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Validation of the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) Equations in a CKD Population: A
Nikitha Murthy1, Alyssa Sanchez1, Janani Rangaswami2
1Department of Cardiology, Loma Linda Medical Center, Loma Linda, California.
Insights
The American Heart Association (AHA) PREVENT equations show good performance in predicting cardiovascular events in patients with chronic kidney disease (CKD). Performance is best in earlier stages of CKD, suggesting timely intervention opportunities.
Area of Science:
- Nephrology
- Cardiology
- Public Health
Background:
- Cardiovascular kidney metabolic (CKM) syndrome is a significant public health concern, contributing to cardiovascular mortality.
- Evaluating risk prediction models is crucial for managing CKM syndrome, especially in patients with chronic kidney disease (CKD).
Purpose of the Study:
- To assess the performance of the American Heart Association (AHA) PREVENT (Predicting Risk of Cardiovascular Disease EVENTs) equations in a large CKD population.
- To compare the PREVENT equations' accuracy against the Pooled Cohort Equations (PCE) across different strata of kidney function.
Main Methods:
- Veterans with estimated glomerular filtration rate (eGFR) were categorized into three groups based on kidney function (eGFR > 60, 30-59, 15-29).
- The study evaluated base PREVENT equations for total cardiovascular disease (CVD), atherosclerotic cardiovascular disease (ASCVD), and heart failure (HF), including add-on equations with albuminuria.
- Model performance was measured using the c-index for discrimination and calibration curve slopes for calibration.
Main Results:
- PREVENT equations demonstrated moderate discrimination (c-indices ranging from 0.633 to 0.673) and variable calibration (slopes 0.78-1.27).
- PREVENT-ASCVD showed modestly better discrimination than PCE and superior calibration.
- Equation discrimination declined with worsening kidney function, but albuminuria add-on equations improved prediction in less advanced CKD stages.
Conclusions:
- The PREVENT equations are valuable for risk prediction in CKD patients, performing optimally in earlier stages of the disease.
- These findings highlight opportunities for timely application of preventive and disease-modifying therapies in CKM syndrome.
- Risk stratification using PREVENT equations can guide clinical management in patients with varying degrees of kidney function impairment.
Key Points:
The Predicting Risk of Cardiovascular Disease EVENTs equations optimize risk prediction in the modern era of increasing cardiovascular kidney metabolic disease prevalence. Risk prediction is most helpful to intensify therapy in those with less advanced kidney disease. In those with advanced kidney disease, risk indices provide little utility beyond biomarkers alone such as eGFR and albuminuria.
Background:
Cardiovascular kidney metabolic syndrome is a growing public health problem and leading cause of cardiovascular mortality. We evaluated model performance of the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations in a large single health system population in patients with CKD and across strata of kidney function and compared findings where applicable to those of the pooled cohort equations (PCE).
Methods:
Veterans with eGFR measurements were divided into three groups: eGFR ≥60, eGFR 30-59, and eGFR 15-29. We assessed the base PREVENT equations for total cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure as well as add-on equations using albuminuria. Accuracy of equations was assessed with c-index as a measure of discrimination and calibration curve slopes as a measure of calibration.
Results:
Overall c-index was 0.651 for PREVENT-CVD, 0.633 for PREVENT-ASCVD, and 0.673 for PREVENT-heart failure. Discrimination of PREVENT-ASCVD was modestly greater than that of PCE (0.633 versus 0.629, respectively). For all three base PREVENT equations, discrimination declined with advancing kidney disease. Overall calibration slopes for the PREVENT equations ranged from 0.78 to 1.27. PREVENT-ASCVD had superior calibration compared to PCE with a calibration slope of 1.27 and 0.53, respectively. In those with urine albumin to creatinine ratio (UACR) measurements, discrimination of UACR add-on equations improved from base equations in those with less advanced kidney disease (c-index 0.624 for PREVENT-CVD UACR versus 0.607 for PREVENT-CVD base equation in those with eGFR ≥60, and c-index 0.586 for PREVENT-CVD UACR versus 0.563 for PREVENT-CVD base equation in those with eGFR 30-59). This trend was similar for the PREVENT-ASCVD and PREVENT-heart failure UACR add-on equations.
Conclusions:
In a CKD population, the PREVENT equations perform best in those less advanced stages of CKD and cardiovascular kidney metabolic, presenting an opportunity to apply disease-preventing and disease-modifying therapeutics in a timely manner.
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