Phase 2 Trial Evaluating Minocycline for Geographic Atrophy in Age-Related Macular Degeneration: A Nonrandomized Controlled Trial.

Importance: Existing therapies to slow geographic atrophy (GA) enlargement in age-related macular degeneration (AMD) have relatively modest anatomic efficacy, require intravitreal administration, and increase the risk of neovascular AMD. Additional therapeutic approaches are desirable. Objective: To evaluate the safety and possible anatomic efficacy of oral minocycline, a microglial inhibitor, for the treatment of GA in AMD. Design, Setting, and Participants: This was a phase 2, prospective, single-arm, 45-month, nonrandomized controlled trial conducted from December 2016 to April 2023. Patients with GA from AMD in 1 or both eyes were recruited from the National Institutes of Health (Bethesda, Maryland) and Bristol Eye Hospital (Bristol, UK). Study data were analyzed from September 2022 to May 2023. Intervention: After a 9-month run-in phase, participants began oral minocycline, 100 mg, twice daily for 3 years. Main Outcomes and Measures: The primary outcome measure was the difference in rate of change of square root GA area on fundus autofluorescence between the 24-month treatment phase and 9-month run-in phase. Results: Of the 37 participants enrolled (mean [SD] age, 74.3 [7.6] years; 21 female [57%]), 36 initiated the treatment phase. Of these participants, 21 (58%) completed at least 33 months, whereas 15 discontinued treatment (8 by request, 6 for adverse events/illness, and 1 death). Mean (SE) square root GA enlargement rate in study eyes was 0.31 (0.03) mm per year during the run-in phase and 0.28 (0.02) mm per year during the treatment phase. The primary outcome measure of mean (SE) difference in enlargement rates between the 2 phases was -0.03 (0.03) mm per year (P = .39). Similarly, secondary outcome measures of GA enlargement rate showed no differences between the 2 phases. The secondary outcome measures of mean difference in rate of change between 2 phases were 0.2 letter score per month (95% CI, -0.4 to 0.9; P = .44) for visual acuity and 0.7 µm per month (-0.4 to 1.8; P = .20) for subfoveal retinal thickness. Of the 129 treatment-emergent adverse events among 32 participants, 49 (38%) were related to minocycline (with no severe or ocular events), including elevated thyrotropin level (15 participants) and skin hyperpigmentation/discoloration (8 participants). Conclusions and Relevance: In this phase 2 nonrandomized controlled trial, oral minocycline was not associated with a decrease in GA enlargement over 24 months, compared with the run-in phase. This observation was consistent across primary and secondary outcome measures. Oral minocycline at this dose is likely not associated with slower rate of enlargement of GA in AMD.

A deep network DeepOpacityNet for detection of cataracts from color fundus photographs.

BACKGROUND: Cataract diagnosis typically requires in-person evaluation by an ophthalmologist. However, color fundus photography (CFP) is widely performed outside ophthalmology clinics, which could be exploited to increase the accessibility of cataract screening by automated detection. METHODS: DeepOpacityNet was developed to detect cataracts from CFP and highlight the most relevant CFP features associated with cataracts. We used 17,514 CFPs from 2573 AREDS2 participants curated from the Age-Related Eye Diseases Study 2 (AREDS2) dataset, of which 8681 CFPs were labeled with cataracts. The ground truth labels were transferred from slit-lamp examination of nuclear cataracts and reading center grading of anterior segment photographs for cortical and posterior subcapsular cataracts. DeepOpacityNet was internally validated on an independent test set (20%), compared to three ophthalmologists on a subset of the test set (100 CFPs), externally validated on three datasets obtained from the Singapore Epidemiology of Eye Diseases study (SEED), and visualized to highlight important features. RESULTS: Internally, DeepOpacityNet achieved a superior accuracy of 0.66 (95% confidence interval (CI): 0.64-0.68) and an area under the curve (AUC) of 0.72 (95% CI: 0.70-0.74), compared to that of other state-of-the-art methods. DeepOpacityNet achieved an accuracy of 0.75, compared to an accuracy of 0.67 for the ophthalmologist with the highest performance. Externally, DeepOpacityNet achieved AUC scores of 0.86, 0.88, and 0.89 on SEED datasets, demonstrating the generalizability of our proposed method. Visualizations show that the visibility of blood vessels could be characteristic of cataract absence while blurred regions could be characteristic of cataract presence. CONCLUSIONS: DeepOpacityNet could detect cataracts from CFPs in AREDS2 with performance superior to that of ophthalmologists and generate interpretable results. The code and models are available at https://github.com/ncbi/DeepOpacityNet ( https://doi.org/10.5281/zenodo.10127002 ).

Cataracts are cloudy areas in the eye that impact sight. Diagnosis typically requires in-person evaluation by an ophthalmologist. In this study, a computer program was developed that can identify cataracts from specialist photographs of the eye. The computer program successfully identified cataracts and was better able to identify these than ophthalmologists. This computer program could be introduced to improve the diagnosis of cataracts in eye clinics.

Deep-GA-Net for Accurate and Explainable Detection of Geographic Atrophy on OCT Scans.

Objective: To propose Deep-GA-Net, a 3-dimensional (3D) deep learning network with 3D attention layer, for the detection of geographic atrophy (GA) on spectral domain OCT (SD-OCT) scans, explain its decision making, and compare it with existing methods. Design: Deep learning model development. Participants: Three hundred eleven participants from the Age-Related Eye Disease Study 2 Ancillary SD-OCT Study. Methods: A dataset of 1284 SD-OCT scans from 311 participants was used to develop Deep-GA-Net. Cross-validation was used to evaluate Deep-GA-Net, where each testing set contained no participant from the corresponding training set. En face heatmaps and important regions at the B-scan level were used to visualize the outputs of Deep-GA-Net, and 3 ophthalmologists graded the presence or absence of GA in them to assess the explainability (i.e., understandability and interpretability) of its detections. Main Outcome Measures: Accuracy, area under receiver operating characteristic curve (AUC), area under precision-recall curve (APR). Results: Compared with other networks, Deep-GA-Net achieved the best metrics, with accuracy of 0.93, AUC of 0.94, and APR of 0.91, and received the best gradings of 0.98 and 0.68 on the en face heatmap and B-scan grading tasks, respectively. Conclusions: Deep-GA-Net was able to detect GA accurately from SD-OCT scans. The visualizations of Deep-GA-Net were more explainable, as suggested by 3 ophthalmologists. The code and pretrained models are publicly available at https://github.com/ncbi/Deep-GA-Net. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Proliferative Retinopathy Associated with Repeated High-Altitude Exposure in a Patient with Sickle Cell Trait.

Broadhead Geoffrey K., Henry E. Wiley, David Peprah, Kenneth Olumba, and Alisa T. Thavikulwat. Proliferative retinopathy associated with repeated high-altitude exposure in a patient with sickle cell trait. High Alt Med Biol. 23:369-371, 2022.-Sickle cell trait (SCT), a carrier state characterized by one normal copy of the beta-globin gene (producing hemoglobin A) and one abnormal variant (producing hemoglobin S), is typically asymptomatic and very low risk for manifestations of hemoglobinopathy, including development of retinopathy. Reported cases of proliferative retinopathy in patients with SCT have occurred in the context of concurrent ocular or systemic disease. We report a case of an otherwise healthy patient with SCT who developed proliferative retinopathy requiring surgical intervention in the setting of significant exposure to high altitude through increased work hours as a flight attendant in the month leading to her presentation. Significant high-altitude exposure may contribute to development of retinopathy in patients with sickle trait. Practitioners should consider the possibility of sickle cell retinopathy in patients with sickle trait in these circumstances.

Assessment of Retinal Changes Following Intravitreal Aflibercept in 2 Patients With Von Hippel-Lindau Disease-Related Retinal Capillary Hemangioblastoma.

This case report of 2 patients describes peripheral Von Hippel-Lindau disease associated with a series of aflibercept injections.

Deep Learning-Based Modeling of the Dark Adaptation Curve for Robust Parameter Estimation.

Purpose: This study investigates deep-learning (DL) sequence modeling techniques to reliably fit dark adaptation (DA) curves and estimate their key parameters in patients with age-related macular degeneration (AMD) to improve robustness and curve predictions. Methods: A long-short-term memory autoencoder was used as the DL method to model the DA curve. The performance was compared against the classical nonlinear regression method using goodness-of-fit and repeatability metrics. Experiments were performed to predict the latter portion of the curve using data from early measurements. The prediction accuracy was quantified as the rod intercept time (RIT) prediction error between predicted and actual curves. Results: The two models had comparable goodness-of-fit measures, with root mean squared error (RMSE; SD) = 0.11 (0.04) log-units (LU) for the classical model and RMSE = 0.13 (0.06) LU for the DL model. Repeatability of the curve fits evaluated after introduction of random perturbations, and after performing repeated testing, demonstrated superiority of the DL method, especially among parameters related to cone decay. The DL method exhibited superior ability to predict the curve and RIT using points prior to -2 LU, with 3.1 ± 3.1 minutes RIT prediction error, compared to 19.1 ± 18.6 minutes RIT error for the classical method. Conclusions: The parameters obtained from the DL method demonstrated superior robustness as well as predictability of the curve. These could provide important advances in using multiple DA curve parameters to characterize AMD severity. Translational Relevance: Dark adaptation is an important functional measure in studies of AMD and curve modeling using DL methods can lead to improved clinical trial end points.

Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration.

Introduction - Retinal layer segmentation in optical coherence tomography (OCT) images is an important approach for detecting and prognosing disease. Automating segmentation using robust machine learning techniques lead to computationally efficient solutions and significantly reduces the cost of labor-intensive labeling, which is traditionally performed by trained graders at a reading center, sometimes aided by semi-automated algorithms. Although several algorithms have been proposed since the revival of deep learning, eyes with severe pathological conditions continue to challenge fully automated segmentation approaches. There remains an opportunity to leverage the underlying spatial correlations between the retinal surfaces in the segmentation approach. Methods - Some of these proposed traditional methods can be expanded to utilize the three-dimensional spatial context governing the retinal image volumes by replacing the use of 2D filters with 3D filters. Towards this purpose, we propose a spatial-context, continuity and anatomical relationship preserving semantic segmentation algorithm, which utilizes the 3D spatial context from the image volumes with the use of 3D filters. We propose a 3D deep neural network capable of learning the surface positions of the layers in the retinal volumes. Results - We utilize a dataset of OCT images from patients with Age-related Macular Degeneration (AMD) to assess performance of our model and provide both qualitative (including segmentation maps and thickness maps) and quantitative (including error metric comparisons and volumetric comparisons) results, which demonstrate that our proposed method performs favorably even for eyes with pathological changes caused by severe retinal diseases. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for patients with a wide range of AMD severity scores (0-11) were within 0.84±0.41 and 1.33±0.73 pixels, respectively, which are significantly better than some of the other state-of-the-art algorithms. Conclusion - The results demonstrate the utility of extracting features from the entire OCT volume by treating the volume as a correlated entity and show the benefit of utilizing 3D autoencoder based regression networks for smoothing the approximated retinal layers by inducing shape based regularization constraints.

Longitudinal characterization and treatment response of retinal arterial macroaneurysms in adult-onset coats disease.

Purpose: To perform longitudinal analysis of retinal arterial macroaneurysms in 3 patients with adult-onset Coats disease. Observations: Three eyes of three patients with adult-onset Coats disease were followed longitudinally for 4-15 years. Ultra-widefield images and montage color fundus photographs of affected eyes were analyzed. Size, retinal location, and grading for predominant characteristic (hemorrhagic, exudative, or quiescent) of each individual macroaneurysm were followed longitudinally from the time of onset. Fifty-one individual retinal arterial macroaneurysms were identified. The distance of any lesion-associated hemorrhage or exudation present from the foveal center was measured. Macroaneurysms were located in all quadrants of the retina, with the majority (37/51) graded as hemorrhagic at lesion onset. Hemorrhagic and exudative macroaneurysms that entered the quiescent phase remained quiescent for an average of 26 months. Seven macroaneurysms were found to have hemorrhage or exudation that came within 125 µm of the fovea and all three eyes followed demonstrated a longitudinal decrease in visual acuity despite laser and intravitreal injection therapy. At the initial visit, visual acuities ranged from 20/40 to 20/200, but decreased to 20/80 to 20/320 by the last follow-up visit. Conclusion and Importance: There are many challenges in treating patients with adult-onset Coats disease. Long-term loss of visual acuity often results from sequelae of hemorrhage and exudation affecting the macula.

Evaluation of Cone- and Rod-Mediated Parameters in Dark Adaptation Testing as Outcome Measures in Age-Related Macular Degeneration.

PURPOSE: To investigate the suitability of 6 rod- or cone-mediated dark adaptation (DA) parameters as outcome measures for clinical trials in age-related macular degeneration (AMD), including their retest reliability, association with age and disease severity, and measurable longitudinal change over time. DESIGN: Prospective, longitudinal study (Clinicaltrials.gov: NCT01352975). PARTICIPANTS: A total of 191 patients with AMD and older participants followed longitudinally over 5 years. METHODS: Dark adaptation testing was performed using the AdaptDx dark adaptometer with a maximum test time of 40 minutes. A 2-part exponential-linear curve was fitted to obtain values for cone decay, cone plateau, time to rod-cone break, rod intercept time (RIT), rod adaptation rate (S2), and area under the curve. Intersession retest reliability was assessed in tests performed within 2 weeks using the Bland-Altman analysis. The relationship of DA parameters with age, AMD severity, and reticular pseudodrusen (RPD) presence was evaluated using linear mixed models. MAIN OUTCOME MEASURES: Retest reliability, association with disease severity, and longitudinal change of 6 DA parameters. RESULTS: A total of 1329 DA curves were analyzed. Rod intercept time was the parameter that showed the greatest reliability (intraclass correlation coefficient of 0.88) and greatest association with age, AMD severity, and RPD (marginal R2 of 0.38), followed by the rod-mediated parameters area under the curve and rod-cone break. Cone plateau appeared constant at lower RIT values but increased with progressive rod dysfunction (RIT > 22.8 minutes) with a slope of 0.07 log units per 10 minutes RIT prolongation. Therefore, it might provide additional information in the advanced stages of AMD. CONCLUSIONS: Age-related macular degeneration severity and RPD presence are each associated with large differences in multiple DA curve parameters. In addition, substantial differences in some parameters occur with age, even accounting for AMD severity and RPD status. This supports the 2-hit hypothesis of age and disease status on DA (and perhaps AMD pathophysiology itself). Of the DA parameters, RIT has the highest retest reliability, closest correlation with AMD severity and RPD, and largest longitudinal changes. This underscores the suitability of RIT as an outcome measure in clinical trials. The cone plateau increases only in advanced stages of kinetic rod dysfunction, indicating rod dysfunction preceding cone dysfunction and degeneration in the temporal sequence of pathology in AMD.

MULTIMODAL ASSESSMENTS OF DRUSENOID PIGMENT EPITHELIAL DETACHMENTS IN THE AGE-RELATED EYE DISEASE STUDY 2 ANCILLARY SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY STUDY COHORT.

PURPOSE: To identify features correlating with drusenoid pigment epithelial detachment (DPED) progression in the Age-Related Eye Disease Study 2 Ancillary spectral-domain optical coherence tomography study cohort. METHODS: In this retrospective analysis of a prospective longitudinal study, eyes with intermediate age-related macular degeneration and DPEDs were followed longitudinally with annual multimodal imaging. RESULTS: Thirty-one eyes of 25 participants (mean age 72.6 years) in the Age-Related Eye Disease Study 2 Ancillary spectral-domain OCT substudy (A2A study) had DPED identified in color fundus images. Spectral-domain optical coherence tomography inspection confirmed a subretinal pigment epithelium drusenoid elevation of ≥433 µm diameter in 25 eyes (80.6%). Twenty-four of these eyes were followed longitudinally (median 4.0 years), during which 7 eyes (29.2%) underwent DPED collapse (with 3/7 further progressing to geographic atrophy), 6 (25.0%) developing neovascular age-related macular degeneration, and 11 (45.8%) maintaining DPED persistence without late age-related macular degeneration. On Kaplan-Meier analysis, mean time to DPED collapse was 3.9 years. Both DPED collapse and progression to neovascular age-related macular degeneration were preceded by the presence of hyperreflective foci over the DPED. CONCLUSION: The natural history of DPED comprises collapse (sometimes followed by the development of atrophy), vascularization followed by exudation, or DPED persistence. Spectral-domain optical coherence tomography can confirm retinal pigment epithelial elevation caused by drusenoid accumulation and facilitate the identification of high-risk features that correlate with progression.

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. DESIGN: DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. PARTICIPANTS: A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). METHODS: Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. MAIN OUTCOME MEASURES: Mean squared error (MSE). RESULTS: On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. CONCLUSIONS: DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.

Improving Interpretability in Machine Diagnosis: Detection of Geographic Atrophy in OCT Scans.

Purpose: Manually identifying geographic atrophy (GA) presence and location on OCT volume scans can be challenging and time consuming. This study developed a deep learning model simultaneously (1) to perform automated detection of GA presence or absence from OCT volume scans and (2) to provide interpretability by demonstrating which regions of which B-scans show GA. Design: Med-XAI-Net, an interpretable deep learning model was developed to detect GA presence or absence from OCT volume scans using only volume scan labels, as well as to interpret the most relevant B-scans and B-scan regions. Participants: One thousand two hundred eighty-four OCT volume scans (each containing 100 B-scans) from 311 participants, including 321 volumes with GA and 963 volumes without GA. Methods: Med-XAI-Net simulates the human diagnostic process by using a region-attention module to locate the most relevant region in each B-scan, followed by an image-attention module to select the most relevant B-scans for classifying GA presence or absence in each OCT volume scan. Med-XAI-Net was trained and tested (80% and 20% participants, respectively) using gold standard volume scan labels from human expert graders. Main Outcome Measures: Accuracy, area under the receiver operating characteristic (ROC) curve, F1 score, sensitivity, and specificity. Results: In the detection of GA presence or absence, Med-XAI-Net obtained superior performance (91.5%, 93.5%, 82.3%, 82.8%, and 94.6% on accuracy, area under the ROC curve, F1 score, sensitivity, and specificity, respectively) to that of 2 other state-of-the-art deep learning methods. The performance of ophthalmologists grading only the 5 B-scans selected by Med-XAI-Net as most relevant (95.7%, 95.4%, 91.2%, and 100%, respectively) was almost identical to that of ophthalmologists grading all volume scans (96.0%, 95.7%, 91.8%, and 100%, respectively). Even grading only 1 region in 1 B-scan, the ophthalmologists demonstrated moderately high performance (89.0%, 87.4%, 77.6%, and 100%, respectively). Conclusions: Despite using ground truth labels during training at the volume scan level only, Med-XAI-Net was effective in locating GA in B-scans and selecting relevant B-scans within each volume scan for GA diagnosis. These results illustrate the strengths of Med-XAI-Net in interpreting which regions and B-scans contribute to GA detection in the volume scan.

Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.

PURPOSE: To develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP) in the context of age-related macular degeneration (AMD). DESIGN: Application of deep learning models to the Age-Related Eye Disease Study 2 (AREDS2) dataset. PARTICIPANTS: FAF and CFP images (n = 11 535) from 2450 AREDS2 participants. Gold standard labels from reading center grading of the FAF images were transferred to the corresponding CFP images. METHODS: A deep learning model was trained to detect RPD in eyes with intermediate to late AMD using FAF images (FAF model). Using label transfer from FAF to CFP images, a deep learning model was trained to detect RPD from CFP (CFP model). Performance was compared with 4 ophthalmologists using a random subset from the full test set. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC), κ value, accuracy, and F1 score. RESULTS: The FAF model had an AUC of 0.939 (95% confidence interval [CI], 0.927-0.950), a κ value of 0.718 (95% CI, 0.685-0.751), and accuracy of 0.899 (95% CI, 0.887-0.911). The CFP model showed equivalent values of 0.832 (95% CI, 0.812-0.851), 0.470 (95% CI, 0.426-0.511), and 0.809 (95% CI, 0.793-0.825), respectively. The FAF model demonstrated superior performance to 4 ophthalmologists, showing a higher κ value of 0.789 (95% CI, 0.675-0.875) versus a range of 0.367 to 0.756 and higher accuracy of 0.937 (95% CI, 0.907-0.963) versus a range of 0.696 to 0.933. The CFP model demonstrated substantially superior performance to 4 ophthalmologists, showing a higher κ value of 0.471 (95% CI, 0.330-0.606) versus a range of 0.105 to 0.180 and higher accuracy of 0.844 (95% CI, 0.798-0.886) versus a range of 0.717 to 0.814. CONCLUSIONS: Deep learning-enabled automated detection of RPD presence from FAF images achieved a high level of accuracy, equal or superior to that of ophthalmologists. Automated RPD detection using CFP achieved a lower accuracy that still surpassed that of ophthalmologists. Deep learning models can assist, and even augment, the detection of this clinically important AMD-associated lesion.

Longitudinal Study of Peripapillary Thinning in Sickle Cell Hemoglobinopathies.

PURPOSE: To determine the rate of retinal nerve fiber layer (RNFL) thinning in patients with sickle cell hemoglobinopathies. DESIGN: This was a prospective cohort study. METHODS: Sixty-seven patients averaging 35.8 ± 11.5 years of age at enrollment with electrophoretically confirmed sickle cell hemoglobinopathies followed by the University of Illinois at Chicago retina clinic for ≥1 year were included. Exclusion criteria included a history of diabetes, uncontrolled hypertension, glaucoma, ocular opacities, other retinopathies, and previous retinal procedures. The optic nerve head RNFL thicknesses were measured with spectral-domain optical coherence tomography (Heidelberg Engineering, Inc) at enrollment and subsequent follow-ups. Linear mixed models were used to estimate rates of thinning. RESULTS: A total of 122 eyes were followed for 3.8 ± 2.0 years (range 1-8 years). Mean global peripapillary RNFL thickness was 100.9 ± 13.0 µm at baseline. Global peripapillary RNFL thickness decreased at a rate of 0.98 µm per year (95% confidence interval [CI] 0.77-1.19 µm/year). A history of stroke was associated with a faster rate of global RNFL thinning (1.72 ± 0.20 vs 0.79 ± 0.12 µm/year, P < .001), whereas a history of hypertension was associated with a slower rate of thinning (0.33 ± 0.27 vs 1.14 ± 0.12 µm/year, P = .002). CONCLUSIONS: Peripapillary RNFL thinning in patients with sickle cell hemoglobinopathies occurred faster in patients with a history of stroke and slower in patients with controlled hypertension. Future studies will compare these rates to those of healthy age- and race-matched individuals.

Pathophysiology and management of glaucoma associated with phakomatoses.

The phakomatoses, encephalotrigeminal angiomatosis (ETA; Sturge-Weber Syndrome), neurofibromatosis type 1 (NF1 or von Recklinghausen disease), Von Hippel-Lindau (VHL) disease, tuberous sclerosis (TSC), oculodermal melanocytosis (ODM), and phakomatosis pigmentovascularis are a group of neurocutaneous disorders that have characteristic systemic and ocular manifestations. Through many different mechanisms, they may cause glaucomatous damage of the optic nerve and subsequent vision loss varying from mild to severe. Glaucoma commonly affects patients with ETA (43-72%), orbito-facial NF1 (23-50%), and ODM (10%). Rarely, it may present as neovascular glaucoma in VHL and TSC. In ETA, glaucoma typically occurs ipsilateral to the port-wine stain, which is caused by a mutation in the GNAQ gene. Specifically, mechanical malformation of the anterior chamber angle and elevated episcleral venous pressure has been implicated as causes of glaucoma in ETA. In NF1, which is caused by a mutation in the NF1 tumor suppressor gene, glaucoma commonly occurs ipsilateral to lid plexiform neurofibromas. Histological studies of eyes with NF1 have revealed direct anterior chamber infiltration by neurofibromas, secondary angle closure, fibrovascularization, and developmental angle abnormalities as mechanisms of glaucoma. Lastly, phakomatosis pigmentovascularis is a rare combination of ODM and port-wine stain. Affected patients are at very high risk of developing glaucoma. Despite the many different mechanisms of glaucomatous damage, management follows similar principles as that for congenital glaucoma and primary open angle glaucoma. First-line therapy is topical intraocular pressure-lowering eye drops. Surgical management, including goniotomy, trabeculotomy, trabeculectomy, and tube shunt placement may be required for more severe cases.

Evolution of Geographic Atrophy in Participants Treated with Ranibizumab for Neovascular Age-related Macular Degeneration.

PURPOSE: To evaluate the risk factors, incidence, and rate of progression of geographic atrophy (GA) in eyes with neovascular age-related macular degeneration (nAMD) treated with ranibizumab. DESIGN: Post-hoc analysis of a prospective clinical study. PARTICIPANTS: 69 participants with nAMD in at least one eye. METHODS: Participants were prospectively treated in the study eye with 0.5 mg intravitreal ranibizumab. Study eyes received 4 monthly injections followed by pro re nata injections until a fluid-free macula was achieved on optical coherence tomography. Risk factors assessed included baseline demographics, treatment, and ocular characteristics on imaging. Eyes were evaluated on fundus autofluorescence (FAF) for GA. The rate of GA area growth in study and fellow eyes was analyzed by linear regression of square-root transformed areas. MAIN OUTCOME MEASURES: Development of new-onset GA and rate of GA area growth measured on ocular imaging, including FAF images of the study eyes. RESULTS: Sixty-nine participants (mean age 78.8±7.8 years) with an average of 40.0±13.6 months of follow-up were analyzed. Twenty-two of 69 study eyes (32%) were treatment naïve. During their first year of the study, participants received an average of 9.2±3.3 injections in the study eye. Of 63 study eyes with quality baseline images, 22 (35%) had pre-existing GA. Of the remaining 41 eyes, 7 (17%) developed new-onset GA during study follow-up. Those who developed new GA were older (all ≥79 years old) and had received fewer study injections on average (6.9 vs. 10.4 injections at 1 year) compared to those who did not develop new GA. Of the 12 treatment naïve study eyes without GA at baseline, 1 (8.3%) developed new GA during the study. In 21 study eyes with quantifiable GA area, eyes with GA present at baseline (16/21) enlarged by 0.34±0.26 mm/year, compared to 0.19±0.12 mm/year in eyes developing new-onset GA (5/21). CONCLUSIONS: While 17% of study eyes without GA present at baseline receiving ranibizumab developed new GA, the role of ranibizumab in the development of GA is unclear. Further prospective longitudinal studies are required to determine the eyes most at risk of developing GA in the setting of anti-VEGF treatment.

The effects of gender and age on the range of the normal human electro-oculogram.

PURPOSE: To define the normal ranges for the slow oscillations (SO) and fast oscillations (FO) of the electro-oculogram (EOG) recorded to International Society for Clinical Electrophysiology of Vision (ISCEV) standards. The effects of age and gender on the EOG ranges were examined. METHODS: ISCEV standard SOs and FOs were recorded from 121 subjects (51 % male) aged from 7 to 72 years. Study variables for the SO were dark trough (DT) and light peak (LP) amplitudes (µV), times to DT and LP (min), and the Arden ratio (LP/DT amplitude). The FO was fit by a sine wave and peak-to-peak amplitude (µV), phase (°), and peak-to-trough (PT) ratios derived. The effects of age, gender and pupil size on EOG parameters were examined by multiple regression analysis. RESULTS: The average Arden ratio was 2.5. Arden ratio decreased with age at a rate of 0.13 per decade of age (R (2) = 0.14, P < 0.0001). The 5th percentile of the Arden ratio decreased from 2.0 to 1.7 between 10 and 60 years of age. Median time to LP was 9 min (interquartile range 8-9 min). Time to LP was age-dependent and increased by 2 min for subjects over 55 years of age compared with those less than 25 years. EOG amplitudes were greater in women than in men (P < 0.005). The average PT ratio was 1.18, which was not affected by age or gender. Time to reach the light trough of the FO was 40 s, which increased with age (1.1 s/decade). No correlation was observed between Arden ratio and PT ratio. CONCLUSIONS: The major strength of this study is the definition of the normal range and associated lower limits of ISCEV standard EOGs based on recordings from 121 subjects balanced by gender and spanning the 1st through 8th decades of life. Decreased Arden ratio and increased time to LP are associated with aging, which is likely due to the intricate mechanisms involved in generation of the light rise. Differences between the FO and SO with respect to the effects of aging are consistent with separate generation of these two EOG signals.

Evaluation of Cataract Surgery Outcomes at the Ban Phaeo Mobile Eye Clinic, Thailand.

PURPOSE: To evaluate the quality and efficacy of the Ban Phaeo Mobile Eye Clinic (BPMEC) model in providing cataract surgery to rural Thailand. METHODS: Retrospective review of 4-week postoperative visual acuity (VA) of 7040 patients receiving unilateral cataract surgery at 75 sites from October 2008 to March 2011. BPMEC conducted screening, surgery, and follow-up at partnering local hospitals that otherwise do not offer eye care. The primary outcome was achievement of VA ≥20/70, which was secondarily analyzed by cataract extraction method (phacoemulsification, phaco, or extracapsular cataract extraction, ECCE) and intraocular lens (IOL) type (foldable acrylic or rigid polymethylmethacrylate). RESULTS: Preoperatively, 249 eyes (4%) had uncorrected VA ≥20/70. Four weeks postoperatively, 5957 (85%) had VA ≥20/70, 3288 (47%) had VA ≥20/40, and 69 (1%) were lost to follow-up. Phaco was performed in 6324 eyes (90%), 1581 (25%) receiving foldable IOLs. A total of 1403 eyes (89%) undergoing phaco with a foldable IOL had postoperative VA ≥20/70, compared to 4058 (85%) and 496 (69%) of those undergoing phaco with a rigid IOL (p < 0.05) and ECCE with a rigid IOL (p < 0.001), respectively. Overall, 37 eyes were left aphakic. Reported intraocular complications included zonular dehiscence (37 eyes), retained lens fragment (2 eyes), wound leakage (1 eye), and posterior capsule tear (1 eye). CONCLUSIONS: The 4-week uncorrected postoperative VAs met the World Health Organization's quality target of VA ≥20/70 for eyes undergoing phaco and were comparable to those published by an urban hospital in Bangkok.