Introduction. Diabetic nephropathy (DN) is a chronic microvascular complication of diabetes mellitus, leading to end-stage kidney disease and increased mortality. Early detection and treatment are essential to prevent DN. This study aims to develop a diagnostic prediction model for early DN. Methods. This retrospective analysis study was conducted on 205 patients with type 2 diabetes mellitus (T2DM) treated between September 2019 and September 2022. Patients with stage A1 albumin-to-creatinine ratio (ACR) (< 30 mg/g) were categorized as the simple diabetes mellitus group (n = 134), and those with ACR 30-300 mg/g at stage A2 were classified as the early diabetic nephropathy group (n = 71). Relevant ultrasound parameters and hematological indices were selected through univariate and multivariate screenings. A nomogram model was constructed based on the results of multi-factor screening. Internal validation was performed by using Bootstrap methods with 1000 repetitions, receiver operating characteristic (ROC) curve analysis evaluated model differentiation, calibration curves verified model consistency, and decision curve analysis assessed clinical utility. Results. Multivariate logistic regression identified renal artery resistance index (RI), renal cortex shear wave velocity (SWV), Cystatin C (CysC), Retinol-binding protein (RBP), and Glycated Hemoglobin (HbA1C) as significant factors for early DN (all P < .05). The nomogram model showed good differentiation and consistency and has high clinical value and practicality in predicting DN. Conclusion. The prediction model for early DN, based on conventional ultrasound parameters and hematological indices, demonstrates good prediction efficiency and clinical practicability.
Cheng et al. (Fri,) studied this question.
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