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

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 m 2, 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.