Construction and validation of a neovascular glaucoma nomogram in patients with diabetic retinopathy after pars plana vitrectomy

Abstract

BACKGROUND Neovascular glaucoma (NVG) is likely to occur after pars plana vitrectomy (PPV) for diabetic retinopathy (DR) in some patients, thus reducing the expected benefit. Understanding the risk factors for NVG occurrence and building effective risk prediction models are currently required for clinical research. AIM To develop a visual risk profile model to explore factors influencing DR after surgery. METHODS We retrospectively selected 151 patients with DR undergoing PPV. The patients were divided into the NVG (NVG occurrence) and No-NVG (No NVG occurrence) groups according to the occurrence of NVG within 6 months after surgery. Independent risk factors for postoperative NVG were screened by logistic regression. A nomogram prediction model was established using R software, and the model’s prediction accuracy was verified internally and externally, involving the receiver operator characteristic curve and correction curve. RESULTS After importing the data into a logistic regression model, we concluded that a posterior capsular defect, preoperative vascular endothelial growth factor ≥ 302.90 pg/mL, glycosylated hemoglobin ≥ 9.05%, aqueous fluid interleukin 6 (IL-6) ≥ 53.27 pg/mL, and aqueous fluid IL-10 ≥ 9.11 pg/mL were independent risk factors for postoperative NVG in patients with DR (P < 0.05). A nomogram model was established based on the aforementioned independent risk factors, and a computer simulation repeated sampling method was used to internally and externally verify the nomogram model. The area under the curve (AUC), sensitivity, and specificity of the model were 0.962 [95% confidence interval (95%CI): 0.932-0.991], 91.5%, and 82.3%, respectively. The AUC, sensitivity, and specificity of the external validation were 0.878 (95%CI: 0.746-0.982), 66.7%, and 95.7%, respectively. CONCLUSION A nomogram constructed based on the risk factors for postoperative NVG in patients with DR has a high prediction accuracy. This study can help formulate relevant preventive and treatment measures.