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Updated: May 17, 2025
A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro
Published on: June 2, 2022
Risk factors for and a preliminary prediction model of coronary artery calcification in patients beginning
Hao Xiong1, Cuifang Sun1, Jie Song1
1Department of Nephrology, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China.
Insights
Vascular calcification (VC) is common in new hemodialysis patients. Age, comorbidity index, and diabetes are key risk factors, enabling a new prediction model for early intervention.
Area of Science:
- Nephrology
- Cardiology
- Biomedical Engineering
Background:
- Vascular calcification (VC) is a significant risk factor for cardiovascular events in maintenance hemodialysis (MHD) patients.
- Limited data exists on VC predictors in patients initiating hemodialysis.
Purpose of the Study:
- Identify risk factors for VC in new hemodialysis patients.
- Develop a predictive model for VC progression in this population.
Main Methods:
- 86 new MHD patients were analyzed for demographic, medical, and laboratory data.
- Coronary artery calcification (CAC) assessed via computed tomography (Agatston score).
- Serum fetuin-A levels measured; regression analysis and neural networks used for prediction modeling.
Main Results:
- 72.09% of patients exhibited CAC.
- Age, body mass index, diabetes, comorbidity index, and coronary artery calcification branches correlated with CAC score.
- Age (OR 1.07), comorbidity index (OR 1.72), and diabetes (OR 3.97) were independent risk factors for CAC.
Conclusions:
- Age, comorbidity index, and diabetes are independent risk factors for CAC in incident hemodialysis patients.
- A novel VC prediction model using these factors can aid in early identification and clinical intervention for MHD patients.
Background And Hypothesis:
Vascular calcification (VC) is an important risk factor for cardiovascular events in patients undergoing maintenance hemodialysis (MHD); however, there is limited data on VC-related factors in patients beginning hemodialysis. Thus, this study aimed to determine the risk factors of VC and to establish a prediction model for evaluating VC progression in new patients undergoing hemodialysis.
Methods:
This study selected 86 patients who initiated in-center MHD between March 2021 and November 2022. Demographic characteristics, medical history, and laboratory data were collected. Coronary artery calcification (CAC) was assessed based on the Agatston vascular score determined via computed tomography. Serum levels of the VC inhibitors fetuin-A was quantified via enzyme-linked immunosorbent assays. Univariate and multivariate regression analyses were conducted to determine the risk factors for VC, and a neural network-based approach was adopted to construct a VC prediction model.
Results:
The average age of the patients was 56.74 ± 12.79 years, and 65.1% were male. CAC was observed in 72.09% of patients. Age, body mass index, diabetes, the comorbidity index, and the number of coronary artery branches with calcification were positively correlated with the CAC score, whereas plasma fetuin-A levels was negatively correlated. The multivariate logistic regression analysis revealed that age [odds ratio (OR) 1.07, 95%CI 1.00-1.14], the comorbidity index [OR 1.72, 95%CI 1.16-2.57], diabetes [OR 3.97, 95%CI 1.16-13.58] were independent risk factors for CAC; these factors were used to establish a simple scoring model to predict VC risk.
Conclusion:
Age, the comorbidity index, diabetes were identified as independent risk factors for CAC in patients beginning hemodialysis, and the new VC prediction model based on these factors may help identify VC in patients undergoing MHD, facilitating clinical interventions.
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