To review the potential use of circulating cardiovascular biomarkers and dynamic risk prediction models to improve cardiovascular disease (CVD) risk estimation.
Key Findings:
Current cardiovascular risk estimation systems have moderate discriminative accuracy (c-index 0.67 to 0.84).
Static risk models may misclassify risk in specific subgroups, necessitating personalized approaches.
Dynamic risk prediction using biomarkers can enhance the accuracy of CVD risk estimates.
Interpretation:
Incorporating circulating biomarkers and dynamic modelling can lead to more personalized and effective CVD prevention strategies.
Limitations:
Current risk estimation systems have not significantly evolved over decades.
Static models may not accurately reflect individual risk due to biological variability.
Conclusion:
Future research should focus on developing multi-marker dynamic risk prediction models and assessing their public health impact and cost-effectiveness.
