Related Experiment Video
Updated: Jun 11, 2026
Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
A retrospective study on development and internal validation of cardiovascular disease risk prediction model for
1Department of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Insights
Cardiovascular disease (CVD) risk in chronic kidney disease (CKD) patients is better predicted by a new model including inflammatory and imaging markers. This model improves upon traditional risk factors for CKD stages 3-5.
Area of Science:
- Nephrology
- Cardiology
- Biomarkers
Background:
- Cardiovascular disease (CVD) is the primary cause of death in chronic kidney disease (CKD) patients.
- Traditional CVD risk factors are less effective in predicting outcomes in advanced CKD.
- Novel biomarkers are needed to improve CVD risk prediction in CKD.
Purpose of the Study:
- To develop and validate a 5-year CVD risk prediction model for CKD stages 3-5.
- To integrate clinical, laboratory, and imaging parameters into the prediction model.
- To enhance the accuracy of CVD risk assessment in CKD patients.
Main Methods:
- Recruited 301 CKD stages 3-5 patients with follow-up until July 2022.
- Utilized LASSO and multivariable logistic regression to identify predictors.
- Internally validated the model using tenfold cross-validation and assessed performance with c-statistic and Hosmer-Lemeshow test.
Main Results:
- 169 (56.1%) patients experienced a CVD event within 5 years.
- C-reactive protein (CRP) and interventricular septum thickness (IVS) improved predictive performance when added to traditional factors.
- The full model achieved an AUC of 0.845 (95% CI [0.802-0.888]) with excellent calibration.
Conclusions:
- A validated clinical risk prediction model for 5-year CVD events in CKD stages 3-5 was established.
- The model incorporates readily available clinical, inflammatory, and imaging variables.
- This tool aims to improve CVD risk prediction and management in CKD patients.
Background:
Cardiovascular disease (CVD) is the leading cause of mortality in chronic kidney disease (CKD) patients. Traditional CVD risk factors exhibit diminished predictive utility in advanced CKD, necessitating integration of non-traditional biomarkers. Previous prediction models based only on traditional CVD risk show limitations and inaccuracies. This study aimed to develop and validate a 5-year CVD risk prediction model combining clinical, laboratory, and imaging parameters for CKD stages 3-5 patients.
Methods:
Three hundred and one patients with CKD stage 3-5 were recruited from January 2010 to January 2022 and followed up until July 2022. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression were used to identify baseline predictors for model development including clinical data, medication history and laboratory parameters, regression modeling was performed using logistic regression and internally validated using tenfold cross-validation. Discrimination and calibration of resulting prediction models were assessed using c-statistic and P-value of the Hosmer-Lemeshow test. Decision curve analysis was performed to assess clinical effectiveness.
Results:
During follow-up, 169 (56.1%) experienced a first CVD event within 5 years. The median time of occurrence was 10 months. From 29 candidate variables, 11 independent predictors were identified. Through nested model comparisons, we demonstrated that adding inflammatory marker C-reactive protein (CRP) and echocardiographic marker interventricular septum thickness (IVS) markers to traditional risk factors progressively improved predictive performance. The full model-integrating clinical, inflammatory, and imaging parameters-achieved the highest discrimination (area under the curve (AUC), 0.845, 95% confidence interval (CI), [0.802-0.888]) and best fit (Akaike information criterion (AIC), 311.531), with excellent calibration (Hosmer-Lemeshow P = 0.332).
Conclusions:
This study established and validated a clinical risk prediction model based on readily available variables in clinical practice, aiming to predict the risk of CVD events in patients with CKD stages 3-5 over a 5-year period.
Related Concept Videos
01:25Chronic Kidney Disease I: Introduction
01:28Chronic Kidney Disease III: Interprofessional Care
01:28Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration
01:18Chronic Kidney Disease IV: Nursing Management
01:24Chronic Kidney Disease II: Clinical Manifestations
01:30Acute Kidney Injury IV: Diagnostic Studies and Prevention