BACKGROUND
Diabetes mellitus (DM) has become one of the most serious public health problems in the 21st century. chronic complications associated with type 2 DM (T2DM) increase the rate of disability, leading to untimely death and reduce the quality of life. In these complications, diabetic retinopathy (DR) is the most common one and could lead to secondary blindness. Despite retinal screening is first-of-choice for DR diagnosis, the limits of such screening equipments and experienced image readers restricted its applications, especially in those rural areas where DR risks even higher. Therefore, it’s essential to construct an easy-to-implement predictive model of the risk of DR in order to help predict individual morbidity and identify the risk factors of DR.
OBJECTIVE
Diabetic retinopathy (DR) has a high incidence rate in diabetic patients, the quality of life of whom will be seriously affected if not treated in time. This study aims to develop a risk prediction model for DR in type 2 diabetic patients.
METHODS
According to the retrieval strategy, inclusion and exclusion criteria, the relevant Meta analyses on DR risk factors were searched and evaluated. The pooled odds ratio (OR) or relative risk (RR) of each risk factor was obtained and calculated for β coefficients using logistic regression (LR) model. Besides, an electronic patient-reported outcome questionnaire was developed and 60 cases of DR and non-DR T2DM patients were investigated to validate the developed model. Receiver operating characteristic curve (ROC) was drawn to verify the prediction accuracy of the model.
RESULTS
After retrieving, eight Meta analysis with a total of 15654 cases and 12 risk factors associated with the onset of DR in T2DM, including weight loss surgery, myopia, lipid-lowing drugs, blood glucose control, course of T2DM, glycosylated hemo-globin, fasting blood glucose, hypertension, gender, insulin treatment, residence, and smoking were included for LR modeling. These factors, followed by the respective β coefficient was bariatric surgery(-0.942), myopia(-0.357), lipid-lowering drug follow-up <3y(-0.994), lipid-lowering drug follow-up >3y(-0.223), course of T2DM(0.174), glycated hemoglobin (0.372), fasting blood sugar(0.223), insulin therapy(0.688), rural residence(0.199), smoking(-0.083), hypertension(0.405), male(0.548), blood sugar control(-0.400) with constant term α = -0.949 in the constructed model. The area under receiver operating characteristic curve (AUC) of ROC curve of the model in the external validation was 0.912. An application was presented as an example of use.
CONCLUSIONS
In this study, the risk prediction model of DR was developed, which make individualized assessment for the susceptible DR population feasible and need to be further verified with large sample size application.