Dr Ramesh Nadarajah qualified in medicine at the University of Cambridge and obtained a MA (Hons) in neuroscience. He worked as a junior doctor in London he has undertaken speciality training in Cardiology, most recently at Leeds General Infirmary. After becoming a member of the Royal College of Physicians he was awarded a clinical research training fellowship by the British Heart Foundation and PhD enrichment scheme award from the Alan Turing Institute to complete a PhD in the use of machine learning to predict incident atrial fibrillation using national linked health record datasets. The algorithm developed in this PhD (FIND-AF) is now being tested in a BHF-funded prospective clinical validation.

Ramesh’s research incorporates the use of large routinely-collected datasets to answer clinical questions and develop and validate prediction models, particularly in the area of atrial fibrillation, heart failure and stroke. For his work Ramesh has worn national and international prizes, and he is a member of the British Cardiovascular Society Royal College of Physicians of London and European Society of Cardiology.

About Ramesh’s Big Data for Complex Disease Fellowship project

Heart failure (HF) is a condition consisting of symptoms (e.g. breathlessness, ankle swelling), clinical signs, and structural and/or functional abnormality of the heart. As many cases of HF are diagnosed each year in the NHS as the four most common causes of cancer combined, and HF has a worse prognosis than each of bladder, prostate and breast cancer. Heart failure is split into two types; where the heart is weak (heart failure with reduced ejection fraction) and where the heart does not relax enough to pump effectively (heart failure with preserved ejection fraction, HFpEF). HFpEF is becomingly increasingly common, and is forecast to become the most common type of HF. However, little is known about the scale of how many people have HFpEF in the UK, and how often HFpEF causes hospital admission or death after diagnosis. Also, many of these patients have delays to diagnosis, even after presenting to their GP with symptoms consistent with HFpEF.

This Fellowship aims to use whole population, national linked health data to investigate how common HFpEF is, how people do after diagnosis, and whether inequalities affect this. It will also seek to develop a prediction model for HFpEF to help identify people with HFpEF earlier, in the primary care setting, so they can get the right treatment earlier.