中华眼底病杂志

中华眼底病杂志

2型糖尿病患者糖尿病视网膜病变风险预测模型的建立和初步验证

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目的 建立并初步验证糖尿病(DM)视网膜病变(DR)风险预测模型预测2型DM(T2DM)患者DR发生的可行性。 方法 检查确诊的T2DM患者315例630只眼纳入研究。其中,男性132例264只眼;女性183例366只眼。平均年龄(67.28±12.17)岁;平均DM病程(10.86±7.81)年。根据随机数字表法将患者随机分为模型组、检验组,分别为252例504只眼、63例126只眼。详细记录患者性别、年龄、DM病程、学历;测量身高、体重。均行免散瞳眼底彩色照相、眼轴长度(AL)测量,以及空腹血糖、糖化血红蛋白(HbA1c)、血肌酐、血尿酸、甘油三酯、总胆固醇、高密度脂蛋白、低密度脂蛋白和尿蛋白等实验室检查。采用多因素logistic回归分析DR发生的相关因素,并在此基础上建立回归方程即风险预测模型,受试者工作特征曲线(ROC曲线)确定方程的曲线下面积,以约登指数最大为标准确定回归方程的诊断临界值。检验组评估预测模型的可行性。 结果 模型组504只眼中,DR 170只眼;非DR 334只眼。检验组126只眼中,DR 45只眼;非DR 81只眼。多因素logistic回归分析结果显示,AL[β=–0.196,比值比(OR)=0.822,P<0.001]、年龄(β=–0.079,OR=0.924,P<0.001)、DM病程(β=0.048,OR=1.049,P=0.001)、HbA1c(β=0.184,OR=1.202,P=0.020)、尿蛋白(β=1.298,OR=3.661,P<0.001)与DR呈显著相关。其回归方程简化为P=7.018–0.196X1–0.079X2+0.048X3+0.148X4+1.298X5,其中X1=眼轴,X2=年龄,X3=DM病程,X4=HbA1c,X5=尿蛋白。风险预测模型的ROC曲线下面积为0.800,诊断临界值为–1.485。将检验组患者相关因素代入预测模型,其敏感性为84.0%,特性为59.0%,ROC曲线下面积为0.756。 结论 T2DM患者AL、年龄、DM病程、HbA1c、尿蛋白与DR呈显著相关;检验组验证风险预测模型的灵敏性为84.0%,特异性为59.0%,ROC曲线下面积为0.756,提示其具有预测DR患病风险的可行性。

Objective To establish a risk prediction model of diabetic retinopathy (DR) for type 2 diabetic patients (T2DM). Methods A total of 315 T2DM patients (600 eyes) were enrolled in the study. There were 132 males (264 eyes) and 183 females (366 eyes). The mean age was (67.28±12.17) years and the mean diabetes duration was (10.86±7.81) years. The subjects were randomly assigned to model group and check group, each had 252 patients (504 eyes) and 63 patients (126 eyes) respectively. Some basic information including gender, age, education degree and diabetes duration were collected. The probable risk factors of DR including height, weight, blood pressure, fasting glucose, glycosylated hemoglobin (HbA1c), blood urea, serum creatinine, uric acid, triglyceride, total cholesterol, high-density lipoprotein, low density lipoprotein cholesterol and urinary protein. The fundus photograph and the axial length were measured. Multivariate logistic regression was used to analyze the correlative factors of DR and establish the regression equation (risk model). Receiver operating characteristic (ROC) curves were used to determine the cut-off point for the score. The maximum Youden Index was used to determine the threshold of the equation. The check group was used to check the feasibility of the predictive model. Results Among 504 eyes in the model group, 170 eyes were DR and 334 eyes were not. Among 126 eyes in the check group, 45 eyes were DR and 81 eyes were not. Multivariate logistic regression analysis revealed that axial length [β=–0.196, odds ratio (OR)=0.822,P<0.001], age (β=-0.079,OR=0.924,P<0.001), diabetes duration (β=0.048,OR=1.049,P=0.001), HbA1c (β=0.184,OR=1.202,P=0.020), urinary protein (β=1.298,OR=3.661,P<0.001) were correlated with DR significantly and the simplified calculation of the score of DR were as follows:P=7.018–0.196X1–0.079X2+0.048X3+0.148X4+1.298X5 (X1= axial length, X2=age, X3=diabetes duration, X4=glycosylated hemoglobin, X5= urinary protein). The area under the ROC curve for the score DR was 0.800 and the cut-off point of the score was -1.485. The elements of the check group were substituted into the equation to calculate the scores and the scores were compared with the diagnostic threshold to ensure the patients in high-risk of DR. The result of the score showed 84% sensitivity and 59% specificity. ROC curve for the score to predict DR was 0.756. Conclusion Axial length, age, diabetes duration, HbA1c and urinary protein have significant correlation with DR. The sensitivity and specificity of the risk model to predict DR are 84.0% and 59.0% respectively. The area under the ROC curve was 0.756.

关键词: 糖尿病视网膜病变/预防和控制; 模型,统计学; 预测

Key words: Diabetic retinopathy/prevention & control; Models, statistical; Forecasting

引用本文: 吕喆, 陈亦棋, 沈丽君, 林丽, 陈立锋, 李亮, 吴韩飞, 黎楚岚, 毛剑波. 2型糖尿病患者糖尿病视网膜病变风险预测模型的建立和初步验证. 中华眼底病杂志, 2017, 33(3): 257-261. doi: 10.3760/cma.j.issn.1005-1015.2017.03.009 复制

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