Hui Dongna, Sun Yiyang, Xu Shixin, Liu Junjie, He Ping, Deng Yuhui, Huang Huaxiong, Zhou Xiaoshuang, Li Rongshan
Institute of Biomedical Sciences, Shanxi University, No. 92 Wucheng Road, Xiaodian District, Taiyuan, 030006, Shanxi, China.
Department of Nephrology, Shanxi Provincial People's Hospital, No. 29 Shuangta Street, Yingze District, Taiyuan, 030012, Shanxi, China.
Int Urol Nephrol. 2023 Mar;55(3):687-696. doi: 10.1007/s11255-022-03322-1. Epub 2022 Sep 7.
The heterogeneity of Type 2 Diabetes Mellitus (T2DM) complicated with renal diseases has not been fully understood in clinical practice. The purpose of the study was to propose potential predictive factors to identify diabetic kidney disease (DKD), nondiabetic kidney disease (NDKD), and DKD superimposed on NDKD (DKD + NDKD) in T2DM patients noninvasively and accurately.
Two hundred forty-one eligible patients confirmed by renal biopsy were enrolled in this retrospective, analytical study. The features composed of clinical and biochemical data prior to renal biopsy were extracted from patients' electronic medical records. Machine learning algorithms were used to distinguish among different kidney diseases pairwise. Feature variables selected in the developed model were evaluated.
Logistic regression model achieved an accuracy of 0.8306 ± 0.0057 for DKD and NDKD classification. Hematocrit, diabetic retinopathy (DR), hematuria, platelet distribution width and history of hypertension were identified as important risk factors. Then SVM model allowed us to differentiate NDKD from DKD + NDKD with accuracy 0.8686 ± 0.052 where hematuria, diabetes duration, international normalized ratio (INR), D-Dimer, high-density lipoprotein cholesterol were the top risk factors. Finally, the logistic regression model indicated that DD-dimer, hematuria, INR, systolic pressure, DR were likely to be predictive factors to identify DKD with DKD + NDKD.
Predictive factors were successfully identified among different renal diseases in type 2 diabetes patients via machine learning methods. More attention should be paid on the coagulation factors in the DKD + NDKD patients, which might indicate a hypercoagulable state and an increased risk of thrombosis.
2型糖尿病（T2DM）合并肾脏疾病的异质性在临床实践中尚未得到充分认识。本研究的目的是提出潜在的预测因素，以无创且准确地识别T2DM患者中的糖尿病肾病（DKD）、非糖尿病肾病（NDKD）以及NDKD合并DKD（DKD+NDKD）。
本回顾性分析研究纳入了241例经肾活检确诊的合格患者。从患者的电子病历中提取肾活检前的临床和生化数据特征。使用机器学习算法对不同的肾脏疾病进行两两区分。对所开发模型中选择的特征变量进行评估。
逻辑回归模型对DKD和NDKD分类的准确率为0.8306±0.0057。血细胞比容、糖尿病视网膜病变（DR）、血尿、血小板分布宽度和高血压病史被确定为重要危险因素。然后，支持向量机模型使我们能够以0.8686±0.052的准确率区分NDKD和DKD+NDKD，其中血尿、糖尿病病程、国际标准化比值（INR）、D-二聚体、高密度脂蛋白胆固醇是主要危险因素。最后，逻辑回归模型表明，D-二聚体、血尿、INR、收缩压、DR可能是识别DKD与DKD+NDKD的预测因素。
通过机器学习方法成功识别了2型糖尿病患者不同肾脏疾病中的预测因素。DKD+NDKD患者的凝血因子应得到更多关注，这可能表明存在高凝状态和血栓形成风险增加。
