Development and validation of new glomerular filtration rate predicting models for Chinese patients with type 2 diabetes. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Chen, Jinxia; Tang, Hua; Huang, Hui; Lv, Linsheng; Wang, Yanni; Liu, Xun; Lou, Tanqi
• Is Part Of: Journal of translational medicine. Volume 13:Issue 1(2015)
• Page Start: 1
• Page End: 7
• Publication Date: 2015-12
• Subjects: Glomerular filtration rate -- Type 2 diabetes -- Artificial neural network -- Serum creatinine -- Body mass index; Medicine, Experimental -- Periodicals; Human experimentation in medicine -- Periodicals; Therapeutics -- Periodicals; 615.50724
• DOI: 10.1186/s12967-015-0674-y
• Languages: English
• ISSNs: 1479-5876

Abstract

Abstract Background Previous 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. Methods We 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. Results Based 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). Conclusions ANN3 based on sex, age, serum creatinine and BMI is the optimal model for GFR estimation in Chinese patients with type 2 diabetes.