• Hang Guo, Fei Han, Jing-Ru Qu, Cong-Qing Pan, Bei Sun, and Li-Ming Chen.
• NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
• Ann. Med. 2024 Dec 1; 56 (1): 24139202413920.

AimTo develop and validate a model for predicting diabetic retinopathy (DR) in patients with type 2 diabetes.MethodsAll risk factors with statistical significance in the DR prediction model were scored by their weights. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve, Kaplan-Meier curve, calibration curve and decision curve analysis. The prediction model was externally validated using a validation cohort from a Chinese hospital.ResultsIn this meta-analysis, 21 cohorts involving 184,737 patients with type 2 diabetes were examined. Sex, smoking, diabetes mellitus (DM) duration, albuminuria, glycated haemoglobin (HbA1c), systolic blood pressure (SBP) and TG were identified to be statistically significant. Thus, they were all included in the model and scored according to their weights (maximum score: 35.0). The model was validated using an external cohort with median follow-up time of 32 months. At a critical value of 16.0, the AUC value, sensitivity and specificity of the validation cohort are 0.772 ((95% confidence interval (95%CI): 0.740-0.803), p < .01), 0.715 and 0.775, respectively. The calibration curve lied close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated that the model had notably higher net benefits. The external validation results proved the reliability of the risk prediction model.ConclusionsThe simple DR prediction model developed has good overall calibration and discrimination performance. It can be used as a simple tool to detect patients at high risk of DR.

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