Diabetes Metab J.  2021 Sep;45(5):708-718. 10.4093/dmj.2020.0117.

Screening Tools Based on Nomogram for Diabetic Kidney Diseases in Chinese Type 2 Diabetes Mellitus Patients

  • 1ADR Monitoring Department, Henan Medical Products Administration & Center for ADR Monitoring of Henan, Zhengzhou, China
  • 2Zhengzhou University Affiliated Cancer Hospital, Zhengzhou, China
  • 3Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
  • 4Department of Infection Control, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
  • 5Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
  • 6Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China


The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM.
A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools.
Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387).
LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.


Diabetes mellitus, type 2; Diabetic nephropathies; Nomograms; Risk factors
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