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.

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.