Adoption of the PREVENT (Predicting Risk of Cardiovascular Disease EVENTs) Risk Algorithm: Potential International...

Area of Science:

• Cardiology
• Nephrology
• Preventive Medicine

Background:

• The PREVENT (Predicting Risk of cardiovascular disease EVENTs) risk algorithm was developed to better incorporate metabolic factors into cardiovascular risk assessment.
• Chronic kidney disease (CKD) significantly impacts cardiovascular risk, necessitating improved risk stratification tools.

Purpose of the Study:

• To compare the performance of the PREVENT algorithm against established cardiovascular risk calculators (Framingham, pooled cohort equation, SCORE2).
• To evaluate risk stratification accuracy, particularly for individuals with chronic kidney disease and those categorized as moderate risk by standard methods.

Main Methods:

• A simulated cohort of adults aged 40-75 years was created, including individuals with normal and abnormal estimated glomerular filtration rates (eGFR).
• The study analyzed concordance and reclassification rates between PREVENT and comparator algorithms, focusing on moderate-risk classifications.

Main Results:

• PREVENT demonstrated a clear correlation between decreased eGFR and increased cardiovascular risk.
• Concordance with standard algorithms varied widely (6%-88%) when moderate risk was identified.
• PREVENT reclassified risk significantly: it assigned lower risk in 18%-88% of normal eGFR simulations and higher risk in 4%-94% of abnormal eGFR simulations.

Conclusions:

• The PREVENT algorithm substantially reclassifies cardiovascular risk, offering potential shifts in prevention strategies.
• A tendency for PREVENT to assign lower risk with normal eGFR might impact preventive therapy initiation.
• Healthcare systems should monitor the public health impact of changes in prevention practices driven by new risk algorithms.