Zhongguo quanke yixue (Mar 2024)
A Nomogram Prediction Model and Validation Study on the Risk of Complicated Diabetic Nephropathy in Type 2 Diabetes Patients
Abstract
Background Diabetes nephropathy (DN) is a common complication of diabetes patients. The prediction and validation of its risk will help identify high-risk patients in advance and take intervention measures to avoid or delay the progress of nephropathy. Objective To analyze the risk factors affecting the complication of DN in patients with type 2 diabetes mellitus (T2DM) , construct a risk prediction model for the risk of DN in T2DM patients and validate it. Methods A total of 5 810 patients with T2DM admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2016 to June 2021 were selected as the study subjects and divided into the DN group (n=481) and non-DN group (n=5 329) according to the complication of DN. A 1∶1 case-control matching was performed on 481 of these DN patients and non-DN patients by gender and age (±2 years) , and the matched 962 T2DM patients were randomly divided into the training group (n=641) and validation group (n=321) based on a 2∶1 ratio. Basic data of patients, such as clinical characteristics, laboratory test results and other related data, were collected. LASSO regression was applied to optimize the screening variables, and a nomogram prediction model was developed using multivariate Logistic regression analysis. The discriminability, calibration and clinical validity of the prediction model were evaluated by using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow calibration curve, and decision curve analysis (DCA) , respectively. Results There were significant differences in gender, age, BMI, course of diabetes, white blood cell count, total cholesterol, triacylglycerol, low-density lipoprotein cholesterol, serum creatinine, hypertension, systolic blood pressure, diastolic blood pressure, glycosylated hemoglobin, apolipoprotein B, 24-hour urinary micro total protein, qualitative urinary protein between the DN and non-DN group (P<0.05) . Five predictor variables associated with the risk of DN in patients with T2DM were screened using LASSO regression analysis, and the results combined with multivariate Logistic regression analysis showed that duration of diabetes, total cholesterol, serum creatinine, hypertension, and qualitative urinary protein were risk factors for the complication of DN in T2DM patients (P<0.05) . The area under the ROC curve (AUC) for the risk of DN in the training group of the model was 0.866 (95%CI=0.839-0.894) , and the AUC for predicting the risk of DN in the validation group was 0.849 (95%CI=0.804-0.889) based on the predictor variables. The Hosmer-Lemeshow calibration curve fit was good (P=0.748 for the training group; P=0.986 for the validation group) . DCA showed that the use of nomogram prediction model was more beneficial in predicting DN when the threshold probability of patients was 0.15 to 0.95. Conclusion The nomogram prediction model containing five predictor variables (diabetes duration, total cholesterol, serum creatinine, hypertension, qualitative urinary protein) developed in this study can be used to predict the risk of DN in patients with T2DM.
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