糖尿病视网膜病变患者发病危险因素:基于SS-OCTA检测指标的分析

目的 探讨糖尿病视网膜病变(DR)发病的危险因素,建立发病概率预测模型,为加强防治DR提供依据.方法 纳入60例DR患者为DR组,60例糖尿病非DR(NDR)患者为NDR组,记录两组患者的性别、年龄、糖尿病病程及相关SS-OCTA指标,包括黄斑中心凹厚度(CMT)、视网膜外层厚度、视网膜色素上皮层(RPE)厚度、光感受...

Risk factors for patients with diabetic retinopathy:an analysis based indicators from SS-OCTA

Objective　To investigate the risk factors for patients with diabetic retinopathy (DR) and establish a prediction model for the incidence of DR, so as to provide the basis for strengthening the prevention and treatment of DR.Methods　Totally sixty patients with DR were included as the DR group and 60 diabetic patients without DR (NDR) as the NDR group, and the gender, age, duration of diabetes, and related optical coherence tomography angiography（SS-OCTA）parameters in the two groups, including foveal thickness (CMT), outer retinal thickness, retinal pigment epithelial layer (RPE) thickness, photoreceptor layer (IS/OS) thickness, superficial retinal capillary plexus blood flow density (SCP) (excluding great vessels), deep retinal capillary plexus blood flow density (DCP), choriocapillaris perfusion (CPI), and choroidal vascular index (CVI), were recorded, and DR-related risk factors were screened by independent sample t-test and chi-square test, and binary logistic regression analysis of related factors was performed by stepwise analysis to establish a prediction model for the incidence, and receiver operating characteristic curve (ROC curve) was used to evaluate the established model and the diagnostic efficacy of each factor. Results　Patients in the DR group were older than those in the NDR group, had a longer duration of diabetes, and had a higher proportion of male patients than those in the NDR group, and the differences were statistically significant (all P<0.05). Univariate Logistic regression analysis after primary screening of all factors revealed that age, gender, duration of diabetes, IS/OS layer thickness, SCP (removal of great vessels), DCP, and CPI were associated with DR incidence (all P<0.05). The results of multivariate Logistic regression analysis showed that duration of diabetes, IS/OS layer thickness, SCP (except great vessels), and DCP were independent risk factors for the incidence of DR; the maximum area under the ROC curve of the established model was 0. 951, which had a good predictive value. Conclusion　Duration of diabetes, SCP (except great vessels), DCP, and IS/OS layer thickness are independent risk factors for the incidence of DR. Logistic regression prediction model can accurately predict the incidence of DR.