To obtain renal volume parameters by using three-dimensional ultrasound, and to establish a predictive model for the diagnosis of diabetic nephropathy (DN) based on clinical data.

Sixty-two subjects with type 2 diabetes mellitus complicated with renal damage were enrolled. Based on the pathological results of renal biopsy, the patients were divided into either a DN group (n=35) or a non-diabetic renal diseases (NDRD) group (n=27). Kidney volume was obtained by three-dimensional ultrasound, and the body volume index was used to obtain the kidney volume index. Gender, age, weight, height, body mass index, body surface area, systolic blood pressure/diastolic blood pressure, urinary protein, glomerular filtration rate, plasma urea nitrogen, serum creatinine, urinary creatinine, fasting blood glucose, history of diabetes, diabetes treatment, diabetic retinopathy, hematuria, kidney volume, and kidney volume index were compared between the two groups. Clinical data indexes and kidney volume index were selected by Logistic regression to establish a predictive model for the diagnosis of DN.

Urinary protein, glomerular filtration rate, plasma urea nitrogen, serum creatinine, history of diabetes, diabetic retinopathy, and hematuria differed significantly between the two groups (t=4.8056, 2.3748, 5.0350, 4.0205, 4.3821, 5.9283; χ²=2.9606, 3.1691; all P<0.05). There was also a statistically significant difference in the right kidney volume index between the two groups (t=2.7166, P<0.05). There was no significant difference in gender, age, weight, height, body mass index, body surface area, diastolic blood pressure, urinary creatinine, fasting blood glucose, diabetes treatment, volume of both kidneys, or left kidney volume index (all P>0.05). A predictive model was constructed by using renal kidney volume and the main clinical parameters. The area under the ROC curve of this model for diagnosing DN was 0.9217 (95% confidence interval: 0.8557-0.9877); with an optimal threshold of 0.2069, the specificity, sensitivity, accuracy, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were 85.19%, 85.71%, 85.48%, 0.8824, 0.8214, 5.7857, and 0.1677, respectively.

The predictive model for the diagnosis of DN based on three-dimensional ultrasound provides a new diagnostic method for DN, which is of great significance for the clinical diagnosis and treatment of DN.