Abstract:Objective: To explore the influencing factors of poor response to anti vascular endothelial growth factor (VEGF) treatment in patients with diabetic retinopathy (DR), and to build a prediction model based on the influencing factors, so as to provide reference for clinical individualized treatment.Method: A retrospective analysis was conducted on the clinical data of 1250 DR patients (1250 eyes) who received anti VEGF treatment in the hospital from July 2022 to August 2025. The patients were randomly divided into a training set (n=875) and a validation set (n=375) in a 7:3 ratio. Patients in the training set were divided into a poor response group (n=266) and a good response group (n=609) based on their treatment response 1 month after 3 sessions of anti VEGF therapy. The basic information of patients was collected. The influencing factors of poor response to anti-VEGF treatment in DR Patients were analyzed through univariate and multivariate Logistic regression. A nomogram prediction model was constructed based on the influencing factors. The correction curve and the receiver operating characteristic (ROC) curve were drawn to verify and evaluate the model. The decision curve was adopted to evaluate the actual clinical benefits of the nomogram model.Result: There was no statistical significant difference in the general information of patients between the training set and the validation set (P>0.05). Multivariate Logistic regression analysis showed that the classification of diabetic macular edema (DME) - serous retinal detachment, macular fovea retinal thickness (CMT) before treatment, best corrected visual acuity (BCVA) before treatment, destruction of ellipsoidal zone (EZ), glycosylated hemoglobin (HbA1c) before treatment, and neutrophil count before treatment were all risk factors for poor response to anti VEGF treatment in DR patients (OR>1, P<0.05). A nomogram risk model was drawn based on risk factors. The C-index of the training set for predicting poor response to anti-VEGF treatment was 0.880 (95%CI: 0.855-0.904), and that of the validation set was 0.867 (95%CI: 0.828-0.906). The ROC curves were drawn. The area under the curve (AUC) of the prediction model in the training set and validation set was 0.884 (95%CI: 0.859-0.908) and 0.880 (95%CI: 0.841-0.919), respectively, suggesting that the model had good discrimination. The decision curve showed that the net benefit rate of the training set and validation set thresholds within the range of 0.06 to 0.99 was greater than 0.Conclusion: DME classification serous retinal detachment, pre-treatment CMT, pre-treatment BCVA, EZ disruption, pre-treatment HbA1c, and pre-treatment neutrophil count levels are all risk factors for poor anti VEGF treatment response in DR patients. The column chart risk prediction model constructed based on this has high predictive power and can provide reference for the early development of targeted intervention strategies in clinical p
