Development and validation of new glomerular filtration rate predicting models for Chinese patients with type 2 diabetes

Journal of Translational Medicine

Research

Hui Huang¹  Xun Liu²  Tanqi Lou²  Yanni Wang²  Hua Tang²  Jinxia Chen³  Linsheng Lv⁴ 

[1] Department of Cardiology, Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China;Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, 510630, Guangzhou, China;Division of Nephrology, Department of Internal Medicine, The Third Affiliated Hospital of Sun Yat-sen University, 510630, Guangzhou, China;Institute of Nephrology, Guangdong Medical College, Zhanjiang, Guangdong, China;Operation Room, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China;

关键词: Glomerular filtration rate;    Type 2 diabetes;    Artificial neural network;    Serum creatinine;    Body mass index;   

DOI  :  10.1186/s12967-015-0674-y

 received in 2015-05-28, accepted in 2015-09-14,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundPrevious researches has depicted that the performance of the recommended glomerular filtration rate (GFR)-estimating equations in the type 2 diabetic population is inferior to that in the non-diabetic population. We attempted to develop new GFR-predicting models for use in Chinese patients with type 2 diabetes in this study.MethodsWe enrolled 519 type 2 diabetic patients including a development data-set (n = 276), an internal validation data-set (n = 138) and an external validation data-set (n = 105) to establish new GFR-predicting models. 99mTc-DTPA-GFR revised by the dual sample method was referred to as the gold GFR standard.ResultsBased on sex, age, serum creatinine and new predictor variables [body mass index (BMI), hemoglobinA1C, and urinary albumin creatinine ratio], eight new regression models and eight artificial neural network (ANN) models were developed. In the external validation group, only ANN3 was superior in both precision and accuracy over the original CKD-EPI equation (precision, 20.5 vs. 24.2 mL/min/1.73 m2, P < 0.001; 30 % accuracy, 88.6 vs. 80.6 %, P = 0.02).ConclusionsANN3 based on sex, age, serum creatinine and BMI is the optimal model for GFR estimation in Chinese patients with type 2 diabetes.

【 授权许可】

CC BY   
© Chen et al. 2015