[1]赵芳,裴超,蔡志鹏,等.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[J].眼科新进展,2021,41(7):643-646.[doi:10.13389/j.cnki.rao.2021.0132]
 ZHAO Fang,PEI Chao,CAI Zhipeng,et al.Risk factors for patients with diabetic retinopathy:an analysis based indicators from SS-OCTA[J].Recent Advances in Ophthalmology,2021,41(7):643-646.[doi:10.13389/j.cnki.rao.2021.0132]
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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/HTML
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《眼科新进展》[ISSN:1003-5141/CN:41-1105/R]

卷:
41卷
期数:
2021年7期
页码:
643-646
栏目:
应用研究
出版日期:
2021-07-05

文章信息/Info

Title:
Risk factors for patients with diabetic retinopathy:an analysis based indicators from SS-OCTA
作者:
赵芳裴超蔡志鹏张红
100040 北京市,中国中医科学院眼科医院
Author(s):
ZHAO FangPEI Chao CAI ZhipengZHANG Hong
Eye Hospital, China Academy of Chinese Medical Sciences, Beijing 100040, China
关键词:
糖尿病视网膜病变OCTALogistic回归分析预测模型
Keywords:
diabetic retinopathy optical coherence tomography angiography logistic regression analysis predictive model
分类号:
R774.1
DOI:
10.13389/j.cnki.rao.2021.0132
文献标志码:
A
摘要:
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、光感受器层(IS/OS)厚度、视网膜浅层毛细血管丛血流密度(SCP)(除去大血管)、视网膜深层毛细血管丛血流密度(DCP)、脉络膜毛细血管灌注(CPI)、脉络膜血管指数(CVI)等数据,通过独立样本t检验及卡方检验筛选出DR相关危险因素,并通过逐步分析法对相关因素进行二元Logistic回归分析,建立发病概率预测模型,并利用受试者工作特征曲线(ROC曲线)评价所建立模型及各因素的诊断效能。结果 DR组患者年龄高于NDR组,糖尿病病程长于NDR组,男性患者占比例高于NDR组,差异均具有统计学意义(均为P<0.05)。单因素Logistic回归分析对所有因素进行初筛后发现,年龄、性别、糖尿病病程、IS/OS层厚度、SCP(去除大血管)、DCP、CPI与DR发病存在相关性(均为P<0.05)。多因素Logistic回归分析结果显示,糖尿病病程、IS/OS层厚度、SCP(除去大血管)、DCP是DR发病的独立危险因素;建立的模型ROC曲线下面积最大为0.951,具有较好的预测价值。结论 糖尿病病程、SCP(除去大血管)、DCP、IS/OS层厚度是DR发病的独立危险因素,Logistic回归预测模型能较为准确地预测DR发病概率。
Abstract:
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.

参考文献/References:

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备注/Memo

备注/Memo:
北京市科委项目(编号:Z191100006619066)
更新日期/Last Update: 2021-07-05