Design and validation of a scoring model for differential diagnosis of diabetic nephropathy and nondiabetic renal diseases in type 2 diabetic patients

Abstract

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.

A retrospective cohort study of clinical characteristics and outcomes of type 2 diabetic patients with kidney disease

“…Progression of T2DM, poor glycemic control, DR, deterioration of kidney function, hematuria, hypertension can guide to differentiation between DN and NDKD in many studies ( Li et al, 2020 ; Popa et al, 2021 ; Saini, Kochar & Poonia, 2021 ), which were consistent with our findings. Pallayova et al (2015) found that a strong predictor of NDKD was low serum HbA1c level.…”

Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science

“…The CNN model for detecting plants and flowers, was discussed in this article (Wassan et al, 2021). Li et al devise a scoring model to help patients with type 2 diabetes distinguish between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) (Li et al, 2020). In this study, the authors compare and contrast various machine learning-based classifiers.…”

Development and validation of a diagnostic nomogram model for predicting monoclonal gammopathy of renal significance

“…Therefore, determining whether renal injury is associated with MIg holds considerable importance for treatment decisions and patient prognosis assessments. Although various prediction models have been employed for clinical prediction and prognostic assessment in various diseases 3 , 4 , prediction models for the diagnosis of MGRS lesions are notably absent in existing literature. There, in this study, we aimed to conduct a retrospective investigation into the correlation between clinical indicators and the diagnosis of MGRS lesions.…”