Approved Research

Open Access Integrated Therapeutic and Diagnostic Platforms for Personalized Cardiovascular Medicine

Lay summary

Scientific rationale: Rare diseases such as familial hypercholesterolaemia and inherited heart diseases often go undiagnosed. People with these rare diseases can be identified using algorithms to screen electronic health records, including genetic data. Similarly, the future risk of cardiovascular events in patients, even with common forms of heart disease, can be estimated using algorithms applied to clinical and genetic data.

Aims: We aim to discover algorithms within UK Biobank data to identify patients with rare diseases such as familial hypercholesterolaemia and estimate the risk of future health outcomes. We also intend to use similar methods to identify patients with common forms of heart disease and predict future health events.

Project duration: 3 years.

Public health impact: Discovering algorithms to identify patients with rare and common heart diseases will help screen populations and prevent future health events. This would be beneficial not only to individuals who might have a missed diagnosis but also possibly improve the efficiency and lower the cost of overburdened hospital services.