Echocardiography in the Detection and Monitoring of Heart Failure

Echocardiography in the Detection and Monitoring of Heart Failure

Zamorano JoseL
European Cardiovascular Disease 2006
Published: June 2006
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Heart failure (HF) is a growing and increasingly important chronic disease of the Western world, occurring in at least 2% of the adult population and rising to 3% in those aged over 75 years. It is characterised by inadequate systemic perfusion due to impairment of the cardiac pump function. Clinical HF is a progressive condition, typically with high morbidity and mortality rates. It therefore places a significant burden on healthcare resources. One of the keys to reducing the mortality, morbidity, and cost of HF is accurate and early diagnosis of left ventricular systolic dysfunction (LVSD). This is essential for successfully addressing underlying diseases or causes and in selection of appropriate therapies.

According to the recently released American College of Cardiology (ACC)/American Heart Association (AHA) guidelines for the diagnosis and management of HF, transthoracic echocardiography is “The single most useful diagnostic test in the evaluation of patients with HF…” As therapeutic techniques such as cardiac resynchronisation therapy (CRT) gain clinical acceptance, new echocardiographic imaging modalities such as live 3-D imaging and specialised analysis tools for dynamically assessing regional volumes and differences in timing of contraction promise to play an expanding role in the management of HF. This article discusses these and other new imaging and analysis technologies in echocardiography that are directed at improving accuracy and reproducibility in HF assessment and monitoring.

Aetiology and Prognosis in HF

The most common cause of HF is LVSD (approximately 60% of patients). In this category, most cases are a result of end-stage coronary artery disease (CAD), either with a history of myocardial infarction (MI), chronically underperfused yet viable myocardium or a combination of the two. Other disease processes that can lead to HF include valvular heart disease, congenital heart disease, diabetes, hypertensive disease and idiopathic and toxic (e.g. alcohol-induced) cardiomyopathies. Less common causes include viral infections of the heart muscle, thyroid disorders, disorders of the heart rhythm, or a combinations of these.

HF associated with LVSD is characterised by progressive structural change in the LV known as remodelling. While the disease progresses, myocyte hypertrophy and elongation gives rise to LV dilatation and hypertrophy. In this situation, stroke volume is increased without an actual increase in ejection fraction (EF). This results in increased wall tension impaired subendocardial myocardial perfusion, and may provoke ischaemia. While this dilatation progresses, separation of the valve leaflets can lead to mitral and tricuspid regurgitation. This may further diminish the cardiac output and increase end-systolic volumes and ventricular wall stress, therefore leading to further dilation, pulmonary congestion and myocardial dysfunction.

LV volumes and EF are therefore important prognostic indicators for morbidity and mortality in HF patients. 5

Echocardiography in the Diagnosis and Monitoring of HF

Echocardiographic Analysis of EF

Measurement of EF typically uses manual planimetry of 2-D areas according to Simpson’s single plane or bi-plane method of disks. This method can be time-consuming and has been shown to exhibit inaccuracies when compared with the gold standard of magnetic resonance imaging (MRI). Chief sources of inter- and intra-observer variability derive from inconsistent acquisition methods or image plane selection and subjective boundary definition by readers. Variable image quality, particularly in technically difficult patients, may further exacerbate many of these inaccuracies. A further important source of error lies in the geometric assumptions used in the calculation of volumes from 2-D data.

Several new imaging technologies have recently emerged that can significantly improve the speed, reproducibility and accuracy of EF measurements. These can be divided into four categories based on the benefits they offer and are summarised in Table 1.

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