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External Validation of the American Heart Association PREVENT Cardiovascular Disease Risk Equations
Britton Scheuermann1, Alexandra Brown2, Trenton Colburn3
1College of Health and Human Sciences, Kansas State University, Manhattan.
Insights
The American Heart Association
Area of Science:
- Cardiovascular Disease Epidemiology
- Risk Prediction Modeling
- Public Health Surveillance
Background:
- Cardiovascular disease (CVD) risk assessment is crucial for treatment initiation and patient communication.
- Previous risk assessment tools required updates for improved accuracy.
- The American Heart Association developed the PREVENT equations to enhance CVD risk prediction.
Purpose of the Study:
- To evaluate the prognostic capabilities, calibration, and discrimination of the PREVENT equations.
- To assess the performance of PREVENT equations in a representative US general population sample.
- To compare the PREVENT equations against existing Pooled Cohort Equations (PCEs).
Main Methods:
- Utilized data from the National Health and Nutrition Examination Survey (NHANES) 1999-2010.
- Included adult participants with 10-year follow-up data.
- Assessed model discrimination using receiver-operator characteristic curves and calibration via predicted vs. observed risk slopes.
Main Results:
- PREVENT risk estimates showed a significant association with increased CVD mortality.
- PREVENT equations demonstrated excellent discrimination (C statistic, 0.890) but moderate underfitting.
- PREVENT models outperformed PCEs based on the net reclassification index.
Conclusions:
- The PREVENT equations exhibit excellent discrimination for CVD risk prediction.
- Modest calibration discrepancies suggest potential for refinement.
- Findings support the utilization of PREVENT equations as intended by the American Heart Association.
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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