PURPOSE:The purpose of this study is to evaluate patients with uveal metastasis based on primary tumor site.METHODS:Retrospective analysis from Wills Eye Hospital, Philadelphia, PA, USA, for uveal metastasis clinical features and outcomes based on the primary tumor site.RESULTS:There were 2214 uveal metastases diagnosed in 1111 consecutive patients. The demographics included mean age of 60 years (median 61 years), Caucasian race (88%), and female gender (64%). The tumor was unilateral (82%) and primary site was established before uveal metastasis (67%). The primary tumor originated in the breast (37%), lung (26%), kidney (4%), gastrointestinal (GI) tract (4%), cutaneous melanoma (2%), lung carcinoid (2%), prostate (2%), thyroid (1%), pancreas (1%), other sites (3%), and unknown (16%). Comparative analysis of the 5 most common primary sites (breast, lung, kidney, GI tract, and cutaneous melanoma), revealed metastasis at mean age (57, 62, 66, 61, 59 years), as unilateral tumor (74%, 86%, 85%, 93%, 85%), with mean number of metastasis/eye (1.9, 1.7, 1.0, 1.1, 2.0), and in females (99%, 46%, 26%, 25%, 30%). Choroidal metastases measured mean base (9.3, 10.2, 9.1, 11.0, 7.3 mm), mean thickness (2.4, 3.6, 4.4, 4.0, 2.9 mm), and demonstrated predominant color yellow (94%, 91%, 56%, 97%, 36%). Of the 769 patients with documented follow-up, mean patient survival was poor (22.2, 11.5, 8.6, 12.4, 11.4 months) and Kaplan–Meier analysis revealed 3-year survival (33%, 19%, 0%, 14%, 21%) and 5-year survival (24%, 13%, 0%, 14%, 21%). The worst survival was found in patients with pancreatic metastasis (mean 4.2 months) and best survival with lung carcinoid (92% at 5 years).CONCLUSION:In a tertiary referral service, uveal metastasis originates from cancer in the breast, lung, kidney, GI tract, cutaneous melanoma, or others. Overall prognosis is poor with 5-year survival at 23% and worst survival with pancreatic metastasis whereas best survival with lung carcinoid metastasis.

ObjectiveEvaluate individual factors that impact adherence to eye care follow-up in patients with diabetes.Design and methodsA 4-year retrospective chart review was conducted for 1968 patients with diabetes over age 40 from an urban academic center. Data collected included demographics, insurance, visual acuity, smoking status, medications, dates of dilated fundus examinations (DFE), and reported hemoglobin A1C and blood glucose levels. The primary outcome was timely DFE follow-up adherence following the initial eye exam visit.ResultsOverall, 41.6% of patients adhered to initial follow-up eye care recommendations. Multivariable analysis demonstrated that patients with severe diabetic retinopathy (DR) were more adherent than patients with mild DR (OR 1.86). Other variables associated with increased adherence were visual impairment and reported A1C or blood glucose. Smoking was associated with decreased adherence. Ethnicity and insurance were also significantly associated with adherence. Longitudinal follow-up rates were influenced by additional factors, including ethnicity and neighborhood deprivation index.ConclusionsPatients with moderate to severe DR and/or visual impairment were more likely to adhere to timely DFE follow-up. This could relate to the presence of visual symptoms and/or other systemic manifestations of diabetes. Smokers were less likely to adhere to timely DFE follow-up. One hypothesis is patients who smoke have other symptomatic health problems which patients prioritize over asymptomatic ocular disorders. In order to reduce vision loss from DR, practitioners should be aware that patients with mild and moderate DR, patients with normal vision, and smokers are at greater risk for poor follow-up eye care adherence.

Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform the field of glaucoma. Autonomous DL algorithms are capable of maximizing information embedded in digital fundus photographs and ocular coherence tomographs to outperform ophthalmologists in disease detection. Other unsupervised algorithms such as principal component analysis (axis learning) and archetypal analysis (corner learning) facilitate visual field interpretation and show great promise to detect functional glaucoma progression and differentiate it from non-glaucomatous changes when compared with conventional software packages. Forecasting tools such as the Kalman filter may revolutionize glaucoma management by accounting for a host of factors to set target intraocular pressure goals that preserve vision. Activation maps generated from DL algorithms that process glaucoma data have the potential to efficiently direct our attention to critical data elements embedded in high throughput data and enhance our understanding of the glaucomatous process. It is hoped that AI will realize more accurate assessment of the copious data encountered in glaucoma management, improving our understanding of the disease, preserving vision, and serving to enhance the deep bonds that patients develop with their treating physicians.

Objective: To determine the prevalence of depressive symptoms in an adult ophthalmic patient population and to delineate correlates. Design: Cross-sectional study. Participants: Adult patients (⩾18 years) were approached in general and sub-specialty cornea, retina, and glaucoma ophthalmic clinics. A total of 367 patients from the four clinics were enrolled. Methods: Depressive symptoms were assessed using the Patient Health Questionnaire-9. A cut-off score of ⩾10 was used to indicate clinically significant depressive symptoms. Patient Health Questionnaire-9 scores were used to evaluate bivariate relationships between depressive symptoms and distance visual acuity, ocular diagnosis, diabetes status, smoking status, demographic information, and medications. Results: The majority of patients were female (52.9%) and Caucasian (48.6%). The mean age was 52.0 years (standard deviation: 16.7). Clinically significant depressive symptoms were present in 19.9% of patients overall; this rate varied slightly by clinic. Patients with low vision and blindness (visual acuity worse than 20/60) were more likely to have depressive symptoms (odds ratio = 2.82; 95% confidence interval: 1.90–4.21). Smoking and diabetes were also associated with depressive symptoms (odds ratio = 3.11 (2.66–3.64) and 3.42 (1.90–6.16), respectively). Conclusion: In a sample of urban ophthalmic adult patients, depressive symptoms were highly associated with low vision, smoking, and diabetes. This information can be used to target interventions to those at greatest risk of depressive symptoms.