Positron Emission Tomography / Magnetic Resonance Imaging in Aortic Stenosis

Aortic stenosis is the most common valve disease requiring surgery in the Western world. It is defined by progressive calcification and fibrosis of the valve leaflets and restricted valve opening. This in turn exposes the heart muscle (left ventricle) to increasing pressure leading to heart muscle thickening (left ventricular hypertrophy, LVH) to normalise wall stress and maintain heart output (stroke volume). The only treatment available is relief of pressure overload by surgical or minimally invasive valve replacement (TAVI). Transthyretin (TTR) amyloidosis is a condition characterised by deposition of insoluble transthyretin protein (a small protein tetramer produced in the liver) in various tissues, predominantly in the heart. Although there are inherited forms caused by specific TTR gene mutations, most cases occur in older individuals with non-mutated TTR (wild-type). The finding of TTR plaques in elderly individuals is relatively common; in a post-mortem study 22-25% of patients over the age of 80 had evidence of cardiac amyloid deposition. However, there is significant progressive amyloid accumulation in a small percentage of individuals leading to heart muscle thickening and heart failure. No medical treatments are currently licensed although several agents are at advanced stages of clinical trials. As both the above conditions are increasingly common in the elderly population and characterised by increased heart muscle thickening, there is the potential for them to coexist unrecognised in individual patients. The prevalence of cardiac amyloidosis in clinical populations with significant aortic stenosis is not known however small series have estimated somewhere in the region of 6-29%. Other data have suggested that patients with aortic stenosis and concurrent cardiac amyloidosis have an adverse prognosis even despite AVR. It is therefore important to identify aortic stenosis patients with coexistent amyloidosis both in terms of predicting prognosis and be