The measurement of healthcare outcomes is now a routine part of everyday medical practice. Although it originated in the 1850s through pioneers such as Florence Nightingale and Ernest Armory Codman, the main period of progression has been seen over the last twenty years. The most significant changes have involved large scale collection of simple mortality and morbidity data, through which quality improvement programmes have enabled major improvements in outcomes for most medical and surgical specialties. In the 21st century, there is demand for similar improvements to continue, which has proven challenging. In an attempt to satisfy this demand, a number of groups are promoting the routine use of more advanced widespread outcome measures for different disease states. In order to create frameworks for such outcome measures, evidence for the specific measures which are of benefit, are required for each disease state.

In this thesis, I explored the feasibility of three emerging outcome measures in a cohort of patients with proximal aortic disease: 1) measurement of longitudinal health-related quality of life, 2) measurement of physical activity and sleep with wrist-worn biosensors, and 3) assessment of flow patterns in the thoracic aorta using magnetic resonance imaging and computational fluid dynamics. In each of these areas, a prospective study was performed to assess the performance of these measures. In summary, there were three key findings from this work. Firstly, longitudinal measurement of health-related quality of life is not only feasible, but it provides significantly more value than morbidity data alone. Policy makers should seriously consider collecting such data longitudinally on a national basis to help better discriminate quality. Secondly, measurement of wrist-worn physical activity data has the potential to predict complications and could act as a surrogate for health-related quality of life, which is sometimes challenging to collect. Policy makers should also consider collecting such data at a national level. Lastly, computational fluid dynamics assessment of the thoracic aorta is an exceptionally powerful tool to assess abnormal blood flow patterns. Whilst further work is required, it is close to being mature enough for use in everyday clinical practice, where it may support the diagnosis, management, and follow-up of patients with thoracic aortic disease.