Racial Differences in Diagnostic Accuracy of Retinal Nerve Fiber Layer Thickness in Primary Open-Angle Glaucoma.

Purpose: To evaluate the diagnostic accuracy of retinal nerve fiber layer thickness (RNFLT) by spectral-domain optical coherence tomography (OCT) in primary open-angle glaucoma (POAG) in eyes of African (AD) and European descent (ED). Design: Comparative diagnostic accuracy analysis by race. Participants: 379 healthy eyes (125 AD and 254 ED) and 442 glaucomatous eyes (226 AD and 216 ED) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. Methods: Spectralis (Heidelberg Engineering GmbH) and Cirrus (Carl Zeiss Meditec) OCT scans were taken within one year from each other. Main Outcome Measures: Diagnostic accuracy of RNFLT measurements. Results: Diagnostic accuracy for Spectralis-RNFLT was significantly lower in eyes of AD compared to those of ED (area under the receiver operating curve [AUROC]: 0.85 and 0.91, respectively, P=0.04). Results for Cirrus-RNFLT were similar but did not reach statistical significance (AUROC: 0.86 and 0.90 in AD and ED, respectively, P =0.33). Adjustments for age, central corneal thickness, axial length, disc area, visual field mean deviation, and intraocular pressure yielded similar results. Conclusions: OCT-RNFLT has lower diagnostic accuracy in eyes of AD compared to those of ED. This finding was generally robust across two OCT instruments and remained after adjustment for many potential confounders. Further studies are needed to explore the potential sources of this difference.

Association of Long-Term Intraocular Pressure Variability and Rate of Ganglion Complex Thinning in Patients With Glaucoma.

PURPOSE: To evaluate the association of mean intraocular pressure (IOP) and IOP variability (IOP fluctuation [SD of IOP] and the IOP range) with the rate of ganglion cell complex (GCC) layer thinning over time in patients with glaucoma. DESIGN: Prospective cohort study. METHODS: Participants with at least 4 visits and 2 years of follow-up of optical coherence tomography tests were included. A linear mixed-effect model was used to investigate the association of IOP parameters with the rates of GCC thinning. Subgroup analyses were conducted for eyes with early (MD ≥ -6 dB), and moderate to advanced stage (MD < -6 dB) at baseline. RESULTS: The cohort consisted of 369 eyes of 249 glaucoma patients (282 early glaucoma and 87 moderate to advanced glaucoma) with mean (standard deviation [SD]) age of 68.2 (10.7) years over 5.1 years of follow-up. The mean rate of GCC change was -0.59 (95% confidence interval [CI], -0.67 to -0.52) µm per year. In multivariable models, faster annual rate of GCC thinning was associated with a higher IOP fluctuation (-0.17 [95% CI, -0.23 to -0.11] µm per 1-mmHg higher, P < .001) or higher IOP range (-0.07 [95% CI, -0.09 to -0.05] µm per 1-mmHg higher, P < .001) after adjustment for mean IOP and other confounding factors. Similar results were found for early and moderate to advanced stages of glaucoma. CONCLUSIONS: IOP variability showed an independent association with macular change in patients with glaucoma regardless of severity at baseline, even after adjustment for mean IOP, supporting its potential value as a therapeutic target for clinical decision-making.

Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data.

A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

Purpose: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Methods: Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). Results: The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88). Conclusions: The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Translational Relevance: Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.

Central visual field damage in glaucoma eyes with choroidal microvasculature dropout with and without high axial myopia.

PURPOSE: To characterise the relationship between a deep-layer microvasculature dropout (MvD) and central visual field (VF) damage in primary open-angle glaucoma (POAG) patients with and without high axial myopia. DESIGN: Cross-sectional study. METHODS: Seventy-one eyes (49 patients) with high axial myopia and POAG and 125 non-highly myopic POAG eyes (97 patients) were enrolled. Presence, area and angular circumference of juxtapapillary MvD were evaluated on optical coherence tomography angiography B-scans and en-face choroidal images. RESULTS: Juxtapapillary MvD was detected more often in the highly myopic POAG eyes (43 eyes, 86%) than in the non-highly myopic eyes (73 eyes, 61.9%; p=0.002). In eyes with MvD, MvD area and angular circumference (95% CI) were significantly larger in the highly myopic eyes compared with the non-highly myopic eyes (area: (0.69 (0.40, 0.98) mm2 vs 0.31 (0.19, 0.42) mm2, p=0.011) and (angular circumference: 84.3 (62.9, 105.8) vs 74.5 (58.3, 90.9) degrees, p<0.001), respectively. 24-2 VF mean deviation (MD) was significantly worse in eyes with MvD compared with eyes without MvD in both groups (p<0.001). After adjusting for 24-2 MD VF, central VF defects were more frequently found in eyes with MvD compared with eyes without MvD (82.7% vs 60.9%, p<0.001). In multivariable analysis, higher intraocular pressure, worse 24-2 VF MD, longer axial length and greater MvD area and angular circumference were associated with worse 10-2 VF MD. CONCLUSIONS: MvD was more prevalent and larger in POAG eyes with high myopia than in non-highly myopic POAG eyes. In both groups, eyes with MvD showed worse glaucoma severity and more central VF defects.

Displacement of the Lamina Cribrosa With Acute Intraocular Pressure Increase in Brain-Dead Organ Donors.

Purpose: To examine deformations of the optic nerve head (ONH) deep tissues in response to acute elevation of intraocular pressure (IOP). Methods: Research-consented brain-dead organ donors underwent imaging by spectral domain optical coherence tomography (OCT). OCT imaging was repeated while the eye was sequentially maintained at manometric pressures of 10, 30, and 50 mm Hg. Radial scans of the ONH were automatically segmented by deep learning and quantified in three dimensions by a custom algorithm. Change in lamina cribrosa (LC) depth and choroidal thickness was correlated with IOP and age by linear mixed-effect models. LC depth was computed against commonly utilized reference planes. Results: Twenty-six eyes from 20 brain-dead organ donors (age range, 22-62 years; median age, 43 years) were imaged and quantified. LC depth measured against a reference plane based on Bruch's membrane (BM), BM opening, and an anterior sclera canal opening plane showed both a reduction and an increase in LC depth with IOP elevation. LC depth universally increased in depth when measured against a sclera reference plane. Choroidal (-0.5222 µm/mm Hg, P < 0.001) and retinal nerve fiber layer thickness (-0.0717 µm/mm Hg, P < 0.001) significantly thinned with increasing IOP. The magnitude of LC depth change with IOP was significantly smaller with increasing age (P < 0.03 for all reference planes). Conclusions: LC depth changes with IOP reduce with age and are significantly affected by the reference plane of choice, which highlights a need for standardizing LC metrics to properly follow progressive remodeling of the loadbearing tissues of the ONH by OCT imaging and for the definition of a reference database.

Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

PRCIS: An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma diagnosis in eyes with and without high myopia. BACKGROUND/AIMS: To evaluate the diagnostic accuracy of a multimodal DL classifier using wide OCT optic nerve head cube scans in eyes with and without axial high myopia. MATERIALS AND METHODS: Three hundred seventy-one primary open angle glaucoma (POAG) eyes and 86 healthy eyes, all without axial high myopia [axial length (AL) ≤ 26 mm] and 92 POAG eyes and 44 healthy eyes, all with axial high myopia (AL > 26 mm) were included. The multimodal DL classifier combined features of 3 individual VGG-16 models: (1) texture-based en face image, (2) retinal nerve fiber layer (RNFL) thickness map image, and (3) confocal scanning laser ophthalmoscope (cSLO) image. Age, AL, and disc area adjusted area under the receiver operating curves were used to compare model accuracy. RESULTS: Adjusted area under the receiver operating curve for the multimodal DL model was 0.91 (95% CI = 0.87, 0.95). This value was significantly higher than the values of individual models [0.83 (0.79, 0.86) for texture-based en face image; 0.84 (0.81, 0.87) for RNFL thickness map; and 0.68 (0.61, 0.74) for cSLO image; all P ≤ 0.05]. Using only highly myopic eyes, the multimodal DL model showed significantly higher diagnostic accuracy [0.89 (0.86, 0.92)] compared with texture en face image [0.83 (0.78, 0.85)], RNFL [0.85 (0.81, 0.86)] and cSLO image models [0.69 (0.63, 0.76)] (all P ≤ 0.05). CONCLUSIONS: Combining OCT-based RNFL thickness maps with texture-based en face images showed a better ability to discriminate between healthy and POAG than thickness maps alone, particularly in high axial myopic eyes.

Retinal electrophysiologic response to IOP elevation in living human eyes.

PURPOSE: The relationships between intraocular pressure (IOP), ocular perfusion pressure (OPP), retinal perfusion, and retinal electrophysiologic responses have been explored experimentally across several animal models. These studies have demonstrated that elevated IOP reduces OPP, and when this reduction in OPP exceeds the autoregulatory capacity of the retina vasculature, retinal perfusion and electrophysiologic responses are reduced. This study aimed to evaluate these interactions for the first time in the living human eye. METHODS: Five eyes from three research-consented brain-dead organ donors underwent optical coherence tomography with angiographic (OCT/A; Spectralis, Heidelberg Engineering) and electroretinographic (ERG, Diagnosys LLC) measurements while IOP was manometrically-elevated stepwise to pressures of 10, 30 and 50 mmHg. Systemic blood pressure (BP) was monitored continuously during testing. Correlation analysis was applied to assess association between ERG and OPP changes. In a single eye, prolonged IOP elevation was induced with viscoelastic injection and serial ERG measurements were obtained. RESULTS: Reductions in inner retinal function defined by photopic ERG were observed with elevation in IOP and concomitant reduction in OPP. Reductions, especially in b-wave, and photopic negative response (PhNR) amplitudes and implicit times were significantly correlated with elevation in IOP and reduction in OPP. There were more appreciable changes in perfusion and functional responses in eyes tested while systemic blood pressure was lower. With prolonged IOP elevation, selective loss of the PhNR response was observed. CONCLUSIONS: In the living human eye, retinal perfusion and inner retinal function are acutely impacted by elevation of IOP, and this impact is related to systemic BP and OPP. This novel approach provides a viable model to study the autoregulatory responses to IOP elevation in the living human eye.

Fast Progressors in Glaucoma: Prevalence Based on Global and Central Visual Field Loss.

PURPOSE: To determine the prevalence of fast global and central visual field (VF) progression in individuals with glaucoma under routine care. DESIGN: Observational study. PARTICIPANTS: Six hundred ninety-three eyes of 461 individuals with glaucoma followed up over a median of 4.5 years. METHODS: This study included (1) patients at a private ophthalmology clinic in Melbourne, Australia, and (2) individuals in 2 prospective longitudinal observational studies across 3 sites in the United States. All individuals had a diagnosis of glaucoma and were under routine care, and had performed 5 or more reliable 24-2 VF tests over a 1- to 5-year period. Ordinary least squares regression analyses were used to calculate the rate of global mean deviation (MD) change over time and the rate of the mean total deviation values of the 12 test locations within the central 10° region (MTD10) for each eye. MAIN OUTCOME MEASURES: Prevalence of progression based on the rate of MD and the MTD10 change across various fixed cutoffs and cutoffs based on the estimated normal distribution (from the positive slopes). RESULTS: Based on the MD and the MTD10, 12.5% and 11.7% of the eyes, respectively, exhibited a rate of change that was less than -1.0 dB/year (being a rate that typically is defined as "fast progression" for MD values), and 29.0% of the eyes showed a change of less than -0.5 dB/year on MTD10. Furthermore, 12.7% and 9.1% of the eyes exhibited a rate of change that exceeded the 1% cutoff of the estimated normal distribution MD and the MTD10 values, respectively. CONCLUSIONS: This study found that approximately 1 in 8 eyes with glaucoma receiving routine care showed fast progression based on global MD values (< -1.0 dB/year) and that nearly 1 in 3 eyes showed a < -0.5 dB/year decline centrally. These findings highlight the clinical importance of assessing progressive central VF loss and reinforce the need for new therapies to prevent functional disability in a notable proportion of individuals who continue to exhibit fast progression. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Association Between Rate of Ganglion Cell Complex Thinning and Rate of Central Visual Field Loss.

Importance: Whether rapid ganglion cell complex (GCC) thinning during an initial follow-up period is associated with rates of central visual field loss over time is unclear but important to understand because risk of glaucoma progression can help guide treatment intensity. Objective: To investigate the association between the rate of GCC thinning during initial follow-up and the rate of central visual field loss. Design, Setting, and Participants: This retrospective cohort study assessed patients older than 18 years with glaucoma at a tertiary glaucoma center who were followed up from June 18, 2014, to January 11, 2019. Data analysis for the current study was undertaken in March 2022. Main Outcomes and Measures: Initial rates of GCC thinning were obtained from global GCC thickness values of the first 3 optical coherence tomography (OCT) scans. Rates of central visual field loss were assessed as the change in central (10-2) visual field mean deviation during the 4.7-year follow-up period by univariable and multivariable linear mixed-effects models. Eyes were categorized as slow (>-1 µm/y) or fast (≤-1 µm/y) progressors based on rates of GCC thinning. Results: The cohort consisted of 202 eyes of 139 patients (mean [SD] age, 68.7 [10.0] years; 72 male [51.8%]); 44 African American patients (31.7%), 13 Asian patients (9.4%), 80 White patients (57.6%), and 2 patients who identified as other race and ethnicity (1.4%) were analyzed. The rate of GCC change was -0.56 µm/y (95% CI, -0.66 to -0.46 µm/y) during a mean initial follow-up of 1.8 years (95% CI, 1.7-2.0 years). A total of 163 eyes (80.7%) were slow OCT progressors, and 39 (19.3%) were fast OCT progressors, with rates of GCC thinning of -0.3 µm/y (95% CI, -0.4 to -0.2 µm/y) and -1.6 µm/y (-1.8 to -1.3 µm/y), respectively. The rates of 10-2 visual field mean deviation worsening among slow and fast OCT progressors were -0.10 dB/y (95% CI, -0.16 to 0.00 dB/y) and -0.34 dB/y (95% CI, -0.51 to -0.16 dB/y), respectively (difference, -0.26 dB/y; 95% CI, -0.45 to -0.07 dB/y; P = .008). Conclusions and Relevance: In this cohort study, rapid GCC thinning during an initial follow-up period was associated with faster rates of central visual field decline. These findings support use of longitudinal macular OCT scans assisting clinical decision-making for glaucoma and also may guide possible intensification of therapy in high-risk patients.

Vision-Related Quality of Life Among Healthy, Preperimetric Glaucoma, and Perimetric Glaucoma Patients.

PURPOSE: To investigate the association of vision-related quality of life (VRQOL) with the central visual field and macular ganglion cell complex (GCC) thickness in healthy control participants, patients with preperimetric glaucoma, and patients with perimetric glaucoma. DESIGN: Retrospective cross-sectional study. METHODS: A total of 39 healthy, 34 preperimetric glaucoma, and 145 perimetric glaucoma patients completed the 25-item National Eye Institute Visual Function Questionnaire (NEI-VFQ). A linear mixed-effect models was used to investigate the association between the glaucoma stage as measured by binocular 10-2 visual field mean sensitivity (VFMS) and GCC thickness with the Rasch-calibrated NEI-VFQ score. RESULTS: A total of 436 eyes from 218 participants (mean age = 67.2 [95% CI = 65.1 to 69.2] years) were enrolled. VRQOL calculated by the NEI-VFQ Rasch-calibrated score was worst for patients with perimetric glaucoma (50.7 [95% CI = 47.2 to 54.2]), followed by patients with preperimetric glaucoma (41.2 [95% CI = 34.5 to 47.9]) and healthy controls (29.3 [95% CI = 24.0 to 34.7]. Worse VRQOL had a moderate association with a worse global binocular 10-2 VFMS (-3.4 [95% CI = -5.0 to -1.9] dB per 1 score; P < .001; adjusted R2 = 0.27), but not with a thinner global GCC in the better eye (-0.1 [95% CI = -0.2 to 0.1] µm per 1 score; P =.0485; adjusted R2 = 0.17). CONCLUSIONS: These findings suggest that patients with perimetric and preperimetric glaucoma have worse VRQOL than patients with healthy eyes. As compared to macular thickness measurements, the central visual field is more strongly associated with VRQOL and may better help to identify patients in need of intervention.

Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements.

PURPOSE: To estimate central 10-degree visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thickness (RNFL) measurements in glaucoma with artificial intelligence. DESIGN: Artificial intelligence (convolutional neural networks) study. METHODS: This study included 5352 SD-OCT scans and 10-2 VF pairs from 1365 eyes of 724 healthy patients, patients with suspected glaucoma, and patients with glaucoma. Convolutional neural networks (CNNs) were developed to estimate the 68 individual sensitivity thresholds of 10-2 VF map using all-sectors (CNNA) and temporal-sectors (CNNT) RNFL thickness information of the SD-OCT circle scan (768 thickness points). 10-2 indices including pointwise total deviation (TD) values, mean deviation (MD), and pattern standard deviation (PSD) were generated using the CNN-estimated sensitivity thresholds at individual test locations. Linear regression (LR) models with the same input were used for comparison. RESULTS: The CNNA model achieved an average pointwise mean absolute error of 4.04 dB (95% confidence interval [CI] 3.76-4.35) and correlation coefficient (r) of 0.59 (95% CI 0.52-0.64) over 10-2 map and the mean absolute error and r of 2.88 dB (95% CI 2.63-3.15) and 0.74 (95% CI 0.67-0.80) for MD, and 2.31 dB (95% CI 2.03-2.61) and 0.59 (95% CI 0.51-0.65) for PSD estimations, respectively, significantly outperforming the LRA model. CONCLUSIONS: The proposed CNNA model improved the estimation of 10-2 VF map based on circumpapillary SD-OCT RNFL thickness measurements. These artificial intelligence methods using SD-OCT structural data show promise to individualize the frequency of central VF assessment in patients with glaucoma and would enable the reallocation of resources from patients at lowest risk to those at highest risk of central VF damage.

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization.

Purpose: To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and identify the salient areas of the photographs most important for each model's decision-making process. Design: Evaluation of a diagnostic technology. Subjects Participants and Controls: Overall 66 715 photographs from 1636 OHTS participants and an additional 5 external datasets of 16 137 photographs of healthy and glaucoma eyes. Methods: Data-efficient image Transformer models were trained to detect 5 ground-truth OHTS POAG classifications: OHTS end point committee POAG determinations because of disc changes (model 1), visual field (VF) changes (model 2), or either disc or VF changes (model 3) and Reading Center determinations based on disc (model 4) and VFs (model 5). The best-performing DeiT models were compared with ResNet-50 models on OHTS and 5 external datasets. Main Outcome Measures: Diagnostic performance was compared using areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities. The explainability of the DeiT and ResNet-50 models was compared by evaluating the attention maps derived directly from DeiT to 3 gradient-weighted class activation map strategies. Results: Compared with our best-performing ResNet-50 models, the DeiT models demonstrated similar performance on the OHTS test sets for all 5 ground-truth POAG labels; AUROC ranged from 0.82 (model 5) to 0.91 (model 1). Data-efficient image Transformer AUROC was consistently higher than ResNet-50 on the 5 external datasets. For example, AUROC for the main OHTS end point (model 3) was between 0.08 and 0.20 higher in the DeiT than ResNet-50 models. The saliency maps from the DeiT highlight localized areas of the neuroretinal rim, suggesting important rim features for classification. The same maps in the ResNet-50 models show a more diffuse, generalized distribution around the optic disc. Conclusions: Vision Transformers have the potential to improve generalizability and explainability in deep learning models, detecting eye disease and possibly other medical conditions that rely on imaging for clinical diagnosis and management.

A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design.

PURPOSE: To investigate the efficacy of a deep learning regression method to predict macula ganglion cell-inner plexiform layer (GCIPL) and optic nerve head (ONH) retinal nerve fiber layer (RNFL) thickness for use in glaucoma neuroprotection clinical trials. DESIGN: Cross-sectional study. PARTICIPANTS: Glaucoma patients with good quality macula and ONH scans enrolled in 2 longitudinal studies, the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovations in Glaucoma Study. METHODS: Spectralis macula posterior pole scans and ONH circle scans on 3327 pairs of GCIPL/RNFL scans from 1096 eyes (550 patients) were included. Participants were randomly distributed into a training and validation dataset (90%) and a test dataset (10%) by participant. Networks had access to GCIPL and RNFL data from one hemiretina of the probe eye and all data of the fellow eye. The models were then trained to predict the GCIPL or RNFL thickness of the remaining probe eye hemiretina. MAIN OUTCOME MEASURES: Mean absolute error (MAE) and squared Pearson correlation coefficient (r2) were used to evaluate model performance. RESULTS: The deep learning model was able to predict superior and inferior GCIPL thicknesses with a global r2 value of 0.90 and 0.86, r2 of mean of 0.90 and 0.86, and mean MAE of 3.72 µm and 4.2 µm, respectively. For superior and inferior RNFL thickness predictions, model performance was slightly lower, with a global r2 of 0.75 and 0.84, r2 of mean of 0.81 and 0.82, and MAE of 9.31 µm and 8.57 µm, respectively. There was only a modest decrease in model performance when predicting GCIPL and RNFL in more severe disease. Using individualized hemiretinal predictions to account for variability across patients, we estimate that a clinical trial can detect a difference equivalent to a 25% treatment effect over 24 months with an 11-fold reduction in the number of patients compared to a conventional trial. CONCLUSIONS: Our deep learning models were able to accurately estimate both macula GCIPL and ONH RNFL hemiretinal thickness. Using an internal control based on these model predictions may help reduce clinical trial sample size requirements and facilitate investigation of new glaucoma neuroprotection therapies. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Macula structural and vascular differences in glaucoma eyes with and without high axial myopia.

AIMS: To identify clinically relevant parameters for identifying glaucoma in highly myopic eyes, an investigation was conducted of the relationship between the thickness of various retinal layers and the superficial vessel density (sVD) of the macula with axial length (AL) and visual field mean deviation (VFMD). METHODS: 270 glaucoma patients (438 eyes) participating in the Diagnostic Innovations in Glaucoma cross-sectional study representing three axial myopia groups (non-myopia: n=163 eyes; mild myopia: n=218 eyes; high myopia (AL>26 mm): n=57 eyes) who completed macular optical coherence tomography (OCT) and OCT-angiography imaging were included. Associations of AL and VFMD with the thickness of the ganglion cell inner plexiform layer (GCIPL), macular retinal nerve fibre layer (mRNFL), ganglion cell complex (GCC), macular choroidal thickness (mCT) and sVD were evaluated. RESULTS: Thinner Global GCIPL and GCC were significantly associated with worse VFMD (R2=34.5% and R2=32.9%; respectively p<0.001), but not with AL (all p>0.1). Thicker mRNFL showed a weak association with increasing AL (R2=2.4%; p=0.005) and a positive association with VFMD (global R2=19.2%; p<0.001). Lower sVD was weakly associated with increasing AL (R2=1.8%; p=0.028) and more strongly associated with more severe glaucoma VFMD (R2=29.6%; p<0.001). Thinner mCT was associated with increasing AL (R2=15.5% p<0.001) and not associated with VFMD (p=0.194). mRNFL was thickest while mCT was thinnest in all sectors of high myopic eyes. CONCLUSIONS: As thinner GCIPL and GCC were associated with increasing severity of glaucoma but were not significantly associated with AL, they may be useful for monitoring glaucoma in highly myopic eyes.

Smoking and progressive retinal nerve fibre layer thinning in glaucoma.

BACKGROUND/AIMS: To investigate the relationship between smoking and smoking intensity, and the rate of retinal nerve fibre layer (RNFL) thinning in patients with primary open angle glaucoma (POAG). METHODS: In this longitudinal study, patients with POAG who had at least 3 years of follow-up with a minimum of 5 visits of optical coherence tomography (OCT) were enrolled. The smoking intensity was calculated as the pack-year at the baseline OCT. Univariable and multivariable linear mixed models were used to determine the effect of each parameter on the rates of RNFL thinning over time. Non-linear least-squares estimation with piecewise regression model was used to investigate the cut-off point for the relationship between circumpapillary RNFL thinning and smoking intensity. RESULTS: A total of 466 eyes of 314 patients were included over the mean (95% CI) follow-up of 6.6 (6.4 to 6.7) years. Of the 314 patients, 121 (39%) had reported any history of smoking. Greater smoking intensity was associated with faster RNFL thinning (-0.06 (95% CI -0.11 to 0.00) µm/year per 10 pack-year higher; p=0.031) after adjusted for confounding factors. RNFL thinning became significantly faster when smoking intensity was >8 pack-year. CONCLUSIONS: Smoking intensity is associated with faster rates of RNFL thinning. Evaluation of smoking intensity might add information to the assessment of risk of glaucoma progression. Future studies are required to explore if withdrawing smoking as a modifiable risk factor can decrease progression in patients with glaucoma.

Long-term variability of retinal nerve fibre layer thickness measurement in patients with glaucoma of African and European descents.

BACKGROUND: To examine long-term retinal nerve fibre layer thickness (RNFLT) variability and associated clinical factors in African (AD) and European descent (ED) individuals with glaucoma. METHODS: This retrospective cohort study included glaucoma eyes of AD and ED from Diagnostic Innovations in Glaucoma Study/The African Descent and Glaucoma Evaluation Study with ≥4 visits/2 years of follow-up. We calculated optic nerve head RNFLT variability per-examination/visit as the absolute error of its residuals across follow-up. Full, baseline and parsimonious linear-mixed models were fit to evaluate the effects of clinical factors (demographics and ocular characteristics, prior/intervening glaucoma surgeries and cataract extraction (CE), RNFLT thinning rate, scan quality, visit/testing frequency, etc) on RNFLT variability in both races. RESULTS: There were 376 and 625 eyes (226 and 349 participants) of AD and ED, and the mean (95% CI) RNFLT variability was 1.62 (1.52, 1.71) µm and 1.42 (1.34, 1.50) µm, respectively (p=0.002). AD and ED had some shared predictors of RNFLT variability, including intraocular pressure fluctuation and scan quality, although the effects varied (p<0.05). In both races, intervening CE was most strongly correlated with higher RNFLT variability (ß: 0.24-0.92, p<0.05). After excluding eyes with intervening CE, RNFLT variability was reduced and the small racial difference was no longer significant (AD: 1.40 (1.31, 1.48) µm vs ED: 1.34 (1.27, 1.40) µm; p=0.280). CONCLUSIONS: Although some predictors were identified, long-term RNFLT variability appeared small for both AD and ED eyes. Moreover, the racial difference did not remain once intervening CE, the strongest predictor of variability, was eliminated. Our findings inform on strategies to optimise structural assessment and suggest that, when accounting for relevant factors, RNFLT is reliable across races.

Association of Intraocular Pressure With Retinal Nerve Fiber Layer Thinning in Patients With Glaucoma.

Importance: Higher intraocular pressure variability may be associated with faster structural changes in patients with glaucoma. Objectives: To investigate the association of mean intraocular pressure and intraocular pressure variability (defined as the SD of intraocular pressure and the intraocular pressure range) with the rate of retinal nerve fiber layer thinning over time in patients with glaucoma. Design, Setting, and Participants: In this retrospective analysis of a longitudinal cohort, patients were enrolled from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation study. A total of 815 eyes (564 with perimetric glaucoma and 251 with preperimetric glaucoma) from 508 patients with imaging follow-up for a mean of 6.3 years from December 2008 to October 2020 were studied. Data were analyzed from November 2021 to March 2022. Main Outcomes and Measures: In this longitudinal study, eyes with at least 4 visits and 2 years of follow-up optical coherence tomography and intraocular pressure measurement were included. A linear mixed-effect model was used to investigate the association of intraocular pressure parameters with the rates of retinal nerve fiber layer thinning. Dominance analysis was performed to determine the relative importance of the intraocular pressure parameters. Results: Of 508 included patients, 280 (55.1%) were female, 195 (38.4%) were African American, 24 (4.7%) were Asian, 281 (55.3%) were White, and 8 (1.6%) were another race or ethnicity; the mean (SD) age was 65.5 (11.0) years. The mean rate of retinal nerve fiber layer change was -0.67 (95% CI, -0.73 to -0.60) µm per year. In multivariable models adjusted for mean intraocular pressure and other confounding factors, faster annual rate of retinal nerve fiber layer thinning was associated with a higher SD of intraocular pressure (-0.20[ 95% CI, -0.26 to -0.15] µm per 1-mm Hg higher; P < .001) or higher intraocular pressure range (-0.05 [95% CI, -0.06 to -0.03] µm per 1-mm Hg higher; P < .001). Conclusions and Relevance: In this study, intraocular pressure variability was independently associated with structural change in patients with glaucoma, even after adjustment for mean intraocular pressure, supporting its potential value in clinical management.

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.

Purpose: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indices on perimetry can be enhanced by adding global retinal nerve fiber layer (RNFL) data and whether model performance is influenced by the racial composition of the training and testing sets. Design: Retrospective, longitudinal cohort study. Participants: Patients with open-angle glaucoma (OAG) or glaucoma suspects enrolled in the African Descent and Glaucoma Evaluation Study or Diagnostic Innovation in Glaucoma Study. Methods: We developed a Kalman filter (KF) with tonometry and perimetry data (KF-TP) and another KF with tonometry, perimetry, and global RNFL data (KF-TPO), comparing these models with one another and with 2 linear regression (LR) models for predicting mean deviation (MD) and pattern standard deviation values 36 months into the future for patients with OAG and glaucoma suspects. We also compared KF model performance when trained on individuals of European and African descent and tested on patients of the same versus the other race. Main Outcome Measures: Predictive accuracy (percentage of MD values forecasted within the 95% repeatability interval) differences among the models. Results: Among 362 eligible patients, the mean ± standard deviation age at baseline was 71.3 ± 10.4 years; 196 patients (54.1%) were women; 202 patients (55.8%) were of European descent, and 139 (38.4%) were of African descent. Among patients with OAG (n = 296), the predictive accuracy for 36 months in the future was higher for the KF models (73.5% for KF-TP, 71.2% for KF-TPO) than for the LR models (57.5%, 58.0%). Predictive accuracy did not differ significantly between KF-TP and KF-TPO (P = 0.20). If the races of the training and testing set patients were aligned (versus nonaligned), the mean absolute prediction error of future MD improved 0.39 dB for KF-TP and 0.48 dB for KF-TPO. Conclusions: Adding global RNFL data to existing KFs minimally improved their predictive accuracy. Although KFs attained better predictive accuracy when the races of the training and testing sets were aligned, these improvements were modest. These findings will help to guide implementation of KFs in clinical practice.

Frequency of Optical Coherence Tomography Testing to Detect Progression in Glaucoma.

PRCIS: With high specificity and less variability than perimetry, more frequent testing resulted in shorter time to detect progression, though a 6-month testing interval provides a reasonable trade-off for following glaucoma patients using optical coherence tomography (OCT). PURPOSE: To investigate the time to detect progression in glaucomatous eyes using different OCT test intervals. MATERIALS AND METHODS: Participants with manifest glaucoma from the African Descent and Glaucoma Evaluation Study (ADAGES), a multicenter, prospective, observational cohort study, were included. A total of 2699 OCT tests from 171 glaucomatous and 149 normal eyes of 182 participants, with at least 5 tests and 2 years of follow-up, were analyzed. Computer simulations (n=10,000 eyes) were performed to estimate time to detect progression of global circumpapillary retinal nerve fiber layer thickness (cpRNFL) measured with OCT tests. Simulations were based on different testing paradigms (every 4, 6, 12, and 24 mo) and different rates of change (µm/year). Time to detect significant progression ( P <0.05) at 80% and 90% power were calculated for each paradigm and rate of cpRNFL change. RESULTS: As expected, more frequent testing resulted in shorter time to detect progression. Although there was clear disadvantage for testing at intervals of 24 versus 12 months (~22.4% time [25 mo] increase in time to progression detection) and when testing 12 versus 6 months (~22.1% time [20 mo] increase), the improved time to detect progression was less pronounced when comparing 6 versus 4 months (~11.5% time [10 mo] reduction). CONCLUSION: With high specificity and less variability than perimetry, a 6-month testing interval provides a reasonable trade-off for following glaucoma patients using OCT.