Faculty Bibliography

The glaucoma treatment paradigm is starting to change from a more reactive approach that relies on topical medications to a more proactive approach that leverages procedural interventions. This evolution toward interventional glaucoma has been enabled by a growing array of lower-risk minimally invasive procedures such as laser trabeculoplasty, minimally invasive glaucoma surgery, and procedural pharmaceuticals. A common feature of these glaucoma interventions-as with all glaucoma interventions-is the need for early, prompt, and accurate diagnosis. The present review summarizes new and upcoming developments in glaucoma diagnostics. These include technologies and techniques for home-based intraocular pressure measurement, novel visual field platforms, photography- and optical coherence tomography-based visualization, and artificial intelligence applications. They also include emerging technologies such as mitochondrial flavoprotein fluorescence imaging, detection of apoptosing retinal cells, collector channel visualization, and genetic testing. These diagnostic modalities have the potential to circumvent the limitations of traditional diagnostic methods. By increasing the frequency and feasibility of obtaining valuable glaucoma data with more rapid detection of disease and progression, these diagnostics may enable an interventional approach to glaucoma treatment for the betterment of patient care.

Since the Artificial Intelligence Committee of the American Society of Retina Specialists developed the initial task force report in 2020, the artificial intelligence (AI) field has seen further adoption of US Food and Drug Administration-approved AI platforms and significant development of AI for various retinal conditions. With expansion of this technology comes further areas of challenges, including the data sources used in AI, the democracy of AI, commercialization, bias, and the need for provider education on the technology of AI. The overall focus of this committee report is to explore these recent issues as they relate to the continued development of AI and its integration into ophthalmology and retinal practice.

OBJECTIVE:To determine the rate of Medicare opt-out among optometrists and ophthalmologists and to contrast the differences in the characteristics and geographic distribution of these populations. DESIGN/METHODS:A retrospective cross-sectional study. SETTING/METHODS:Using a publicly available Centers for Medicare & Medicaid Services (CMS) data set, we collated data for ophthalmologists and optometrists who opted out in each year between 2005 and 2023. We calculated the rate of opt-out annually in each year window and cumulatively from 2005 to 2023. Comparative analysis was used to identify clinician characteristics associated with opt-out. MAIN OUTCOMES AND MEASURES/METHODS:Both annual and cumulative rate of ophthalmologist and optometrist opt-out from Medicare. RESULTS:The estimated prevalence of Medicare opt-outs was 0.52% (77/14,807) for ophthalmologists and 0.38% (154/40,526) for optometrists. Ophthalmologists opting out were predominantly male (67.5%), had a longer practice duration (average 31.8 years), and were more often located in urban areas (83.1%), compared to optometrists (53.2% male, average 19.6 years in practice, 59.1% in urban areas, p = 0.04, p<0.001, p<0.001 respectively). Approximately 83% of ophthalmologists were either anterior segment or oculoplastics specialties, while the majority (52.1%) of optometrists were in optometry-only practices; >75% of identified clinicians were in private practice. Geographical distribution across the US showed variable opt-out rates, with the top 3 states including Oklahoma (3.4%), Arizona (2.1%), and Kansas (1.6%) for ophthalmology and Idaho (4.3%), Montana (3.1%), and Wyoming (1.4%) for optometry. CONCLUSIONS AND RELEVANCE/CONCLUSIONS:Few ophthalmologists and optometrists opt-out of Medicare but this trend has significantly increased since 2012. Of those who disenrolled from Medicare, 83% of ophthalmologists were in urbanized areas while 41% of optometrists were in non-urbanized areas. Because reasons for Medicare opt-out cannot be solely determined by administrative data, further investigation is warranted given the potential impact on healthcare accessibility.

IMPORTANCE/UNASSIGNED:Patients with retinal artery occlusions (RAOs) are recommended to have emergent stroke workup, although the true risk of death and subsequent vascular events post-RAO is not clear. OBJECTIVE/UNASSIGNED:To determine short-term and long-term rates of stroke, myocardial infarction (MI), and death in patients after RAO compared with a control cohort. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This retrospective cohort study used aggregated electronic health records from January 1, 2003, through April 14, 2023, from TriNetX, a network with data from more than 111 million patients. Patients with RAO and a cataract control group were identified and matched for age, sex, race, and comorbidities, including hypertension, diabetes, hyperlipidemia, and smoking status. Patients were excluded if they had a stroke or MI within 2 years before the diagnosis of RAO or cataract. EXPOSURE/UNASSIGNED:International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, diagnosis code for RAO or age-related cataract. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Rate of death, stroke, and MI at 2 weeks, 30 days, 1 year, 5 years, and 10 years after RAO compared with matched controls. RESULTS/UNASSIGNED:There were a total of 34 874 patients with at least 1 year of follow-up in the RAO cohort. The mean (SD) age at the RAO event was 66 (15.2) years. The rate of death after RAO diagnosis was higher than after cataract diagnosis at 2 weeks (0.14% vs 0.06%; relative risk [RR], 2.45; 95% CI, 1.46-4.12; risk difference [RD], 0.08%; 95% CI, 0.04%-0.13%; P < .001), 30 days (0.29% vs 0.14%; RR, 2.10; 95% CI, 1.49-2.97; RD, 0.15%; 95% CI, 0.08%-0.22%; P < .001), 1 year (3.51% vs 1.99%; RR, 1.78; 95% CI, 1.61-1.94; RD, 1.41%; 95% CI, 1.17%-1.66%; P < .001), 5 years (22.74% vs 17.82%; RR, 1.28; 95% CI, 1.23-1.33; RD, 4.93%; 95% CI, 4.17%-5.68%; P < .001), and 10 years (57.86% vs 55.38%; RR, 1.05; 95% CI, 1.02-1.07; RD, 2.47%; 95% CI, 1.25%-3.69%; P < .001). Risk of stroke after RAO was higher at 2 weeks (1.72% vs 0.08%; RR, 21.43; 95% CI, 14.67-31.29; RD, 1.64%; 95% CI, 1.50%-1.78%; P < .001), 30 days (2.48% vs 0.18%; RR, 14.18; 95% CI, 10.94-18.48; RD, 2.31%; 95% CI, 2.14%-2.47%; P < .001), 1 year (5.89% vs 1.13%; RR, 5.20; 95% CI, 4.67-5.79; RD, 4.64%; 95% CI, 4.37%-4.91%; P < .001), 5 years (10.85% vs 4.86%; RR, 2.24; 95% CI, 2.09-2.40; RD, 6.00%; 95% CI, 5.50%-6.50%; P < .001), and 10 years (14.59% vs 9.18%; RR, 1.59; 95% CI, 1.48-1.70; RD, 5.41%; 95% CI, 4.62%-6.21%; P < .001). Risk of MI after RAO was higher at 2 weeks (0.16% vs 0.06%; RR, 3.00; 95% CI, 1.79-5.04; RD, 0.11%; 95% CI, 0.06%-0.16%; P < .001), 30 days (0.27% vs 0.10%; RR, 2.61; 95% CI, 1.78-3.83; RD, 0.17%; 95% CI, 0.10%-0.23%; P < .001), 1 year (1.66% vs 0.97%; RR, 1.72; 95% CI, 1.51-1.97; RD, 0.59%; 95% CI, 0.42%-0.76%; P < .001), 5 years (6.06% vs 5.00%; RR, 1.21; 95% CI, 1.12-1.31; RD, 1.07%; 95% CI, 0.64%-1.50%; P < .001), and 10 years (10.55% vs 9.43%; RR, 1.12; 95% CI, 1.04-1.21; RD, 1.13%; 95% CI, 0.39%-1.87%; P = .003). CONCLUSIONS AND RELEVANCE/UNASSIGNED:This study showed an increased risk of death, stroke, and MI in patients with RAO at both short-term and long-term intervals after RAO compared with a matched control population diagnosed with cataract. These findings suggest a potential need for multidisciplinary evaluation and long-term systemic follow-up of patients post-RAO.

PURPOSE/UNASSIGNED:Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. METHOD/UNASSIGNED:We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. RESULTS/UNASSIGNED:Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. CONCLUSIONS/UNASSIGNED:The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. TRANSLATIONAL RELEVANCE/UNASSIGNED:TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.