: Proliferative diabetic retinopathy and diabetic macular edema can be a potentially sight-threatening disease if not treated correctly. It is directly correlated to the duration of diabetes, and how well managed the patients diabetes is. In the last 15 years, the treatment of diabetic eye disease has taken a quantum leap in methodology due to the group of biological agents named anti-vascular endothelial growth factor (anti-VEGF). The introduction of the first biological agent has revolutionized the treatment, not only in diabetic eye disease but also across most inflammatory eye diseases causing leakage of fluid from the blood vessels i.e. age-related macular degeneration. The availability of these biological agents despite their considerable costs have significantly improved the outcome measured in visual acuity compared to more traditional treatment of diabetic retinopathy in the form of sole laser treatment and glycemic control. The agents demonstrate a favorable safety profile, but if the rarest and most severe side effect occurs, there is a potential total loss of vision. This review aims to make an overview of the current pharmaceutical therapeutic options in the treatment of diabetic macular edema. This includes laser therapy, intravitreal steroids and with a primary focus on intravitreal anti-vascular endothelial growth factors.
Purpose: To examine the real-world performance of a support vector machine learning software (RetinaLyze) in order to identify the possible presence of diabetic retinopathy (DR) in patients with diabetes via software implementation in clinical practice. Methods: 1001 eyes from 1001 patients—one eye per patient—participating in the Danish National Screening Programme were included. Three independent ophthalmologists graded all eyes according to the International Clinical Diabetic Retinopathy Disease Severity Scale with the exact level of disease being determined by majority decision. The software detected DR and no DR and was compared to the ophthalmologists’ gradings. Results: At a clinical chosen threshold, the software showed a sensitivity, specificity, positive predictive value and negative predictive value of 84.9% (95% CI: 81.8–87.9), 89.9% (95% CI: 86.8–92.7), 92.1% (95% CI: 89.7–94.4), and 81.0% (95% CI: 77.2–84.7), respectively, when compared to human grading. The results from the routine screening were 87.0% (95% CI: 84.2–89.7), 85.3% (95% CI: 81.8–88.6), 89.2% (95% CI: 86.3–91.7), and 82.5% (95% CI: 78.5–86.0), respectively. AUC was 93.4%. The reference graders Conger’s Exact Kappa was 0.827. Conclusion: The software performed similarly to routine grading with overlapping confidence intervals, indicating comparable performance between the two groups. The intergrader agreement was satisfactory. However, evaluating the updated software alongside updated clinical procedures is crucial. It is therefore recommended that further clinical testing before implementation of the software as a decision support tool is conducted.
Purpose: The hospital registration of retinal artery occlusions in the Danish National Patient Registry has not previously been validated. In this study, the diagnosis codes were validated to ensure the diagnoses had an acceptable validity for research. The validation was performed both for the overall diagnosis population and at the subtype diagnosis level. Methods: The medical records for all patients with retinal artery occlusion with an incident hospital record in the years 2017–2019 in Northern Jutland (Denmark) were assessed in this population-based validation study. Furthermore, fundus images and two-person verification were assessed for the included patients when available. The positive prediction values for the overall diagnosis of retinal artery occlusion, as well as for the central or branch subtypes, were calculated. Results: A total of 102 medical records were available for review. The overall positive prediction value for a retinal artery occlusion diagnosis was 79.4% (95% CI: 70.6–86.1%), while the overall positive prediction value at the subtype diagnosis level was 69.6% (95% CI: 60.1–77.7%), with 73.3% (95% CI: 58.1–85.4%) for branch retinal artery occlusion and 71.2% (95% CI: 56.9–82.9%) for central retinal artery occlusion. For the stratified analyses at the subtype diagnosis, age, sex, diagnosis year, and primary or secondary diagnosis, the positive prediction values ranged from 73.5 to 91.7%. In the stratified analyses at the subtype level, the positive prediction values ranged from 63.3 to 83.3%. The differences among the positive prediction values of the individual strata of both analyses were not statistically significant. Conclusions: the validities of the retinal artery occlusion and subtype level diagnoses are comparable to other validated diagnoses and considered acceptable for use in research.
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