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.

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.

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.

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.

RimNet: A Deep Neural Network Pipeline for Automated Identification of the Optic Disc Rim.

Purpose: Accurate neural rim measurement based on optic disc imaging is important to glaucoma severity grading and often performed by trained glaucoma specialists. We aim to improve upon existing automated tools by building a fully automated system (RimNet) for direct rim identification in glaucomatous eyes and measurement of the minimum rim-to-disc ratio (mRDR) in intact rims, the angle of absent rim width (ARW) in incomplete rims, and the rim-to-disc-area ratio (RDAR) with the goal of optic disc damage grading. Design: Retrospective cross sectional study. Participants: One thousand and twenty-eight optic disc photographs with evidence of glaucomatous optic nerve damage from 1021 eyes of 903 patients with any form of primary glaucoma were included. The mean age was 63.7 (± 14.9) yrs. The average mean deviation of visual fields was -8.03 (± 8.59). Methods: The images were required to be of adequate quality, have signs of glaucomatous damage, and be free of significant concurrent pathology as independently determined by glaucoma specialists. Rim and optic cup masks for each image were manually delineated by glaucoma specialists. The database was randomly split into 80/10/10 for training, validation, and testing, respectively. RimNet consists of a deep learning rim and cup segmentation model, a computer vision mRDR measurement tool for intact rims, and an ARW measurement tool for incomplete rims. The mRDR is calculated at the thinnest rim section while ARW is calculated in regions of total rim loss. The RDAR was also calculated. Evaluation on the Drishti-GS dataset provided external validation (Sivaswamy 2015). Main Outcome Measures: Median Absolute Error (MAE) between glaucoma specialists and RimNet for mRDR and ARW. Results: On the test set, RimNet achieved a mRDR MAE of 0.03 (0.05), ARW MAE of 31 (89)°, and an RDAR MAE of 0.09 (0.10). On the Drishti-GS dataset, an mRDR MAE of 0.03 (0.04) and an mRDAR MAE of 0.09 (0.10) was observed. Conclusions: RimNet demonstrated acceptably accurate rim segmentation and mRDR and ARW measurements. The fully automated algorithm presented here would be a valuable component in an automated mRDR-based glaucoma grading system. Further improvements could be made by improving identification and segmentation performance on incomplete rims and expanding the number and variety of glaucomatous training images.

A Metascore of Multiple Imaging Methods to Measure Long-Term Glaucoma Structural Progression.

Purpose: To develop a structural metascore (SMS) that combines measurements from different devices and expresses them on a single scale to facilitate their long-term analysis. Methods: Three structural measurements (Heidelberg Retina Tomograph II [HRT] rim area, HD-Cirrus optical coherence tomography [OCT] average retinal nerve fiber layer [RNFL] thickness, Spectralis OCT RNFL global thickness) were normalized on a scale of 0 to 100 and converted to a reference value. The resultant metascores were plotted against time. SMS performance was evaluated to predict future values (internal validation), and correlations between the average grades assigned by three clinicians were compared with the SMS slopes (external validation). Results: The linear regression fit with the variance approach, and adjustment to a Spectralis equivalent was the best-performing approach; this was denominated metascore. Plots were created for 3416 eyes of 1824 patients. The average baseline age (± standard deviation) was 69.8 (±13.9), mean follow-up was 11.6 (±4.7) years, and mean number of structural scans per eye was 10.0 (±4.7). The mean numbers of scans per device were 3.8 (±2.5), 5.0 (±2.9), and 1.3 (±3.0) for HRT, Cirrus, and Spectralis, respectively. The metascore slopes' median was -0.3 (interquartile range 1.1). Correlations between the average grades assigned by the three clinicians and the metascore slopes were -0.51, -0.49, and -0.69 for the first (structural measurement printouts alone), second (metascore plots alone), and third (printouts + metascore plots) series of gradings, respectively. The average absolute predictive ability was 7.63/100 (whereas 100 = entire normalized scale). Conclusions: We report a method that converts Cirrus global RNFL and HRT global rim area normalized measurements to Spectralis global RNFL equivalent values to facilitate long-term structural follow-up. Translational Relevance: Because glaucoma changes usually occur slowly, patients are often examined with different instruments during their follow-up, a method that "unifies" structural measurements provided by different devices, which could assist patients' longitudinal structural follow-up.

Structural-Functional Glaucoma Progression Trajectory in 2-Dimensional Space.

PRCIS: We describe a method that provides rapid visualization of glaucomatous change in a 2-dimensional (2D) structural and functional (S/F) space. PURPOSE: To describe a method to visualize glaucomatous change in a 2D S/F space. DESIGN: This was a retrospective longitudinal observational study. SUBJECTS: Group I included 64 normal and 64 glaucomatous eyes used to develop the structural score. Group II included 957 glaucomatous eyes used to plot the structural-functional progression vectors. METHODS: Subjects were arranged in 2 groups. Group I was a cross-sectional group used to develop a structural score which were applied to longitudinal measurements of patients in group II for vectoral analysis. Visual field index was used as a functional score. Vectors were created for each eye to define structural (x) and functional (y) progression. The structural and functional components were calculated with linear models of optical coherence tomography scores and visual field index. The resultant vector and its confidence interval were plotted in 2D S/F space. MAIN OUTCOME MEASURES: Combined structural-functional glaucomatous progression. RESULTS: Group I included 64 normal and 64 glaucomatous eyes. We calculated 957 vectors (957 eyes of 582 OAG patients) in group II. The mean (±SD) follow-up period was 6.9 (±1.5) years and mean baseline mean deviation (MD) was -4.3 (±5.4). Preperimetric, mild, moderate, and severe groups included 159, 288, 299, and 211 eyes, respectively. Mean baseline MDs in these groups were 0.8, -1.0, -3.7, and -11.2 dB, and mean vector slopes were 0.88, 1.00, 1.98, and 2.69. CONCLUSION: We present a method that presents glaucoma progression in a 2D S/F space. This approach integrates a large amount of longitudinal numerical data and provides the clinician with a rapid and intuitive summary of the patient's glaucoma trajectory.

The Trajectory of Glaucoma Progression in 2-Dimensional Structural-Functional Space.

PURPOSE: To present a method that allows visualization of functional and structural change in 2-dimensional space. DESIGN: Retrospective, longitudinal, observational study. PARTICIPANTS: Patients from the Stein Eye Institute, UCLA from 1993 through 2017. METHODS: Patients were arranged into 2 cohorts. Cohort 1 was used to create a structural score for the horizontal axis of the structural-functional (S-F) 2-dimensional space. The visual field (VF) index was used for the vertical axis as the functional score. Cohort 2 was used to apply those scores for analysis of S-F progression with a combined vector. The first cohort included eyes with mild glaucoma (abnormal glaucoma hemifield test results, pattern standard deviation <0.05 on 2 examinations, or mean deviation [MD] >-5 dB) and normal control participants. The second cohort included all stages of open-angle glaucoma with ≥5 OCT retinal nerve fiber layer scans, ≥5 reliable visual field (VF) results, and follow-up of ≥4 years. MAIN OUTCOME MEASURES: Vectors were created for each eye to represent the trajectory of glaucoma progression over time. Each vector was defined by structural (x-axis) and functional (y-axis) components. The structural component was calculated with a linear model of Heidelberg Retina Tomograph (Heidelberg Engineering, Heidelberg, Germany) scores over time. The functional component was calculated with a linear model of VF measurements over time. The resultant vector and its confidence interval were plotted in 2-dimensional S-F space. Eyes were divided into severity stages based on baseline MD. A mean vector was calculated for each severity stage. RESULTS: We obtained 290 vectors from 290 eyes of 196 patients. The mean ± standard deviation follow-up period was 14.6±3.1 years. Average age was 58.6±8.8 years. Preperimetric, mild, moderate, and severe categories included 41, 89, 97, and 63 eyes, respectively. Mean baseline MDs were 0.8 dB, -0.95 dB, -3.57 dB, and -11.51 dB, respectively, and mean vector slopes for each severity categories were 0.79, 0.95, 1.95, and 2.08, respectively. Cook's distance removed 131 (7.1%) and 137 (7.4%) outliers from the structure and function regressions, respectively. CONCLUSIONS: We report a method to visualize the trajectory of a patient's glaucoma progression in a 2-dimensional S-F space. The slope of the trajectory of glaucoma progression is a function of the severity of the disease.