Quantification of Visual Field Variability in Glaucoma: Implications for Visual Field Prediction and Modeling

Quantification and Predictors of Visual Field Variability in Healthy, Glaucoma Suspect, and Glaucomatous Eyes Using SITA-Faster

PURPOSE

The newly released Swedish Interactive Thresholding Algorithm (SITA)-Faster (SFR) has significantly shorter testing durations compared with older SITA algorithms, but its variability is uncertain. This study quantified and established threshold limits of test-retest variability across the 24-2 test grid using SFR.

DESIGN

Cross-sectional study with prospective longitudinal arm.

PARTICIPANTS

1426 eyes of 787 patients with healthy, suspected glaucoma, or manifest glaucoma eyes from hospital- and university- eye clinics.

METHODS

Two SFR tests per eye at a baseline visit and at two follow-up visits.

MAIN OUTCOME MEASURES

Pointwise variability measured by test-retest difference in pointwise sensitivity between tests one and two, mean global variability (test-retest variance) measured by average of pointwise variability for each participant, global sensitivity, and reliability indices of each eye.

RESULTS

Of the 1426 eyes, 540 eyes (37.9%) had a diagnosis of glaucoma, 753 eyes (52.8%) were suspected of having glaucoma, and the remaining 133 eyes (9.3%) were healthy. Of 74 152 pointwise sensitivities obtained, the mean test-retest difference was 2.17 ± 2.9 dB, whereas the mean test-retest variance for each participant was 2.17 ± 1.2 dB. Pointwise and global variability increased with worsening threshold sensitivity and (MD), respectively, and was greater for peripheral compared with central test locations. In the longitudinal cohort, no significant difference in mean test-retest variance was found across the 3 visits (mean variability, 2.10 dB vs. 2.16 dB vs. 2.16 dB at visits F0 vs. F1 vs. F2; P = 0.53, repeated-measures analysis of variance). Baseline MD (-0.19 dB; 95% CI, -0.22 to 0.16 dB; P < 0.0001) and abnormally high sensitivity on glaucoma hemifield test (1.14 dB; 95% CI, 0.78-1.51 dB; P < 0.0001) were significantly associated with increased variability. Finally, test-retest MD showed minimal change around the recommended 15% false-positive cutoff threshold.

CONCLUSIONS

The variability of SFR increases with worsening threshold sensitivity, is stable over time, and is greater for peripheral compared with central test locations. Worse baseline MD and abnormally high sensitivity are significant predictors of increased variability. A cutoff of 15% in false-positive results may be inappropriate as a threshold for judging test reliability in SFR.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Association of macular vessel density and ganglion cell complex thickness with central visual field progression in glaucoma

BACKGROUND/AIMS

To evaluate the association of macular vessel density (VD) and ganglion cell complex (GCC) thickness with 10-2 central visual field (CVF) progression in glaucoma.

METHODS

In this retrospective cohort study, patients with glaucoma from Diagnostic Innovation in Glaucoma Study with≥five 10-2 visual field (VF) tests and 3-year follow-up before optical coherence tomography (OCT) and OCT angiography (OCTA) imaging were included. Whole-image GCC thickness (wiGCC) and superficial VD (wiVD) were obtained from 6*6 macula scans. The association of wiVD and wiGCC with past rate of 10-2 VF mean deviation worsening, and with past CVF progression (defined using clustered linear regression criteria) was evaluated using linear mixed models after adjusting for confounders.

RESULTS

From 238 eyes (141 patients), 25 eyes (11%) of 16 patients were CVF progressors. In the multivariable analysis of the association between OCT/OCTA parameters and past rate of 10-2 CVF worsening, lower wiVD (β=-0.04 (-0.05, -0.02); p<0.001; R2=0.32) and wiGCC (β=-0.01 (-0.01, 0.00); p=0.004; R2=0.21) were significantly associated with faster CVF worsening. For the association between OCT/OCTA parameters and past CVF progression, the multivariable analysis showed that a lower wiVD was significantly associated with increased odds of past CVF progression (OR=1.23 (1.06, 1.44) per 1% lower; p=0.008), while wiGCC did not show correlation.

CONCLUSIONS

Lower macular VD and GCC were associated with faster worsening of CVF, and lower macular VD was associated with increased odds of CVF progression. Assessment of macular OCT and OCTA may help detect glaucoma eyes with CVF progression.

Endpoints for clinical trials in ophthalmology

With the identification of novel targets, the number of interventional clinical trials in ophthalmology has increased. Visual acuity has for a long time been considered the gold standard endpoint for clinical trials, but in the recent years it became evident that other endpoints are required for many indications including geographic atrophy and inherited retinal disease. In glaucoma the currently available drugs were approved based on their IOP lowering capacity. Some recent findings do, however, indicate that at the same level of IOP reduction, not all drugs have the same effect on visual field progression. For neuroprotection trials in glaucoma, novel surrogate endpoints are required, which may either include functional or structural parameters or a combination of both. A number of potential surrogate endpoints for ophthalmology clinical trials have been identified, but their validation is complicated and requires solid scientific evidence. In this article we summarize candidates for clinical endpoints in ophthalmology with a focus on retinal disease and glaucoma. Functional and structural biomarkers, as well as quality of life measures are discussed, and their potential to serve as endpoints in pivotal trials is critically evaluated.

Multiple (frontloaded) visual field tests increase identification of very slow mean deviation progression in glaucoma

OBJECTIVE

To determine the effect of frontloading (multiple) visual field (VF) tests at the same visit for detecting mean deviation (MD) change in slowly progressive glaucoma.

METHODS

This was a computer simulation study. Baseline MD (range, 0 to -12 dB) and progression rate (range, 0 to -0.4 dB/year, non-inclusive) were generated for 10,000 patients. Each patient had 6 simulated "stable" baseline VF tests. Then follow-up VFs (up to 10 years) were generated by incorporating progression rate and within-visit and between-visit variability. The independent variables were number of VF tests per visit (one non-frontloaded or two frontloaded), VF reliability (100%, 85%, or 70%), repeat testing because of unreliable results (yes or no), and follow-up interval (6-monthly or yearly). The outcomes were detection of progression (MD slope that was negative and significant at p < 0.05), MD at detection, and number of years to detection.

RESULTS

Frontloading identified more progressors (62.7%-79.2%) compared with non-frontloading (31.0%-36.7%) at 10 years (p < 0.0001). Six-monthly follow-ups led to greater detection than yearly intervals. Progressors detected by both methods were detected by the non-frontloaded method sooner (up to 0.26 years), but this was small and not clinically significant (MD difference, 0.06 dB). An increase (less severe) in MD, an increase (slower) in progression rate, and an increase in SD of baseline VFs decreased the likelihood of detecting progression.

CONCLUSIONS

Frontloading VF tests at 6-monthly intervals improve detection rates of MD progression in slowly progressive glaucoma patients compared with performing 1 test per visit at yearly intervals.

Use of artificial intelligence in forecasting glaucoma progression

Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

[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.

Visual Field Prediction: Evaluating the Clinical Relevance of Deep Learning Models

Purpose

Two novel deep learning methods using a convolutional neural network (CNN) and a recurrent neural network (RNN) have recently been developed to forecast future visual fields (VFs). Although the original evaluations of these models focused on overall accuracy, it was not assessed whether they can accurately identify patients with progressive glaucomatous vision loss to aid clinicians in preventing further decline. We evaluated these 2 prediction models for potential biases in overestimating or underestimating VF changes over time.

Design

Retrospective observational cohort study.

Participants

All available and reliable Swedish Interactive Thresholding Algorithm Standard 24-2 VFs from Massachusetts Eye and Ear Glaucoma Service collected between 1999 and 2020 were extracted. Because of the methods' respective needs, the CNN data set included 54 373 samples from 7472 patients, and the RNN data set included 24 430 samples from 1809 patients.

Methods

The CNN and RNN methods were reimplemented. A fivefold cross-validation procedure was performed on each model, and pointwise mean absolute error (PMAE) was used to measure prediction accuracy. Test data were stratified into categories based on the severity of VF progression to investigate the models' performances on predicting worsening cases. The models were additionally compared with a no-change model that uses the baseline VF (for the CNN) and the last-observed VF (for the RNN) for its prediction.

Main Outcome Measures

PMAE in predictions.

Results

The overall PMAE 95% confidence intervals were 2.21 to 2.24 decibels (dB) for the CNN and 2.56 to 2.61 dB for the RNN, which were close to the original studies' reported values. However, both models exhibited large errors in identifying patients with worsening VFs and often failed to outperform the no-change model. Pointwise mean absolute error values were higher in patients with greater changes in mean sensitivity (for the CNN) and mean total deviation (for the RNN) between baseline and follow-up VFs.

Conclusions

Although our evaluation confirms the low overall PMAEs reported in the original studies, our findings also reveal that both models severely underpredict worsening of VF loss. Because the accurate detection and projection of glaucomatous VF decline is crucial in ophthalmic clinical practice, we recommend that this consideration is explicitly taken into account when developing and evaluating future deep learning models.

Hierarchical Censored Bayesian Analysis of Visual Field Progression

Purpose

To develop a Bayesian model (BM) for visual field (VF) progression accounting for the hierarchical, censored and heteroskedastic nature of the data.

Methods

Three versions of a hierarchical BM were developed: a simple linear (Hi-linear); censored at 0 dB (Hi-censored); heteroskedastic censored (Hi-HSK). For the latter, we modeled the test variability according to VF sensitivity using a large test-retest cohort (1396 VFs, 146 eyes with glaucoma). We analyzed a large cohort of 44,371 VF tests from 3352 eyes from five glaucoma clinics. We quantified the bias in the estimated rate-of-progression, the detection of progression (Hit-rate [HR]), the median time-to-progression and the prediction error of future observations (mean absolute error [MAE]). HR and time-to-progression were compared at matched false-positive-rate (FPR), quantified using permutations of a separate test-retest cohort (360 tests, 30 eyes with glaucoma). BMs were compared to simple linear regression and Permutation-Analyses-of Pointwise-Linear-Regression. Differences in time-to-progression were tested using survival analysis.

Results

Censored models showed the smallest bias in the rate-of-progression. The three BMs performed very similarly in terms of HR and time-to-progression and always better than the other methods. The average reduction in time-to-progression was 37% with the BMs (P < 0.001) at 5% FPR. MAE for prediction was very similar among methods.

Conclusions

Bayesian hierarchical models improved the detection of VF progression. Accounting for censoring improves the precision of the estimates, but minimal effect is provided by accounting for heteroskedasticity.

Translational Relevance

These results are relevant for quantification of VF progression in practice and for clinical trials.

Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management

PURPOSE OF REVIEW

The field of artificial intelligence has grown exponentially in recent years with new technology, methods, and applications emerging at a rapid rate. Many of these advancements have been used to improve the diagnosis and management of glaucoma. We aim to provide an overview of recent publications regarding the use of artificial intelligence to enhance the detection and treatment of glaucoma.

RECENT FINDINGS

Machine learning classifiers and deep learning algorithms have been developed to autonomously detect early structural and functional changes of glaucoma using different imaging and testing modalities such as fundus photography, optical coherence tomography, and standard automated perimetry. Artificial intelligence has also been used to further delineate structure-function correlation in glaucoma. Additional 'structure-structure' predictions have been successfully estimated. Other machine learning techniques utilizing complex statistical modeling have been used to detect glaucoma progression, as well as to predict future progression. Although not yet approved for clinical use, these artificial intelligence techniques have the potential to significantly improve glaucoma diagnosis and management.

SUMMARY

Rapidly emerging artificial intelligence algorithms have been used for the detection and management of glaucoma. These algorithms may aid the clinician in caring for patients with this complex disease. Further validation is required prior to employing these techniques widely in clinical practice.

Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility

Purpose

To investigate intereye associations of visual field (VF) defects.

Methods

We selected 24-2 VF pairs of both eyes from 63,604 patients tested on the same date with mean deviation (MD) ≥ −12 dB. VFs were decomposed into one normal and 15 defect patterns previously identified using archetypal analysis. VF pattern weighting coefficients were correlated between the worse and better eyes, as defined by MD. VF defect patterns (weighting coefficients > 10%) in the better eye were predicted from weighting coefficients of the worse eye by logistic regression models, which were evaluated by area under the receiver operating characteristic curve (AUC).

Results

Intereye correlations of archetypal VF patterns were strongest for the same defect pattern between fellow eyes. The AUCs for predicting the presence of 15 defect patterns in the better eye based on the worse eye ranged from 0.69 (superior nasal step) to 0.92 (near total loss). The AUC for predicting superior paracentral loss was 0.89. Superior paracentral loss in the better eye was positively correlated with coefficients of superior paracentral loss, central scotoma, superior altitudinal defect, nasal hemianopia, and inferior paracentral loss in the worse eye, and negatively correlated with coefficients of the normal VF, superior peripheral defect, concentric peripheral defect, and temporal wedge. The parameters are presented in the descending order of statistical significance.

Conclusions

VF patterns of the worse eye are predictive of VF defects in the better eye.

Translational Relevance

Our models can potentially assist clinicians to better interpret VF loss under measurement uncertainty.

Optical coherence tomography and optical coherence tomography angiography in glaucoma: diagnosis, progression, and correlation with functional tests

The present review will summarize the most updated findings with regards to optical coherence tomography and optical coherence tomography angiography in glaucoma, highlighting their clinical use for detection and monitoring of the disease, and their correlation to functional tests (such as visual field) widely employed in the asset of modern glaucoma clinics.