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.

PURPOSE: This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements and the disc damage likelihood scale (DDLS) to measure the rate of optic disc rim loss in a large cohort of glaucoma patients. METHODS: A deep neural network was used to calculate RADAR and DDLS for each optic disc photograph (ODP). Patient demographics, diagnosis, intraocular pressure (IOP), and mean deviation (MD) from perimetry were analyzed as risk factors for faster progression of RADAR. Receiver operating characteristic (ROC) curves were used to compare RADAR and DDLS in their utility to distinguish glaucoma from glaucoma suspect (GS) and for detecting glaucoma progression. RESULTS: A total of 13,679 ODPs with evidence of glaucomatous optic nerve damage from 4106 eyes of 2407 patients with glaucoma or GS were included. Of these eyes, 3264 (79.5%) had a diagnosis of glaucoma, and 842 (20.5%) eyes were GS. Mean ± SD baseline RADAR of GS and glaucoma were 0.67 ± 0.13 and 0.57 ± 0.18, respectively (P < 0.001). Older age, greater IOP fluctuation, baseline MD, right eye, and diagnosis of secondary open-angle glaucoma were associated with slope of RADAR. The mean baseline DDLS of GS and glaucoma were 3.78 and 4.39, respectively. Both RADAR and DDLS showed a less steep slope in advanced glaucoma. In glaucoma, the change of RADAR and DDLS correlated with the corresponding change in MD. RADAR and DDLS had a similar ability to discriminate glaucoma from GS and detect disease progression. Area under the ROC curve of RADAR and DDLS was 0.658 and 0.648. CONCLUSIONS: Automated calculation of RADAR and DDLS with a neural network can be used to evaluate the extent and long-term rate of optic disc rim loss and is further evidence of long-term nerve fiber loss in treated patients with glaucoma. TRANSLATIONAL RELEVANCE: Our study provides a large clinic-based experience for RADAR and DDLS measurements in GS and glaucoma with a neural network.

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.