Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up.

(1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92-0.94) for any DR and 0.90 (95% CI, 0.89-0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient.

Effect of Lipids on Diabetic Retinopathy in a Large Cohort of Diabetic Patients after 10 Years of Follow-Up.

(1) Background: Diabetic retinopathy (DR) remains the leading cause of low vision and blindness in young adults of working age. Although the most important risk factors-such as the duration of diabetes mellitus (DM) and glycemic control measured by HbA1c-are known, the effects of lipids are not as clear. The aim of the present study is to analyze the effects of lipids on the development of DR. (2) Methods: This is a retrospective study of a population of 175,645 DM2 patients, during the period 2010 to 2020, in which the effects of different lipid factors are studied. (3) Results: The variables that most influenced the development of DR in our study, based on significance and cumulative hazard (CH), were arterial hypertension (CH 1.217, p < 0.001), HbA1c levels (CH 1.162, p = 0.001), microalbuminuria (CH 1.012, p < 0.001), LDL-C cholesterol (CH 1.007, p = 0.012), TC/HDL-C index (CH 1.092, p < 0.001), No-HDL-C/HDL-C index (CH 1.065, p = 0.002), the use of statins (CH 1.001, p = 0.005), and body mass index (CH 1.007, p < 0.001). (4) Conclusions: LDL-cholesterol, TC/HDL-C, and No-HDL-C/HDL-C indices are related to the development of DR, and there is a protective effect of HDL-cholesterol and the use of fibrates.

Real-world outcomes of a clinical decision support system for diabetic retinopathy in Spain.

Objective: The aim of present study was to evaluate our clinical decision support system (CDSS) for predicting risk of diabetic retinopathy (DR). We selected randomly a real population of patients with type 2 diabetes (T2DM) who were attending our screening programme. Methods and analysis: The sample size was 602 patients with T2DM randomly selected from those who attended the DR screening programme. The algorithm developed uses nine risk factors: current age, sex, body mass index (BMI), duration and treatment of diabetes mellitus (DM), arterial hypertension, Glicated hemoglobine (HbA1c), urine-albumin ratio and glomerular filtration. Results: The mean current age of 67.03±10.91, and 272 were male (53.2%), and DM duration was 10.12±6.4 years, 222 had DR (35.8%). The CDSS was employed for 1 year. The prediction algorithm that the CDSS uses included nine risk factors: current age, sex, BMI, DM duration and treatment, arterial hypertension, HbA1c, urine-albumin ratio and glomerular filtration. The area under the curve (AUC) for predicting the presence of any DR achieved a value of 0.9884, the sensitivity of 98.21%, specificity of 99.21%, positive predictive value of 98.65%, negative predictive value of 98.95%, α error of 0.0079 and ß error of 0.0179. Conclusion: Our CDSS for predicting DR was successful when applied to a real population.

Diabetic Macular Edema Pathophysiology: Vasogenic versus Inflammatory.

Diabetic macular edema (DME) can cause blindness in diabetic patients suffering from diabetic retinopathy (DR). DM parameters controls (glycemia, arterial tension, and lipids) are the gold standard for preventing DR and DME. Although the vascular endothelial growth factor (VEGF) is known to play a role in the development of DME, the pathological processes leading to the onset of this disease are highly complex and the exact sequence in which they occur is still not completely understood. Angiogenesis and inflammation have been shown to be involved in the pathogenesis of this disease. However, it still remains to be clarified whether angiogenesis following VEGF overexpression is a cause or a consequence of inflammation. This paper provides a review of the data currently available, focusing on VEGF, angiogenesis, and inflammation. Our analysis suggests that angiogenesis and inflammation act interdependently during the development of DME. Knowledge of DME etiology seems to be important in treatments with anti-VEGF or anti-inflammatory drugs. Current diagnostic techniques do not permit us to differentiate between both etiologies. In the future, diagnosing the physiopathology of each patient with DME will help us to select the most effective drug.