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Development of prognostic model for patients at CKD stage 3a and 3b in South Central China using computational
Qiongjing Yuan1, Haixia Zhang1,2, Yanyun Xie1
1Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
Insights
A computational model accurately predicts chronic kidney disease (CKD) progression in stage 3 patients. This tool identifies high-risk individuals for personalized treatment, improving outcomes for CKD stage 3a and 3b.
Area of Science:
- Nephrology
- Computational Intelligence
- Biostatistics
Background:
- Chronic kidney disease (CKD) stage 3 is heterogeneous, with subgroups CKD stage 3a and 3b defined by estimated glomerular filtration rate (eGFR).
- Significant differences exist in CKD prevalence, demographics, and environmental factors between China and Western populations.
- Understanding CKD progression in specific populations, like South Central China, is crucial for effective management.
Purpose of the Study:
- To develop and validate a computational intelligence model (CKD stage 3 Modeling, CSM) for distinguishing CKD stage 3a and 3b.
- To identify risk factors associated with the progression of CKD stage 3a and 3b to CKD stage 5.
- To create a prognostic model for CKD stage 3a/3b patients in South Central China.
Main Methods:
- Utilized a computational intelligence model (CSM) incorporating data distribution rules, Pearson correlation coefficient (PCC), Spearman correlation (SCC), logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (Nnet).
- Applied RF to identify risk factors for CKD progression from stage 3a/3b to stage 5.
- Analyzed data from 1090 CKD stage 3 patients, with 455 progressing to CKD stage 5 over a median follow-up of 4 years.
Main Results:
- Common risk factors for CKD stage 3a/3b progression to stage 5 included albumin, creatinine, and total protein.
- Specific factors for CKD stage 3a to stage 5 progression were proteinuria, direct bilirubin, and hemoglobin.
- Risk factors for CKD stage 3b to stage 5 progression included low-density lipoprotein cholesterol, diabetes, and eosinophil percentage.
Conclusions:
- The CKD stage 3 Modeling (CSM) can serve as a point-of-care test for screening high-risk CKD patients.
- The model facilitates individualized therapeutic management strategies for CKD progression.
- Identifying specific risk factors aids in targeted interventions for CKD stage 3a and 3b patients.
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.
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