Development and validation of a predictive risk model based on retinal geometry for an early assessment of diabetic retinopathy

AIMS

This study aimed to develop and validate a risk nomogram prediction model based on the retinal geometry of diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM) and to investigate its clinical application value.

METHODS

In this study, we collected the clinical data of 410 patients with T2DM in the Second Affiliated Hospital of Chongqing Medical University between October 2020 and March 2022. Firstly, the patients were randomly divided into a development cohort and a validation cohort in a ratio of 7:3. Then, the modeling factors were selected using the least absolute shrinkage and selection operator (LASSO). Subsequently, a nomogram prediction model was built with these identified risk factors. Two other models were constructed with only retinal vascular traits or only clinical traits to confirm the performance advantage of this nomogram model. Finally, the model performances were assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA).

RESULTS

Five predictive variables for DR among patients with T2DM were selected by LASSO regression from 33 variables, including fractal dimension, arterial tortuosity, venular caliber, duration of diabetes mellitus (DM), and insulin dosage (P< 0.05). A predictive nomogram model based on these selected clinical and retinal vascular factors presented good discrimination with an AUC of 0.909 in the training cohort and 0.876 in the validation cohort. By comparing the models, the retinal vascular parameters were proven to have a predictive value and could improve diagnostic sensitivity and specificity when combined with clinical characteristics. The calibration curve displayed high consistency between predicted and actual probability in both training and validation cohorts. The DCA demonstrated that this nomogram model led to net benefits in a wide range of threshold probability and could be adapted for clinical decision-making.

CONCLUSION

This study presented a predictive nomogram that might facilitate the risk stratification and early detection of DR among patients with T2DM.