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.

Validation of a Diagnostic Support System for Diabetic Retinopathy Based on Clinical Parameters.

Purpose: To validate a clinical decision support system (CDSS) that estimates risk of diabetic retinopathy (DR) and to personalize screening protocols in type 2 diabetes mellitus (T2DM) patients. Methods: We utilized a CDSS based on a fuzzy random forest, integrated by fuzzy decision trees with the following variables: current age, sex, arterial hypertension, diabetes duration and treatment, HbA1c, glomerular filtration rate, microalbuminuria, and body mass index. Validation was made using the electronic health records of a sample of 101,802 T2DM patients. Diagnosis was made by retinal photographs, according to EURODIAB guidelines and the International Diabetic Retinopathy Classification. Results: The prevalence of DR was 19,759 patients (19.98%). Results yielded 16,593 (16.31%) true positives, 72,617 (71.33%) true negatives, 3165 (3.1%) false positives, and 9427 (9.26%) false negatives, with an accuracy of 0.876 (95% confidence interval [CI], 0.858-0.886), sensitivity of 84% (95% CI, 83.46-84.49), specificity of 88.5% (95% CI, 88.29-88.72), positive predictive value of 63.8% (95% CI, 63.18-64.35), negative predictive value of 95.8% (95% CI, 95.68-95.96), positive likelihood ratio of 7.30, and negative likelihood ratio of 0.18. The type 1 error was 0.115, and the type 2 error was 0.16. Conclusions: We confirmed a good prediction rate for DR from a representative sample of T2DM in our population. Furthermore, the CDSS was able to offer an individualized screening protocol for each patient according to the calculated risk confidence value. Translational Relevance: Results from this study will help to establish a novel strategy for personalizing screening for DR according to patient risk factors.