Improved pulse wave analysis for diagnosing and monitoring heart failure

Abstract

Heart failure is treatable, but in the United Kingdom, the one-, five- and ten-year mortality rates are 24.1, 54.5 and 75.5%, respectively. The poor prognosis reflects, in part, the lack of specific, simple and affordable diagnostic techniques: the disease is often advanced by the time a diagnosis is made. Moreover, 79% of UK heart failure diagnoses are made only after an emergency hospital admission despite 41% of patients visiting their GP in the preceding five years with at least one the main symptoms of heart failure – breathlessness, fatigue or ankle swelling. The survival rates following a diagnosis in primary care are, as expected, better than the average, but there are clear missed opportunities for early diagnosis and intervention. There is also a significant disconnect between clinical guidelines and actual patient diagnoses: between 2010–2013, only 24% of UK patients were diagnosed according to the pathway recommended by the European Society of Cardiology. All of these factors necessitate an improved diagnosis pipeline. Arterial waves carry information about the performance of the heart and vessels, and previous studies have demonstrated that certain metrics derived from pressure–velocity-based wave intensity analysis are significantly altered in the presence of heart failure. The pressure wave- form, however, can only be obtained accurately using invasive methods, which has inhibited clinical adoption. This thesis investigates the potential for a new form of wave intensity – based instead on noninvasive ultrasound measurements of arterial diameter and velocity – to improve the heart failure diagnosis pipeline. Diameter and pressure are intrinsically related, but this relationship is fundamentally nonlinear, as the arterial wall exhibits properties such as viscoelasticity and strain stiffening. Through one- 5 dimensional computational modelling of blood flow in a virtual population of 600 people, this thesis has shown the two forms of wave intensity are similar regardless of these nonlinearities. Using a second virtual population of 2000 people – with half modelled as healthy controls and half with heart failure through an impaired stroke volume – this thesis has also shown that, when analysed with machine learning-based classification techniques, metrics derived from this diameter-based wave intensity can identify heart failure in individual patients with 99% recall and 95% precision. Computational results must be corroborated with experimental findings before they can be truly accepted. This thesis also, therefore, describes a first-in-man trial where ultrafast ultrasound-based measurements of wave intensity were made in controls and heart failure patients. Statistically significant differences were identified in multiple wave intensity metrics representative of both systolic and diastolic function; the results are promising regarding the introduction of noninvasive wave intensity to the clinic.