Clinical journal of the American Society of Nephrology : CJASN · 2026
Validation of the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) Equations in a CKD Population: A Nationwide Veterans Analysis.
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Murthy Nikitha, Sanchez Alyssa, Rangaswami Janani, Khan Sadiya S, Mathew Roy O
Аннотация
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.
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.