Construction of a nomogram model to predict the risk of retinopathy of prematurity reactivate after intravitreal anti-vascular endothelial growth factor therapy: a retrospective study.

Abstract

Objective: To explore the risk factors for the reactivate of retinopathy of prematurity (ROP) after intravitreal injection of anti-vascular endothelial growth factor (VEGF) and to construct a nomogram model to predict the risk of ROP reactivate.

Methods: A retrospective analysis was conducted on 185 ROP children who underwent anti-VEGF treatment at the First Affiliated Hospital of Zhengzhou University from January 2017 to October 2023. They were randomly divided into a training set (129 cases) and a validation set (56 cases) at a ratio of 7:3. The training set was further divided into a reactivate group (n = 18) and a non-reactivate group (n = 111) based on whether ROP recurred after treatment. Multivariable logistic regression analysis was used to screen for risk factors for ROP reactivate. A nomogram model was constructed using R software and validated using the validation set. The discrimination, calibration, and clinical net benefit of the model were evaluated using the receiver operating characteristic curve (ROC curve), calibration curve, and decision curve analysis, respectively.

Results: Multivariable logistic regression analysis showed that the number of red blood cell transfusions, use of pulmonary surfactant (PS) 2 times or more, and preoperative fundus hemorrhage were independent risk factors for ROP reactivate (P < 0.05). The area under the ROC curve (AUC) of the training set was 0.810 (95% CI: 0.706-0.914), and that of the validation set was 0.756 (95% CI: 0.639-0.873). The Hosmer-Leme show goodness-of-fit test indicated a good fit of the model (P = 0.31). Calibration curve analysis and decision curve analysis suggested high predictive efficacy and clinical application value of the model.

Conclusions: The number of red blood cell transfusions, use of PS 2 times or more, and preoperative fundus hemorrhage are independent risk factors for ROP reactivate. The nomogram model constructed based on these factors has high predictive efficacy and clinical application value.