Causes, distribution and consequences of valvular heart disease: analyses of large-scale datasets (Dr. Dexter Canoy and Prof Kazem Rahimi)
Causes, distribution and consequences of valvular heart disease: analyses of large-scale datasets
DESCRIPTION OF PROJECT
Valvular heart diseases (VHD) affect an increasing number of adults worldwide. Despite this, our knowledge of their burden, underlying causes and consequences is limited. Indeed, a large proportion of VHD is still considered to be 'degenerative' with no clear understanding of its causes and no established preventative strategies. Most previous research in this field has been based on small-scale mechanistic studies or cross-sectional studies with their inherent limitations. Recent accumulation of Big Data from routine health records (such as the UK Clinical Practice Research Datalink [CPRD]), registries and large-scale cohorts (such as the UK Biobank) provides an unprecedented opportunity to investigate the burden and determinants of VHD and help identify potentially modifiable risk factors. Early analyses from our group have shown highly promising results, for instance, by identifying blood pressure as a major risk factor for certain valvular conditions. The more recent imaging substudy of the UK Biobank, which involves cardiac MRI studies from up to 100,000 participants as well as genetic data, will enable more in-depth analyses of the causes of VHD. For this DPhil project, the student will acquire and use very large datasets from different sources to investigate the epidemiology of VHD. The specific focus of the studies to be undertaken will depend the student’s skills and interests. As a member of the team, the student will become involved in the design, conduct and interpretation of other related Big Data projects.
This research opportunity would be suitable for a candidate with a quantitative background (e.g. MSc in biostatistics, epidemiology or bioinformatics) with interest in public health and cardiovascular medicine. The project is also suitable for a student with medical background, in which case some experience with statistical packages (preferably R) and good understanding of epidemiological concepts would be a strong advantage. For medical doctors, learning opportunities include training in advanced statistical methods, epidemiology and usage of statistical packages such as R. For non-clinical candidates, learning opportunities involve training in epidemiological study designs as well as advanced machine learning techniques for interrogation of some of the world’s largest and most complex datasets to address questions of high relevance to public health globally.
This project will be part of a new interdisciplinary programme entitled ‘Deep Medicine’ at the George Institute for Global Health. The research team provides expert individual supervision and support from several of experienced and enthusiastic researchers with backgrounds in clinical medicine, statistics, epidemiology, computer science and informatics. Further support in grant writing, high-impact scientific publications and career development will be provided. As well as the specific training detailed above, students will have access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.