Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning.

PURPOSE: Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL). DESIGN: Development of a DL algorithm to predict VF progression. METHODS: 3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up from a tertiary academic center were included. Serial VF mean deviation (MD) rates of change were estimated with linear-regression. VF progression was defined as negative MD slope with p<0.05. A Siamese Neural Network with ResNet-152 backbone pre-trained on ImageNet was designed to predict VF progression using serial optic-disc photographs (ODP), and baseline retinal nerve fiber layer (RNFL) thickness. We tested the model on a separate dataset (427 eyes) with RNFL data from different OCT. The Main Outcome Measure was Area under ROC curve (AUC). RESULTS: Baseline average (SD) MD was 3.4 (4.9)dB. VF progression was detected in 900 eyes (29%). AUC (95% CI) for model incorporating baseline ODP and RNFL thickness was 0.813 (0.757-0.869). After adding the second and third ODPs, AUC increased to 0.860 and 0.894, respectively (p<0.027). This model also had highest AUC (0.911) for predicting fast progression (MD rate <1.0 dB/year). Model's performance was similar when applied to second dataset using RNFL data from another OCT device (AUC=0.893; 0.837-0.948). CONCLUSIONS: DL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.

A Bayesian Hierarchical Spatial Longitudinal Model Improves Estimation of Local Macular Rates of Change in Glaucomatous Eyes.

Purpose: Demonstrate that a novel Bayesian hierarchical spatial longitudinal (HSL) model improves estimation of local macular ganglion cell complex (GCC) rates of change compared to simple linear regression (SLR) and a conditional autoregressive (CAR) model. Methods: We analyzed GCC thickness measurements within 49 macular superpixels in 111 eyes (111 patients) with four or more macular optical coherence tomography scans and two or more years of follow-up. We compared superpixel-patient-specific estimates and their posterior variances derived from the latest version of a recently developed Bayesian HSL model, CAR, and SLR. We performed a simulation study to compare the accuracy of intercept and slope estimates in individual superpixels. Results: HSL identified a significantly higher proportion of significant negative slopes in 13/49 superpixels and a significantly lower proportion of significant positive slopes in 21/49 superpixels than SLR. In the simulation study, the median (tenth, ninetieth percentile) ratio of mean squared error of SLR [CAR] over HSL for intercepts and slopes were 1.91 (1.23, 2.75) [1.51 (1.05, 2.20)] and 3.25 (1.40, 10.14) [2.36 (1.17, 5.56)], respectively. Conclusions: A novel Bayesian HSL model improves estimation accuracy of patient-specific local GCC rates of change. The proposed model is more than twice as efficient as SLR for estimating superpixel-patient slopes and identifies a higher proportion of deteriorating superpixels than SLR while minimizing false-positive detection rates. Translational Relevance: The proposed HSL model can be used to model macular structural measurements to detect individual glaucoma progression earlier and more efficiently in clinical and research settings.

Hypotony Failure Criteria in Glaucoma Surgical Studies and Their Influence on Surgery Success.

PURPOSE: Review hypotony failure criteria used in glaucoma surgical outcome studies and evaluate their impact on success rates. DESIGN: Systematic literature review and application of hypotony failure criteria to 2 retrospective cohorts. PARTICIPANTS: A total of 934 eyes and 1765 eyes undergoing trabeculectomy and deep sclerectomy (DS) with a median follow-up of 41.4 and 45.4 months, respectively. METHODS: Literature-based hypotony failure criteria were applied to patient cohorts. Intraocular pressure (IOP)-related success was defined as follows: (A) IOP ≤ 21 mmHg with ≥ 20% IOP reduction; (B) IOP ≤ 18 mmHg with ≥ 20% reduction; (C) IOP ≤ 15 mmHg with ≥ 25% reduction; and (D) IOP ≤ 12 mmHg with ≥ 30% reduction. Failure was defined as IOP exceeding these criteria in 2 consecutive visits > 3 months after surgery, loss of light perception, additional IOP-lowering surgery, or hypotony. Cox regression estimated failure risk for different hypotony criteria, using no hypotony as a reference. Analyses were conducted for each criterion and hypotony type (i.e., numerical [IOP threshold], clinical [clinical manifestations], and mixed [combination of numerical or clinical criteria]). MAIN OUTCOME MEASURES: Hazard ratio (HR) for failure risk. RESULTS: Of 2503 studies found, 278 were eligible, with 99 studies (35.6%) lacking hypotony failure criteria. Numerical hypotony was predominant (157 studies [56.5%]). Few studies used clinical hypotony (3 isolated [1.1%]; 19 combined with low IOP [6.8%]). Forty-nine different criteria were found, with IOP < 6 mmHg, IOP < 6 mmHg on ≥ 2 consecutive visits after 3 months, and IOP < 5 mmHg being the most common (41 [14.7%], 38 [13.7%], and 13 [4.7%] studies, respectively). In both cohorts, numerical hypotony posed the highest risk of failure (HR, 1.51-1.21 for criteria A to D; P < 0.001), followed by mixed hypotony (HR, 1.41-1.20 for criteria A to D; P < 0.001), and clinical hypotony (HR, 1.12-1.04; P < 0.001). Failure risk varied greatly with various hypotony definitions, with the HR ranging from 1.02 to 10.79 for trabeculectomy and 1.00 to 8.36 for DS. CONCLUSIONS: Hypotony failure criteria are highly heterogenous in the glaucoma literature, with few studies focusing on clinical manifestations. Numerical hypotony yields higher failure rates than clinical hypotony and can underestimate glaucoma surgery success rates. Standardizing failure criteria with an emphasis on clinically relevant hypotony manifestations is needed. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Detecting Fast Progressors: Comparing a Bayesian Longitudinal Model to Linear Regression for Detecting Structural Changes in Glaucoma.

PURPOSE: Demonstrate that a novel Bayesian hierarchical spatial longitudinal (HSL) model identifies macular superpixels with rapidly deteriorating ganglion cell complex (GCC) thickness more efficiently than simple linear regression (SLR). DESIGN: Prospective cohort study. SETTING: Tertiary Glaucoma Center. SUBJECTS: One hundred eleven eyes (111 patients) with moderate to severe glaucoma at baseline and ≥4 macular optical coherence tomography scans and ≥2 years of follow-up. OBSERVATION PROCEDURE: Superpixel-patient-specific GCC slopes and their posterior variances in 49 superpixels were derived from our latest Bayesian HSL model and Bayesian SLR. A simulation cohort was created with known intercepts, slopes, and residual variances in individual superpixels. MAIN OUTCOME MEASURES: We compared HSL and SLR in the fastest progressing deciles on (1) proportion of superpixels identified as significantly progressing in the simulation study and compared to SLR slopes in cohort data; (2) root mean square error (RMSE), and SLR/HSL RMSE ratios. RESULTS: Cohort- In the fastest decile of slopes per SLR, 77% and 80% of superpixels progressed significantly according to SLR and HSL, respectively. The SLR/HSL posterior SD ratio had a median of 1.83, with 90% of ratios favoring HSL. Simulation- HSL identified 89% significant negative slopes in the fastest progressing decile vs 64% for SLR. SLR/HSL RMSE ratio was 1.36 for the fastest decile of slopes, with 83% of RMSE ratios favoring HSL. CONCLUSION: The Bayesian HSL model improves the estimation efficiency of local GCC rates of change regardless of underlying true rates of change, particularly in fast progressors.

Efficacy of Smoothing Algorithms to Enhance Detection of Visual Field Progression in Glaucoma.

Purpose: To evaluate and compare the effectiveness of nearest neighbor (NN)- and variational autoencoder (VAE)-smoothing algorithms to reduce variability and enhance the performance of glaucoma visual field (VF) progression models. Design: Longitudinal cohort study. Subjects: 7150 eyes (4232 patients), with ≥ 5 years of follow-up and ≥ 6 visits. Methods: Vsual field thresholds were smoothed with the NN and VAE algorithms. The mean total deviation (mTD) and VF index rates, pointwise linear regression (PLR), permutation of PLR (PoPLR), and the glaucoma rate index were applied to the unsmoothed and smoothed data. Main Outcome Measures: The proportion of progressing eyes and the conversion to progression were compared between the smoothed and unsmoothed data. A simulation series of noiseless VFs with various patterns of glaucoma damage was used to evaluate the specificity of the smoothing models. Results: The mean values of age and follow-up time were 62.8 (standard deviation: 12.6) years and 10.4 (standard deviation: 4.7) years, respectively. The proportion of progression was significantly higher for the NN and VAE smoothed data compared with the unsmoothed data. VF progression occurred significantly earlier with both smoothed data compared with unsmoothed data based on mTD rates, PLR, and PoPLR methods. The ability to detect the progressing eyes was similar for the unsmoothed and smoothed data in the simulation data. Conclusions: Smoothing VF data with NN and VAE algorithms improves the signal-to-noise ratio for detection of change, results in earlier detection of VF progression, and could help monitor glaucoma progression more effectively in the clinical setting. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Optical Coherence Tomographic Optic Nerve Head Morphology in Myopia III: The Exposed Neural Canal Region in Healthy Eyes-Implications for High Myopia.

PURPOSE: To determine the prevalence and magnitude of optical coherence tomography (OCT) exposed neural canal (ENC), externally oblique choroidal border tissue (EOCBT), and exposed scleral flange (ESF) regions in 362 non-highly myopic (spherical equivalent -6.00 to 5.75 diopters) eyes of 362 healthy subjects. DESIGN: Cross-sectional study. METHODS: After OCT optic nerve head (ONH) imaging, Bruch membrane opening (BMO), the anterior scleral canal opening (ASCO), and the scleral flange opening (SFO) were manually segmented. BMO, ASCO, and SFO points were projected to the BMO reference plane. The direction and magnitude of BMO/ASCO offset as well as the magnitude of ENC, EOCBT, and ESF was calculated within 30° sectors relative to the foveal-BMO axis. Hi-ESF eyes demonstrated an ESF ≥100 µm in at least 1 sector. Sectoral peri-neural canal choroidal thickness (pNC-CT) was measured and correlations between the magnitude of sectoral ESF and proportional pNC-CT were assessed. RESULTS: Seventy-three Hi-ESF (20.2%) and 289 non-Hi-ESF eyes (79.8%) were identified. BMO/ASCO offset as well as ENC, EOCBT, and ESF prevalence and magnitude were greatest inferior temporally where the pNC-CT was thinnest. Among Hi-ESF eyes, the magnitude of each ENC region correlated with the BMO/ASCO offset magnitude, and the sectors with the longest ESF correlated with the sectors with proportionally thinnest pNC-CT. CONCLUSIONS: ONH BMO/ASCO offset, either as a cause or result of ONH neural canal remodeling, corresponds with the sectoral location of maximum ESF and minimum pNC-CT in non-highly myopic eyes. Longitudinal studies to characterize the development and clinical implications of ENC Hi-ESF regions in non-highly myopic and highly myopic eyes are indicated.

Predictors of Glaucomatous Progression in Individuals with Small and Large Optic Discs.

PURPOSE: To identify factors associated with glaucomatous progression in individuals with small and large optic discs. DESIGN: Retrospective review. SUBJECTS: 4505 individuals with glaucoma at UCLA; 233 (59.7%) with small discs, 157 (40.3%) with large discs. METHODS: Small and large disc sizes were defined by OCT or Heidelberg Retinal Tomography as disc area ≤ 5% (≤ 1.3 mm2) and ≥ 95% (≥ 2.9 mm2), respectively. Medical records were reviewed for demographics, systemic comorbidities, glaucoma type, ocular comorbidities, and ocular surgery. Logistic regression was used to identify predictors of visual field (VF) progression in individuals with small and large discs and predictors of large versus small discs. MAIN OUTCOME MEASURES: The VF deterioration with mean deviation, pointwise linear regression, and glaucoma rate index (GRI); large vs. small disc. RESULTS: In individuals with small discs, Asian versus non-Hispanic White ethnicity was associated with increased progression (adjusted odds ratio [aOR] = 4.05; 95% confidence interval [CI] = 1.12-14.59 for GRI). Higher intraocular pressure (IOP) range and peak were associated with increased progression in individuals with both small discs (aOR = 1.12; 95% CI = 1.00-1.27 and aOR = 1.05; 95% CI = 1.00-1.10 per 1 mmHg for range and peak with GRI) and large discs (aOR = 1.35; 95% CI = 1.12-1.66 and aOR = 1.11; 95% CI = 1.03-1.20 per 1 mmHg for range and peak with GRI). Multivariable predictors of having large vs. small discs included vasospastic phenotype (aOR = 2.58; 95% CI = 1.35-5.19) and Black (aOR = 20.46; 95% CI = 8.33-61.84), Hispanic/Latino (aOR = 9.65; 95% CI = 4.14-25.39), Asian (aOR = 4.87; 95% CI = 2.96-8.1), and other (aOR = 2.79; 95% CI = 1.69-4.63) versus non-Hispanic White ethnicity. CONCLUSIONS: Increased odds of glaucomatous progression were associated with Asian vs. non-Hispanic White ethnicity in glaucoma patients with small optic discs, as well as with increased IOP range and peak in those with small and large discs. Individuals with a vasospastic phenotype and those from racial and ethnic minority backgrounds had increased odds of having large vs. small optic discs. Further characterization of discernible phenotypes would improve disease prognostication and help individualize glaucoma treatment. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Long-term Treatment Outcomes for Malignant Glaucoma.

PURPOSE: To report the contributing factors to the successful long-term treatment outcomes of a large series of patients with malignant glaucoma (MG). DESIGN: Retrospective, interventional, consecutive case series. PARTICIPANTS: This study used data collected from 1997 to 2022 from the Glaucoma Division of the Stein Eye Institute, University of California, Los Angeles (UCLA). All patients with MG who underwent treatment at UCLA were enrolled. METHODS: The following demographic and clinical data were collected and analyzed for their relevance to successful treatment: age, gender, ethnicity, glaucoma family history, visual acuity (VA), intraocular pressure (IOP), lens status, prior glaucoma diagnosis, prior ocular surgery, prior use of antiglaucoma agents, ultrasonic axial length, qualitative anterior chamber (AC) depth, and treatment methods and outcomes. MAIN OUTCOME MEASURES: Anatomical success was defined as restoration of normal AC depth, indicating relief of the MG episode. Complete success was defined as anatomical success and the reduction of IOP to < 21 mmHg without further surgery, with or without medications. RESULTS: A total of 74 eyes of 73 patients were identified with a diagnosis of MG. The median (interquartile range) age of the patients at the time of MG presentation was 70 years (19.5) and 49 (75.4%) patients were female. The most common prior diagnosis before MG was primary angle closure glaucoma (PACG) (34 eyes, 51.5%). The initiating event for 30 eyes (45.5%) was glaucoma surgery and for 21 eyes (31.8%) was cataract surgery. Most eyes were pseudophakic (57, 86.4%). Fifty-six eyes underwent medical treatment; MG resolved in 2 eyes with medical treatment alone. Nine eyes (7 eyes = treatment naïve; 2 eyes = failed medical treatment) underwent laser treatment and MG resolved in 5 eyes. Among the 55 eyes which had surgical treatment, 52 eyes failed medical treatment and 3 eyes were treatment naïve. The anatomical success rate with surgical treatment was 96.4% and the most commonly performed surgical procedure was combined pars plana antero-central vitrectomy, hyaloido-zonulectomy, and iridectomy. CONCLUSIONS: Female gender, PACG, and glaucoma surgery were predisposing factors for the development of MG. Medical treatment alone for MG was inadequate in the vast majority of cases. A surgical technique consisting of combined pars plana antero-central vitrectomy, hyaloido-zonulectomy and iridectomy consistently produced high long-term success. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Prediction of Central Visual Field Measures From Macular OCT Volume Scans With Deep Learning.

Purpose: Predict central 10° global and local visual field (VF) measurements from macular optical coherence tomography (OCT) volume scans with deep learning (DL). Methods: This study included 1121 OCT volume scans and 10-2 VFs from 289 eyes (257 patients). Macular scans were used to estimate 10-2 VF mean deviation (MD), threshold sensitivities (TS), and total deviation (TD) values at 68 locations. A three-dimensional (3D) convolutional neural network based on the 3D DenseNet121 architecture was used for prediction. We compared DL predictions to those from baseline linear models. We carried out 10-fold stratified cross-validation to optimize generalizability. The performance of the DL and baseline models was compared based on correlations between ground truth and predicted VF measures and mean absolute error (MAE; ground truth - predicted values). Results: Average (SD) MD was -9.3 (7.7) dB. Average (SD) correlations between predicted and ground truth MD and MD MAE were 0.74 (0.09) and 3.5 (0.4) dB, respectively. Estimation accuracy deteriorated with worsening MD. Average (SD) Pearson correlations between predicted and ground truth TS and MAEs for DL and baseline model were 0.71 (0.05) and 0.52 (0.05) (P < 0.001) and 6.5 (0.6) and 7.5 (0.5) dB (P < 0.001), respectively. For TD, correlation (SD) and MAE (SD) for DL and baseline models were 0.69 (0.02) and 0.48 (0.05) (P < 0.001) and 6.1 (0.5) and 7.8 (0.5) dB (P < 0.001), respectively. Conclusions: Macular OCT volume scans can be used to predict global central VF parameters with clinically relevant accuracy. Translational Relevance: Macular OCT imaging may be used to confirm and supplement central VF findings using deep learning.

Prediction of visual field progression with serial optic disc photographs using deep learning.

AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up. METHODS: 3919 eyes (2259 patients) with ≥2 ODPs at least 2 years apart, and ≥5 24-2 VF exams spanning ≥3 years of follow-up were included. Serial VF mean deviation (MD) rates of change were estimated starting at the fifth visit and subsequently by adding visits until final visit. VF progression was defined as a statistically significant negative slope at two consecutive visits and final visit. We built a twin-neural network with ResNet50-backbone. A pair of ODPs acquired up to a year before the VF progression date or the last VF in non-progressing eyes were included as input. Primary outcome measures were area under the receiver operating characteristic curve (AUC) and model accuracy. RESULTS: The average (SD) follow-up time and baseline VF MD were 8.1 (4.8) years and -3.3 (4.9) dB, respectively. VF progression was identified in 761 eyes (19%). The median (IQR) time to progression in progressing eyes was 7.3 (4.5-11.1) years. The AUC and accuracy for predicting VF progression were 0.862 (0.812-0.913) and 80.0% (73.9%-84.6%). When only fast-progressing eyes were considered (MD rate < -1.0 dB/year), AUC increased to 0.926 (0.857-0.994). CONCLUSIONS: A deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.

Differential Responses of Retinal Neurons and Glia Revealed via Proteomic Analysis on Primary and Secondary Retinal Ganglion Cell Degeneration.

To explore the temporal profile of retinal proteomes specific to primary and secondary retinal ganglion cell (RGC) loss. Unilateral partial optic nerve transection (pONT) was performed on the temporal side of the rat optic nerve. Temporal and nasal retinal samples were collected at 1, 4 and 8 weeks after pONT (n = 4 each) for non-biased profiling with a high-resolution hybrid quadrupole time-of-flight mass spectrometry running on label-free SWATHTM acquisition (SCIEX). An information-dependent acquisition ion library was generated using ProteinPilot 5.0 and OneOmics cloud bioinformatics. Combined proteome analysis detected 2531 proteins with a false discovery rate of <1%. Compared to the nasal retina, 10, 25 and 61 significantly regulated proteins were found in the temporal retina at 1, 4, and 8 weeks, respectively (p < 0.05, FC ≥ 1.4 or ≤0.7). Eight proteins (ALDH1A1, TRY10, GFAP, HBB-B1, ALB, CDC42, SNCG, NEFL) were differentially expressed for at least two time points. The expressions of ALDH1A1 and SNCG at nerve fibers were decreased along with axonal loss. Increased ALDH1A1 localization in the inner nuclear layer suggested stress response. Increased GFAP expression demonstrated regional reactivity of astrocytes and Muller cells. Meta-analysis of gene ontology showed a pronounced difference in endopeptidase and peptidase inhibitor activity. Temporal proteomic profiling demonstrates established and novel protein targets associated with RGC damage.

Phenotypic expressions of the optic disc in primary open-angle glaucoma.

BACKGROUND: Which phenotypes are we able to recognize in the optic nerve of patients with primary open angle glaucoma? METHODS: Retrospective interventional case series. 885 eyes from 885 patients at an outpatient tertiary care centre who met specified criteria for POAG were included. Disc photographs were classified by three glaucoma specialists into the following phenotypes according to their predominant characteristics: (1) concentric rim thinning, (2) focal rim thinning, (3) acquired pit of the optic nerve (APON), (4) tilted, (5) extensive peripapillary atrophy (PPA), and (6) broad rim thinning. Demographic, medical, and ocular data were collected. Kruskal-Wallis was used as a non-parametric test and pairwise comparison was performed by using Wilcoxon rank sum test corrected. RESULTS: Phenotypic distribution was as follows: 398(45%) focal thinning, 153(18%) concentric thinning, 153(17%) broad thinning, 109(12%) tilted, 47(5%) extensive PPA and 25(3%) APON. Phenotypic traits of interest included a higher proportion of female patients with the focal thinning phenotype (p = 0.015); myopia (p = 0.000), Asian race (OR: 8.8, p = 0.000), and younger age (p = 0.000) were associated with the tilted phenotype; the concentric thinning patients had thicker RNFL (p = 0.000), higher MD (p = 0.008) and lower PSD (p = 0.043) than broad thinning, despite no difference in disc sizes (p = 0.849). The focal thinning group had a localized VF pattern with high PSD compared to concentric thinning (p = 0.005). CONCLUSION: We report six phenotypic classifications of POAG patients with demographic and ocular differences between phenotypes. Future refinement of phenotypes should allow enhanced identification of genetic associations and improved individualization of patient care.

Comparing Rates of Change in Moderate to Advanced Glaucoma: Retinal Nerve Fiber Layer Versus Bruch Membrane Opening-Minimum Rim Width.

PURPOSE: To compare rates of change (RoC) of peripapillary retinal nerve fiber layer (RNFL) and Bruch membrane opening-based minimum rim width (BMO-MRW) thickness in moderate-to-advanced glaucoma. DESIGN: Prospective cohort study. METHODS: Longitudinal optical coherence tomography (OCT) optic nerve head volume scans of 113 eyes of 113 glaucoma patients with moderate-to-advanced or central damage were exported. This study estimated and compared global and sectoral RoC with linear mixed effects models and simple linear regression (SLR) of RNFL and BMO-MRW thickness. Permutation analyses were used to test significance of RoC in the SLR model. It also compared longitudinal signal-to-noise ratios (LSNR) defined as RoC divided by residual standard deviation (SD) between the two groups. RESULTS: Mean (SD) follow-up and median (IQR) OCT scan sessions were 5.2 (1.3) years and 10 (8-11), respectively. Baseline average (SD) visual field mean deviation was -9.2 (5.8) dB. Based on SLR, a higher proportion of significant negative RNFL RoC was observed compared to BMO-MRW in the inferotemporal (35% vs 20%; P = .015) and inferonasal (42% vs 17%; P < .001) sectors. Permutation analyses also demonstrated a higher proportion of worsening RNFL RoC than BMO-MRW in the inferotemporal (P = .026) and inferonasal (P < .001) sectors along with overall lower positive RoC. Longitudinal signal-to-noise ratios for RNFL were significantly more negative than for BMO-MRW globally, and in the inferotemporal, inferonasal, and superonasal sectors (P ≤ .01). CONCLUSIONS: Longitudinal RNFL OCT measurements are more likely to detect structural change and demonstrate better LSNR compared with BMO-MRW in eyes with central or moderate-to-advanced glaucoma damage at baseline.

Long-Term Visual Field Outcomes After Ahmed Glaucoma Valve Implantation.

PURPOSE: To measure visual field (VF) rates of change after Ahmed Glaucoma Valve (AGV) implantation and to investigate risk factors for progression. DESIGN: Retrospective, clinical cohort study. METHODS: Patients who underwent AGV implantation with at least 4 eligible postoperative VFs and 2 years of follow-up were included. Baseline, intraoperative, and postoperative data were collected. VF progression was explored with 3 methods: mean deviation (MD) rate; glaucoma rate index (GRI); and pointwise linear regression (PLR). For a subset of eyes with sufficient preoperative and postoperative VFs, rates were compared between the 2 periods. RESULTS: A total of 173 eyes were included. The intraocular pressure (IOP) and number of glaucoma medications were significantly reduced from a median (interquartile range [IQR]) of 23.5 (12.1) mm Hg at baseline to 12.8 (4.0) mm Hg at final follow-up, and from (mean ± SD) 3.3 ± 1.2 to 2.2 ± 1.4, respectively. A total of 38 eyes (22%) showed VF progression, and 101 eyes (58%) were stable as assessed by all 3 methods, which accounted for 80% of all eyes. The rate of VF decline by MD and GRI was a median (IQR) of -0.30 (0.8) dB/y and -2.30 (10.6) (of -100), respectively. When comparing progression before and after surgery, the reduction was not statistically significant with any of the methods. The peak IOP (after 3 postoperative months) was associated with VF deterioration, with a 7% increase in risk per each additional millimeter of mercury (mm Hg). CONCLUSIONS: To our knowledge, this is the largest published series reporting long-term VF outcomes after glaucoma drainage device implantation. There is a continued, significant rate of VF decline after AGV surgery.

Associations Between Niacin Intake and Glaucoma in the National Health and Nutrition Examination Survey.

PRCIS: This study examined the association between dietary niacin intake and glaucoma in the 2005-2008 National Health and Nutrition Examination Survey (NHANES). Increased niacin intake was associated with lower odds of glaucoma overall and among women. PURPOSE: To examine the association between dietary niacin intake and glaucoma in the 2005-2008 NHANES. MATERIALS AND METHODS: This cross-sectional study included adult participants of the 2005-2008 NHANES. The exposure was dietary niacin intake, which was examined as a continuous and categorical variable. The outcome was glaucoma as defined by regraded disc images. Covariates included age, sex, race/ethnicity, education level, income, body mass index, smoking status, alcohol use, cardiovascular disease, diabetes mellitus, daily energy intake, vitamin B2 and B6 consumption, and macular degeneration. Adjusting for all covariates, logistic regression was performed to examine the association between niacin intake and glaucoma in the overall population and stratified by sex. RESULTS: The weighted population included 5371 individuals (109,734,124 weighted), of whom 55 (1.0%) had glaucoma. Each 1 mg increase in niacin intake was associated with a 6% decreased odds of glaucoma odds [adjusted odds ratio (aOR) = 0.94, 95% CI = 0.90, 0.98]. Among women, increased niacin intake was associated with decreased odds of glaucoma both with niacin as a continuous (aOR = 0.89, 95% CI = 0.80, 0.99 per 1 mg increase in niacin intake) and binary variable (aOR = 0.35, 95% CI = 0.14, 0.90 for higher vs lower niacin intake). CONCLUSIONS: In the 2005-2008 NHANES population, higher levels of niacin intake were associated with decreased odds of glaucoma overall and in women. Further studies are needed to examine the potential protective effects of niacin on glaucoma risk.

The Success Rate of Glaucoma Drainage Device Revision.

PRCIS: We report the survival of surgical revision to glaucoma drainage devices for several indications in a large cohort of patients, with an overall success rate of 45% at 36 months. PURPOSE: To evaluate the outcomes of surgical revision for complications of glaucoma drainage devices. METHODS: Three hundred thirty-five eyes of 318 patients who underwent tube revision or removal at University of California Los Angeles (UCLA) Jules Stein Eye Institute between 1997 and 2019 were included. The pre-defined primary outcome measure was surgical success of the initial revision, defined as resolution of the condition with no additional revisions required, no functionally significant change in vision, and no instances of intraocular pressure > 21 mmHg at 2 consecutive visits postoperatively. Kaplan-Meier survival analysis was applied to evaluate survival at 36 months based on these criteria. The Wilcoxon paired test was used to compare mean preoperative and postoperative intraocular pressure, medication usage, and visual acuity. RESULTS: Overall, survival of revised tubes at 36 months was 45%. The 4 most common indications for revision were exposure of the implant (42% of all revisions), occlusion (14%), corneal failure or threat of failure (12%), and hypotony (11%). Survival at 36 months for each of these indications was 44%, 45%, 52%, and 37%, respectively. CONCLUSIONS: These results suggest that eyes with glaucomatous damage with long-term glaucoma drainage device complications can still have a reasonably successful outcome when a revision is performed. However, with substantial rates of vision loss and a frequent need for additional revisions to manage complications, managing patient expectations for success and making them aware of the likelihood of additional surgeries or failure is important.

A Neural Network for Automated Image Quality Assessment of Optic Disc Photographs.

This study describes the development of a convolutional neural network (CNN) for automated assessment of optic disc photograph quality. Using a code-free deep learning platform, a total of 2377 optic disc photographs were used to develop a deep CNN capable of determining optic disc photograph quality. Of these, 1002 were good-quality images, 609 were acceptable-quality, and 766 were poor-quality images. The dataset was split 80/10/10 into training, validation, and test sets and balanced for quality. A ternary classification model (good, acceptable, and poor quality) and a binary model (usable, unusable) were developed. In the ternary classification system, the model had an overall accuracy of 91% and an AUC of 0.98. The model had higher predictive accuracy for images of good (93%) and poor quality (96%) than for images of acceptable quality (91%). The binary model performed with an overall accuracy of 98% and an AUC of 0.99. When validated on 292 images not included in the original training/validation/test dataset, the model's accuracy was 85% on the three-class classification task and 97% on the binary classification task. The proposed system for automated image-quality assessment for optic disc photographs achieves high accuracy in both ternary and binary classification systems, and highlights the success achievable with a code-free platform. There is wide clinical and research potential for such a model, with potential applications ranging from integration into fundus camera software to provide immediate feedback to ophthalmic photographers, to prescreening large databases before their use in research.

Association of Blood Pressure With Rates of Macular Ganglion Cell Complex Thinning in Patients With Glaucoma.

Importance: There are scarce data on the association of blood pressure measures with subsequent macular structural rates of change in patients with glaucoma. Objective: To investigate the association of baseline blood pressure measures with rates of change of the macular ganglion cell complex in patients with central or moderate to advanced glaucoma damage at baseline. Design, Setting, and Participants: This prospective cohort study, conducted from August 2021 to August 2022, used data from patients in the Advanced Glaucoma Progression Study at the University of California, Los Angeles. Participants were between 39 and 80 years of age and had more than 4 macular imaging tests and 2 or more years of follow-up. Exposures: A diagnosis of glaucoma with either central damage or a visual field mean deviation worse than -6 dB. Main Outcomes and Measures: The main outcome was the association of blood pressure measures with ganglion cell complex rates of change. Macular ganglion cell complex thickness rates of change were estimated with a bayesian hierarchical model. This model included relevant demographic and clinical factors. Blood pressure measures, intraocular pressure, and their interactions were added to the model to assess the association of baseline blood pressure measures with global ganglion cell complex rates of change. Results: The cohort included 105 eyes from 105 participants. The mean (SD) age, 10-2 visual field mean deviation, and follow-up time were 66.9 (8.5) years, -8.3 (5.3) dB, and 3.6 (0.4) years, respectively, and 67 patients (63.8%) were female. The racial and ethnic makeup of the cohort was 15 African American (14.3%), 23 Asian (21.9%), 12 Hispanic (11.4%), and 55 White (52.4%) individuals based on patient self-report. In multivariable analyses, female sex, history of taking blood pressure medications, higher intraocular pressure, thicker central corneal thickness, shorter axial length, higher contrast sensitivity at 12 cycles per degree, and higher baseline 10-2 visual field mean deviation were associated with faster ganglion cell complex thinning. Lower diastolic blood pressure was associated with faster rates of ganglion cell complex thinning at higher intraocular pressures. For intraocular pressures of 8 and of 16 mm Hg (10% and 90% quantiles, respectively), every 10 mm Hg-lower increment of diastolic blood pressure was associated with 0.011 µm/y slower and -0.130 µm/y faster rates of ganglion cell complex thinning, respectively. Conclusions and Relevance: In this cohort study, a combination of lower diastolic blood pressure and higher intraocular pressure at baseline was associated with faster rates of ganglion cell complex thinning. These findings support consideration of evaluating and addressing diastolic blood pressure as a therapeutic measure in patients with glaucoma if supported by appropriate clinical trials.

DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage.

Purpose: To report an image analysis pipeline, DDLSNet, consisting of a rim segmentation (RimNet) branch and a disc size classification (DiscNet) branch to automate estimation of the disc damage likelihood scale (DDLS). Design: Retrospective observational. Participants: RimNet and DiscNet were developed with 1208 and 11 536 optic disc photographs (ODPs), respectively. DDLSNet performance was evaluated on 120 ODPs from the RimNet test set, for which the DDLS scores were graded by clinicians. Reproducibility was evaluated on a group of 781 eyes, each with 2 ODPs taken within 4 years apart. Methods: Disc damage likelihood scale calculation requires estimation of optic disc size, provided by DiscNet (VGG19 network), and the minimum rim-to-disc ratio (mRDR) or absent rim width (ARW), provided by RimNet (InceptionV3/LinkNet segmentation model). To build RimNet's dataset, glaucoma specialists marked optic disc rim and cup boundaries on ODPs. The "ground truth" mRDR or ARW was calculated. For DiscNet's dataset, corresponding OCT images provided "ground truth" disc size. Optic disc photographs were split into 80/10/10 for training, validation, and testing, respectively, for RimNet and DiscNet. DDLSNet estimation was tested against manual grading of DDLS by clinicians with the average score used as "ground truth." Reproducibility of DDLSNet grading was evaluated by repeating DDLS estimation on a dataset of nonprogressing paired ODPs taken at separate times. Main Outcome Measures: The main outcome measure was a weighted kappa score between clinicians and the DDLSNet pipeline with agreement defined as ± 1 DDLS score difference. Results: RimNet achieved an mRDR mean absolute error (MAE) of 0.04 (± 0.03) and an ARW MAE of 48.9 (± 35.9) degrees when compared to clinician segmentations. DiscNet achieved 73% (95% confidence interval [CI]: 70%, 75%) classification accuracy. DDLSNet achieved an average weighted kappa agreement of 0.54 (95% CI: 0.40, 0.68) compared to clinicians. Average interclinician agreement was 0.52 (95% CI: 0.49, 0.56). Reproducibility testing demonstrated that 96% of ODP pairs had a difference of ≤ 1 DDLS score. Conclusions: DDLSNet achieved moderate agreement with clinicians for DDLS grading. This novel approach illustrates the feasibility of automated ODP grading for assessing glaucoma severity. Further improvements may be achieved by increasing the number of incomplete rims sample size, expanding the hyperparameter search, and increasing the agreement of clinicians grading ODPs.

Comparison of Ganglion Cell Layer and Ganglion Cell/Inner Plexiform Layer Measures for Detection of Early Glaucoma.

PURPOSE: To test the hypothesis that macular ganglion cell layer (GCL) measurements detect early glaucoma with higher accuracy than ganglion cell/inner plexiform layer (GCIPL) thickness measurements. DESIGN: Cross-sectional study. PARTICIPANTS: The first cohort included 58 glaucomatous eyes with visual field mean deviation (MD) ≥ -6 dB and 125 normal eyes. The second cohort included 72 glaucomatous and 73 normal/glaucoma suspect (GS) eyes with scans able to create GCL/GCIPL deviation maps. METHODS: In the first cohort, 8 × 8 GCL and GCIPL grids were exported and 5 superior and inferior sectors were defined. Global and sectoral GCL and GCIPL measures were used to predict glaucoma. In the second cohort, proportions of scan areas with abnormal (< 5% and < 1% cutoffs) and supernormal (> 95% and > 99% cutoffs) thicknesses on deviation maps were calculated. The extents of GCL and GCIPL abnormal areas were used to predict glaucoma. MAIN OUTCOME MEASURES: Extents of abnormal GCL/GCIPL regions and areas under receiver operating characteristic curves (AUROC) for prediction of glaucoma were compared between GCL or GCIPL measures. RESULTS: The average ± standard deviation MDs were -3.7 ± 1.6 dB and -2.7 ± 1.8 dB in glaucomatous eyes in the first and second cohorts, respectively. Global GCIPL thickness measures (central 18° × 18° macular region) performed better than GCL for early detection of glaucoma (AUROC, 0.928 vs. 0.884, respectively; P = 0.004). Superior and inferior sector 3 thickness measures provided the best discrimination with both GCL and GCIPL (inferior GCL AUROC, 0.860 vs. GCIPL AUROC, 0.916 [P = 0.001]; superior GCL AUROC, 0.916 vs. GCIPL AUROC, 0.900 [P = 0.24]). The extents of abnormal GCL regions at a 1% cutoff in the central elliptical area were 17.5 ± 22.2% and 6.4 ± 10.8% in glaucomatous and normal/GS eyes, respectively, versus 17.0 ± 22.2% and 5.7 ± 10.5%, respectively, for GCIPL (P = 0.06 for GCL and 0.002 for GCIPL). The extents of GCL and GCIPL supernormal regions were mostly similar in glaucomatous and normal eyes. The best performance for prediction of glaucoma in the second cohort was detected at a P value of < 1% within the entire scan for both GCL and GCIPL (AUC, 0.681 vs. 0.668, respectively; P = 0.29). CONCLUSIONS: Macular GCL and GCIPL thicknesses are equivalent for identifying early glaucoma with current OCT technology. This is likely explained by limitations of inner macular layer segmentation and concurrent changes within the inner plexiform layer in early glaucoma.