Improving cardiovascular risk prediction to support treatment decisions
Cardiovascular disease (CVD) is a major health concern that continues to grow. CVD is already responsible for more deaths globally, than any other disease and the huge burden it places upon healthcare systems and society is predicted to become even greater.
30 % of cardiovascular disease associated mortality occurs in individuals without elevated, conventional risk factors. Therefore, there is a clinical need to expand the number of available diagnostic tools for evaluating an individual’s risk of CVD.1