A Nomogram for Predicting Vision-Threatening Diabetic Retinopathy Among Mild Diabetic Retinopathy Patients: A Case–Control and Prospective Study of Type 2 Diabetes

Abstract

Aim This study aims to develop a nomogram for predicting vision-threatening diabetic retinopathy (VTDR) in type 2 diabetes mellitus (T2DM) with mild non-proliferative diabetic retinopathy (NPDR) patients. Materials and Methods In case–control analysis, 440 patients with mild NPDR or VTDR were enrolled to identify predictors and develop a nomogram. In the prospective cohort, 120 T2DM patients with mild NPDR were enrolled for external validation. Sensitivity, specificity, and area under the receiver operating characteristic (AUC) were calculated to evaluate the predictive performance of the nomogram. Results In case–control analysis, 2-h C-peptide (OR = 0.85, 95% CI: 0.75 to 0.95, p = 0.006), sural nerve conduction impaired (SNCI) (mildly: OR = 2.18, 95% CI: 1.10 to 4.33, p = 0.026; moderately/severely: 3.66, 95% CI: 1.74 to 7.70, p < 0.001) and UACR (microalbuminuria: OR = 2.37, 95% CI: 1.25 to 4.48, p = 0.008; macroalbuminuria: 4.02, 95% CI: 1.61 to 10.06, p = 0.003) were identified as independent predictors. The concordance index of the prediction nomogram was 0.76 in the training set. In the test set, sensitivity, specificity, and AUC were 84.8%, 60.6%, and 0.73, respectively. In the prospective cohort, median follow-up period was 42 months, and 15 patients (12.5%) developed VTDR. Sensitivity, specificity, and AUC of prediction were 66.7%, 89.5%, and 0.75, respectively. Conclusion Introducing 2-h C-peptide, UACR, and SNCI, the nomogram demonstrated a good discriminatory power for predicting risk of VTDR in mild NPDR individuals.

Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy

“…After integrating all independent factors, this study included five variables, Age, BMI, SBP, Duration, and HbA1C, in the construction of a column chart model. Discrimination analysis showed that the training set AUC was 0.7193 and the testing set AUC was 0.6897, which was lower than Ke, J.’s prediction model ( 18 ). However, it should be noted that the variables used in Ke, J.’s model, including 2-hour C-peptide, UASCR, and SNCI, are not commonly used clinical observation indicators, indicating that their model may not be widely applicable in the real world ( 18 ).…”

“…Discrimination analysis showed that the training set AUC was 0.7193 and the testing set AUC was 0.6897, which was lower than Ke, J. 's prediction model (18). However, it should be noted that the variables used in Ke, J.…”

“…However, it should be noted that the variables used in Ke, J. 's model, including 2-hour C-peptide, UASCR, and SNCI, are not commonly used clinical observation indicators, indicating that their model may not be widely applicable in the real world (18). In contrast, this study included five clinical data that are relatively easy to obtain (Age, BMI, SBP, Duration, and HbA1C) as predictive factors in the model.…”

“…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 ( 18 ).…”