Comparative Review of Cardiovascular Disease Risk Models: Bridging Evidence and Clinical Practice

Abstract

The reviewed file provides a comprehensive comparative analysis of cardiovascular disease (CVD) risk
prediction models, synthesizing evidence from over 100 studies and evaluating models' clinical
performance, validation, and practical application across diverse settings. Cardiovascular disease remains
the leading global cause of morbidity and mortality, making risk stratification vital for preventive care. The
evolution in risk assessment has progressed from simple risk factor identification, exemplified by the
foundational Framingham Risk Score, to advanced multivariable models such as QRISK3, SCORE2, ASCVD
Pooled Cohort Equations, Reynolds Risk Score, PROCAM, and models developed specifically for country or
region-specific populations. Innovations extend to recent models like PREVENT and machine learning-driven
approaches, which integrate novel biomarkers, imaging data, and socio-demographic factors for improved
risk stratification.
The review highlights that while established models generally demonstrate similar discrimination (C-
statistics between 0.70 and 0.85), calibration and applicability vary significantly between populations,
necessitating recalibration or development of tailored models for local contexts. Recent models also
emphasize lifetime risk, integrate non-traditional risk factors, and deploy artificial intelligence to identify
complex patterns, though challenges persist regarding transparency, equity, and generalizability. The
paper underscores the need for continuous validation, recalibration, and inclusive model development to
address disparities, as well as effective integration of risk assessment into electronic health records and
clinical workflows. The ultimate goal of refined risk modeling remains to better guide preventive
interventions, inform clinical guidelines, and reduce the global burden of CVD through targeted
identification and management of high-risk individuals.