Principal Investigator: Dr Fu Siong Ng
Institution: Imperial College LondonTags: 48666, Arrhythmia, cardiometabolic, remodelling, Risk Stratification, sudden cardiac death, ventricle
Our research has two aims. Firstly, to study the electrical activity of the heart in individuals that have a higher risk of heart rhythm disturbance, called “arrhythmia”. Secondly, to establish if we can develop methods to accurately predict which individuals are at increased risk of arrhythmia using existing demographic and clinical information. We know that medical problems associated with heart disease, such as obesity and diabetes, are becoming increasingly common and can cause arrhythmia. In some instances, these can be life-threatening. Our ability to identify patients at risk of arrhythmia has been limited to standard 12-lead electrical recordings (ECG). Using the data from the UK BioBank, we aim to conduct more detailed investigation to establish links between clinical measurements and the heart’s structure and electrical activity. Using a method called machine learning, we also aim to develop ways to predict patients’ current and/or future risk of arrhythmia. We will do this alongside another ongoing study, in which we will use a more sophisticated 252-lead ECG and combine it with heart scans (also called ECGI), to better understand how common medical conditions affect the heart’s electrical activity. This study will therefore improve our understanding of how demographic and clinical factors affect or influence the electrical activity of the heart and guide future research.