0.0002)). These results confirm that the reliability of a visual field examination is subjective and decreases with age because of its difficulty and the personal compliance of the patient toward this examination.

Application of Pattern Recognition Analysis to Optimize Hemifield Asymmetry Patterns for Early Detection of Glaucoma

Purpose

To assess the diagnostic utility of a new hemifield asymmetry analysis derived using pattern recognition contrast sensitivity isocontours (CSIs) within the Humphrey Field Analyzer (HFA) 24-2 visual field (VF) test grid. The performance of an optimal CSI-derived map was compared against a commercially available clustering method (Glaucoma Hemifield Test, GHT).

Methods

Five hundred VF results of 116 healthy subjects were used to determine normative distribution limits for comparisons. Pattern recognition analysis was applied to HFA 24-2 sensitivity data to determine CSI theme maps delineating clusters for hemifield comparisons. Then, 1019 VF results from 228 glaucoma patients were assessed using different clustering methods to determine the true-positive rate. We also assessed additional 354 VF results of 145 healthy subjects to determine the false-positive rate.

Results

The optimum clustering method was the CSI-derived seven-theme class map, which identified more glaucomatous VFs compared with the GHT map. The seven-class theme map also identified more cases compared with the five-, six-, and eight-class maps, suggesting no effect of number of clusters. Integrating information regarding the location of glaucomatous defects to the CSI clusters did not improve detection rate.

Conclusions

A clustering map derived using CSIs improved detection of glaucomatous VFs compared with the currently available GHT. An optimized CSI-derived map may serve as an additional means to aid earlier detection of glaucoma.

Translational Relevance

Pattern recognition–derived theme maps provide a means for guiding test point selection for asymmetry analysis in glaucoma assessment.

Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning

PURPOSE

Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine learning-based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices.

DESIGN

Development and comparison of a prognostic index.

METHOD

Visual fields from 2085 eyes of 1214 subjects were used to identify glaucoma progression patterns using machine learning. Visual fields from 133 eyes of 71 glaucoma patients were collected 10 times over 10 weeks to provide a no-change, test-retest dataset. The parameters of all methods were identified using visual field sequences in the test-retest dataset to meet fixed 95% specificity. An independent dataset of 270 eyes of 136 glaucoma patients and survival analysis were used to compare methods.

RESULTS

The time to detect progression in 25% of the eyes in the longitudinal dataset using global mean deviation (MD) was 5.2 (95% confidence interval, 4.1-6.5) years; 4.5 (4.0-5.5) years using region-wise, 3.9 (3.5-4.6) years using point-wise, and 3.5 (3.1-4.0) years using machine learning analysis. The time until 25% of eyes showed subsequently confirmed progression after 2 additional visits were included were 6.6 (5.6-7.4) years, 5.7 (4.8-6.7) years, 5.6 (4.7-6.5) years, and 5.1 (4.5-6.0) years for global, region-wise, point-wise, and machine learning analyses, respectively.

CONCLUSIONS

Machine learning analysis detects progressing eyes earlier than other methods consistently, with or without confirmation visits. In particular, machine learning detects more slowly progressing eyes than other methods.

Development of a Visual Field Simulation Model of Longitudinal Point-Wise Sensitivity Changes From a Clinical Glaucoma Cohort

Purpose

To develop a new visual field simulation model that can recreate real-world longitudinal results at a point-wise level from a clinical glaucoma cohort.

Methods

A cohort of 367 glaucoma eyes from 265 participants seen over 10.1 ± 2.5 years were included to obtain estimates of “true” longitudinal visual field point-wise sensitivity and estimates of measurement variability. These two components were then combined to reconstruct visual field results in a manner that accounted for correlated measurement error. To determine how accurately the simulated results reflected the clinical cohort, longitudinal variability estimates of mean deviation (MD) were determined by calculating the SD of the residuals from linear regression models fitted to the MD values over time for each eye in the simulated and clinical cohorts. The new model was compared to a previous model that does not account for spatially correlated errors.

Results

The SD of all the residuals for the clinical and simulated cohorts was 1.1 dB (95% confidence interval [CI]: 1.1–1.2 dB) and 1.1 dB (95% CI: 1.1–1.1 dB), respectively, whereas it was 0.4 dB (95% CI: 0.4–0.4 dB) using the previous simulation model that did not account for correlated errors.

Conclusions

A new simulation model accounting for correlated measurement errors between visual field locations performed better than a previous model in estimating visual field variability in glaucoma.

Translational Relevance

This model can provide a powerful framework to better understand use of visual field testing in clinical practice and trials and to evaluate new methods for detecting progression.