BACKGROUND: We aim to design a scoring model for differential diagnosis between diabetic nephropathy (DN) and nondiabetic renal disease (NDRD) in type 2 diabetic patients through a combination of clinical variables. METHODS: A total of 170 patients with type 2 diabetes who underwent kidney biopsies were included and divided into three groups according to pathological findings: DN group (n = 46), MIX group (DN + NDRD, n = 54), NDRD group (n = 70). Clinical characteristics and laboratory data were collected and compared among groups. Variables with a significant statistical difference between DN and NDRD patients were analyzed by logistic regression to predict the presence of NDRD; then a scoring model was established based on the regression coefficient and further validated in an independent cohort of 67 patients prospectively. RESULTS: On biopsy, 72.9% of patients had NDRD, and the most common pathological type was membranous nephropathy. The established scoring model for predicting NDRD included five predictors: age, systolic blood pressure, hemoglobin, duration of diabetes, and absence of diabetic retinopathy. The model demonstrated good discrimination and calibration (area under curve [AUC] 0.863, 95% CI, 0.800-0.925; Hosmer-Lemeshow [H-L] P = .062). Furthermore, high prediction accuracy (AUC = 0.900; 95% CI, 0.815-0.985) in the validation cohort proved the stability of the model. CONCLUSIONS: We present a simple, robust scoring model for predicting the presence of NDRD with high accuracy (0.85) for the first time. This decision support tool provides a noninvasive method for differential diagnosis of DN and NDRD, which may help clinicians assess the risk-benefit ratio of kidney biopsy for type 2 diabetic patients with renal impairment.
BACKGROUND: We aim to design a scoring model for differential diagnosis between diabetic nephropathy (DN) and nondiabetic renal disease (NDRD) in type 2 diabeticpatients through a combination of clinical variables. METHODS: A total of 170 patients with type 2 diabetes who underwent kidney biopsies were included and divided into three groups according to pathological findings: DN group (n = 46), MIX group (DN + NDRD, n = 54), NDRD group (n = 70). Clinical characteristics and laboratory data were collected and compared among groups. Variables with a significant statistical difference between DN and NDRDpatients were analyzed by logistic regression to predict the presence of NDRD; then a scoring model was established based on the regression coefficient and further validated in an independent cohort of 67 patients prospectively. RESULTS: On biopsy, 72.9% of patients had NDRD, and the most common pathological type was membranous nephropathy. The established scoring model for predicting NDRD included five predictors: age, systolic blood pressure, hemoglobin, duration of diabetes, and absence of diabetic retinopathy. The model demonstrated good discrimination and calibration (area under curve [AUC] 0.863, 95% CI, 0.800-0.925; Hosmer-Lemeshow [H-L] P = .062). Furthermore, high prediction accuracy (AUC = 0.900; 95% CI, 0.815-0.985) in the validation cohort proved the stability of the model. CONCLUSIONS: We present a simple, robust scoring model for predicting the presence of NDRD with high accuracy (0.85) for the first time. This decision support tool provides a noninvasive method for differential diagnosis of DN and NDRD, which may help clinicians assess the risk-benefit ratio of kidney biopsy for type 2 diabeticpatients with renal impairment.