Qiongjing Yuan1, Haixia Zhang1,2, Yanyun Xie1, Wei Lin3, Liangang Peng4, Liming Wang5, Weihong Huang6, Song Feng7, Xiangcheng Xiao8. 1. Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China. 2. Department of Nephrology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou, 215000, Jiangsu, China. 3. Department of Pathology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China. 4. Changsha Aeronautical Vocational and Technical College, Changsha, 410014, Hunan, China. 5. Bitvalue Technology (Hunan) Company Limited, Xiangjiang Road, Changsha, 410082, China. 6. Mobile Health Ministry of Education-China Mobile Joint Laboratory, Xiangya Hospital, Central South University, Changsha, 410008, China. 7. Network Information Center, Xiangya Hospital, Central South University, Xiangya Road, Changsha, 410008, China. fs205@sina.com. 8. Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China. xiaoxc@csu.edu.cn.
Abstract
BACKGROUND: Chronic kidney disease (CKD) stage 3 was divided into two subgroups by eGFR (45 mL/ min 1.73 m2). There is difference in prevalence of CKD, racial differences, economic development, genetic, and environmental backgrounds between China and Western countries. METHODS: We used a computational intelligence model (CKD stage 3 Modeling, CSM) to distinguish CKD stage 3 with CKD stage 3a/3b by data distribution rules, pearson correlation coefficient (PCC), spearman correlation (SCC) analysis, logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (Nnet) to develop Prognostic Model for patients with CKD stage 3a/3b in South Central China. Furthermore, we used RF to discover risk factors of progression of CKD stage 3a and 3b to CKD stage 5. 1090 cases of CKD stage 3 patients in Xiangya Hospital were collected. Among them, 455 patients progressed to CKD stage 5 in a median follow-up of 4 years (IQR 4.295, 4.489). RESULTS: We found that the common risk factors for progression of CKD stage 3a/3b to CKD stage 5 included albumin, creatinine, total protein, etc. Proteinuria, direct bilirubin, hemoglobin, etc. accounted for the progression from stage CKD stage 3a to stage 5. The risk factors for CKD stage 3b progression to stage 5 included low-density lipoprotein cholesterol, diabetes, eosinophil percentage, etc. CONCLUSIONS: CSM could be used as a point-of-care test to screen patients at high risk for disease progression, might allowing individualized therapeutic management.
BACKGROUND:Chronic kidney disease (CKD) stage 3 was divided into two subgroups by eGFR (45 mL/ min 1.73 m2). There is difference in prevalence of CKD, racial differences, economic development, genetic, and environmental backgrounds between China and Western countries. METHODS: We used a computational intelligence model (CKD stage 3 Modeling, CSM) to distinguish CKD stage 3 with CKD stage 3a/3b by data distribution rules, pearson correlation coefficient (PCC), spearman correlation (SCC) analysis, logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (Nnet) to develop Prognostic Model for patients with CKD stage 3a/3b in South Central China. Furthermore, we used RF to discover risk factors of progression of CKD stage 3a and 3b to CKD stage 5. 1090 cases of CKD stage 3 patients in Xiangya Hospital were collected. Among them, 455 patients progressed to CKD stage 5 in a median follow-up of 4 years (IQR 4.295, 4.489). RESULTS: We found that the common risk factors for progression of CKD stage 3a/3b to CKD stage 5 included albumin, creatinine, total protein, etc. Proteinuria, direct bilirubin, hemoglobin, etc. accounted for the progression from stage CKD stage 3a to stage 5. The risk factors for CKD stage 3b progression to stage 5 included low-density lipoprotein cholesterol, diabetes, eosinophil percentage, etc. CONCLUSIONS: CSM could be used as a point-of-care test to screen patients at high risk for disease progression, might allowing individualized therapeutic management.