Key result
PREVENT model provides clearer individualized CVD risk classification than Framingham and Reynolds models.
Why the study?
Several cardiovascular risk calculation systems exist, but comparative evaluation of their risk factor effects and individualization, including the new PREVENT model, was needed.
Does the PREVENT model provide more detailed cardiovascular risk assessment compared to Framingham and Reynolds systems in simulated scenarios?
Does the PREVENT model provide more detailed cardiovascular risk assessment compared to Framingham and Reynolds systems in simulated scenarios?
The PREVENT model offers more detailed and individualized cardiovascular risk classification compared to older models like Framingham and Reynolds.
PREVENT may refine risk stratification in simulations; leaves open whether it improves clinical decisions or outcomes versus established scores.
BACKGROUND: Cardiovascular diseases (CVD) are a largely preventable cause of death worldwide. There are many risk factors that influence CVD, some of which are modifi-able and others non-modifiable. Several systems, such as the widely used Framingham, Reynolds, and the most recently developed Predicting Risk of Cardiovascular Disease EVENTs (PREVENT), are used to calculate an individual's CVD risk based on the effects of these risk factors. METHODS: This study compared CVD risk scores calculated using the Framingham, Reynolds, and PREVENT systems. The risk factors common to these 3 systems were determined to be age, gender, smoking status, high-density lipoprotein cholesterol, total cholesterol, and systolic blood pressure, and the effects of these risk factors on CVD risk were evaluated. The numerical risk scores obtained because of the effects of risk factors were presented comparatively in tables created based on gender and smoking status. RESULTS: According to the results obtained, it was observed that the PREVENT system provides more individualized results by including renal, metabolic, and social markers in the calculation and provides a more detailed assessment by also considering the age of risk. CONCLUSION: These features may enable PREVENT to classify both low- and high-risk patients more clearly. This classification of patients may contribute to changing the course of CVD, a major global disease, by enabling appropriate interventions and treatments.
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Atıcı et al. (2026) studied Cardiovascular disease risk (n=4,320). PREVENT risk model vs. Framingham and Reynolds risk models was evaluated on Cardiovascular disease risk score classification. The PREVENT model provided more individualized cardiovascular risk assessments and clearer classification of low- and high-risk patients compared to the Framingham and Reynolds models.
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