Accelerated Epigenetic Aging is Associated with Faster Glaucoma Progression: A DNA Methylation Study.

Purpose: To investigate the association between epigenetic age acceleration and glaucoma progression. Design: Retrospective cohort study. Participants: 100 primary open-angle glaucoma (POAG) patients with fast progression and 100 POAG patients with slow progression. Methods: Subjects were classified as fast or slow progressors based on rates of change in standard automated perimetry (SAP) mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness. Epigenetic age was calculated using the Horvath, Hannum, PhenoAge, and GrimAge clocks from DNA methylation profiles obtained from blood samples. Age acceleration (AgeAccel) was defined as the residual from a linear regression of epigenetic age on chronologic age, with positive values suggesting faster biological aging. Multivariable logistic regression models estimated the association between AgeAccel and likelihood of fast progression, adjusting for confounders. Main Outcome Measures: Difference in epigenetic age acceleration between fast and slow glaucoma progressors. Results: The mean rate of SAP MD change in the fastest progressing eye was -1.06 dB/year (95% CI: -1.28 to -0.85) for fast progressors compared to -0.10 dB/year (95% CI: -0.16 to -0.04) for slow progressors (P<0.001). For RNFL thickness, corresponding values were -1.60 µm/year (95% CI: -1.97 to -1.23) and -0.76 µm/year (95% CI: -1.04 to -0.48), respectively (P<0.001). Fast progressors demonstrated significantly greater age acceleration compared to slow progressors for the Horvath clock (mean difference = 2.93 years, 95% CI: 1.48 to 4.39, P<0.001) and Hannum clock (mean difference = 1.24 years, 95% CI: 0.03 to 2.46, P=0.045). In multivariable models, each year of Horvath AgeAccel was associated with 15% higher odds of fast progression (OR 1.15, 95% CI 1.07-1.23, P<0.001), after adjusting for sex, race, intraocular pressure, central corneal thickness, baseline disease severity, smoking status and follow-up time. Hannum and GrimAge clocks also showed significant associations with fast progression. The association between AgeAccel and fast progression was stronger in subjects with relatively low IOP during follow-up. Conclusion: Accelerated epigenetic aging was associated with faster glaucoma progression. These findings suggest that faster biological age, as reflected in DNA methylation, may increase optic nerve susceptibility to damage, highlighting epigenetic age as a potential prognostic biomarker.

Big data for imaging assessment in glaucoma.

Glaucoma is the leading cause of irreversible blindness worldwide, with many individuals unaware of their condition until advanced stages, resulting in significant visual field impairment. Despite effective treatments, over 110 million people are projected to have glaucoma by 2040. Early detection and reliable monitoring are crucial to prevent vision loss. With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. Leveraging vast data sources, these technologies promise to enhance clinical practice and public health outcomes by enabling earlier disease detection, progression forecasting, and deeper understanding of underlying mechanisms. This review evaluates the use of Big Data and AI in glaucoma research, providing an overview of most relevant topics and discussing various models for screening, diagnosis, monitoring disease progression, correlating structural and functional changes, assessing image quality, and exploring innovative technologies such as generative AI.

Association Between Greater Social Vulnerability and Delayed Glaucoma Surgery.

PURPOSE: Timing of surgical intervention in glaucoma is crucial to preserving sight. While ocular characteristics that increase surgical risk are known, the impact of neighborhood-level social risk factors such as the Social Vulnerability Index (SVI) and Area Deprivation Index (ADI) on time to glaucoma surgery is unknown. The objective of this study was to evaluate the association between SVI or ADI scores and the timing of glaucoma surgical intervention. DESIGN: Retrospective cohort study. METHODS: Adult subjects with open-angle glaucoma were identified from the Bascom Palmer Glaucoma Repository using International Classification of Disease-10 codes. Subject demographics, ocular characteristics, and standard automated perimetry data were extracted. Geocoded data were obtained using subject residences and American Community Survey data. Univariable and multivariable time-to-event survival analyses using accelerated failure time models were completed to evaluate whether geocoded SVI and ADI scores accelerated or delayed time to glaucoma surgery from initial glaucoma diagnosis in the electronic health record. RESULTS: A total of 10,553 eyes from 6934 subjects were evaluated, of which 637 eyes (6.0%) from 568 subjects (8.2%) underwent glaucoma surgery. Mean age was 68.3 ± 13.5 years, with 57.9% female, 21.5% Black, and 34.5% Hispanic subjects. Mean follow-up time was 5.0 ± 2.1 years, with time to surgery of 3.2 ± 1.9 years. Multivariable accelerated failure time models demonstrated that higher mean intraocular pressure (time ratio [TR] 0.27 per 5 mm Hg higher; 95% confidence interval [CI]: 0.23-0.31, P < .001), faster standard automated perimetry rate of progression (TR 0.74 per 0.5 dB/year faster; 95% CI: 0.69-0.78, P < .001), moderate (TR 0.69; 95% CI: 0.56-0.85, P < .001) or severe baseline severity (TR 0.39; 95% CI: 0.32-0.47, P < .001), and thinner central corneal thickness (TR 0.85 per 50 µm thinner; 95% CI: 0.77-0.95, P = .003) all accelerated time to surgery. In contrast, overall SVI delayed surgery (TR 1.11 per 25% increase; 95% CI: 1.03-1.20, P = .006). Specifically, SVI Themes 1 (TR 1.08; 95% CI: 1.01-1.17, P = .037) and 4 (TR 1.11; 95% CI: 1.03-1.19, P = .006) were significant. Patients from the most deprived neighborhoods (highest national ADI quartile) had a 68% increase in time to surgery compared to the least deprived quartile (TR 1.68; 95% CI: 1.20-2.36, P = .002). CONCLUSIONS: Residence in areas with higher SVI or ADI scores was associated with delayed glaucoma surgery after controlling for demographic and ocular parameters. Awareness of such disparities can guide initiatives aimed at achieving parity in health outcomes.

Impact of Social Vulnerability Index on the Development of Severe Visual Impairment or Blindness From Glaucoma.

PURPOSE: To evaluate whether geocoded social risk factor data predict the development of severe visual impairment or blindness due to glaucoma during follow-up using a large electronic health record (EHR) database. DESIGN: Cohort study. METHODS: Patients diagnosed with open-angle glaucoma (OAG) at a tertiary care institution. All eyes had glaucomatous visual field defects at baseline. Sociodemographic and ocular data were extracted from the EHR, including age, gender, self-reported race and ethnicity, insurance status, OAG type, prior glaucoma laser or surgery, baseline disease severity using Hodapp-Anderson-Parrish criteria, mean intraocular pressure (IOP) during follow-up, and central corneal thickness. Social vulnerability index (SVIndex) data at the census tract level were obtained using geocoded patient residences. Mixed-effects Cox proportional hazard models were completed to assess for the development of severe visual impairment or blindness during follow-up, defined as BCVA ≤ 20/200 at least at the last two clinic visits or standard automated perimetry (SAP) mean deviation (MD) ≤ -22dB confirmed on two tests. RESULTS: A total of 4,046 eyes from 2,826 patients met inclusion criteria and were followed for an average of 4.3 ± 2.2 years. Severe visual impairment or blindness developed in 79 eyes (2.0%) from 76 patients (2.7%) after an average of 3.4 ± 1.8 years, leading to an incidence rate of severe visual impairment or blindness of 0.5% per year. Older age (adjusted hazards ratio [HR] 1.36 per decade, P = .007), residence in areas with higher SVIndex (HR 1.56 per 25% increase, P < .001), higher IOP during follow-up (HR 3.01 per 5 mmHg increase, P < .001), and moderate or severe glaucoma at baseline (HR 7.31 and 26.87, P < .001) were risk factors for developing severe visual impairment or blindness. Concordance index of the model was 0.88. Socioeconomic, minority status/language, and housing type/transportation SVIndex themes were key contributors to developing severe visual impairment or blindness. CONCLUSIONS: Risk factors for developing glaucoma-related severe visual impairment or blindness included older age, elevated IOP during follow-up, moderate or severe disease at baseline, and residence in areas associated with greater social vulnerability. In addition to ocular risk factors, geocoded EHR data regarding social risk factors could help identify patients at high risk of developing glaucoma-related visual impairment.

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 2 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: Eight thousand seven hundred eighty-five follow-up sequences of 5 consecutive OCT tests from 3253 eyes (1859 subjects) were included in the study. The 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 < .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.

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. PARTICIPANTS: A total of 155 eyes (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 previous intraocular surgery. Subjects underwent standard automated perimetry (SAP) at baseline and annually for 5 years. Standard automated perimetry 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 because of a nonglaucomatous etiology. Linear mixed-effects models were used to compare rates of change in SAP mean deviation (MD) between the 2 groups. Intraocular pressure (IOP) control was assessed by percentage of visits with IOP < 18 mmHg and mean IOP. MAIN OUTCOME MEASURES: Rate of change in SAP MD during follow-up. RESULTS: A total of 730 SAP tests were evaluated (average of 4.7 tests per eye). The average SAP MD at baseline was -12.8 ± 8.3 decibels (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 to 17.5 mmHg had significantly faster rates of SAP MD loss compared with 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% to 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. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

Optical Coherence Tomography Versus Optic Disc Photo Assessment in Glaucoma Screening.

PRCIS: Optical coherence tomography (OCT) and optic disc photography present valuable but distinct capabilities for glaucoma screening. OBJECTIVE: This review article examines the strengths and limitations of OCT and optic disc photography in glaucoma screening. METHODS: A comprehensive literature review was conducted, focusing on the accuracy, feasibility, cost-effectiveness, and technological advancements in OCT and optic disc photography for glaucoma screening. RESULTS: OCT is highly accurate and reproducible but faces limitations due to its cost and less portable nature, making widespread screening challenging. In contrast, optic disc photos are more accessible and cost-effective but are hindered by subjective interpretation and inconsistent grading reliability. A critical challenge in glaucoma screening is achieving a high PPV, particularly given the low prevalence of the disease, which can lead to a significant number of false positives. The advent of artificial intelligence (AI) and deep learning models shows potential in improving the diagnostic accuracy of optic disc photos by automating the detection of glaucomatous optic neuropathy and reducing subjectivity. However, the effectiveness of these AI models hinges on the quality of training data. Using subjective gradings as training data, will carry the limitations of human assessment into the AI system, leading to potential inaccuracies. Conversely, training AI models using objective data from OCT, such as retinal nerve fiber layer thickness, may offer a promising direction. CONCLUSION: Both OCT and optic disc photography present valuable but distinct capabilities for glaucoma screening. An approach integrating AI technology might be key in optimizing these methods for effective, large-scale screening programs.

Socioeconomic Disparities in Glaucoma Severity at Initial Diagnosis: A Nationwide Electronic Health Record Cohort Analysis.

PURPOSE: To assess disparities in initial disease severity among open-angle glaucoma (OAG) patients. DESIGN: Cross-sectional study. METHODS: In this analysis of Epic Cosmos, an aggregated electronic health record dataset encompassing >213 million patients, OAG patients examined in ophthalmology or optometry clinics between January 1, 2013, and June 1, 2023, were evaluated. OAG severity at presentation was classified as mild, moderate, or severe using International Classification of Disease-10 codes. Demographics, social vulnerability index (SVI) scores, and rural-urban commuting area codes were evaluated as predictors of disease stage using ordinal logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. RESULTS: Of 245,669 patients, 38.1% had mild, 32.5% moderate, and 29.3% severe disease at presentation. In multivariable analyses, significant determinants of worse severity included older age (OR: 1.23 per decade, 95% CI: 1.22-1.23), male sex (OR: 1.37, 95% CI: 1.35-1.39), Black race (OR: 1.61, 95% CI: 1.58-1.65), Hispanic ethnicity (OR: 1.15, 95% CI: 1.11-1.18), non-commercial insurance or uninsured status (OR: 2.53, 95% CI: 2.33-2.74), secondary OAGs (eg, pseudoexfoliative glaucoma - OR: 1.65, 95% CI: 1.58-1.72), and higher socioeconomic SVI scores (OR: 1.25 for highest versus lowest quartile, 95% CI: 1.22-1.28). Black and Hispanic patients were diagnosed at younger ages compared to White patients (mean ages: 67.8 ± 12.3 and 68.1 ± 12.8 vs 73.3 ± 11.8 years respectively, P < .001). CONCLUSIONS: Worse OAG at presentation was associated with older age, male sex, Black race, Hispanic ethnicity, non-commercial insurance or uninsured status, secondary OAGs, and greater socioeconomic vulnerability in this nationwide cohort. These findings can help tailor screening programs towards vulnerable populations.

Performance of Linear Mixed Models in Estimating Structural Rates of Glaucoma Progression Using Varied Random Effect Distributions.

Purpose: To compare how linear mixed models (LMMs) using Gaussian, Student t, and log-gamma (LG) random effect distributions estimate rates of structural loss in a glaucomatous population using OCT and to compare model performance to ordinary least squares (OLS) regression. Design: Retrospective cohort study. Subjects: Patients in the Bascom Palmer Glaucoma Repository (BPGR). Methods: Eyes with ≥ 5 reliable peripapillary retinal nerve fiber layer (RNFL) OCT tests over ≥ 2 years were identified from the BPGR. Retinal nerve fiber layer thickness values from each reliable test (signal strength ≥ 7/10) and associated time points were collected. Data were modeled using OLS regression as well as LMMs using different random effect distributions. Predictive modeling involved constructing LMMs with (n - 1) tests to predict the RNFL thickness of subsequent tests. A total of 1200 simulated eyes of different baseline RNFL thickness values and progression rates were developed to evaluate the likelihood of declared progression and predicted rates. Main Outcome Measures: Model fit assessed by Watanabe-Akaike information criterion (WAIC) and mean absolute error (MAE) when predicting future RNFL thickness values; log-rank test and median time to progression with simulated eyes. Results: A total of 35 862 OCT scans from 5766 eyes of 3491 subjects were included. The mean follow-up period was 7.0 ± 2.3 years, with an average of 6.2 ± 1.4 tests per eye. The Student t model produced the lowest WAIC. In predictive models, all LMMs demonstrated a significant reduction in MAE when estimating future RNFL thickness values compared with OLS (P < 0.001). Gaussian and Student t models were similar and significantly better than the LG model in estimating future RNFL thickness values (P < 0.001). Simulated eyes confirmed LMM performance in declaring progression sooner than OLS regression among moderate and fast progressors (P < 0.01). Conclusions: LMMs outperformed conventional approaches for estimating rates of OCT RNFL thickness loss in a glaucomatous population. The Student t model provides the best model fit for estimating rates of change in RNFL thickness, although the use of the Gaussian or Student t distribution in models led to similar improvements in accurately estimating RNFL loss. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

Single Administration of Bimatoprost Implant: Effects on 24-Hour Intraocular Pressure and 1-Year Outcomes.

PURPOSE: To evaluate the effects of a single bimatoprost implant administration on 24-hour intraocular pressure (IOP) lowering at 8 weeks, and 1-year IOP-lowering efficacy and safety outcomes. DESIGN: Multicenter, open-label, 12-month, phase 3b study (NCT04285580). PARTICIPANTS: Adults with open-angle glaucoma or ocular hypertension. METHODS: Participants (n = 31) received 10-µg bimatoprost implant in the study eye on day 1; IOP (sitting and/or supine) was measured with pneumatonometry every 2 hours throughout a 24-hour period at baseline and week 8. IOP was measured by Goldmann applanation tonometry (GAT) at hour 0 (8 am ± 1 hour) at baseline, weeks 8 and 16, and months 6, 9, and 12. MAIN OUTCOME MEASURES: The primary endpoint was the week-8 hour-matched change from baseline in habitual position IOP over 24 hours assessed with pneumatonometry. Hour 0 IOP change from baseline measured with GAT in study eyes that received no additional (rescue) IOP-lowering treatment, treatment-emergent adverse events (TEAEs), and central corneal endothelial cell density (CECD) were evaluated through 12 months. RESULTS: The mean (standard deviation [SD]) baseline IOP at hour 0 was 24.2 (2.70) mmHg and 25.3 (7.15) mmHg by GAT and pneumatonometry, respectively. Pneumatonometer measurements of IOP taken over 24 hours at week 8 with the participant in habitual position (sitting from 8 am to 10 pm, supine from 12 am to 6 am) showed consistent IOP lowering through the day and night and reduced fluctuation in IOP. The range in IOP measurements over 24 hours was reduced from baseline by a mean (SD) of -1.6 (2.98) mmHg. All 31 bimatoprost implant-treated participants completed the 12-month study; 23 (74%) required no rescue IOP-lowering treatment. The mean (SD) IOP reduction from baseline at month 12 in nonrescued eyes was -4.3 (3.35) mmHg. The most common TEAE was conjunctival hyperemia (incidence 35.5%, 11/31). No implant-treated eye had a ≥ 15% loss in CECD from baseline. CONCLUSIONS: A single intracameral administration of the bimatoprost implant lowered IOP in the habitual position consistently throughout the day and night at week 8. The majority of participants continued to have reduced IOP for 1 year without additional therapy. The 1-year safety profile was favorable. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

Hazard Detection During Simulated Driving in Glaucoma Patients.

PRCIS: In this cross-sectional study, glaucoma patients showed slower reaction times (RTs) to hazardous situations when compared with control subjects during simulated driving. Worse RTs were associated with a greater magnitude of visual field loss. PURPOSE: The purpose of this study was to evaluate the impact of different hazardous traffic conditions on driving performance in glaucoma patients using a high-fidelity driving simulator. METHODS: The cross-sectional study was performed 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. 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 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 and 1.14 s slower per 10 dB 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 with controls. Individualized assessment of driving fitness using hazardous scenarios in driving simulators could be helpful in providing an assessment of driving risk in glaucoma patients.

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.

Systematic User-centered Design of a Prototype Clinical Decision Support System for Glaucoma.

Purpose: To rigorously develop a prototype clinical decision support (CDS) system to help clinicians determine the appropriate timing for follow-up visual field testing for patients with glaucoma and to identify themes regarding the context of use for glaucoma CDS systems, design requirements, and design solutions to meet these requirements. Design: Semistructured qualitative interviews and iterative design cycles. Participants: Clinicians who care for patients with glaucoma, purposefully sampled to ensure a representation of a range of clinical specialties (glaucoma specialist, general ophthalmologist, optometrist) and years in clinical practice. Methods: Using the established User-Centered Design Process framework, we conducted semistructured interviews with 5 clinicians that addressed the context of use and design requirements for a glaucoma CDS system. We analyzed the interviews using inductive thematic analysis and grounded theory to generate themes regarding the context of use and design requirements. We created design solutions to address these requirements and used iterative design cycles with the clinicians to refine the CDS prototype. Main Outcome Measures: Themes regarding decision support for determining the timing of visual field testing for patients with glaucoma, CDS design requirements, and CDS design features. Results: We identified 9 themes that addressed the context of use for the CDS system, 9 design requirements for the prototype CDS system, and 9 design features intended to address these design requirements. Key design requirements included the preservation of clinician autonomy, incorporation of currently used heuristics, compilation of data, and increasing and communicating the level of certainty regarding the decision. After completing 3 iterative design cycles using this preliminary CDS system design solution, the design was satisfactory to the clinicians and was accepted as our prototype glaucoma CDS system. Conclusions: We used a systematic design process based on the established User-Centered Design Process to rigorously develop a prototype glaucoma CDS system, which will be used as a starting point for a future, large-scale iterative refinement and implementation process. Clinicians who care for patients with glaucoma need CDS systems that preserve clinician autonomy, compile and present data, incorporate currently used heuristics, and increase and communicate the level of certainty regarding the decision. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

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.

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.

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.