Key result
PREVENT risk equations linked to ~9% higher 10-year CVD mortality per 1% risk increase.
Why the study?
Do the PREVENT equations improve cardiovascular risk prediction compared to the Pooled Cohort Equations in the US general population?
Observational (n=172,900,000)
Yes
Do the PREVENT equations improve cardiovascular risk prediction compared to the Pooled Cohort Equations in the US general population?
Hazard Ratio: 1.09 (95% CI 1.087–1.094)
The AHA PREVENT equations demonstrate excellent discrimination and outperform the older Pooled Cohort Equations for predicting 10-year CVD mortality in a representative US population.
May support PREVENT use in risk assessment; extends observational validation but leaves open randomized confirmation.
Importance: The American Heart Association's Predicting Risk of Cardiovascular Disease Events (PREVENT) equations were developed to extend and improve on previous cardiovascular disease (CVD) risk assessments for the purpose of treatment initiation and patient-clinician communication. Objective: To assess prognostic capabilities, calibration, and discrimination of the PREVENT equations in a study sample representative of the noninstitutionalized, US general population. Design, Setting, and Participants: This prognostic study used data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2010 data cycles. Participants included adults for whom 10-year follow-up data were available. Data curation and analyses took place from December 2023 through May 2024. Main Outcomes and Measures: Primary measures were risk estimated by the PREVENT equations, as well as risk estimates from the previous Pooled Cohort Equations (PCEs). The primary outcome was composite CVD-related mortality at 10 years of follow-up. Additional analyses compared the PREVENT equations against the PCEs. Model discrimination was assessed with receiver-operator characteristic curves and Harrell C statistic from proportional hazard regression; model calibration was determined as the slope of predicted versus observed risk. Results: The study cohort, accounting for NHANES complex survey design, consisted of 172.9 million participants (mean age, 45.0 years [95% CI, 44.6-45.4 years]; 52.1% women [95% CI, 51.5%-52.6%]). In analyses adjusted for the NHANES survey design, a 1% increase in PREVENT risk estimates was statistically significantly associated with increased CVD mortality risk (hazard ratio, 1.090; 95% CI, 1.087-1.094). PREVENT risk scores demonstrated excellent discrimination (C statistic, 0.890; 95% CI, 0.881-0.898) but moderate underfitting of the model (calibration slope, 1.13; 95% CI, 1.06-1.21). PREVENT risk models performed statistically significantly better than the PCEs, as assessed by the net reclassification index (0.093; 95% CI, 0.073-0.115). Conclusions and Relevance: In this prognostic study of the PREVENT equations, PREVENT risk estimates demonstrated excellent discrimination and only modest discrepancies in calibration. These findings provided evidence supporting utilization of the PREVENT equations for application in the intended population as suggested by the American Heart Association.
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Scheuermann et al. (2024) conducted an observational in Cardiovascular disease (n=172,900,000). PREVENT Cardiovascular Disease Risk Equations vs. Pooled Cohort Equations (PCEs) was evaluated on Composite CVD-related mortality at 10 years of follow-up (HR 1.090, 95% CI 1.087-1.094). The PREVENT risk equations were significantly associated with 10-year CVD mortality (HR 1.090 per 1% increase; 95% CI 1.087-1.094) and demonstrated excellent discrimination (C statistic 0.890).
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