PREVENT equations predicted risk for incident CVD in adults aged 30 to 79 y

Area of Science:

• Cardiovascular science
• Nephrology
• Epidemiology

Background:

• Cardiovascular disease (CVD) remains a leading cause of mortality globally.
• Accurate risk prediction is crucial for implementing timely preventive strategies.
• Existing cardiovascular risk calculators have limitations in diverse populations.

Purpose of the Study:

• To develop and validate the American Heart Association's PREVENT equations for CVD risk prediction.
• To assess the performance of the new equations across various demographic groups.
• To provide a more equitable tool for cardiovascular risk assessment.

Main Methods:

• Development of the PREVENT equations using large, diverse datasets.
• Internal and external validation of the equations using multiple cohorts.
• Assessment of calibration and discrimination across different subgroups.
• Comparison with existing CVD risk prediction models.

Main Results:

• The PREVENT equations demonstrated good calibration and discrimination for predicting CVD events.
• Performance was consistent across different racial and ethnic groups.
• The equations showed improved accuracy compared to some existing models in specific populations.
• Validation confirmed the generalizability of the developed risk scores.

Conclusions:

• The American Heart Association's PREVENT equations offer a validated tool for CVD risk prediction.
• These equations can aid clinicians in identifying individuals at high risk.
• The PREVENT equations aim to improve the equity and accuracy of cardiovascular risk assessment.