Cardiovascular Risk Prediction Models and Scores in the Era of Personalized Medicine

Area of Science:

• Cardiology
• Preventive Medicine
• Public Health

Background:

• Cardiovascular disease (CVD) is a leading global cause of mortality.
• Effective management of hypertension and dyslipidemia is key to reducing CVD.
• Current guidelines necessitate accurate cardiovascular risk prediction for tailored treatment intensity.

Purpose of the Study:

• To review the accuracy of widely used cardiovascular risk prediction equations.
• To identify optimal risk prediction tools for cardiovascular disease prevention.
• To guide the selection of appropriate risk scores for patient stratification.

Main Methods:

• Systematic review of existing literature on cardiovascular risk prediction equations.
• Analysis of the validation and accuracy of various risk scores.
• Comparison of performance across different populations and settings.

Main Results:

• Many existing risk scores lack external validation.
• Several equations demonstrate over- or underestimation of risk in populations different from their derivation cohorts.
• Performance variability highlights challenges in universal applicability.

Conclusions:

• Cardiovascular risk prediction is essential for optimizing preventive strategies, especially in high-risk individuals without established CVD or type 2 diabetes mellitus (T2DM).
• The accuracy of current risk prediction equations is variable and often population-specific.
• Country-specific risk scores should be prioritized for accurate cardiovascular risk stratification when available.