Advanced Methods for Clinical Outcome Prediction in Acquired Heart Disease

Abstract

Acquired heart disease, which includes conditions such as coronary artery disease (CAD) and heart failure, continues to pose a large impediment on the individuals that suffer from it as well as on society in general. CAD is the leading cause of death in the Western world, and the burden of CAD continues to rise in developing countries[1]. Heart failure is a chronic disease with frequent, costly re-hospitalizations[2]. The majority of heart failure cases results from CAD. Overall, it has been estimated that by the year 2020, nearly 20.5 million deaths worldwide will be due to cardiovascular disease. Predictive thinking plays a fundamental role in prevention of adverse cardiac events, and the improvement of our ability to make accurate predictions is one of the driving forces behind clinical research. Focusing on patients with acquired heart disease for prevention of recurrent cardiac events and mortality may contribute to efficient healthcare, because these patients are at high risk of needing medical attention and may benefit most from additional treatment. This thesis overarches several disciplines, including methodology of prediction modelling, laboratory assessment and cardiovascular imaging, in order to evaluate and improve clinical outcome prediction in patients with known, acquired heart disease.