Diabetic retinopathy risk prediction in patients with type 2 diabetes mellitus using a nomogram model - Wang, Qian; Zeng, Ni; Tang, Hongbo; Yang, Xiaoxia; Yao, Qu; Zhang, Lin; Zhang, Han; Zhang, Ying; Nie, Xiaomei; Liao, Xin; Jiang, Feng (Frontiers in Endocrinology, 2022)
Background: This study aims to develop diabetic retinopathy (DR) hazard nomogram for a Chinese population of patients with type 2 diabetes mellitus (T2DM). Methods: We constructed a nomogram model by including data from 213 patients with T2DM between January 2019 to May 2021 in Affiliated Hospital of Zunyi Medical University. We used basic statistics and biochemical indicator tests to assess the risk of DR in patients with T2DM. The patient data was used to evaluate DR risk using R software and a Least Absolute Shrinkage and Selection Operator (LASSO) predictive model. Using multivariable cox regression, we examined the risk factors of DR to reduce the LASSO penalty. The validation model, decision curve analysis, and C-index were tested on the calibration plot. The bootstrapping methodology was used to internally validate the accuracy of the nomogram. Results: The LASSO algorithm identified the following eight predictive variables from the 16 independent variables: disease duration, BMI, fasting blood glucose (FPG), glycated hemoglobin Alanine aminotransferase (HbA1c), homeostatic model assessment-insulin resistance (HOMA-IR), triglyceride (TG), total cholesterol (TC), and vitamin D3 (VitD). The C-index was 0.848 (95% CI: 0.798–0.898), indicating the accuracy of the model. In interval validation, high scores (0.816) are possible from an analysis of a DR nomogram's decision curve to predict DR. Conclusion: We developed a non-parametric technique to predict the risk of DR based on disease duration, BMI, FPG, HbA1c, HOMA-IR, TG, TC, and VitD.