Key result
A 12-factor clinical risk model predicts diabetic retinopathy in T2DM with an AUC of ~0.91.
Why the study?
A risk prediction model was needed to provide individualized assessment for diabetic retinopathy in patients with type 2 diabetes mellitus.
Meta-Analysis (n=15,654)
Effect estimate: AUC 0.912
A newly developed risk prediction model using 12 common clinical and lifestyle factors can accurately predict the risk of diabetic retinopathy in patients with type 2 diabetes.
May support targeted retinopathy screening in type 2 diabetes; leaves open prospective validation of clinical impact.
To establish a risk prediction model and make individualized assessment for the susceptible diabetic retinopathy (DR) population in type 2 diabetic mellitus (T2DM) patients. 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. After retrieving, eight meta-analyses with a total of 15,654 cases and 12 risk factors associated with the onset of DR in T2DM, including weight loss surgery, myopia, lipid-lowing drugs, intensive glucose control, course of T2DM, glycated hemoglobin (HbA1c), fasting plasma 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), HbA1c (0.372), fasting plasma glucose (0.223), insulin therapy (0.688), rural residence (0.199), smoking (- 0.083), hypertension (0.405), male (0.548), intensive glycemic control (- 0.400) with constant term α (- 0.949) in the constructed model. The area under receiver operating characteristic curve (AUC) of the model in the external validation was 0.912. An application was presented as an example of use. In conclusion, the risk prediction model of DR is developed, which makes individualized assessment for the susceptible DR population feasible and needs to be further verified with large sample size application.
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Zhu et al. (2023) conducted a meta-analysis in Diabetic retinopathy in type 2 diabetes mellitus (n=15,654). 12 clinical risk factors (including HbA1c, fasting plasma glucose, hypertension, and diabetes duration) vs. Absence of risk factors was evaluated on Prediction accuracy (AUC) of the risk prediction model in external validation (AUC 0.912). A logistic regression risk prediction model incorporating 12 clinical risk factors accurately predicted the risk of diabetic retinopathy in patients with type 2 diabetes, achieving an AUC of 0.912 in external validation.
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