■本研究旨在探索特定的生化指标,并构建2型糖尿病(T2D)患者糖尿病肾病(DKD)的风险预测模型。
■这项研究包括234名T2D患者,其中166人患有DKD,2021年1月至2022年7月在吉林大学第一医院就诊。临床特征,比如年龄,性别,和典型的血液学参数,被收集并用于建模。五种机器学习算法[极限梯度提升(XGBoost),梯度增压机(GBM),支持向量机(SVM)逻辑回归(LR),和随机森林(RF)]用于识别关键的临床和病理特征,并建立DKD的风险预测模型。此外,从吉林大学第三医院收集70例患者(nT2D=20,nDKD=50)的临床数据进行外部验证.
■RF算法在预测发展到DKD方面表现最佳,确定五个主要指标:估计肾小球滤过率(eGFR),糖化白蛋白(GA),尿酸,HbA1c,锌(Zn)。预测模型显示出足够的预测准确性,在内部验证集和外部验证集中,曲线下面积(AUC)值为0.960(95%CI:0.936-0.984)和0.9326(95%CI:0.8747-0.9885),分别。RF模型的诊断效能(AUC=0.960)显著高于RF模型中筛选的具有最高特征重要性的五个特征中的每一个。
■使用RF算法构建的在线DKD风险预测模型是基于其在内部验证中的强大性能而选择的。
UNASSIGNED: This study aimed to explore specific biochemical indicators and construct a risk prediction model for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D).
UNASSIGNED: This study included 234 T2D patients, of whom 166 had DKD, at the First Hospital of Jilin University from January 2021 to July 2022. Clinical characteristics, such as age, gender, and typical hematological parameters, were collected and used for modeling. Five machine learning algorithms [Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF)] were used to identify critical clinical and pathological features and to build a risk prediction model for DKD. Additionally, clinical data from 70 patients (nT2D = 20, nDKD = 50) were collected for external validation from the Third Hospital of Jilin University.
UNASSIGNED: The RF algorithm demonstrated the best performance in predicting progression to DKD, identifying five major indicators: estimated glomerular filtration rate (eGFR), glycated albumin (GA), Uric acid, HbA1c, and Zinc (Zn). The prediction model showed sufficient predictive accuracy with area under the curve (AUC) values of 0.960 (95% CI: 0.936-0.984) and 0.9326 (95% CI: 0.8747-0.9885) in the internal validation set and external validation set, respectively. The diagnostic efficacy of the RF model (AUC = 0.960) was significantly higher than each of the five features screened with the highest feature importance in the RF model.
UNASSIGNED: The online DKD risk prediction model constructed using the RF algorithm was selected based on its strong performance in the internal validation.