Abstract:AIM: 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 predictive model based on the influencing factors, so as to provide reference for clinical individualized treatment.
METHODS: A retrospective analysis was conducted on the clinical data of DR patients who received anti-VEGF treatment in the hospital from July 2022 to August 2025. The patients were randomly divided into a training set and a validation set 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 mo 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. Based on the identified influencing factors, a nomogram prediction model was constructed. The model was validated and evaluated by calibration curves and receiver operating characteristic(ROC)curves. Decision curve analysis was adopted to assess the clinical net benefit of the nomogram model.
RESULTS:This study included 1 250 DR patients(1 250 eyes), 875 training subjects(age 60.82±10.54 y, 262 males and 613 females), and 375 validation subjects(age 59.70±10.61 y, 100 males and 275 females). Among the patients in the training set, there were 266 cases(266 eyes, age 61.33±9.92 y, 82 males, 184 females)with poor response and 609 cases(609 eyes, age 60.59±10.80 y, 180 males, 429 females)with good response. No statistically significant differences were observed in baseline patient characteristics or treatment response rate between the training set and validation set(P>0.05). Multivariate Logistic regression analysis showed that the classification of diabetic macular edema(DME)-serous retinal detachment, central macular thickness(CMT)before treatment, best corrected visual acuity(BCVA)before treatment, disruption 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(all OR>1, P<0.05). A nomogram risk model was plotted 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 plotted. 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 the verification set threshold in the range of 0.06-0.99 was greater than 0. Within the threshold probability range, this model for clinical decision-making can obtain positive net benefits.
CONCLUSION: DME serous retinal detachment subtype, 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 nomogram risk prediction model constructed based on it has high predictive power and can provide a reference for the early development of targeted intervention strategies in clinical practice.
