Cardiovascular disease (CVD) is a group of common disorders affecting the heart and blood vessels and are among the top causes of death worldwide. According to WHO, they account for up to 30% of all deaths, of which 85% are caused by heart attacks or strokes. These common and acute events are often linked to other underlying problems in the heart or vessels. Heart rhythm disturbances such as atrial fibrillation (AF) is the most common heart arrhythmia and research suggest it is linked to ~20% of all strokes. In European countries management of AF accounts for up to 3% of the total health care expenses, and it is a growing problem predicted to affect 18 million people by 2050.
Doctors have improved predicting of what type of patient that might develop serious problems from CVD, such as strokes or heart attacks, and now have better medicines to prevent serious events. There is, however, still a lot of work to do. Some patients still have strokes when taking these medicines, and others face different issues such as bleeding or heart failure.
If we can improve our ability to predict who is at risk for certain problems caused by CVD, and which treatment will offer the best course for each individual patient, we can make a big difference in how long and how well they live.
Our goal is to improve the clinical prognosis and quality of life for people with CVD by finding new ways to detect patients at higher risk and selecting the right treatments. We aim to achieve this by using the medical records, genetic data, and images from patients in the UK-Biobank, to develop an artificial intelligence tool relying on a mix of well-known and more sophisticated mathematical and computer algorithm strategies. This tool will help us better manage and care for patients with CVD.
