论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
2 型糖尿病伴蛋白尿患者糖尿病肾病诺模图预测模型的建立与验证
Authors Zhou DM, Wei J, Zhang TT, Shen FJ, Yang JK
Received 17 January 2022
Accepted for publication 23 March 2022
Published 8 April 2022 Volume 2022:15 Pages 1101—1110
DOI https://doi.org/10.2147/DMSO.S357357
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Ming-Hui Zou
Purpose: To establish and validate the nomogram model for predicting diabetic nephropathy (DN) in type 2 diabetes mellitus (T2DM) patients with proteinuria.
Methods: A total of 102 patients with T2DM and proteinuria who underwent renal biopsy were included in this study. According to pathological classification of the kidney, the patients were divided into two groups, namely, a DN group (52 cases) and a non-diabetic renal disease (NDRD) group (50 cases). The clinical data were collected, and the factors associated with diabetic nephropathy (DN) were analyzed with multivariate logistic regression. A nomogram model for predicting DN risk was constructed by using R4.1 software. Receiver operator characteristic (ROC) curves were generated, and the K-fold cross-validation method was used for validation. A consistency test was performed by generating the correction curve.
Results: Systolic blood pressure (SBP), diabetic retinopathy (DR), hemoglobin (Hb), fasting plasma glucose (FPG) and triglyceride/cystatin C (TG/Cys-C) ratio were independent factors for DN in T2DM patients with proteinuria (P< 0.05). The nomogram model had good prediction efficiency. If the total score of the nomogram exceeds 200, the probability of DN is as high as 95%. The area under the ROC curve was 0.9412 (95% confidence interval (CI) = 0.8981– 0.9842). The 10-fold cross-validation showed that the prediction accuracy of the model was 0.8427. The Hosmer-Lemeshow (H-L) test showed that there was no significant difference between the predicted value and the actual observed value (X 2 = 6.725, P = 0.567). The calibration curve showed that the fitting degree of the DN nomogram prediction model was good.
Conclusion: The nomogram model constructed in the present study improves the diagnostic efficiency of DN in T2DM patients with proteinuria, and it has a high clinical value.
Keywords: diabetic nephropathy, non-diabetic renal disease, nomogram model, type 2 diabetes mellitus