Dynamic risk estimation systems to better predict and prevent cardiovascular disease

Cardiovascular risk scores are imperfect and may result in the unnecessary prescription of life-long therapies in some and failure to recognize and treat others at increased risk of events. New approaches are required to improve cardiovascular risk estimates for use in primary care.

Currently, all cardiovascular risk models are static and expected to predict risk at 10-years from a single baseline assessment. They do not recognize that risk factors change or reflect the impact of preventative measures on risk.

I hypothesize that the trajectory of individual risk factors will provide additional prognostic information with dynamicity more accurately quantifying risk compared to static models.

Using a combination of harmonized general population data sets with repeated measures, novel advanced modelling techniques and incorporation of traditional risk factors alongside newer circulating biomarkers, I will develop a dynamic risk estimation system for the primary prevention of cardiovascular disease.

Using routine care data, I will validate this approach for use in practice and develop the data infrastructure needed to address current inequalities in risk assessment and enable future implementation studies.

I anticipate that a dynamic risk estimation system will provide better individualised estimates of risk and could help to prevent cardiovascular events.