Short-term Detection of Fast Progressors in Glaucoma: The Fast Progression Assessment through Clustered Evaluation (Fast-PACE) Study

PURPOSE

To evaluate the performance of an intensive, clustered testing approach in identifying eyes with rapid glaucoma progression over 6 months in the Fast Progression Assessment through Clustered Evaluation (Fast-PACE) Study.

DESIGN

Prospective cohort study.

PARTICIPANTS

A total of 125 eyes from 65 primary open-angle glaucoma (POAG) subjects.

METHODS

Subjects underwent 2 sets of 5 weekly visits (clusters) separated by an average of 6 months and then were followed with single visits every 6 months for an overall mean follow-up of 25 months (mean of 17 tests). Each visit consisted of testing with standard automated perimetry (SAP) 24-2 and 10-2, and spectral-domain OCT (SD-OCT). Progression was assessed using trend analyses of SAP mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness. Generalized estimating equations were applied to adjust for correlations between eyes for confidence interval (CI) estimation and hypothesis testing.

MAIN OUTCOME MEASURES

Diagnostic accuracy of the 6-month clustering period to identify progression detected during the overall follow-up.

RESULTS

A total of 19 of 125 eyes (15%, CI, 9%-24%) progressed based on SAP 24-2 MD over the 6-month clustering period. A total of 14 eyes (11%, CI, 6%-20%) progressed on SAP 10-2 MD, and 16 eyes (13%, CI, 8%-21%) progressed by RNFL thickness, with 30 of 125 eyes (24%, CI, 16%-34%) progressing by function, structure, or both. Of the 35 eyes progressing during the overall follow-up, 25 had progressed during the 6-month clustering period, for a sensitivity of 71% (CI, 53%-85%). Of the 90 eyes that did not progress during the overall follow-up, 85 also did not progress during the 6-month period, for a specificity of 94% (CI, 88%-98%). Of the 14 eyes considered fast progressors by SAP 24-2, SAP 10-2, or SD-OCT during the overall follow-up, 13 were identified as progressing during the 6-month cluster period, for a sensitivity of 93% (CI, 66%-100%) for identifying fast progression with a specificity of 85% (CI, 77%-90%).

CONCLUSIONS

Clustered testing in the Fast-PACE Study detected fast-progressing glaucoma eyes over 6 months. The methodology could be applied in clinical trials investigating interventions to slow glaucoma progression and may be of value for short-term assessment of high-risk subjects.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references in the Footnotes and Disclosures at the end of this article.

Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression from Age-Related Changes in OCT scans

PURPOSE

To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.

DESIGN

Retrospective cohort study.

METHODS

Glaucomatous and healthy eyes with ≥5 reliable peripapillary OCT (Spectralis, Heidelberg Engineering) circle scans were included. A weakly supervised time-series learning model, called Noise Positive-Unlabeled (Noise-PU) DL was developed to classify whether sequences of OCT B-scans showed glaucoma progression. The model used two learning schemes, one to identify age-related changes by differentiating test sequences from glaucoma vs. healthy eyes, and the other to identify test-retest variability based on scrambled OCTs of glaucoma eyes. Both models' bases were convolutional neural networks (CNN) and long short-term memory (LSTM) networks which were combined to form a CNN-LSTM model. Model features were combined and jointly trained to identify glaucoma progression, accounting for age-related loss. The DL model's outcomes were compared with ordinary least squares (OLS) regression of retinal nerve fiber layer (RNFL) thickness over time, matched for specificity. The hit ratio was used as a proxy for sensitivity.

RESULTS

8,785 follow-up sequences of 5 consecutive OCT tests from 3253 eyes (1859 subjects) were included in the study. Mean follow-up time was 3.5±1.6 years. In the test sample, the hit ratios of the DL and OLS methods were 0.498 (95%CI:0.470-0.526) and 0.284 (95%CI:0.258-0.309) respectively (P<0.001) when the specificities were equalized to 95%.

CONCLUSION

A DL model was able to identify longitudinal glaucomatous structural changes in OCT B-scans using a surrogate reference standard for progression.

Social history and glaucoma progression: the effect of body mass index, tobacco and alcohol consumption on the rates of structural change in patients with glaucoma

BACKGROUND/AIMS

Although obesity, tobacco and alcohol consumption were linked to the progression of numerous chronic diseases, an association of these social history aspects with glaucoma progression is not yet determined. This study aims to investigate the effect of body mass index (BMI) and history of tobacco and alcohol use on the rates of retinal nerve fibre layer (RNFL) change over time in glaucoma patients.

METHODS

2839 eyes of 1584 patients with glaucoma from the Duke Ophthalmic Registry were included. Patients had at least two spectral-domain optical coherency tomography (SD-OCT) tests over a minimum 6-month follow-up. Self-reported history of alcohol and tobacco consumption was extracted from electronic health records and mean BMI was calculated. Univariable and multivariable linear mixed models were used to determine the effect of each parameter on RNFL change over time.

RESULTS

Mean follow-up time was 4.7±2.1 years, with 5.1±2.2 SD-OCT tests per eye. 43% and 54% of eyes had tobacco or alcohol consumption history, respectively, and 34% were classified as obese. Higher BMI had a protective effect on glaucoma progression (0.014 µm/year slower per each 1 kg/m2 higher; p=0.011). Tobacco and alcohol consumption were not significantly associated with RNFL change rates (p=0.473 and p=0.471, respectively). Underweight subjects presented significantly faster rates of structural loss (-0.768 µm/year; p=0.002) compared with normal weight.

CONCLUSIONS

In a large clinical population with glaucoma, habits of tobacco and alcohol consumption showed no significant effect on the rates of RNFL change. Higher BMI was significantly associated with slower rates of RNFL loss.

Visual Field Outcomes in the Primary Tube Versus Trabeculectomy Study

PURPOSE

To describe visual field outcomes in the Primary Tube Versus Trabeculectomy (PTVT) Study.

DESIGN

Cohort analysis of a prospective multicenter randomized clinical trial.

SUBJECTS

A total of 155 eyes from 155 subjects randomly assigned to treatment with tube shunt surgery (n=84) or trabeculectomy with mitomycin C (n=71).

METHODS

The PTVT Study was a multicenter randomized clinical trial comparing the safety and efficacy of trabeculectomy and tube shunt surgery in eyes without prior intraocular surgery. Subjects underwent standard automated perimetry (SAP) at baseline and annually for five years. SAP tests were deemed reliable if the false positive rate was ≤15%. Tests were excluded if visual acuity was ≤20/400 or loss of ≥2 Snellen lines from baseline were attributed to a non-glaucomatous etiology. Linear mixed-effects models were used to compare rates of change in SAP mean deviation (MD) between the two treatment groups. Intraocular pressure (IOP) control was assessed by percentage of visits with IOP <18 mmHg and mean IOP.

OUTCOME MEASURES

Rate of change in SAP MD during follow-up.

RESULTS

A total of 730 SAP tests were evaluated, with an average of 4.7 tests per eye. The average SAP MD at baseline was -12.8±8.3 dB in the tube group and -12.0±8.4 dB in the trabeculectomy group (p=0.57). The mean rate of change in SAP MD was -0.32±0.39 dB/year in the trabeculectomy group and -0.47±0.43 dB/year in the tube group (p=0.23). Eyes with mean IOP 14-17.5 mmHg had significantly faster rates of SAP MD loss compared to eyes with mean IOP <14 mmHg (-0.59±0.13 vs. -0.27±0.08 dB/year, p=0.012) and eyes with only 50-75% of visits with IOP <18 mmHg had faster rates than those with 100% of visits with IOP <18 mmHg (-0.90±0.16 vs. -0.29±0.08 dB/year, p<0.001). Multivariable analysis identified older age and worse IOP control as risk factors for faster progression in both treatment groups.

CONCLUSIONS

No statistically significant difference in mean rates of visual field change was observed between trabeculectomy and tube shunt surgery in the PTVT Study. Worse IOP control was significantly associated with faster rates of SAP MD loss during follow-up. Older patients were also at risk for faster progression.

Improved Prediction of Perimetric Loss in Glaucomatous Eyes Using Latent Class Mixed Modeling

PURPOSE

To evaluate whether the identification of distinct classes within a population of glaucoma patients improves estimates of future perimetric loss.

DESIGN

Longitudinal cohort study.

PARTICIPANTS

A total of 6558 eyes of 3981 subjects from the Duke Ophthalmic Registry with ≥ 5 reliable standard automated perimetry (SAP) tests and ≥ 2 years of follow-up.

METHODS

Standard automated perimetry mean deviation (MD) values were extracted with associated timepoints. Latent class mixed models (LCMMs) were used to identify distinct subgroups (classes) of eyes according to rates of perimetric change over time. Rates for individual eyes were then estimated by considering both individual eye data and the most probable class membership for that eye. Data were split into training (80%) and test sets (20%), and test set mean squared prediction errors (MSPEs) were estimated using LCMM and ordinary least squares (OLS) regression.

MAIN OUTCOME MEASURES

Rates of change in SAP MD in each class and MSPE.

RESULTS

The dataset contained 52 900 SAP tests with an average of 8.1 ± 3.7 tests per eye. The best-fitting LCMM contained 5 classes with rates of -0.06, -0.21, -0.87, -2.15, and +1.28dB/year (80.0%, 10.2%, 7.5%, 1.3%, and 1.0% of the population, respectively) labeled as slow, moderate, fast, catastrophic progressors, and "improvers" respectively. Fast and catastrophic progressors were older (64.1 ± 13.7 and 63.5 ± 16.9 vs. 57.8 ± 15.8, P < 0.001) and had generally mild-moderate disease at baseline (65.7% and 71% vs. 52%, P < 0.001) than slow progressors. The MSPE was significantly lower for LCMM compared to OLS, regardless of the number of tests used to obtain the rate of change (5.1 ± 0.6 vs. 60.2 ± 37.9, 4.9 ± 0.5 vs. 13.4 ± 3.2, 5.6 ± 0.8 vs. 8.1 ± 1.1, 3.4 ± 0.3 vs. 5.5 ± 1.1 when predicting the fourth, fifth, sixth, and seventh visual fields (VFs) respectively; P < 0.001 for all comparisons). MSPE of fast and catastrophic progressors was significantly lower with LCMM versus OLS (17.7 ± 6.9 vs. 48.1 ± 19.7, 27.1 ± 8.4 vs. 81.3 ± 27.1, 49.0 ± 14.7 vs. 183.9 ± 55.2, 46.6 ± 16.0 vs. 232.4 ± 78.0 when predicting the fourth, fifth, sixth, and seventh VFs respectively; P < 0.001 for all comparisons).

CONCLUSIONS

Latent class mixed model successfully identified distinct classes of progressors within a large glaucoma population that seemed to reflect subgroups observed in clinical practice. Latent class mixed models were superior to OLS regression in predicting future VF observations.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosuremay be found after the references.

Intraocular Pressure and Rates of Macular Thinning in Glaucoma

PURPOSE

To evaluate the effect of intraocular pressure (IOP) on the rates of macular thickness (ganglion cell layer [GCL] and ganglion cell-inner plexiform layer [GCIPL]) change over time measured by spectral-domain (SD) OCT.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Overall, 451 eyes of 256 patients with primary open-angle glaucoma.

METHODS

Data were extracted from the Duke Ophthalmic Registry, a database of electronic medical records of patients observed under routine clinical care at the Duke Eye Center, and satellite clinics. All records from patients with a minimum of 6 months of follow-up and at least 2 good-quality Spectralis SD-OCT macula scans were included. Linear mixed models were used to investigate the relationship between average IOP during follow-up and rates of GCL and GCIPL thickness change over time.

MAIN OUTCOME MEASURES

The effect of IOP on the rates of GCL and GCIPL thickness loss measured by SD-OCT.

RESULTS

Eyes had a mean follow-up of 1.8 ± 1.3 years, ranging from 0.5 to 10.2 years. The average rate of change for GCL thickness was -0.220 μm/year (95% confidence interval [CI], -0.268 to -0.172 μm/year) and for GCIPL thickness was -0.231 μm/year (95% CI, -0.302 to -0.160 μm/year). Each 1-mmHg higher mean IOP during follow-up was associated with an additional loss of -0.021 μm/year of GCL thickness (P = 0.001) and -0.032 μm/year of GCIPL thickness (P = 0.001) after adjusting for potentially confounding factors, such as baseline age, disease severity, sex, race, central corneal thickness, and follow-up time.

CONCLUSIONS

Higher IOP was significantly associated with faster rates of GCL and GCIPL loss over time measured by SD-OCT, even during relatively short follow-up times. These findings support the use of SD-OCT GCL and GCIPL thickness measurements as structural biomarkers for the evaluation of the efficacy of IOP-lowering therapies in slowing down the progression of glaucoma.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Rates of Visual Field Change in Eyes With Optic Disc Drusen

BACKGROUND

Optic disc drusen (ODD) are calcified deposits in the prelaminar portion of the optic nerve head. Although often asymptomatic, these deposits can cause progressive visual field defects and vision loss. The purpose of this study was to evaluate rates of functional loss in eyes with ODD and to investigate risk factors associated with rates of visual field progression.

METHODS

This was a retrospective cohort study including 65 eyes of 43 patients with ODD from the Duke Ophthalmic Registry. All eyes had at least 12 months of follow-up and at least 3 reliable standard automated perimetry (SAP) tests. Linear mixed models were used to estimate rates of SAP mean deviation (MD) loss over time. Univariable and multivariable models were used to assess the effect of clinical variables and intraocular pressure (IOP) on rates of change.

RESULTS

Subjects were followed for an average of 7.6 ± 5.3 years. The mean rate of SAP MD change was -0.23 ± 0.26 dB/year, ranging from -1.19 to 0.13 dB/year. Fifty-seven eyes (87.7%) had slow progression (slower than -0.5 dB/year), 6 eyes (9.2%) had moderate progression (between -0.5 dB/year and -1 dB/year), and 2 eyes (3.1%) had fast progression (faster than -1 dB/year). In multivariable models, older age and worse SAP MD at baseline were significantly associated with faster rates of change. Mean IOP was not associated with faster rates of MD change in both univariable and multivariable analyses.

CONCLUSIONS

Most eyes with ODD had slow rates of visual field loss over time. Age and baseline severity were significantly associated with faster rates of visual field loss.

The Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications

OBJECTIVE

Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022.

DESIGN

Review and conference proceedings.

SUBJECTS

No human or animal subjects or data therefrom were used in the production of this article.

METHODS

A summary of the Workshop was produced with input and approval from all participants.

MAIN OUTCOME MEASURES

Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions.

RESULTS

The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care.

CONCLUSIONS

The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Minimum Rim Width and Peripapillary Retinal Nerve Fiber Layer Thickness for Diagnosing Early to Moderate Glaucoma

PRCIS

In a cross-sectional study from a Brazilian multiracial population, minimum rim width (MRW) and peripapillary retinal nerve fiber layer thickness measurements from OCT showed comparable diagnostic performance in discriminating early to moderate glaucoma from healthy eyes.

PURPOSE

The purpose of this study is to compare the ability of MRW and peripapillary retinal nerve fiber layer thickness (RNFLT) measurements in discriminating early to moderate glaucoma from healthy eyes in a Brazilian population.

METHODS

A total of 155 healthy controls and 118 patients with mild to moderate glaucoma (mean deviation >-12 dB) underwent MRW and RNFLT measurements with optical coherence tomography. Only 1 eye per patient was included in the analysis. A receiver operating characteristic (ROC) regression model was used to evaluate the diagnostic accuracy of MRW and RNFLT, whereas adjusting for age and Bruch membrane opening area. Sensitivities at fixed specificities of 95% were calculated for each parameter.

RESULTS

Global RNFLT and MRW showed comparable area under the ROC curves [0.93 (0.91-0.96) and 0.93 (0.89-0.96), respectively; P =0.973]. Both parameters had similar sensitivities (75% vs. 74%, respectively; P =0.852) at a fixed specificity of 95%. The best sector for diagnosing glaucoma for both parameters was the temporal inferior sector, which showed an area under the ROC curve of 0.93 (0.87-0.96) for RNFLT and 0.91 (0.86-0.95) for MRW ( P =0.320). The temporal inferior sector showed similar sensitivities for RNFLT and MRW measurements (83% vs. 77%, respectively) at a fixed specificity of 95% (P =0.230).

CONCLUSIONS

MRW and RNFLT measurements showed comparable diagnostic performance in discriminating early to moderate glaucoma from healthy eyes in a Brazilian multiracial population.

Identifying Risk Factors for Blindness From Glaucoma at First Presentation to a Tertiary Clinic

PURPOSE

Glaucoma is the leading cause of irreversible blindness, a crippling disability resulting in higher risks of chronic health conditions. To better understand disparities in blindness risk, we identified risk factors of blindness on first presentation to a glaucoma clinic using a large clinical database.

DESIGN

Retrospective cross-sectional study.

METHODS

We used electronic health records of glaucoma patients from the Duke Ophthalmic Registry. International Classification of Diseases codes were used to identify glaucoma and exclude concurrent diseases. Blindness classification was based on the definition of legal blindness. Risk factors included gender, race, marital status, age, intraocular pressure, diabetes history, income level, and education. Odds ratios (ORs) and 95% CIs were calculated for risk factors using univariable and multivariable logistic regression.

RESULTS

Our cohort consisted of 3753 patients, with 192 (5%) blind on first presentation. In univariable models, African American / Black race (OR 2.48, 95% CI 1.83-3.36), single marital status (1.74, 95% CI 1.25-2.44), prior diabetes diagnosis (2.23, 95% CI 1.52-3.27), and higher intraocular pressure (1.29 per 1 SD higher, 95% CI 1.13-1.46) were associated with increased risk of presenting blind, whereas higher annual income (0.75, 95% CI 0.65-0.86) and education (0.77, 95% CI 0.69-0.85) were associated with lower risk. These associations remained significant and in the same direction in a multivariable model apart from income, which became insignificant.

CONCLUSIONS

Using a large real-world clinical database, we identified risk factors associated with presentation with blindness among glaucoma patients. Our results highlight disparities in health care outcomes and indicate the importance of targeted education to reduce disparities in blindness.

Hazard Detection During Simulated Driving in Glaucoma Patients

PRCIS

In this cross-sectional study, glaucoma patients showed slower reaction times (RT) to hazardous situations when compared to control subjects during simulated driving. Worse RTs were associated with greater magnitude of visual field loss.

PURPOSE

To evaluate the impact of different hazardous traffic conditions on driving performance in glaucoma patients using a high-fidelity driving simulator.

METHODS

Cross-sectional study with 52 glaucoma patients and 15 control subjects. A series of hazard scenarios were presented, such as pedestrians crossing the street unexpectedly or vehicles suddenly pulling into the driver's lane. Reaction times (RTs) in seconds (s) from first the evidence of a hazard to the time it took the driver to take the foot off the gas pedal ("Gas off") and the time it took to depress the brake pedal ("Brake On") were compared between groups.

RESULTS

Overall, mean RTs were statistically significantly slower in glaucoma patients (3.39±3.88 s) compared with controls (2.39±1.99 s; P=0.005) for the "Brake On" task but not for the "Gas Off" task (2.74±3.42 s vs. 2.13±1.91 s respectively; P=0.120). For subjects with glaucoma, multivariable models adjusted for age, gender, race, and visual acuity demonstrated significantly slower RTs for worse values of binocular mean sensitivity for both "Gas Off" and "Brake On" tasks (1.12 s and 1.14 s slower per 10dB worse; P=0.009 and P<0.001, respectively). Subjects with glaucoma took significantly longer times to brake for smaller (low saliency) hazards compared with larger (high saliency) hazards (P=0.027).

CONCLUSIONS

RTs in response to hazardous driving situations were slower for glaucoma patients compared to controls. Individualized assessment of driving fitness using hazardous scenarios in driving simulators could be helpful in providing assessment of driving risk in glaucoma patients.

Increasing Rates of Herpes Zoster Ophthalmicus and the COVID-19 Pandemic

Purpose

This epidemiologic study evaluates the variance in incidence of Herpes Zoster (HZ) and Herpes Zoster Ophthalmicus (HZO) within a single healthcare system with an aim to analyze their relationship to the COVID-19 pandemic.

Methods

All patients attending the Duke University Health System (DUHS) from January 1, 2018, to December 31, 2021, were included. General and COVID-related trends of HZO and HZ were analyzed based on new ICD-9 or ICD-10 diagnosis codes, compared with the total number of patients seen at DUHS during this period, and the number of reported COVID-19 cases in North Carolina obtained using the CDC data tracker.

Results

This study included 16,287 cases of HZ of whom 1,294 (7.94%) presented with HZO. The overall incidence of HZO showed an average yearly increase of 5.6%, however HZ incidence decreased by 5.3% per year. When comparing incidence rates of HZO in the 12-months before and after the COVID-19 pandemic onset in the United States (March 2020), the average incidence from March 2020 to February 2021 was 27.6 ± 11.6 compared to 18.0 ± 2.7 from March 2019 to February 2020 (p = 0.01). Moreover, 10/12 (83.3%) of the months had a higher incidence rate of HZO in the post-COVID onset year compared to their corresponding month in the pre-COVID year.

Conclusion

The results show HZO incidence may be increasing, despite an overall lower HZ incidence. This could suggest a distinct mechanism for HZO appearance. The COVID pandemic, directly or indirectly, may have accelerated the already increasing HZO incidence.

Validation of Rates of Mean Deviation Change as Clinically Relevant End Points for Glaucoma Progression

PURPOSE

To investigate whether rates of standard automated perimetry (SAP) mean deviation (MD) over an initial 2-year follow-up period were predictive of events of visual field progression over an extended follow-up.

DESIGN

Longitudinal, prospective, observational study.

PARTICIPANTS

Two hundred forty-six eyes of 168 patients with glaucoma followed up every 6 months for up to 5 years.

METHODS

Patients were required to have a minimum of 5 reliable SAP tests during the first 2 years of follow-up. Events of progression were evaluated using 2 methods: Guided Progression Analysis (GPA; Carl Zeiss Meditec, Inc) and a United States Food and Drug Administration (FDA)-suggested end point. The date of the first test showing progression after the first 2 years was considered to be the event date. Rates of change in SAP MD were calculated for the first 2 years of follow-up, and joint longitudinal survival models were used to assess the risk of faster initial MD loss for subsequent progression based on each event analysis.

MAIN OUTCOME MEASURE

Risk of having an event of progression based on initial rates of SAP MD change.

RESULTS

Fifty-six eye (22.8%) showed an event of progression by the GPA and 51 eyes (20.7%) did so by the FDA end point. Each 0.1-dB/year faster rate of SAP MD loss in the first 2 years was associated with a 26% increase in risk of a GPA progression end point developing (R2 = 76%) and 32% risk of an FDA-based end point developing (R2 = 83%). A reduction of 30% in the rate of MD change in the first 2 years was associated with a 20% reduction in the cumulative probability of a progression event developing over 5 years of follow-up.

CONCLUSIONS

Rates of SAP MD change for eyes with glaucoma calculated over the initial 2 years of follow-up were strongly predictive of events of progression over subsequent follow-up. Our findings give support for the use of slopes of MD change as suitable end points of progression in clinical trials.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT

PURPOSE

To develop and validate a deep learning (DL) model for detection of glaucoma progression using spectral-domain (SD)-OCT measurements of retinal nerve fiber layer (RNFL) thickness.

DESIGN

Retrospective cohort study.

PARTICIPANTS

A total of 14 034 SD-OCT scans from 816 eyes from 462 individuals.

METHODS

A DL convolutional neural network was trained to assess SD-OCT RNFL thickness measurements of 2 visits (a baseline and a follow-up visit) along with time between visits to predict the probability of glaucoma progression. The ground truth was defined by consensus from subjective grading by glaucoma specialists. Diagnostic performance was summarized by the area under the receiver operator characteristic curve (AUC), sensitivity, and specificity, and was compared with conventional trend-based analyses of change. Interval likelihood ratios were calculated to determine the impact of DL model results in changing the post-test probability of progression.

MAIN OUTCOME MEASURES

The AUC, sensitivity, and specificity of the DL model.

RESULTS

The DL model had an AUC of 0.938 (95% confidence interval [CI], 0.921-0.955), with sensitivity of 87.3% (95% CI, 83.6%-91.6%) and specificity of 86.4% (95% CI, 79.9%-89.6%). When matched for the same specificity, the DL model significantly outperformed trend-based analyses. Likelihood ratios for the DL model were associated with large changes in the probability of progression in the vast majority of SD-OCT tests.

CONCLUSIONS

A DL model was able to assess the probability of glaucomatous structural progression from SD-OCT RNFL thickness measurements. The model agreed well with expert judgments and outperformed conventional trend-based analyses of change, while also providing indication of the likely locations of change.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Association of an Objective Structural and Functional Reference Standard for Glaucoma with Quality of Life Outcomes

PURPOSE

To compare self-reported quality-of-life (QoL) outcomes of patients diagnosed as normal, glaucoma suspect, and glaucoma based on an objective reference standard for glaucomatous optic neuropathy (GON).

DESIGN

Cross-sectional study.

PARTICIPANTS

1884 eyes of 1019 patients were included in the study.

METHODS

The data was sourced from the Duke Glaucoma Registry. Eyes were classified according to the presence and topographic correspondence of functional and structural damage, as assessed by parameters from standard automated perimetry (SAP) and spectral-domain OCT (SD-OCT). The objective diagnosis of the worse eye was used to define patient-level diagnosis. To assess QoL in the diagnostic groups, 14 unidimensional vision-related items of the National Eye Institute Visual Functioning Questionnaire (NEI VFQ-25) were used to assess QoL in the diagnostic groups. Association between NEI VFQ-25 Rasch-calibrated scores and diagnostic groups was assessed through multivariable regression that controlled for confounding demographic and socioeconomic variables such as age, sex, race, income, marriage status, insurance status, and highest education level.

MAIN OUTCOME MEASURES

NEI VFQ-25 Rasch scores compared with objective criteria diagnosis based on SAP mean deviation (MD) and SD-OCT retinal nerve fiber layer (RNFL) thickness.

RESULTS

Overall, eyes classified as normal, glaucoma suspect, and glaucoma had decreasing mean scores in SAP MD (0.2 ± 1.0 dB, -0.9 ± 2.4 dB, -6.2 ± 7.0 dB, respectively; P < 0.001) and SD-OCT RNFL thickness (97.8 ± 9.5 μm, 89.0 ± 13.1 μm, 64.5 ± 12.8 μm, respectively; P < 0.001). The mean Rasch-calibrated NEI VFQ-25 score was significantly different among normal, suspect, and glaucoma groups (82.9 ± 13.0, 78.2 ± 14.8, and 72.6 ± 16.2, respectively; P < 0.001). When adjusted for confounding socioeconomic variables, glaucoma patients had significantly worse QoL than those classified as normal (β = -6.8 Rasch score units; P < 0.001).

CONCLUSION

A glaucoma diagnosis, based on an objective reference standard for GON, was significantly associated with worse Rasch-adjusted scores of QoL. Utilization of such objective criteria may provide clinically relevant metrics with potential to improve comparability of research findings and validation of newly proposed diagnostic tools.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

24-2 SITA Standard versus 24-2 SITA Faster in Perimetry-Naive Normal Subjects

PURPOSE

To compare the Swedish Interactive Thresholding Algorithm (SITA) Standard (SS) and SITA Faster (SFR) strategies in normal individuals undergoing standard automated perimetry (SAP) for the first time.

DESIGN

Randomized, comparative, observational case series.

PARTICIPANTS

Seventy-four perimetry-naive healthy individuals.

METHODS

All individuals underwent SAP 24-2 testing with the Humphrey Field Analyzer III (model 850 Zeiss) using the SS and SFR strategies. One eye of each individual was tested. Test order between strategies was randomized, and an interval of 15 minutes was allowed between the tests.

MAIN OUTCOME MEASURES

The following variables were compared: test time, foveal threshold, false-positive errors, number of unreliable tests, mean deviation (MD), visual field index (VFI), pattern standard deviation (PSD), glaucoma hemifield test (GHT), and number of depressed points deviating at P < 5%, P < 2%, P < 1%, and P < 0.5% on the total and pattern deviation probability maps. Specificity of the SS and SFR strategies were compared using Anderson's criteria for abnormal visual fields.

RESULTS

The SFR tests were 60.4% shorter in time compared with SS (P < 0.001) and were associated with a significantly lower PSD (1.75 ± 0.80 decibel [dB] vs. 2.15 ± 1.25 dB; P = 0.016). There were no significant differences regarding the MD, VFI, foveal threshold, GHT, and number of points depressed at P < 5%, P < 2%, P < 1%, and P < 0.5% on the total deviation and pattern deviation probability maps between SS and SFR. When all exams were analyzed and any of Anderson's criteria was applied, the specificity was 68% with SFR and 61% with SS (P = 0.250). The specificities observed with SFR and SS when only the first or second exams were analyzed were also similar (63% vs. 64% and 72% vs. 58%, respectively, P > 0.05).

CONCLUSIONS

The SS and SFR were associated with similar specificities in perimetry-naive individuals. The SFR did not increase the number of depressed points in the total and pattern deviation probability maps. Ophthalmologists should be aware that both strategies are associated with disturbingly high false-positive rates in perimetry-naive individuals.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Spectral-Domain OCT Lens Meridian Position as a Metric to Estimate Postoperative Anatomical Lens Position

PURPOSE

To evaluate the prediction of postoperative anatomical lens position (ALP) using intraoperative spectral-domain optical coherence tomography (SD-OCT) lens anatomy metrics in patients who underwent femtosecond laser-assisted cataract surgery.

METHODS

Intraoperative SD-OCT (Catalys; Johnson & Johnson Vision) and postoperative optical biometry (IOLMaster 700; Carl Zeiss Meditec AG) were used to assess anterior segment landmarks, including lens thickness, lens volume, anterior chamber depth, lens meridian position (LMP), and measured ALP. LMP was defined as the distance from the corneal epithelium to the lens equator, and ALP was defined as the distance from the corneal epithelium to the IOL surface. Eyes were divided into groups according to axial length (> 22.5 mm, 22.5 to 24.5 mm, and > 24.5 mm) and IOL type (Tecnis ZCB00 [Johnson & Johnson Vision]; AcrySof SN-60WF [Alcon Laboratories, Inc], or enVista MX60E [Bausch & Lomb]) to further analyze the correlation between LMP and ALP. Theoretical effective lens position was back-calculated using a specific formula. Primary outcome was correlation between postoperative measured ALP and LMP.

RESULTS

A total of 97 eyes were included in this study. Linear regression analysis displayed a statistically significant correlation between intraoperative LMP and postoperative ALP (R² = 0.522; P < .01). No statistically significant correlation was observed between LMP and lens thickness (R² = 0.039; P = .06) or between ALP and lens thickness (R² = 0.02; P = .992). The greatest predictor for ALP was LMP (β = 0.766, P < .001; R² = 0.523).

CONCLUSIONS

Intraoperative SD-OCT-measured LMP correlated better than anterior chamber depth and axial length to postoperative ALP. Further studies are necessary to analyze the impact of preoperative or intraoperative LMP measurements on postoperative refractive outcomes. [J Refract Surg. 2023;39(3):165-170.].

Comparison of 10-2 and 24-2 Perimetry to Diagnose Glaucoma Using OCT as an Independent Reference Standard

PURPOSE

To compare the performance of the 10-2 test versus 24-2 standard automated perimetry (SAP) test for the diagnosis of glaucoma using OCT as an independent standard for glaucomatous damage.

DESIGN

Cross-sectional study.

PARTICIPANTS

A total of 1375 pairs of 10-2 and 24-2 SAP tests from 569 eyes of 339 subjects were used for the analysis. A total of 440 (77%) eyes had a diagnosis of glaucoma, and 129 (23%) eyes were normal. All participants underwent 10-2 and 24-2 SAP tests within 30 days.

METHODS

Glaucomatous severity was quantified based on OCT macula ganglion cell layer (mGCL) and circumpapillary retinal nerve fiber layer. The area under the receiver operating characteristic (ROC) curve (AUC) was used to compare 10-2 and 24-2 metrics for discriminating healthy eyes from those of glaucoma, at different levels of disease severity.

MAIN OUTCOME MEASURES

Areas under the ROC curves and sensitivities at fixed specificities of 80% and 95%.

RESULTS

The overall AUC for mean deviation (MD) for the 24-2 test (0.808) was significantly higher than that of the 10-2 test (0.742; P < 0.001). When compared at different stages of the disease, the 24-2 test performed generally better than the 10-2 test, notably in the earlier stages of the disease. For early damage (first quartile), the 24-2 MD had an AUC of 0.658 versus 0.590 for 10-2 MD (P = 0.018). For advanced damage (fourth quartile), corresponding values were 0.954 vs. 0.903 (P = 0.013). Similar trends were observed when glaucoma severity was defined based on structural macular damage with mGCL thickness.

CONCLUSIONS

The 24-2 SAP test had better diagnostic accuracy compared with that of the 10-2 test for detecting equivalent levels of glaucomatous damage, as measured by quantitative assessment of retinal nerve fiber layer and macula by OCT.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Scheimpflug-Derived Corneal Lower and Higher Order Aberrations Post Intrastromal Corneal Ring Segments for Keratoconus

Intrastromal corneal ring segments (ICRS) improve corneal topographic symmetry and reduce corneal aberrations through regularization of the corneal surface, thereby functioning as a viable surgical intervention for patients with keratoconus. This study aims to evaluate changes in lower- (LOAs) and higher-order aberrations (HOAs) amongst varying pupil sizes pre- and post- ICRS implantation in keratoconus patients. We specifically investigate the impact of pupil size on total corneal HOAs up to the 6th order. Twenty-one eyes that underwent ICRS implantation were included in this prospective interventional study. LOAs and HOAs measurements at the 6 mm, 4 mm, and 2 mm pupil diameters were collected preoperatively and at 6 months postoperatively using the Zernicke analysis function on a Scheimpflug device. ICRS implantation demonstrated a statistically significant effect in vertical coma with a −0.23 reduction (p = 0.015) for a 4 mm pupil size and a −1.384 reduction (p < 0.001) for 6 mm, with no significant effect at 2 mm. Horizontal coma, astigmatism 0°, astigmatism 45°, trefoil 5th order 30°, and RMS HOA demonstrated significant reductions at 4 mm or 6 mm pupil sizes but not at 2 mm. Our analysis demonstrates a favorable effect of ICRS implantation on larger pupil sizes, suggesting the importance of pupil size as it correlates with HOAs reduction.

A Framework for Automating Psychiatric Distress Screening in Ophthalmology Clinics Using an EHR-Derived AI Algorithm

Purpose

In patients with ophthalmic disorders, psychosocial risk factors play an important role in morbidity and mortality. Proper and early psychiatric screening can result in prompt intervention and mitigate its impact. Because screening is resource intensive, we developed a framework for automating screening using an electronic health record (EHR)-derived artificial intelligence (AI) algorithm.

Methods

Subjects came from the Duke Ophthalmic Registry, a retrospective EHR database for the Duke Eye Center. Inclusion criteria included at least two encounters and a minimum of 1 year of follow-up. Presence of distress was defined at the encounter level using a computable phenotype. Risk factors included available EHR history. At each encounter, risk factors were used to discriminate psychiatric status. Model performance was evaluated using area under the receiver operating characteristic (ROC) curve and area under the precision-recall curve (PR AUC). Variable importance was presented using odds ratios (ORs).

Results

Our cohort included 358,135 encounters from 40,326 patients with an average of nine encounters per patient over 4 years. The ROC and PR AUC were 0.91 and 0.55, respectively. Of the top 25 predictors, the majority were related to existing distress, but some indicated stressful conditions, including chemotherapy (OR = 1.36), esophageal disorders (OR = 1.31), central pain syndrome (OR = 1.25), and headaches (OR = 1.24).

Conclusions

Psychiatric distress in ophthalmology patients can be monitored passively using an AI algorithm trained on existing EHR data.

Translational Relevance

When paired with an effective referral and treatment program, such algorithms may improve health outcomes in ophthalmology.

Corneal Hysteresis and Rates of Neuroretinal Rim Change in Glaucoma

PURPOSE

To evaluate the impact of corneal hysteresis (CH) as a risk factor for progressive neuroretinal rim loss in glaucoma, as measured by spectral-domain OCT of the Bruch's membrane opening minimum rim width (MRW).

DESIGN

Prospective, observational cohort study.

PARTICIPANTS

The study group included 118 eyes of 70 subjects with glaucoma. The average follow-up time for the cohort was 3.9 ± 1.3 years, with an average of 6.4 ± 2.0 spectral-domain OCT tests, ranging from 4 to 12.

METHODS

Corneal hysteresis measurements were acquired at baseline using the Ocular Response Analyzer (Reichert Instruments). Linear mixed models were used to investigate the relationship between the rates of MRW loss and baseline CH. Multivariable analyses adjusted for other putative predictive factors for progression, including mean intraocular pressure (IOP), central corneal thickness (CCT), age, race, and baseline disease severity.

MAIN OUTCOME MEASURES

Effects of CH on the rate of MRW change over time.

RESULTS

Corneal hysteresis had a significant effect on rates of MRW progression over time. Each 1-mmHg lower CH was associated with -0.38 μm/year faster MRW loss (95% confidence interval [CI], -0.70 to -0.06; P = 0.019), after adjustment for other predictive factors. The mean IOP was also significantly associated with progression, with -0.35 μm/year (95% CI, -0.47 to -0.23 μm/year) faster MRW change for each 1-mmHg higher pressure (P < 0.001). In the analysis of predictive strength, the mean IOP was the strongest predictive factor (R2 = 23%), followed by CH (R2 = 14%) and baseline disease severity (R2 = 6%). Central corneal thickness explained only 3% of the variability in slopes of change in global MRW.

CONCLUSIONS

Lower CH measurements were associated with faster loss of the neuroretinal rim in glaucoma, as measured by MRW. The predictive ability of CH was superior to that of CCT. These findings suggest that CH is an important parameter to be considered in assessing the risk of glaucoma progression.

Longitudinal visual field variability and the ability to detect glaucoma progression in black and white individuals

BACKGROUND/AIMS

To investigate racial differences in the variability of longitudinal visual field testing in a 'real-world' clinical population, evaluate how these differences are influenced by socioeconomic status, and estimate the impact of differences in variability on the time to detect visual field progression.

METHODS

This retrospective observational cohort study used data from 1103 eyes from 751 White individuals and 428 eyes from 317 black individuals. Linear regression was performed on the standard automated perimetry mean deviation values for each eye over time. The SD of the residuals from the trend lines was calculated and used as a measure of variability for each eye. The association of race with the SD of the residuals was evaluated using a multivariable generalised estimating equation model with an interaction between race and zip code income. Computer simulations were used to estimate the time to detect visual field progression in the two racial groups.

RESULTS

Black patients had larger visual field variability over time compared with white patients, even when adjusting for zip code level socioeconomic variables (SD of residuals for Black patients=1.53 dB (95% CI 1.43 to 1.64); for white patients=1.26 dB (95% CI 1.14 to 1.22); mean difference: 0.28 (95% CI 0.15 to 0.41); p<0.001). The difference in visual field variability between black and white patients was greater at lower levels of income and led to a delay in detection of glaucoma progression.

CONCLUSION

Black patients had larger visual field variability compared with white patients. This relationship was strongly influenced by socioeconomic status and may partially explain racial disparities in glaucoma outcomes.

Association Between Serum Vitamin D Level and Rates of Structural and Functional Glaucomatous Progression

PRCIS

In a retrospective cohort study, serum vitamin D levels were not associated with rates of structural or functional loss in glaucoma patients, suggesting that low vitamin D level is not a risk factor for progression.

PURPOSE

To investigate the association between serum vitamin D level and rates of functional and structural glaucomatous loss over time.

METHODS

This study included 826 eyes of 536 glaucoma or suspect patients with an average follow-up of 4.8±1.9 years. All patients had at least 1 serum vitamin D measurement, and all eyes had at least 2 reliable standard automated perimetry (SAP) tests and 2 spectral-domain optical coherence tomography (SD OCT) tests with a minimum follow-up of 6 months. Multivariable linear mixed-effects models were used to estimate the association of vitamin D level with rates of change in SAP mean deviation (MD) and OCT retinal nerve fiber layer (RNFL) thickness over time while adjusting for potential confounding factors.

RESULTS

Patients had an average of 3.4±1.7 SAP tests, 4.8±1.9 SD OCT tests, and 2.3±1.9 vitamin D measurements. The average serum vitamin D level was 33.9±13.2 ng/mL. Mean rates of MD and RNFL change were -0.03±0.08 dB/y and -0.68±0.64 µm/y, respectively. After controlling for confounding factors, there was no statistically significant association between mean vitamin D level and rates of MD (β=0.038, 95% CI: -0.006, 0.082, P =0.09) or RNFL loss over time (β=-0.018, 95% CI: -0.092, 0.055, P =0.62).

CONCLUSIONS

We did not find a significant association between vitamin D level and rates of visual field or RNFL loss over time in individuals with glaucoma and glaucoma suspect patients.

Corneal hysteresis: ready for prime time?

PURPOSE OF THE REVIEW

This review summarizes recent findings on corneal hysteresis, a biomechanical property of the cornea. Corneal hysteresis measurements can be easily acquired clinically and may serve as surrogate markers for biomechanical properties of tissues in the back of the eye, like the lamina cribrosa and peripapillary sclera, which may be related to the susceptibility to glaucomatous damage.

RECENT FINDINGS

Several studies have provided evidence of the associations between corneal hysteresis and clinically relevant outcomes in glaucoma. Corneal hysteresis has been shown to be predictive of glaucoma development in eyes suspected of having the disease. For eyes already diagnosed with glaucoma, lower corneal hysteresis has been associated with higher risk of progression and faster rates of visual field loss over time. Such associations appear to be stronger than those for corneal thickness, suggesting that corneal hysteresis may be a more important predictive factor. Recent evidence has also shown that cornealcorrected intraocular pressure measurements may present advantages compared to conventional Goldmann tonometry in predicting clinically relevant outcomes in glaucoma.

SUMMARY

Given the evidence supporting corneal hysteresis as an important risk factor for glaucoma development and its progression, practitioners should consider measuring corneal hysteresis in all patients at risk for glaucoma, as well as in those already diagnosed with the disease.

Rates of Glaucoma Progression Derived from Linear Mixed Models Using Varied Random Effect Distributions

Purpose

To compare the ability of linear mixed models with different random effect distributions to estimate rates of visual field loss in glaucoma patients.

Methods

Eyes with five or more reliable standard automated perimetry (SAP) tests were identified from the Duke Glaucoma Registry. Mean deviation (MD) values from each visual field and associated timepoints were collected. These data were modeled using ordinary least square (OLS) regression and linear mixed models using the Gaussian, Student's t, or log-gamma (LG) distributions as the prior distribution for random effects. Model fit was compared using the Watanabe–Akaike information criterion (WAIC). Simulated eyes of varying initial disease severity and rates of progression were created to assess the accuracy of each model in predicting the rate of change and likelihood of declaring progression.

Results

A total of 52,900 visual fields from 6558 eyes of 3981 subjects were included. Mean follow-up period was 8.7 ± 4.0 years, with an average of 8.1 ± 3.7 visual fields per eye. The LG model produced the lowest WAIC, demonstrating optimal model fit. In simulations, the LG model declared progression earlier than OLS (P < 0.001) and had the greatest accuracy in predicted slopes (P < 0.001). The Gaussian model significantly underestimated rates of progression among fast and catastrophic progressors.

Conclusions

Linear mixed models using the LG distribution outperformed conventional approaches for estimating rates of SAP MD loss in a population with glaucoma.

Translational Relevance

Use of the LG distribution in models estimating rates of change among glaucoma patients may improve their accuracy in rapidly identifying progressors at high risk for vision loss.

Blood Pressure and Glaucomatous Progression in a Large Clinical Population

PURPOSE

To investigate the effect of systemic arterial blood pressure (BP) on rates of progressive structural damage over time in glaucoma.

DESIGN

Retrospective cohort study.

PARTICIPANTS

A total of 7501 eyes of 3976 subjects with glaucoma or suspected of glaucoma followed over time from the Duke Glaucoma Registry.

METHODS

Linear mixed models were used to investigate the effects of BP on the rates of retinal nerve fiber layer (RNFL) loss from spectral-domain OCT (SD-OCT) over time. Models were adjusted for intraocular pressure (IOP), gender, race, diagnosis, central corneal thickness (CCT), follow-up time, and baseline disease severity.

MAIN OUTCOME MEASURE

Effect of mean arterial pressure (MAP), systolic arterial pressure (SAP), and diastolic arterial pressure (DAP) on rates of RNFL loss over time.

RESULTS

A total of 157 291 BP visits, 45 408 IOP visits, and 30 238 SD-OCT visits were included. Mean rate of RNFL change was -0.70 μm/year (95% confidence interval, -0.72 to -0.67 μm/year). In univariable models, MAP, SAP, and DAP during follow-up were not significantly associated with rates of RNFL loss. However, when adjusted for mean IOP during follow-up, each 10 mmHg reduction in mean MAP (-0.06 μm/year; P = 0.007) and mean DAP (-0.08 μm/year; P < 0.001) but not SAP (-0.01 μm/year; P = 0.355) was associated with significantly faster rates of RNFL thickness change over time. The effect of the arterial pressure metrics remained significant after additional adjustment for baseline age, diagnosis, sex, race, follow-up time, disease severity, and corneal thickness.

CONCLUSIONS

When adjusted for IOP, lower MAP and DAP during follow-up were significantly associated with faster rates of RNFL loss, suggesting that levels of systemic BP may be a significant factor in glaucoma progression.

Impact of anxiety and depression on progression to glaucoma among glaucoma suspects

AIMS

To assess the impact of anxiety and depression in the risk of converting to glaucoma in a cohort of glaucoma suspects followed over time.

METHODS

The study included a retrospective cohort of subjects with diagnosis of glaucoma suspect at baseline, extracted from the Duke Glaucoma Registry. The presence of anxiety and depression was defined based on electronic health records billing codes, medical history and problem list. Univariable and multivariable Cox proportional hazards models were used to obtain HRs for the risk of converting to glaucoma over time. Multivariable models were adjusted for age, gender, race, intraocular pressure measurements over time and disease severity at baseline.

RESULTS

A total of 3259 glaucoma suspects followed for an average of 3.60 (2.05) years were included in our cohort, of which 911 (28%) were diagnosed with glaucoma during follow-up. Prevalence of anxiety and depression were 32% and 33%, respectively. Diagnoses of anxiety, or concomitant anxiety and depression were significantly associated with risk of converting to glaucoma over time, with adjusted HRs (95% CI) of 1.16 (1.01, 1.33) and 1.27 (1.07, 1.50), respectively.

CONCLUSION

A history of anxiety or both anxiety and depression in glaucoma suspects was associated with developing glaucoma during follow-up.

Rapid initial OCT RNFL thinning is predictive of faster visual field loss during extended follow-up in glaucoma

PURPOSE

To investigate the relationship between the rate of retinal nerve fiber layer (RNFL) loss during initial follow-up and the magnitude of associated visual field loss during an extended follow-up period.

DESIGN

Retrospective cohort study.

METHODS

A total of 1,150 eyes of 839 glaucoma patients extracted from the Duke Glaucoma Registry. Rates of RNFL loss were obtained from global RNFL thickness values of the first 5 optical coherence tomography (OCT) scans. Rates of visual field loss were assessed using standard automated perimetry mean deviation (SAP MD) during the entire follow-up period. Joint longitudinal mixed effects models were used to estimate rates of change. Eyes were categorized as fast, moderate or slow progressors based on rates of RNFL loss, with cutoffs of ≤-2 µm/year, -2 to -1 µm/year and ≥-1 µm/year, respectively. Univariable and multivariable regressions were completed to identify significant predictors of SAP MD loss.

RESULTS

The rate of RNFL change was -0.76±0.85 µm/y during initial follow-up, which occurred over 3.7±1.5 years. 765 (66%) eyes were slow, 328 (29%) moderate, and 57 (5%) fast progressors, with rates of RNFL thinning of -0.36±0.54 µm/year, -1.34±0.25 µm/year, and -2.87±1.39 µm/year respectively. The rates of SAP MD loss among slow, moderate, and fast OCT progressors were -0.16±0.35 dB/y, -0.32±0.43 dB/y, and -0.71±0.65 dB/y respectively over the extended follow-up period of 6.1±1.9 years (P<0.001). Age, OCT progressor group, and concurrent SAP rate were all significantly associated with the overall rate of SAP MD loss in a multivariable model (all P<0.001).

CONCLUSION

Rapid RNFL thinning during an initial follow-up period was predictive of concurrent and subsequent rates of visual field decline over an extended period.

RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure

Glaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. The cumbersome Standard Automated Perimetry (SAP) test is most frequently used to detect visual loss due to glaucoma. Due to the SAP test’s innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field. RetiNerveNet uses information from the more objective Spectral-Domain Optical Coherence Tomography (SDOCT). RetiNerveNet attempts to trace-back the arcuate convergence of the retinal nerve fibers, starting from the Retinal Nerve Fiber Layer (RNFL) thickness around the optic disc, to estimate individual age-corrected 24-2 SAP values. Recursive passes through the proposed network sequentially yield estimates of the visual locations progressively farther from the optic disc. While all the methods used for our experiments exhibit lower performance for the advanced disease group (possibly due to the “floor effect” for the SDOCT test), the proposed network is observed to be more accurate than all the baselines for estimating the individual visual field values. We further augment the proposed network to additionally predict the SAP Mean Deviation values and also facilitate the assignment of higher weightage to the underrepresented groups in the data. We then study the resulting performance trade-offs of the RetiNerveNet on the early, moderate and severe disease groups.

The Effect of Age on Increasing Susceptibility to Retinal Nerve Fiber Layer Loss in Glaucoma

Purpose

To determine whether aging modifies the effect of intraocular pressure (IOP) on progressive glaucomatous retinal nerve fiber layer (RNFL) thinning over time.

Methods

This was a retrospective cohort study involving patients with glaucoma or suspected of having glaucoma who were followed over time from the Duke Glaucoma Registry. Rates of RNFL loss from spectral-domain optical coherence tomography (SD-OCT) were used to assess disease progression. Generalized estimating equations with robust sandwich variance estimators were used to investigate the effects of the interaction of age at baseline and mean IOP on rates of RNFL loss over time. Models were adjusted for gender, race, diagnosis, central corneal thickness, follow-up time, and baseline disease severity.

Results

The study included 85,475 IOP measurements and 60,026 SD-OCT tests of 14,739 eyes of 7814 patients. Eyes had a mean follow-up time of 3.5 ± 1.9 years. The average rate of change in RNFL thickness was –0.70 µm/year (95% confidence interval, –0.72 to –0.67). There was a significant interaction between age and mean IOP and the rate of RNFL loss (P = 0.001), with older eyes having significantly faster rates of RNFL loss than younger ones for the same level of IOP. The effect of IOP on rates of change was greater in the inferior and superior regions of the optic disc.

Conclusions

Age is a significant modifier of the relationship between IOP and glaucomatous loss in RNFL thickness over time. Older patients may be more susceptible to glaucomatous progression than younger patients at the same level of IOP.

Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs

PURPOSE

To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fundus photographs can predict future development of glaucomatous visual field defects.

DESIGN

Retrospective cohort study.

METHODS

This study included 1,072 eyes of 827 glaucoma-suspect patients with an average follow-up of 5.9 ± 3.8 years. All eyes had normal standard automated perimetry (SAP) at baseline. Additional SAP and fundus photographs were acquired throughout follow-up. Conversion to glaucoma was defined as repeatable glaucomatous defects on SAP. An OCT-trained deep learning algorithm (machine to machine, M2M) was used to predict RNFL thicknesses from fundus photographs. Joint longitudinal survival models were used to assess whether baseline and longitudinal change in M2M's RNFL thickness estimates could predict development of visual field defects.

RESULTS

A total of 196 eyes (18%) converted to glaucoma during follow-up. The mean rate of change in M2M's predicted RNFL thickness was -1.02 μm/y for converters and -0.67 μm/y for non-converters (P < .001). Baseline and rate of change of predicted RNFL thickness were significantly predictive of conversion to glaucoma, with hazard ratios in the multivariable model of 1.56 per 10 μm lower at baseline (95% CI, 1.33-1.82; P < .001) and 1.99 per 1 μm/y faster loss in thickness during follow-up (95% CI, 1.36-2.93; P < .001).

CONCLUSION

Longitudinal changes in a deep learning algorithm's predictions of RNFL thickness measurements based on fundus photographs can be used to predict risk of glaucoma conversion in eyes suspected of having the disease.

Rates of Glaucomatous Structural and Functional Change From a Large Clinical Population: The Duke Glaucoma Registry Study

PURPOSE

To investigate rates of structural and functional change in a large clinical population of glaucoma and glaucoma suspect patients.

DESIGN

Retrospective cohort.

METHODS

Twenty-nine thousand five hundred forty-eight spectral-domain optical coherence tomography (OCT) and 19,812 standard automated perimetry (SAP) tests from 6138 eyes of 3669 patients with ≥6 months of follow-up, 2 good quality spectral-domain OCT peripapillary retinal nerve fiber layer scans, and 2 reliable SAP tests were included. Data were extracted from the Duke Glaucoma Registry, a large database of electronic health records of patients from the Duke Eye Center and satellite clinics. Rates of change for the 2 metrics were obtained using linear mixed models, categorized according to pre-established cutoffs, and analyzed according to the severity of the disease.

RESULTS

Average rates of change were -0.73 ± 0.80 μm per year for global retinal nerve fiber layer thickness and -0.09 ± 0.36 dB per year for SAP mean deviation. More than one quarter (26.6%) of eyes were classified as having at least a moderate rate of change by spectral-domain OCT vs 9.1% by SAP (P < .001). In eyes with severe disease, 31.6% were classified as progressing at moderate or faster rates by SAP vs 26.5% by spectral-domain OCT (P = .055). Most eyes classified as fast by spectral-domain OCT were classified as slow by SAP and vice versa.

CONCLUSION

Although most patients under routine care had slow rates of progression, a substantial proportion had rates that could potentially result in major losses if sustained over time. Both structural and functional tests should be used to monitor glaucoma, and spectral-domain OCT still has a relevant role in detecting fast progressors in advanced disease.

Predicting Age From Optical Coherence Tomography Scans With Deep Learning

Purpose

To assess whether age can be predicted from deep learning analysis of peripapillary spectral-domain optical coherence tomography (SD-OCT) B-scans and to determine the importance of specific retinal areas on the predictions.

Methods

Deep learning (DL) convolutional neural networks were developed to predict chronological age in healthy subjects using peripapillary SD-OCT B-scan images. Models were built using the whole B-scan, as well as using specific regions through image ablation. Cross-validation was used for training and testing the model. Mean absolute error (MAE) and correlations between predicted and observed age were used to evaluate model performance.

Results

A total of 7271 images from 542 eyes of 278 healthy subjects were included. DL predictions of age using the whole B-scan were strongly correlated with chronological age (MAE = 5.82 years; r = 0.860, P < 0.001). The model also accurately discriminated between the lowest and highest tertiles of age, with an area under the receiver operating characteristic curve of 0.962. In general, class activation maps tended to show a diffuse pattern of activation throughout the scan image. For specific structures of the B-scan, the layers with the strongest correlations with chronological age were the choroid and vitreous (both r = 0.736), whereas retinal nerve fiber layer had the lowest correlation (r = 0.492).

Conclusions

A DL algorithm was able to accurately predict age from whole peripapillary SD-OCT B-scans.

Translational Relevance

DL models applied to SD-OCT scans suggest that aging appears to affect several layers in the posterior eye segment.

Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans

Importance

Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors that may affect the accuracy of spectral-domain optical coherence tomography (SD-OCT) scans in detecting glaucomatous damage.

Objective

To develop a segmentation-free deep learning (DL) algorithm for assessment of glaucomatous damage using the entire circle B-scan image from SD-OCT.

Design, Setting, and Participants

This cross-sectional study at a single institution used data from SD-OCT images of eyes with glaucoma (perimetric and preperimetric) and normal eyes. The data set was randomly split at the patient level into a training (50%), validation (20%), and test data set (30%). Data were collected from March 2008 to April 2019, and analysis began April 2018.

Exposures

A convolutional neural network was trained to discriminate glaucomatous from normal eyes using the SD-OCT circle B-scan without segmentation lines.

Main Outcomes and Measures

The ability to discriminate glaucoma from healthy eyes was evaluated by comparing the area under the receiver operating characteristic curve and sensitivity at 80% or 95% specificity for the DL algorithm's predicted probability of glaucoma vs conventional RNFL thickness parameters given by SD-OCT software. The performance was also assessed in preperimetric glaucoma, as well as by visual field severity using Hodapp-Parrish-Anderson criteria.

Results

A total of 20 806 SD-OCT images from 1154 eyes of 635 individuals (612 [53%] with glaucoma and 542 normal eyes [47%]) were included. The mean (SD) age at SD-OCT scan was 70.8 (10.4) years in individuals with glaucoma and 55.8 (14.1) years in controls. There were 187 women (53.3%) in the glaucoma group and 165 (59.8%) in the control group. Of 612 eyes with glaucoma, 432 (70.4%) had perimetric and 180 (29.6%) had preperimetric glaucoma. The DL algorithm had a significantly higher area under the receiver operating characteristic curve than global RNFL thickness (0.96 vs 0.87; difference = 0.08 [95% CI, 0.04-0.12]) and each RNFL thickness sector for discriminating between glaucoma and controls (all P < .001). At 95% specificity, the DL algorithm (81%; 95% CI, 64%-97%) was more sensitive than global RNFL thickness (67%; 95% CI, 58%-76%). The areas under the receiver operating characteristic curve were also significantly greater for the DL algorithm compared with RNFL thickness at each stage of disease, especially preperimetric and mild perimetric glaucoma.

Conclusions and Relevance

A segmentation-free DL algorithm performed better than conventional RNFL thickness parameters for diagnosing glaucomatous damage on OCT scans, especially in early disease. Future studies should investigate how such an approach contributes to diagnostic decisions when combined with other relevant clinical information, such as risk factors and perimetry results.

Effect of Diabetes Control on Rates of Structural and Functional Loss in Patients with Glaucoma

PURPOSE

To investigate the association between levels of diabetes mellitus (DM) control and rates of visual field and retinal nerve fiber layer (RNFL) loss over time in glaucoma.

DESIGN

Retrospective cohort study.

PARTICIPANTS

A total of 351 eyes of 222 patients with type 2 DM with concomitant primary open-angle glaucoma (POAG) or suspected glaucoma extracted from the Duke Glaucoma Registry.

METHODS

All patients had at least 2 reliable standard automated perimetry (SAP) tests, 2 spectral domain OCT (SD-OCT) tests, and 2 glycated hemoglobin (HbA1c) measures over time with a minimum follow-up of 6 months. Values of HbA1c were summarized for each patient as mean, peak, and fluctuation across time. Multivariable linear mixed models were used to estimate the effect of HbA1c on rates of change in SAP mean deviation (MD) and OCT RNFL thickness loss over time while adjusting for various confounding factors.

MAIN OUTCOME MEASURES

Rates of change in MD and RNFL thickness over time.

RESULTS

Subjects had a mean baseline age of 62.5 ± 10.2 years and follow-up time of 6.9 ± 5.1 years. Subjects had an average of 4.8 SAP tests (range, 2-28), 3.6 SD-OCT tests (range, 2-10), and 8.3 HbA1c tests (range, 2-21). Average HbA1c mean was 7.1% ± 1.1% (range, 5.4-11.7), peak HbA1c over time was 8.1% ± 2% (range, 5.5-15.6), and HbA1c fluctuation was 0.6% ± 0.6% (range, 0-4.4). Mean rate of SAP MD change was -0.09 ± 0.20 decibel/year (median -0.06 decibel/year; interquartile range -0.15 to 0.01 decibel/year), and mean rate of RNFL change was -0.83 ± 0.51 μm/year (median -0.76 μm/year; interquartile range -1.06 to 0.56 μm/year). After adjustment for confounding factors, mean, peak, and fluctuation in HbA1c levels were not significantly associated with rates of MD change over time (P = 0.994, P = 0.689, P = 0.920, respectively), nor were rates of change in RNFL loss over time (P = 0.805, P = 0.575, P = 0.770).

CONCLUSIONS

We did not find a significant association between diabetes control, as measured by levels of HbA1c, and rates of visual field or RNFL loss over time in individuals with glaucoma or suspected glaucoma.

Comparing the Rule of 5 to Trend-based Analysis for Detecting Glaucoma Progression on OCT

PURPOSE

The rule of 5 is a simple rule for detecting retinal nerve fiber layer (RNFL) change on spectral-domain OCT (SD-OCT), in which a loss of 5 μm of global RNFL on a follow-up test is considered evidence of significant change when compared with the baseline. The rule is based on short-term test-retest variability of SD-OCT and is often used in clinical practice. The purpose of this study was to compare the rule of 5 with trend-based analysis of global RNFL thickness over time for detecting glaucomatous progression.

DESIGN

Prospective cohort.

PARTICIPANTS

A total of 300 eyes of 210 glaucoma subjects followed for an average of 5.4±1.5 years with a median of 11 (interquartile range, 7-14) visits.

METHODS

Trend-based analysis was performed by ordinary least-squares (OLS) linear regression of global RNFL thickness over time. For estimation of specificity, false-positives were obtained by assessing for progression on series of randomly permutated follow-up visits for each eye, which removes any systematic trend over time. The specificity of trend-based analysis was matched to that of the rule of 5 to allow meaningful comparison of the "hit rate," or the proportion of glaucoma eyes categorized as progressing at each time point, using the original sequence of visits.

MAIN OUTCOME MEASURES

Comparison between hit rates of trend-analysis versus rule of 5 at matched specificity.

RESULTS

After 5 years, the simple rule of 5 identified 37.5% of eyes as progressing at a specificity of 81.1%. At the same specificity, the hit rate for trend-based analysis was significantly greater than that of the rule of 5 (62.9% vs. 37.5%; P < 0.001). If the rule of 5 was required to be repeatable on a consecutive test, specificity improved to 93.4%, but hit rate decreased to 21.0%. At this higher specificity, trend-based analysis still had a significantly greater hit rate than the rule of 5 (47.4% vs. 21.0%, respectively; P < 0.001).

CONCLUSIONS

Trend-based analysis was superior to the simple rule of 5 for identifying progression in glaucoma eyes and should be preferred as a method for longitudinal assessment of global SD-OCT RNFL change over time.

Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning

Purpose

To investigate whether predictions of retinal nerve fiber layer (RNFL) thickness obtained from a deep learning model applied to fundus photographs can detect progressive glaucomatous changes over time.

Design

Retrospective cohort study.

Participants

Eighty-six thousand one hundred twenty-three pairs of color fundus photographs and spectral-domain (SD) OCT images collected during 21 232 visits from 8831 eyes of 5529 patients with glaucoma or glaucoma suspects.

Methods

A deep learning convolutional neural network was trained to assess fundus photographs and to predict SD OCT global RNFL thickness measurements. The model then was tested on an independent sample of eyes that had longitudinal follow-up with both fundus photography and SD OCT. The ability to detect eyes that had statistically significant slopes of SD OCT change was assessed by receiver operating characteristic (ROC) curves. The repeatability of RNFL thickness predictions was investigated by measurements obtained from multiple photographs that had been acquired during the same day.

Main Outcome Measures

The relationship between change in predicted RNFL thickness from photographs and change in SD OCT RNFL thickness over time.

Results

The test sample consisted of 33 466 pairs of fundus photographs and SD OCT images collected during 7125 visits from 1147 eyes of 717 patients. Eyes in the test sample were followed up for an average of 5.3 ± 3.3 years, with an average of 6.2 ± 3.8 visits. A significant correlation was found between change over time in predicted and observed RNFL thickness (r = 0.76; 95% confidence interval [CI], 0.70–0.80; P < 0.001). Retinal nerve fiber layer predictions showed an ROC curve area of 0.86 (95% CI, 0.83–0.88) to discriminate progressors from nonprogressors. For detecting fast progressors (slope faster than 2 μm/year), the ROC curve area was 0.96 (95% CI, 0.94–0.98), with a sensitivity of 97% for 80% specificity and 85% for 90% specificity. For photographs obtained at the same visit, the intraclass correlation coefficient was 0.946 (95% CI, 0.940–0.952), with a coefficient of variation of 3.2% (95% CI, 3.1%–3.3%).

Conclusions

A deep learning model was able to obtain objective and quantitative estimates of RNFL thickness that correlated well with SD OCT measurements and potentially could be used to monitor for glaucomatous changes over time.

A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression

Because of recent advances in computing technology and the availability of large datasets, deep learning has risen to the forefront of artificial intelligence, with performances that often equal, or sometimes even exceed, those of human subjects on a variety of tasks, especially those related to image classification and pattern recognition. As one of the medical fields that is highly dependent on ancillary imaging tests, ophthalmology has been in a prime position to witness the application of deep learning algorithms that can help analyze the vast amount of data coming from those tests. In particular, glaucoma stands as one of the conditions where application of deep learning algorithms could potentially lead to better use of the vast amount of information coming from structural and functional tests evaluating the optic nerve and macula. The purpose of this article is to critically review recent applications of deep learning models in glaucoma, discussing their advantages but also focusing on the challenges inherent to the development of such models for screening, diagnosis and detection of progression. After a brief general overview of deep learning and how it compares to traditional machine learning classifiers, we discuss issues related to the training and validation of deep learning models and how they specifically apply to glaucoma. We then discuss specific scenarios where deep learning has been proposed for use in glaucoma, such as screening with fundus photography, and diagnosis and detection of glaucoma progression with optical coherence tomography and standard automated perimetry.

Translational Relevance

Deep learning algorithms have the potential to significantly improve diagnostic capabilities in glaucoma, but their application in clinical practice requires careful validation, with consideration of the target population, the reference standards used to build the models, and potential sources of bias.

Impact of Intraocular Pressure Control on Rates of Retinal Nerve Fiber Layer Loss in a Large Clinical Population

PURPOSE

To investigate the impact of intraocular pressure (IOP) control on rates of change of spectral-domain OCT (SD-OCT) retinal nerve fiber layer (RNFL) thickness in a large clinical population.

DESIGN

Retrospective cohort study.

PARTICIPANTS

A total of 85 835 IOP measurements and 60 223 SD-OCT tests from 14 790 eyes of 7844 patients.

METHODS

Data were extracted from the Duke Glaucoma Registry, a large database of electronic medical records of patients with glaucoma and suspected disease followed over time at the Duke Eye Center and satellite clinics. All records from patients with a minimum of 6 months of follow-up and at least 2 good-quality SD-OCT scans and 2 clinical visits with Goldmann applanation tonometry were included. Eyes were categorized according to the frequency of visits with IOP below cutoffs of 21 mmHg, 18 mmHg, and 15 mmHg over time. Rates of change for global RNFL thickness were obtained using linear mixed models and classified as slow if change was slower than -1.0 μm/year; moderate if between -1.0 and -2.0 μm/year; and fast if faster than -2.0 μm/year. Multivariable models were adjusted for age, gender, race, diagnosis, central corneal thickness, follow-up time, and baseline disease severity.

MAIN OUTCOME MEASURES

Rates of change in SD-OCT RNFL thickness according to levels of IOP control.

RESULTS

Eyes had a mean follow-up of 3.5±1.9 years. Average rate of change in RNFL thickness was -0.68±0.59 μm/year. Each 1 mmHg higher mean IOP was associated with 0.05 μm/year faster RNFL loss (P < 0.001) after adjustment for potentially confounding variables. For eyes that had fast progression, 41% of them had IOP <21 mmHg in all visits during follow-up, whereas 20% of them had all visits with IOP <18 mmHg, but only 9% of them had all visits with IOP <15 mmHg.

CONCLUSIONS

Intraocular pressure was significantly associated with rates of progressive RNFL loss in a large clinical population. Eyes with stricter IOP control over follow-up visits had a smaller chance of exhibiting fast deterioration. Our findings may assist clinicians in establishing target pressures in clinical practice.

Artificial Intelligence Mapping of Structure to Function in Glaucoma

Purpose

To develop an artificial intelligence (AI)-based structure-function (SF) map relating retinal nerve fiber layer (RNFL) damage on spectral domain optical coherence tomography (SDOCT) to functional loss on standard automated perimetry (SAP).

Methods

The study included 26,499 pairs of SAP and SDOCT from 15,173 eyes of 8878 patients with glaucoma or suspected of having the disease extracted from the Duke Glaucoma Registry. The data set was randomly divided at the patient level in training and test sets. A convolutional neural network (CNN) was initially trained and validated to predict the 52 sensitivity threshold points of the 24-2 SAP from the 768 RNFL thickness points of the SDOCT peripapillary scan. Simulated localized RNFL defects of varied locations and depths were created by modifying the normal average peripapillary RNFL profile. The simulated profiles were then fed to the previously trained CNN, and the topographic SF relationships between structural defects and SAP functional losses were investigated.

Results

The CNN predictions had an average correlation coefficient of 0.60 (P < 0.001) with the measured values from SAP and a mean absolute error of 4.25 dB. Simulated RNFL defects led to well-defined arcuate or paracentral visual field losses in the opposite hemifield, which varied according to the location and depth of the simulations.

Conclusions

A CNN was capable of predicting SAP sensitivity thresholds from SDOCT RNFL thickness measurements and generate an SF map from simulated defects.

Translational Relevance

AI-based SF map improves the understanding of how SDOCT losses translate into detectable SAP damage.

Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs

PURPOSE

To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs.

DESIGN

Evaluation of a machine learning algorithm.

METHODS

An M2M DL algorithm trained with RNFL thickness parameters from spectral-domain optical coherence tomography was applied to a subset of 490 fundus photos of 490 eyes of 370 subjects graded by 2 glaucoma specialists for the probability of glaucomatous optical neuropathy (GON), and estimates of cup-to-disc (C/D) ratios. Spearman correlations with standard automated perimetry (SAP) global indices were compared between the human gradings vs the M2M DL-predicted RNFL thickness values. The area under the receiver operating characteristic curves (AUC) and partial AUC for the region of clinically meaningful specificity (85%-100%) were used to compare the ability of each output to discriminate eyes with repeatable glaucomatous SAP defects vs eyes with normal fields.

RESULTS

The M2M DL-predicted RNFL thickness had a significantly stronger absolute correlation with SAP mean deviation (rho=0.54) than the probability of GON given by human graders (rho=0.48; P < .001). The partial AUC for the M2M DL algorithm was significantly higher than that for the probability of GON by human graders (partial AUC = 0.529 vs 0.411, respectively; P = .016).

CONCLUSION

An M2M DL algorithm performed as well as, if not better than, human graders at detecting eyes with repeatable glaucomatous visual field loss. This DL algorithm could potentially replace human graders in population screening efforts for glaucoma.

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.

Evaluation of contrast sensitivity in patients with advanced glaucoma: comparison of two tests

AIMS

To evaluate contrast sensitivity (CS) in patients with advanced glaucomatous visual field damage, and to compare two clinical CS tests.

METHODS

This was a cross-sectional test-retest study. Twenty-eight patients with open-angle glaucoma, visual acuity (VA) better than 20/40 and visual field mean deviation (MD) worse than -15 dB were enrolled. Patients underwent VA, visual field and CS testing with the Pelli-Robson (PR) chart and the Freiburg Visual Acuity and Contrast Test (FrACT). Retest measurements were obtained within 1 week to 1 month.

RESULTS

Median (IQR) age and MD were 61.5 (55.5 to 69.2) years and -27.7 (-29.7 to -22.7) dB, respectively. Median (IQR) VA was 0.08 logarithm minimum angle of resolution (0.02 to 0.16), corresponding to 20/25 (20/20 to 20/30). Median (IQR) CS was 1.35 (1.11 to 1.51) log units with the PR chart and 1.39 (1.24 to 1.64) log units with FrACT. VA explained less than 40% of the variance in CS (adjusted R²=0.36). CS estimates of both tests were closely related (rho=0.88, p=0.001), but CS was 0.09 log units higher with FrACT compared with the PR chart, and the 95% repeatability intervals (Bland-Altman) were 46% tighter with the PR chart.

CONCLUSIONS

Despite near-normal VA, almost all patients showed moderate to profound deficits in CS. CS measurement provides additional information on central visual function in patients with advanced glaucoma.

Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach

This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects. Training was done to predict RNFL thickness from raw unsegmented scans using conventional RNFL thickness measurements from good quality images as targets, forcing the DL algorithm to learn its own representation of RNFL. The algorithm was tested in three different sets: (1) images without segmentation errors or artefacts, (2) low-quality images with segmentation errors, and (3) images with other artefacts. In test set 1, segmentation-free RNFL predictions were highly correlated with conventional RNFL thickness (r = 0.983, P < 0.001). In test set 2, segmentation-free predictions had higher correlation with the best available estimate (tests with good quality taken in the same date) compared to those from the conventional algorithm (r = 0.972 vs. r = 0.829, respectively; P < 0.001). Segmentation-free predictions were also better in test set 3 (r = 0.940 vs. r = 0.640, P < 0.001). In conclusion, a novel segmentation-free algorithm to extract RNFL thickness performed similarly to the conventional method in good quality images and better in images with errors or other artefacts.

Performance of the Rule of 5 for Detecting Glaucoma Progression between Visits with OCT

PURPOSE

To evaluate whether loss of 5 μm or more in global retinal nerve fiber layer (RNFL) thickness on spectral-domain (SD) between 2 consecutive visits is specific for glaucoma progression.

DESIGN

Prospective cohort.

PARTICIPANTS

Ninety-two eyes of 49 control participants and 300 eyes of 210 glaucoma patients.

METHODS

Patients completed at least 5 standard automated perimetry and SD OCT examinations at 6-month intervals over at least 2 years. Eyes were categorized as progressing from glaucoma if the average RNFL declined by 5 μm or more between 2 consecutive visits. The false-positive proportion was estimated by 2 methods: (1) 5-μm or more loss in control participants and (2) 5-μm or more gain in glaucoma. The false-positive proportion was subtracted from the cumulative proportion of eyes categorized with glaucoma progression to estimate the true progression prevalence.

MAIN OUTCOME MEASURES

False-positive and true progression prevalence of patients with glaucoma detected as progressing on SD OCT.

RESULTS

After 5 years of semiannual testing, the cumulative proportion of false-positive results based on 5-μm or more RNFL losses between visits was 24.8% in the control participants. Although 40.6% of glaucoma eyes were diagnosed with progression at 5 years, only 15.8% would have been considered to show true progression based on the expected false-positive ratio from the control participants (i.e., 40.6%-24.8%). The cumulative proportion of an intervisit gain of 5 μm or more at 5 years was 27.4% in glaucoma eyes, suggesting that only 13.2% of eyes with glaucoma truly had progressed (i.e., 40.6%-27.4%).

CONCLUSIONS

Loss of 5 μm or more in average RNFL thickness between consecutive SD OCT tests is not specific for glaucoma progression. Application of this intervisit rule of 5 can result in a high cumulative proportion of false-positive results over time, which could lead to unnecessary interventions in patients whose disease is stable. More specific diagnostic criteria are needed to help clinicians determine whether patients with glaucoma are progressing so that therapy escalation is both timely and appropriate.

Corneal Biomechanics and Visual Field Progression in Eyes with Seemingly Well-Controlled Intraocular Pressure

PURPOSE

To investigate the incidence and risk factors for glaucomatous visual field progression in eyes with well-controlled intraocular pressure (IOP).

DESIGN

Prospective cohort.

PARTICIPANTS

A total of 460 eyes of 334 patients with glaucoma under treatment.

METHODS

Study subjects had a mean follow-up of 4.3±0.8 years. Patients were classified as well controlled if all IOP measurements were less than 18 mmHg. Rates of visual field progression were calculated using ordinary least-squares linear regression of standard automated perimetry (SAP) mean deviation (MD) values over time. Progression was defined as a significantly negative MD slope (alpha = 0.05).

MAIN OUTCOME MEASURES

Rates of SAP MD change; mean and peak IOP, and IOP fluctuation; and corneal biomechanics: corneal hysteresis (CH), central corneal thickness (CCT), and corneal index.

RESULTS

Of the 179 eyes with well-controlled IOP, 42 (23.5%) demonstrated visual field progression. There was no significant difference between progressing and stable patients in baseline MD (-6.4±7.1 decibels [dB] vs. -6.0±6.2 dB; P = 0.346), mean IOP (11.7±2.0 mmHg vs. 12.1±2.3 mmHg; P = 0.405), IOP fluctuation (1.6±0.6 mmHg vs. 1.6±0.5 mmHg; P = 0.402), or peak IOP (14.3±1.9 mmHg vs. 14.6±2.1 mmHg; P = 0.926). Progressing eyes had significantly lower CH (8.6±1.3 mmHg vs. 9.4±1.6 mmHg; P = 0.014) and thinner CCT (515.1±33.1 μm vs. 531.1±42.4 μm; P = 0.018, respectively) compared with stable eyes. In the multivariate analysis, a 1 standard deviation lower corneal index, a summation of normalized versions of CH and CCT, resulted in a 68% higher risk of progression (odds ratio, 1.68; 95% confidence interval, 1.08-2.62; P = 0.021).

CONCLUSIONS

Approximately one-quarter of eyes with well-controlled IOP may show visual field progression over time. Thin cornea and low CH are main risk factors.

Visual Crowding in Glaucoma

Purpose

Crowding refers to the phenomenon in which objects that can be recognized when viewed in isolation are unrecognizable in clutter. Crowding sets a fundamental limit to the capabilities of the peripheral vision and is essential in explaining performance in a broad array of daily tasks. Due to the effects of glaucoma on peripheral vision, we hypothesized that neural loss in the disease would lead to stronger effects of visual crowding.

Methods

Subjects were asked to discriminate the orientation of a target letter when presented with surrounding flankers. The critical spacing value (scritical), which was required for correct discrimination of letter orientation, was obtained for each quadrant of the visual field. scritical values were correlated with standard automated perimetry (SAP) mean sensitivity (MS) and optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness measurements.

Results

The study involved 13 subjects with mild glaucomatous visual field loss and 13 healthy controls. Glaucomatous eyes had significantly greater (worse) scritical than controls (170.4 ± 27.1 vs. 145.8 ± 28.0 minimum of visual angle, respectively; P = 0.007). scritical measurements were significantly associated with RNFL thickness measurements (R2 = 26%; P < 0.001) but not with SAP MS (P = 0.947).

Conclusions

In glaucoma patients, a pronounced visual crowding effect is observed, even in the presence of mild visual field loss on standard perimetry. scritical was associated with the amount of neural loss quantified by OCT. These results may have implications for understanding how glaucoma patients are affected in daily tasks where crowding effects may be significant.

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs

PURPOSE

To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch membrane opening (BMO-MRW) from spectral-domain optical coherence tomography (SDOCT).

DESIGN

Cross-sectional study.

METHODS

A total of 9282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (20%). A DL convolutional neural network was trained to predict the SDOCT BMO-MRW global and sector values when evaluating optic disc photographs. The predictions of the DL network were compared to the actual SDOCT measurements. The area under the receiver operating curve (AUC) was used to evaluate the ability of the network to discriminate glaucomatous visual field loss from normal eyes.

RESULTS

The DL predictions of global BMO-MRW from all optic disc photographs in the test set (mean ± standard deviation [SD]: 228.8 ± 63.1 μm) were highly correlated with the observed values from SDOCT (mean ± SD: 226.0 ± 73.8 μm) (Pearson's r = 0.88; R² = 77%; P < .001), with mean absolute error of the predictions of 27.8 μm. The AUCs for discriminating glaucomatous from healthy eyes with the DL predictions and actual SDOCT global BMO-MRW measurements were 0.945 (95% confidence interval [CI]: 0.874-0.980) and 0.933 (95% CI: 0.856-0.975), respectively (P = .587).

CONCLUSIONS

A DL network can be trained to quantify the amount of neuroretinal damage on optic disc photographs using SDOCT BMO-MRW as a reference. This algorithm showed high accuracy for glaucoma detection, and may potentially eliminate the need for human gradings of disc photographs.

Mobile Telephone Use and Reaction Time in Drivers With Glaucoma

IMPORTANCE

Combining mobile telephone use with driving is not unusual. However, distracted driving limits driving performance because of limited capacity for persons to divide attention.

OBJECTIVES

To investigate the frequency of mobile telephone use while driving and to assess whether patients with glaucoma had a disproportionate decrease in driving performance while conversing on a mobile telephone compared with healthy participants.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional study of surveys collected from 112 patients with glaucoma and 70 control participants investigating mobile telephone use while driving. A randomly selected subgroup of 37 patients with glaucoma and 28 controls drove in a driving simulator to investigate peripheral event detection performance during distracted driving at the Visual Performance Laboratory, Duke University, Durham, North Carolina. Data collection was performed from December 1, 2016, through April 30, 2017.

EXPOSURES

Participants answered a survey and submitted to a driving simulation test with and without mobile telephone use.

MAIN OUTCOMES AND MEASURES

Survey answers were collected, and distracted driving performance, assessed by reaction time to peripheral stimuli, was analyzed.

RESULTS

Of the 182 participants who answered the survey, the 112 participants with glaucoma included 56 women (50.0%) and had a mean (SD) age of 73.6 (9.6) years. The 70 controls included 49 women (70.0%) and had a mean (SD) age of 68.4 (10.9) years. When asked about mobile telephone use while driving, 30 patients with glaucoma (26.8%) admitted rarely using and 2 (1.8%) sometimes using it. In the control group, 20 participants (28.6%) admitted rarely using and 2 (2.9%) sometimes using the telephone while driving (P = .80). Reaction times to peripheral stimuli were significantly longer among patients with glaucoma compared with controls during mobile telephone use (median [interquartile range], 1.86 [1.42-2.29] seconds vs 1.14 [0.98-1.59] seconds; P = .02). Compared with driving performance while not using a mobile telephone, the mean (SD) increase of 0.85 (0.60) second in reaction time while conversing on the mobile telephone among patients with glaucoma was significantly greater than the mean (SD) increase of 0.68 (0.83) second for controls (P = .03).

CONCLUSIONS AND RELEVANCE

This study's findings indicate that patients with glaucoma use mobile telephones while driving as frequently as healthy participants. However, the findings also suggest that patients with glaucoma may experience a greater decline than healthy participants in their ability to detect peripheral events while driving when also talking on a mobile telephone. Patients with glaucoma should be informed that they may have a higher driving risk that may be worsened by distractions, such as mobile telephone use.