Abstract:AIM: To explore the factors influencing the efficacy of diabetic macular edema(DME)with conbercept and to construct a nomogram prediction model.
METHODS:Retrospective analysis. Patients with DME admitted from January 2021 to October 2025 were selected. All patients received intravitreal conbercept injection once monthly for three consecutive months. Subjects were divided into a good response group and a poor response group according to the reduction in central macular thickness(CMT)at 3 mo after treatment. The clinical data of the patients were collected to analyze factors associated with poor response to conbercept therapy in patients with DME, and to construct a nomogram prediction model for identifying poor responders to conbercept treatment among DME patients.
RESULTS:A total of 400 DME patients(400 eyes)were included in this study. The response group consisted of 312 patients(312 eyes), with 200 males and 112 females, with an average age of 64.85±5.13 y. The non-response group consisted of 88 patients(88 eyes), with 54 males and 34 females, with an average age of 65.14±4.93 y. There were no differences between the two groups in terms of gender, age, alcohol consumption, smoking status, duration of diabetes, edema classification, disease classification, low-density lipoprotein, HRF, ELM and/or EZ continuity interruption, and SRF(all P>0.05). However, there were differences in DR disease duration, pre-treatment BCVA, high-density lipoprotein, glycated hemoglobin, DRIL, and VMIA(all P<0.05). Multivariate Logistic regression analysis showed that DR disease duration ≥1 y(OR=2.351, 95%CI: 1.360-4.065), poor pre-treatment BCVA(OR=3.432, 95%CI: 1.581-6.988), high glycated hemoglobin(OR=1.656, 95%CI: 1.346-2.038), presence of DRIL(OR=3.115, 95%CI: 1.532-6.333), and presence of VMIA(OR=2.526, 95%CI: 1.445-4.415)were all risk factors for poor response to conbercept treatment in DME patients, while elevated high-density lipoprotein(OR=0.025, 95%CI: 0.004-0.163)was a protective factor for poor response to conbercept treatment in DME patients(P<0.05). ROC analysis showed that the area under the curve(AUC)of the nomogram model for predicting poor response to conbercept treatment in DME patients was 0.817(95%CI: 0.776-0.854), with a sensitivity of 73.86% and a specificity of 78.21%. When the threshold probability was >3%, the nomogram model for predicting poor response to conbercept treatment in DME patients was more favorable for all patient treatment plans.
CONCLUSION:The nomogram prediction model constructed in this study is helpful for the clinical early identification of patients with DME who have a high risk of poor response to conbercept treatment, and can provide a reference for the early prevention of poor response to conbercept treatment.
