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

Estimating the Distribution of True Rates of Visual Field Progression in Glaucoma

Purpose

The purpose of this study was to estimate the distribution of the true rates of progression (RoP) of visual field (VF) loss.

Methods

We analyzed the progression of mean deviation over time in series of ≥ 10 tests from 3352 eyes (one per patient) from 5 glaucoma clinics, using a novel Bayesian hierarchical Linear Mixed Model (LMM); this modeled the random-effect distribution of RoPs as the sum of 2 independent processes following, respectively, a negative exponential distribution (the "true" distribution of RoPs) and a Gaussian distribution (the "noise"), resulting in a skewed exGaussian distribution. The exGaussian-LMM was compared to a standard Gaussian-LMM using the Watanabe-Akaike Information Criterion (WAIC). The random-effect distributions were compared to the empirical cumulative distribution function (eCDF) of linear regression RoPs using a Kolmogorov-Smirnov test.

Results

The WAIC indicated a better fit with the exGaussian-LMM (estimate [standard error]: 192174.4 [721.2]) than with the Gaussian-LMM (192595 [697.4], with a difference of 157.2 [22.6]). There was a significant difference between the eCDF and the Gaussian-LMM distribution (P < 0.0001), but not with the exGaussian-LMM distribution (P = 0.108). The estimated mean (95% credible intervals, CIs) "true" RoP (-0.377, 95% CI = -0.396 to -0.359 dB/year) was more negative than the observed mean RoP (-0.283, 95% CI = -0.299 to -0.268 dB/year), indicating a bias likely due to learning in standard LMMs.

Conclusions

The distribution of "true" RoPs can be estimated with an exGaussian-LMM, improving model accuracy.

Translational Relevance

We used these results to develop a fast and accurate analytical approximation for sample-size calculations in clinical trials using standard LMMs, which was integrated in a freely available web application.

Validating Trend-Based End Points for Neuroprotection Trials in Glaucoma

Purpose

The purpose of this study was to evaluate the power of trend-based visual field (VF) progression end points against long-term development of event-based end points accepted by the US Food and Drug Administration (FDA).

Methods

One eye from 3352 patients with ≥10 24-2 VFs (median = 11 years) follow-up were analyzed. Two FDA-compatible criteria were applied to these series to label "true-progressed" eyes: ≥5 locations changing from baseline by more than 7 dB (FDA-7) or by more than the expected test-retest variability (GPA-like) in 2 consecutive tests. Observed rates of progression (RoP) were used to simulate trial-like series (2 years) randomly assigned (1000 times) to a "placebo" or a "treatment" arm. We simulated neuroprotective "treatment" effects by changing the proportion of "true progressed" eyes in the two arms. Two trend-based methods for mean deviation (MD) were assessed: (1) linear mixed model (LMM), testing average difference in RoP between the two arms, and (2) time-to-progression (TTP), calculated by linear regression as time needed for MD to decline by predefined cutoffs from baseline. Power curves with 95% confidence intervals were calculated for trend and event-based methods on the simulated series.

Results

The FDA-7 and GPA-like progression was achieved by 45% and 55% of the eyes in the clinical database. LMM and TTP had similar power, significantly superior to the event-based methods, none of which reached 80% power. All methods had a 5% false-positive rate.

Conclusions

The trend-based methods can efficiently detect treatment effects defined by long-term FDA-compatible progression.

Translational Relevance

The assessment of the power of trend-based methods to detect clinically relevant progression end points.

Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning

Purpose

To assess the performance of a perimetric strategy using structure-function predictions from a deep learning (DL) model.

Methods

Visual field test-retest data from 146 eyes (75 patients) with glaucoma with (median [5th-95th percentile]) 10 [7, 10] tests per eye were used. Structure-function predictions were generated with a previously described DL model using cicumpapillary optical coherence tomography (OCT) scans. Structurally informed prior distributions were built grouping the observed measured sensitivities for each predicted value and recalculated for each subject with a leave-one-out approach. A zippy estimation by sequential testing (ZEST) strategy was used for the simulations (1000 per eye). Ground-truth sensitivities for each eye were the medians of the test-retest values. Two variations of ZEST were compared in terms of speed (average total number of presentations [NP] per eye) and accuracy (average mean absolute error [MAE] per eye), using either a combination of normal and abnormal thresholds (ZEST) or the calculated structural distributions (S-ZEST) as prior information. Two additional versions of these strategies employing spatial correlations were tested.

Results

S-ZEST was significantly faster, with a mean average NP of 213.87 (SD = 28.18), than ZEST, with a mean average NP of 255.65 (SD = 50.27) (P < 0.001). The average MAE was smaller for S-ZEST (1.98; SD = 2.37) than ZEST (2.43; SD = 2.69) (P < 0.001). Spatial correlations further improved both strategies (P < 0.001), but the differences between ZEST and S-ZEST remained significant (P < 0.001).

Conclusions

DL structure-function predictions can significantly improve perimetric tests.

Translational Relevance

DL structure-function predictions from clinically available OCT scans can improve perimetry in glaucoma patients.

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.

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.

The Frontloading Fields Study (FFS): Detecting Changes in Mean Deviation in Glaucoma Using Multiple Visual Field Tests Per Clinical Visit

Purpose

To determine the impact of different numbers of visual field tests per visit for detecting mean deviation changes over time in patients with early glaucoma or suspected glaucoma and to identify a practical approach to maximize change detection.

Methods

Intrasession (n = 322) and intersession (n = 323) visual field results for patients with glaucoma or suspected glaucoma were used to model mean deviation change in 10,000 progressing and 10,000 non-progressing computer-simulated patients over time. Variables assessed in the model included follow-up intervals (0.5, 1, or 2 years), reliability rates (70%, 85%, or 100%) and number of visual field tests performed at each visit (one to four).

Results

Two visual field tests per session compared with one provided higher case detection rates at 2 years (99%–99.8% vs. 34.7%–76.3%, respectively), reduced time to detection (three or four visits vs. six to 10, respectively), and more positive mean deviation score (−4 dB vs. −10 dB, respectively) at the point of mean deviation change identification, especially in the context of unreliable results. Performing two tests per visit offered similar advantages compared with more tests. False positive change detection rates (<2.5%), were similar across all conditions. Patients followed up 6 monthly had less severe mean deviation loss at follow-up compared to 1-year and 2-year follow-up intervals.

Conclusions

Performing two tests per clinical visit at 6 months is practical using SITA-Faster and provides higher detection rates of mean deviation change in comparison with only one test performed per visit and more spaced-out intervals.

Translational Relevance

This model provides guidance for selecting the number of tests per visit to detect mean deviation change.

Variability and Power to Detect Progression of Different Visual Field Patterns

PURPOSE

To compare the variability and ability to detect visual field (VF) progression of 24-2, central 12 locations of the 24-2 and 10-2 VF tests in eyes with abnormal VFs.

DESIGN

Retrospective, multisite cohort.

PARTICIPANTS

A total of 52 806 24-2 and 11 966 10-2 VF tests from 7307 eyes from the Glaucoma Research Network database were analyzed. Only eyes with ≥ 5 visits and ≥ 2 years of follow-up were included.

METHODS

Linear regression models were used to calculate the rates of mean deviation (MD) change (slopes), whereas their residuals were used to assess variability across the entire MD range. Computer simulations (n = 10 000) based on real MD residuals of our sample were performed to estimate power to detect significant progression (P < 5%) at various rates of MD change.

MAIN OUTCOME MEASURES

Time required to detect progression.

RESULTS

For all 3 patterns, the MD variability was highest within the -5 to -20 decibel (dB) range and consistently lower with the 10-2 compared with 24-2 or central 24-2. Overall, time to detect confirmed significant progression at 80% power was the lowest with 10-2 VF, with a decrease of 14.6% to 18.5% when compared with 24-2 and a decrease of 22.9% to 26.5% when compared with central 24-2.

CONCLUSIONS

Time to detect central VF progression was reduced with 10-2 MD compared with 24-2 and C24-2 MD in glaucoma eyes in this large dataset, in part because 10-2 tests had lower variability. These findings contribute to current evidence of the potential value of 10-2 testing in the clinical management of patients with glaucoma and in clinical trial design.

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.

Improving the Power of Glaucoma Neuroprotection Trials Using Existing Visual Field Data

PURPOSE

Selecting reliable visual field (VF) test takers could improve the power of randomized clinical trials in glaucoma. We test this hypothesis via simulations using a large real world data set.

DESIGN

Methodology analysis: assessment of how improving reliability affects sample size estimates.

METHODS

A variability index (VI) estimating intertest variability was calculated for each subject using the residuals of the regression of the mean deviation over time for the first 6 tests in a series of at least 10 examinations for 2,804 patients. Using data from the rest of the series, we simulate VFs at regular intervals for 2 years. To simulate the neuroprotective effect (NE), we reduced the observed progression rate by 20%, 30%, or 50%. The main outcome measure was the sample size to detect a significant difference (P < .05) at 80% power.

RESULTS

In the first experiment, we simulated a trial including one eye per subject, either selecting randomly from the database or prioritizing patients with low VI. We could not reach 80% power for the low NE with the available patients, but the sample size was reduced by 38% and 49% for the 30% and 50% NE, respectively. In the second experiment, we simulated 2 eyes per subject, one of which was the control eye. The sample size (smaller overall) was reduced by 26% and 38% for the 30% and 50% NE by prioritizing patients with low VI.

CONCLUSIONS

Selecting patients with low intertest variability can significantly improve the power and reduce the sample size needed in a trial.

Improving event-based progression analysis in glaucomatous visual fields

Glaucoma is a progressive optic neuropathy with characteristic changes to the optic nerve head and the visual field (VF). Detecting progression of VF damage with Standard Automated Perimetry (SAP) is of paramount importance for clinical care. One common approach to detecting progression is to compare each new VF test to a baseline SAP test (event analysis). This comparison is made difficult by the test–retest variability of SAP, which increases with the level of VF damage, and the limited range of measurement, meaning that damage cannot be assessed below a certain level. We performed a prospective international multi-centre data collection of SAP data on 90 eyes from 90 people with glaucoma and different levels of VF damage over a short period of time (6 tests in 60 days). Data were collected using a fundus tracked perimeter (Compass, CenterVue). We used these data (minus the first test) to develop an improved event analysis that accounts for both the change in variability with damage and the lower bound on the measurement imposed by SAP. Using simulations, we show that our approach is more sensitive compared to previously developed methods, especially in the case of advanced glaucoma, while retaining similar specificity.

Clinical observations of acute onset of myopic optic neuropathy in a real-world setting

AIM

To describe the clinical features of acute myopic onset of optic neuropathy and observe the effects of retrobulbar and systemic glucocorticoid therapy in a real-world setting.

METHODS

A retrospective observational case series included 18 patients with a clinical diagnosis of acute onset of myopic optic neuropathy in a real-world setting. While the patients were using retrobulbar and systemic glucocorticoid therapy, various imaging examination data were analysed, and the clinical features of myopic optic neuropathy were summarized for 6mo to 2y.

RESULTS

The included group of patients with acute onset of myopic optic neuropathy consisted mostly of females (n=11). The visual field (VF) showed abnormalities in bilateral eyes, including the spread of physiological blind spots, central and paracentral dark spots, and centripetal peripheral VF reduction; but central vision with no subjective changes. The visual evoked potential (VEP) was abnormal in all eyes with vision loss. The best corrected visual acuity (BCVA) was improved from 1.04±0.63 to 0.47±0.57 (logMAR) after glucocorticoid treatment (P<0.05). In patients with a short course (within 1wk), recovery was fast and achieved the same BCVA as recorded before the onset within 6d. However, in patients with the long course (1 to 2wk), recovery was slow and did not achieve the BCVA recorded before the onset within 10d. The changes of intraocular pressure (IOP) were not obvious before and after treatment (18.68±5.30 vs 19.55±5.34 mm Hg, P>0.05). There was no recurrence during long-term follow-up observation.

CONCLUSION

The acute onset of myopic optic neuropathy is characterized by BCVA and VF abnormalities in bilateral eyes. Retrobulbar and systemic glucocorticoid therapy is effective.

Comparison of Short- And Long-Term Variability in Standard Perimetry and Spectral Domain Optical Coherence Tomography in Glaucoma

PURPOSE

To assess short- and long-term variability on standard automated perimetry (SAP) and spectral domain optical coherence tomography (SD-OCT) in glaucoma.

DESIGN

Prospective cohort.

METHODS

Ordinary least squares linear regression of SAP mean deviation (MD) and SD-OCT global retinal nerve fiber layer (RNFL) thickness were fitted over time for sequential tests conducted within 5 weeks (short-term testing) and annually (long-term testing). Residuals were obtained by subtracting the predicted and observed values, and each patient's standard deviation (SD) of the residuals was used as a measure of variability. Wilcoxon signed-rank test was performed to test the hypothesis of equality between short- and long-term variability.

RESULTS

A total of 43 eyes of 43 glaucoma subjects were included. Subjects had a mean 4.5 ± 0.8 SAP and OCT tests for short-term variability assessment. For long-term variability, the same number of tests were performed and results annually collected over an average of 4.0 ± 0.8 years. The average SD of the residuals was significantly higher in the long-term than in the short-term period for both tests: 1.05 ± 0.70 dB vs. 0.61 ± 0.34 dB, respectively (P < 0.001) for SAP MD and 1.95 ± 1.86 μm vs. 0.81 ± 0.56 μm, respectively (P < 0.001) for SD-OCT RNFL thickness.

CONCLUSIONS

Long-term variability was higher than short-term variability on SD-OCT and SAP. Because current event-based algorithms for detection of glaucoma progression on SAP and SD-OCT have relied on short-term variability data to establish their normative databases, these algorithms may be underestimating the variability in the long-term and thus may overestimate progression over time.

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 4^th, 6^th and 8^th 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.

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

Purpose

To quantify visual field (VF) variability as a function of threshold sensitivity and location, and to compare weighted pointwise linear regression (PLR) with unweighted PLR and pointwise exponential regression (PER) for data fit and prediction ability.

Methods

Two datasets were used for this retrospective study. The first was used to characterize and estimate VF variability, and included a total of 4,747 eyes of 3,095 glaucoma patients with six or more VFs and 3 years or more of follow-up. After performing PER for each series, standard deviation of residuals was quantified for each decibel of sensitivity as a measure of variability. A separate dataset was used to test and compare unweighted PLR, weighted PLR, and PER for data fit and prediction, and included 261 eyes of 176 primary open-angle glaucoma patients with 10 or more VFs and 6 years or more of follow-up.

Results

The degree of variability changed as a function of threshold sensitivity with a zenith and a nadir at 33 and 11 dB, respectively. Variability decreased with eccentricity and was higher in the central 10° (P < 0.001). Differences among the methods for data fit were negligible. PER was the best model to predict future sensitivity values in the mid term and long term.

Conclusions

VF variability increases with the severity of glaucoma damage and decreases with eccentricity. Weighted linear regression neither improves model fit nor prediction. PER exhibited the best prediction ability, which is likely related to the nonlinear nature of long-term glaucomatous perimetric decay.

Translational Relevance

This study suggests that taking into account heteroscedasticity has no advantage in VF modeling.

Improving the Feasibility of Glaucoma Clinical Trials Using Trend-Based Visual Field Progression Endpoints

Purpose

There have been concerns that short-term clinical trials for evaluating new treatments in glaucoma would require prohibitively large sample sizes when using visual field endpoints, given that glaucoma is often a slowly progressive disease. This study sought to determine the required sample size for such trials using event-based analyses, and whether it can be reduced using trend-based analyses.

Design

Longitudinal, observational study.

Participants

321 eyes of 240 glaucoma participants followed under routine clinical care using 242 visual field for an average of 10 years.

Methods

Sample size requirements were derived using computer simulations that reconstructed "real-world" visual fields by combining estimates of point-wise variability according to different threshold levels and rates of change obtained from the clinical glaucoma cohort. A clinical trial lasting 2 years with testing every 3 months was simulated, assuming that the new treatment halted visual field change in various percentages of participants (or "responders"). Treatment efficacy was evaluated by: (a) Difference in incidence of point-wise event-based progression (similar to the commercially available Guided Progression Analysis), and (b) Difference in rate of visual field mean deviation (MD) change between groups using linear mixed models (LMMs).

Main Outcome Measures

Sample size to detect a statistically significance difference between groups.

Results

Between-group trend-based analyses using LMMs reduced sample size requirements by 85-90% across the range of new treatment effects when compared to the conventional point-wise event-based analysis. To detect the effect of a new treatment that halted progression in 30% of the participants under routine clinical care (equal to a 30% reduction in average rate of MD change) with 90% power, for example, 1924 participants would be required per group using event-based analysis, but only 277 participants per group if LMMs were used.

Conclusions

The feasibility of future glaucoma clinical trials can be substantially improved by evaluating differences in the rate of visual field change between groups.

Comparing 10-2 and 24-2 Visual Fields for Detecting Progressive Central Visual Loss in Glaucoma Eyes with Early Central Abnormalities

Purpose

To compare the ability of 10-2 visual field tests and central 12 locations of the 24-2 tests (C24-2) to detect central visual field progression in glaucoma eyes with early central visual field abnormalities.

Design

Observational cohort study.

Participants

Three-hundred eyes of 180 participants with glaucoma or ocular hypertension.

Methods

Participants with both 10-2 and 24-2 tests performed on ≥3 visits over ≥1-year period were included to estimate the longitudinal variability of 10-2 and C24-2 visual field mean deviation (MD). The variability estimates were then used to reconstruct real-world visual field results by computer simulations, in a scenario where eyes had a baseline 10-2 and C24-2 MD was -2 dB and exhibited various rates of change (-0.25, -0.50, -0.75 and -1.00 dB/year), and the time to detect these changes were evaluated using trend-based analyses.

Main Outcome Measures

Time required to detect progression.

Results

Overall, the time to detect central visual field progression was reduced by 7-9% using the 10-2 compared to C24-2 MD values, equivalent to a total reduction of 0.1-0.3 dB lost. For example, 90% of eyes with a central 10-2 or C24-2 MD loss of -0.50 dB/year would be detected after 5.0 and 5.5 years of semi-annual testing respectively, or after 3.4 and 3.7 years respectively for eyes with a -1.00 dB/year loss.

Conclusions

Trend-based analyses using 10-2 MD resulted in a mild reduction (7-9%) in the time to detect central visual field progression compared to C24-2 MD in glaucoma eyes with early central visual field abnormalities. Further studies are needed to determine whether other progression analyses can better exploit the increased sampling of 10-2 tests. These findings provide evidence-based guidance on the potential value-add of 10-2 testing in the clinical management of glaucoma patients.

Comparison of Visual Field Point-Wise Event-Based and Global Trend-Based Analysis for Detecting Glaucomatous Progression

Purpose

To compare global trend-based and point-wise event-based analysis for detecting visual field progression in eyes with glaucoma.

Methods

The study included a cohort of 367 glaucoma eyes from 265 participants seen over a mean follow-up period of 10 years to develop a computer simulation model of "real-world" visual field results. Progression was evaluated with point-wise event-based analysis using the Guided Progression Analysis (GPA) and global trend-based analysis using the mean deviation (MD) and visual field index (VFI) measures. The specificities of the methods were matched based on a simulated dataset of stable glaucoma eyes, to allow an adequate comparison of their sensitivities for detecting progression.

Results

The 5-year cumulative false-positive rate for the GPA alert of "possible progression" and "likely progression" (significant change from baseline at two and three consecutive visits, respectively) were 34.0% and 7.0%, respectively. At matched specificities, 27.7% eyes were detected as having progressed after 5 years using the GPA "likely progression" criterion, while 24.6% and 23.8% were detected as having progressed using the global trend-based analysis with MD and VFI, respectively. There was a moderate level of agreement between the GPA and global trend-based analyses.

Conclusions

Pointwise event-based and global trend-based methods had similar performances to detect glaucoma progression when rigorously matched for specificity.

Translational Relevance

Although both point-wise event- and global trend-based analyses perform similarly, they could provide complementary information that could be exploited to improve the overall detection of progression in clinical practice and clinical trials.