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Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease: The CRIC
Joshua D Bundy1,2, Mahboob Rahman3, Kunihiro Matsushita4
1Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana.
Insights
New atherosclerotic cardiovascular disease (ASCVD) risk models for chronic kidney disease (CKD) patients show improved prediction using clinical variables and biomarkers compared to general population tools.
Area of Science:
- Nephrology
- Cardiology
- Epidemiology
Background:
- Chronic kidney disease (CKD) significantly increases the risk of atherosclerotic cardiovascular disease (ASCVD).
- Existing ASCVD risk prediction models are not specifically developed for CKD populations, limiting their clinical utility.
- There is a critical need for tailored ASCVD risk assessment tools in individuals with CKD.
Purpose of the Study:
- To develop and validate 10-year ASCVD risk prediction models specifically for patients with CKD.
- To compare the performance of models using traditional clinical variables versus those incorporating novel biomarkers.
- To improve clinical care and prevention strategies for ASCVD in the CKD population.
Main Methods:
- Utilized data from the Chronic Renal Insufficiency Cohort (CRIC) study, including participants without pre-existing cardiovascular disease.
- Defined ASCVD as the first occurrence of adjudicated fatal or nonfatal stroke or myocardial infarction.
- Developed and evaluated models using clinically available variables and novel biomarkers, assessing discrimination, calibration, and reclassification.
Main Results:
- The developed CRIC models demonstrated superior performance compared to the ACC/AHA pooled cohort equations.
- A CRIC model using clinically available variables achieved an AUC of 0.760.
- A biomarker-enriched CRIC model showed the highest AUC of 0.771, significantly outperforming the clinical model and improving risk reclassification.
Conclusions:
- 10-year ASCVD risk prediction models developed in CKD patients, incorporating novel kidney and cardiac biomarkers, outperform general population models.
- These tailored models offer enhanced accuracy for predicting ASCVD events in individuals with CKD.
- The findings support the use of these improved models for better clinical decision-making and prevention strategies in CKD care.
Background:
Individuals with CKD may be at high risk for atherosclerotic cardiovascular disease (ASCVD). However, there are no ASCVD risk prediction models developed in CKD populations to inform clinical care and prevention.
Methods:
We developed and validated 10-year ASCVD risk prediction models in patients with CKD that included participants without self-reported cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. ASCVD was defined as the first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction. Our models used clinically available variables and novel biomarkers. Model performance was evaluated based on discrimination, calibration, and net reclassification improvement.
Results:
Of 2604 participants (mean age 55.8 years; 52.0% male) included in the analyses, 252 had incident ASCVD within 10 years of baseline. Compared with the American College of Cardiology/American Heart Association pooled cohort equations (area under the receiver operating characteristic curve [AUC]=0.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P=0.03), achieving an AUC of 0.736 (95% confidence interval [CI], 0.649 to 0.826). The CRIC model developed using clinically available variables had an AUC of 0.760 (95% CI, 0.678 to 0.851). The CRIC biomarker-enriched model had an AUC of 0.771 (95% CI, 0.674 to 0.853), which was significantly higher than the clinical model (P=0.001). Both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents compared with the pooled cohort equations (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively).
Conclusions:
The 10-year ASCVD risk prediction models developed in patients with CKD, including novel kidney and cardiac biomarkers, performed better than equations developed for the general population using only traditional risk factors.
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