兰州大学学报（医学版）2025，Vol.51Issue(2)：49-59,11.DOI:10.13885/j.issn.1000-2812.2025.02.007
糖尿病视网膜病变伴发糖尿病肾病诊断预测模型的构建与验证分析
Construction and validation analysis of a diagnostic prediction model for diabetic retinopathy complicated with diabetic nephropathy
摘要
Abstract
Objective To construct and select a prediction model for the diagnosis of diabetic retinopathy with diabetic nephropathy,as well as analyze and evaluate the sensitive indicators for such a diagnosis,in order to provide a basis for clinical decision-making.Methods Based on the early warning dataset of diabetic complica-tions in the Chinese People's Liberation Army General Hospital,the dataset was randomly split into a training set and a validation set in accordance with 7∶3,and the three methods of LASSO regression,Support Vector Machine and Random Forest were used to screen the predictors in the training set and to construct three diabet-ic retinopathy with diabetic nephropathy prediction models with the combination of the Logistic regression model;the internal consistency of the models was assessed by 10-fold cross-validation in the training and vali-dation sets,respectively.The internal consistency of the models was assessed by 10-fold cross-validation on the training and validation set,the model differentiation assessed by the Hosmer-Lemeshow test,the receiver opera-tor characteristic and the area under the curve;the model calibration was assessed by the calibration curve,and the clinical validity of the models assessed by the decision curve analysis;the receiver operator characteristic curve was used to evaluate the diabetic retinopathy with diabetic nephropathy prediction model;and the receiver operator characteristic curve was used to evaluate the diabetic retinopathy with diabetic nephropathy prediction model.Receiver operator characteristic curves were used to evaluate the sensitive indicators of diabetic retinopathy with diabetic nephropathy,and the diagnostic value of each sensitive indicator was ana-lyzed.Results LASSO regression screened four variables,support vector machines screened five variables,and random forests screened eight variables.Three prediction models had good prediction performance,and the Random Forest-Logistic regression prediction model was optimal,in which the training set Hosmer-Leme-show test result was P>0.05,the area under the curve was 0.875(95%CI:[0.849,0.902],P<0.001),the specificity was 0.825,the sensitivity was 0.809,the accuracy was 0.815,the Kappa value was 0.614,and the positive predictive value and negative predictive value were respectively 0.712 and 0.890.The results of the calibration curve indicated that the model was well calibrated,and the results of decision curve analysis indicated that the model had good clinical use;the results of the validation set were similar.The analysis of the sensitivity indicators found that urine micro-albumin to urine creatinine ratio,serum creatinine,serum albumin and glycosylated serum protein were the main sensitivity indicators of diabetic retinopathy with diabetic nephrop-athy,and their areas under the curve were all greater than 0.75(P<0.001).Conclusion The Random Forest-Logistic regression model constructed using Random Forest had good predictive performance;the main sensi-tive indicators for diabetic retinopathy with diabetic nephropathy diagnosis were urine microalbumin to urine creatinine ratio,serum creatinine,serum albumin and glycosylated serum protein.关键词
糖尿病肾病/糖尿病视网膜病变/诊断试验评价/预测模型/敏感指标Key words
diabetic nephropathy/diabetic retinopathy/evaluation of diagnosis tests/predictive model/sen-si-tive indicator分类
临床医学引用本文复制引用
游金龙,郑山,刘艳丽,王雪,吕康,刘少东,姜敏,张文君,李媛媛,王敏珍..糖尿病视网膜病变伴发糖尿病肾病诊断预测模型的构建与验证分析[J].兰州大学学报（医学版）,2025,51(2):49-59,11.基金项目
甘肃省杰出青年基金资助项目(23JRRA1019) （23JRRA1019）
武威市市级科技计划项目(WW2202RPZ037) （WW2202RPZ037）
