Background
diabetic retinopathy, panretinal, Diabetic Retinopathy, non-proliferative diabetic retinopathy, VTDR, NPDR, diabetic, Diabetes, T2DM, diabetes
DIABETIC RETINOPATHY, DISEASE, EVENT, DIABETIC RETINOPATHY, NON-PROLIFERATIVE DIABETIC RETINOPATHY, PROLIFERATIVE DIABETIC RETINOPATHY (PDR), DIABETIC MACULAR EDEMA, DIABETES, REGRESSION, DISEASE, DISEASES, DIABETES
According to research conducted by the International Diabetes Federation (IDF), the global prevalence of diabetes was estimated to be 9.3% (463 million people) in 2019, with a predicted increase to 10.2% (578 million people) by 2030 and 10.9% (700 million people) by 2045 (The International Clinical Diabetic Retinopathy Disease Severity Scale categorizes DR into five stages based on disease severity, including the first three stages with low risk, as well as the fourth stage, severe non-proliferative diabetic retinopathy (NPDR), and the fifth stage, proliferative diabetic retinopathy (PDR). Diabetic macular edema (DME) is classified as either present or absent (The main treatments for VTDR include panretinal photocoagulation, intravitreal injection of anti-vascular endothelial growth factor, and vitreoretinal surgery (Logistic regression, as a commonly used statistical method, can be applied in various clinical medical scenarios, including but not limited to the following aspects: 1)Disease Prediction: Logistic regression can predict whether patients have a certain disease based on their clinical data. 2)Risk Assessment: Logistic regression can be utilized to assess the risk of patients developing a specific disease. 3)Diagnostic Assistance: Logistic regression can assist physicians in disease diagnosis. 4)Drug Development: In the drug development process, logistic regression can predict the efficacy and safety of new drugs. By establishing models, researchers can evaluate the therapeutic effects of different drugs on diseases and identify the most promising candidates. 4)Survival Analysis: In clinical observations and epidemiological studies, logistic regression can be used to analyze patients’ survival data, predict their survival rates, and identify factors that affect survival. Overall, logistic regression plays a crucial role in clinical medicine, providing an effective predictive and decision-making tool for physicians and researchers to improve patient health outcomes and the quality of medical services.In clinical prediction models, logistic regression analysis is commonly used for predicting the onset and diagnosis of diseases by analyzing the probability of an individual developing a certain outcome event at different values of the predictive indicator. Through logistic regression analysis of multiple factors, independent influencing factors for the outcome event are identified as predictive indicators in the prediction model. Then, multiple predictive indicators are integrated and analyzed using regression analysis to generate a risk nomogram based on a certain proportion (Currently, there are few clinical prediction model studies on DR and VTDR based on non-imaging data. Ke et al. constructed a risk nomogram for predicting the development of VTDR in mild NPDR patients by introducing three variables, including 2-hour C-peptide, UACR, and sural nerve conduction impairment (SNCI). The model achieved a sensitivity, specificity, and AUC of 66.7%, 89.5%, and 0.75, respectively, in the testing set (In most areas of China, there are standard management and regular physical examination protocols for diabetic patients. However, the screening and management system for diabetic retinopathy (DR) remains incomplete. Therefore, the purpose of this study is to develop and validate a VTDR risk prediction scoring model based on systemic parameters and non-ophthalmic imaging data, using the T2DM cohort study data. The goal is to provide a simple scoring system for regions with limited medical resources and primary healthcare institutions. This system can be used to identify high-risk individuals for VTDR, addressing the gaps in existing medical facilities and enhancing healthcare accessibility for at-risk populations.