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Published on: May 31, 2016
Construction of a miR-15a-based risk prediction model for vascular calcification detection in patients undergoing
Chen Fu1, Yingjie Liu1, Huayu Yang2
1Department of Nephrology, Faculty of Kidney Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, PR China.
Insights
Serum miR-15a, age, and white blood cell count are key predictors for vascular calcification in hemodialysis patients. A new nomogram incorporating these factors offers accurate risk prediction for this common complication.
Area of Science:
- Nephrology and Cardiovascular Research
- Biomarker Discovery
- Clinical Prediction Modeling
Background:
- Vascular calcification (VC) is a prevalent and serious complication in hemodialysis patients, contributing significantly to mortality.
- Accurate biomarkers are needed to predict the onset of VC in this high-risk population.
Purpose of the Study:
- To investigate the association between serum miR-15a levels and VC in hemodialysis patients.
- To develop a predictive model for VC using serum miR-15a and other clinical factors.
Main Methods:
- A cohort of 138 hemodialysis patients was studied, categorized into VC and non-VC groups.
- Logistic regression analysis identified independent risk factors for VC.
- A predictive nomogram was constructed integrating age, dialysis vintage, predialysis nitrogen, WBC count, and serum miR-15a.
Main Results:
- Serum miR-15a, age, and white blood cell (WBC) count were identified as independent risk factors for VC.
- The developed miR-15a-based nomogram demonstrated good predictive performance with an AUC of 0.921.
- The model achieved a sensitivity of 0.722 and specificity of 0.932.
Conclusions:
- Serum miR-15a levels, in conjunction with age and WBC count, are significant independent predictors of VC.
- The developed nomogram provides a valuable tool for predicting VC risk in patients undergoing hemodialysis.
Abstract:
Vascular calcification (VC) is highly prevalent in patients undergoing hemodialysis, and is a significant contributor to the mortality rate. Therefore, biomarkers that can accurately predict the onset of VC are urgently required. Our study aimed to investigate serum miR-15a levels in relation to VC and to develop a predictive model for VC in patients undergoing hemodialysis at the Beijing Friendship Hospital hemodialysis center between 1 January 2019 and 31 December 2020. The patients were categorized into two groups: VC and non-VC. Logistic regression (LR) models were used to examine the risk factors associated with VC. Additionally, we developed an miR-15a-based nomogram based on the results of the multivariate LR analysis. A total of 138 patients under hemodialysis were investigated (age: 58.41 ± 13.22 years; 54 males). VC occurred in 79 (57.2%) patients. Multivariate LR analysis indicated that serum miR-15a, age, and WBC count were independent risk factors for VC. A miR-15a-based nomogram was developed by incorporating the following five predictors: age, dialysis vintage, predialysis nitrogen, WBC count, and miR-15a. The receiver operating characteristic (ROC) curve had an area under the curve of 0.921, diagnostic threshold of 0.396, sensitivity of 0.722, and specificity of 0.932, indicating that this model had good discrimination. This study concluded that serum miR-15a levels, age, and white blood cell (WBC) count are independent risk factors for VC. A nomogram constructed by integrating these risk factors can be used to predict the risk of VC in patients undergoing hemodialysis.
