Establishment and validation of nomogram model for visual prognosis of macular edema secondary to retinal branch vein occlusion treated with ranibizumab
10.3760/cma.j.cn511434-20230320-00127
- VernacularTitle:视网膜分支静脉阻塞继发黄斑水肿雷珠单抗治疗后视力预后的列线图模型构建与验证
- Author:
Yi RU
1
;
Jun FAN
;
Wei LEI
;
Weijuan BEI
Author Information
1. 柳州市红十字会医院药剂科, 柳州 545001
- Keywords:
Branch retinal vein occlusion;
Macular edema;
Ranibizumab;
Visual prognosis;
Nomogram model
- From:
Chinese Journal of Ocular Fundus Diseases
2023;39(6):464-470
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the influencing factors of visual prognosis of macular edema secondary to branch retinal vein occlusion (BRVO-ME) after treatment with ranibizumab, and construct and verify the nomogram model.Methods:A retrospective study. A total of 130 patients with BRVO-ME diagnosed by ophthalmology examination in the Department of Ophthalmology, Liuzhou Red Cross Hospital from January 2019 to December 2021 were selected in this study. All patients received intravitreal injection of ranibizumab. According to the random number table method, the patients were divided into the training set and the test set with a ratio of 3:1, which were 98 patients (98 eyes) and 32 patients (32 eyes), respectively. According to the difference of logarithm of the minimum angle of resolution (logMAR) best corrected visual acuity (BCVA) at 6 months after treatment and logMAR BCVA before treatment, 98 patients (98 eyes) in the training set were divided into good prognosis group (difference ≤-0.3) and poor prognosis group (difference >-0.3), which were 58 patients (58 eyes) and 40 patients (40 eyes), respectively. The clinical data of patients in the two groups were analyzed, univariate and multivariate logistic regression analysis were carried out for the different indicators, and the visualization regression analysis results were obtained by using R software. The consistency index (C-index), convolutional neural network (CNN), calibration curve and receiver operating characteristic (ROC) curve were used to verify the accuracy of the nomogram model.Results:Univariate analysis showed that age, disease course, outer membrane (ELM) integrity, elliptical zone (EZ) integrity, BCVA, center macular thickness (CMT), outer hyperreflective retinal foci (HRF), inner retina HRF, and the blood flow density of retinal deep capillary plexus (DCP) were risk factors affecting the visual prognosis after treatment with ranibizumab in BRVO-ME patients ( P<0.05). Multivariate logistic regression analysis showed that course of disease, ELM integrity, BCVA and outer HRF were independent risk factors for visual prognosis after ranibizumab treatment for BRVO-ME patients ( P<0.05). The ROC area under the curve of the training set and the test set were 0.846[95% confidence interval ( CI) 0.789-0.887) and 0.852 (95% CI 0.794 -0.873)], respectively; C-index were 0.836 (95% CI 0.793-0.865) and 0.845 (95% CI 0.780-0.872), respectively. CNN showed that the error rate gradually stabilized after 300 cycles, with good model accuracy and strong prediction ability. Conclusions:Course of disease, ELM integrity, BCVA and outer HRF were independent risk factors of visual prognosis after ranibizumab treatment in BRVO-ME patients. The nomogram model based on risk factors has good differentiation and accuracy.
