Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease: The CRIC Study
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Key result
A 10-year ASCVD risk prediction model incorporating novel kidney and cardiac biomarkers significantly improved discrimination (AUC 0.771) compared to the standard ACC/AHA pooled cohort equations in patients with chronic kidney disease.
Why the study?
Individuals with CKD are at high risk for ASCVD, but no ASCVD risk prediction models had been developed in CKD populations to inform clinical care and prevention.
Do CKD-specific risk prediction models incorporating novel biomarkers improve 10-year ASCVD risk prediction compared to standard ACC/AHA pooled cohort equations in patients with chronic kidney disease?
Population
2604 CRIC participants with CKD and without self-reported cardiovascular disease
Comparison
Novel CKD-derived ASCVD prediction models vs ACC/AHA pooled cohort equations
Design
Cohort model development and validation study
Follow-up
10 years
Authors
Discussion
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Member takes
Implication
CKD-specific ASCVD risk models may aid stratification; leaves open need for external validation and outcome trials before adoption.
Study Design
Type
Cohort (n=2,604)
Multicenter
Yes
Structured PICO
Do CKD-specific risk prediction models incorporating novel biomarkers improve 10-year ASCVD risk prediction compared to standard ACC/AHA pooled cohort equations in patients with chronic kidney disease?
P
Population
2,604 adults aged 21-74 years with mild-to-moderate chronic kidney disease and no prior cardiovascular disease, followed for up to 10 years.
E
Exposure
CRIC 10-year ASCVD risk prediction models (a clinical model using readily available variables and a biomarker-enriched model including apolipoprotein B, hsCRP, troponin-T, and NT-proBNP)
C
Comparator
American College of Cardiology/American Heart Association (ACC/AHA) pooled cohort equations (PCEs)
O
Outcome
Incident ASCVD (composite of first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction) within 10 yearscomposite
Main Result
Effect estimate: AUC 0.771 (95% CI 0.674 to 0.853)
Absolute Event Rate: 0.771% vs 0.73%
p-value: p=<0.001
In patients with chronic kidney disease, novel 10-year ASCVD risk prediction models incorporating clinical variables and cardiac biomarkers outperformed traditional ACC/AHA pooled cohort equations.
Limitations
- External validation cohorts were selected based on only one assessment of kidney function and had a higher baseline risk of ASCVD.
- Models could not incorporate potentially important imaging variables including coronary artery calcium scores and electrocardiogram data.
- Models overestimated risk in the highest predicted probability groups.
Cite This Study
Bundy et al. (2022) conducted a cohort in Chronic Kidney Disease (n=2,604). CRIC biomarker-enriched ASCVD risk prediction model vs. ACC/AHA pooled cohort equations was evaluated on 10-year incident ASCVD discrimination (AUC) (AUC 0.771, 95% CI 0.674 to 0.853, p=<0.001). A 10-year ASCVD risk prediction model incorporating novel kidney and cardiac biomarkers significantly improved discrimination (AUC 0.771) compared to the standard ACC/AHA pooled cohort equations in patients with chronic kidney disease.
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