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

Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusion: LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.

Keywords: Diabetes mellitus, type 2; Diabetic nephropathies; Nomograms; Risk factors.

Fig. 1.

Nomogram based on different logistic screening models for diabetic kidney disease (DKD): full model (A), laboratorybased model 1 (LBM1) (B), laboratory-based model 2 (LBM2) (C), and simplified model (D), by groups of predicted probabilities. DD, diabetes duration; CHD, coronary heart disease; DR, diabetic retinopathy; SBP, systolic blood pressure; TC, total cholesterol; UA, uric acid; FCP, fasting plasma C-peptide; HbA1c, glycosylated hemoglobin.

Fig. 2.

Receiver operating characteristic curves of different models for diabetic kidney disease screening. LBM1, laboratory-based model 1; LBM2, laboratory-based model 2.

Fig. 3.

Observed and predicted diabetic kidney disease (DKD) incident for different DKD models in participants: full model (A), laboratory-based model 1 (LBM1) (B), laboratory-based model 2 (LBM2) (C), and simplified model (D), by groups of predicted probabilities.