Clinical prediction model for diabetic retinopathy based on ultra-widefield swept-source optical coherence tomography angiography
10.3980/j.issn.1672-5123.2025.6.23
- VernacularTitle:基于UWF-SS-OCTA构建糖尿病视网膜病变的临床预测模型
- Author:
Xinshu LIU
1
,
2
;
Cancan SHI
1
,
2
;
Qing YU
1
,
2
;
Shuwen CHEN
1
,
2
;
Yingyi ZHAO
1
,
2
;
He WANG
1
,
2
;
Mingxin LI
1
,
2
Author Information
1. Graduate School, Xuzhou Medical University, Xuzhou 221004, Jiangsu Province, China
2. Department of Ophthalmology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, Jiangsu Province, China
- Publication Type:Journal Article
- Keywords:
diabetic retinopathy;
ultra-widefield swept-source optical coherence tomography angiography(UWF-SS-OCTA);
prediction model;
choroidal vascular index;
choroid
- From:
International Eye Science
2025;25(6):999-1004
- CountryChina
- Language:Chinese
-
Abstract:
AIM: To explore the risk factors associated with diabetic retinopathy(DR)based on ultra-widefield swept-source optical coherence tomography angiography(UWF-SS-OCTA), and to establish a clinical prediction model.METHODS:A total of 235 patients(235 eyes)with type 2 diabetes mellitus who were treated in the Affiliated Hospital of Xuzhou Medical University from July to November 2024 were selected as the research objects. According to the presence or absence of DR, they were divided into 120 cases(120 eyes)in non-DR group(NDR group)and 115 cases(115 eyes)in non-proliferative DR group(NPDR group). Data on general characteristics, laboratory tests, and OCTA results were collected for both groups. Univariate analysis was employed to identify DR-related risk factors. Logistic regression analysis was conducted to analyze these risk factors and to establish a DR prediction model. The efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve, calibration curve, and decision curve analysis(DCA).RESULTS: The duration of diabetes, fasting blood glucose, blood urea nitrogen(BUN), history of hypertension, and the choroidal vascular index(CVI)were found to be statistically significant in the model(all P<0.05). Specifically, the duration of diabetes, fasting blood glucose, BUN, and history of hypertension were identified as risk factors for DR among diabetic patients, while CVI was recognized as a protective factor. The area under the curve for the model predicting the probability of DR was 0.898(0.859-0.938), with a diagnostic threshold of 0.438. The corresponding sensitivity and specificity were 87.8% and 78.3%, respectively, indicating that the model possesses high predictive value for the occurrence of DR.CONCLUSION: The duration of diabetes, fasting blood glucose, BUN, history of hypertension, and CVI are significantly correlated with DR. The established prediction model demonstrates a substantial screening capability for DR.
