IMPACT OF AORTIC PROSTHETIC HEART VALVE DYSFUNCTION ON LEFT VENTRICULAR AFTERLOAD AND ON THE ACCURACY OF ECHO-DOPPLER MEASUREMENTS

摘要

Left heart side (left ventricle and left atrium) is responsible for delivering the oxygenated blood to all body organs, where a relatively strong left ventricle contraction is needed to deliver around 5 liters of blood per minute. As a consequence, the left heart side experiences a high pressure (～150 mmHg). Therefore, the dysfunction (stenosis or incompetence) in the aortic and/or mitral heart valves in the left side of the heart is more common than the dysfunction in the pulmonary and tricuspid heart valves in the right side of the heart (Yoganathan et al, 2004). Heart valve surgical replacement is the most effective solution in severe functional heart valve disease (Pibarot and Dumesnil, 2009). Almost, half of the total implants of prosthetic heart valves (～300,000) are mechanical (mainly bileaflet). In case of mechanical heart valve (MHV), a lifelong anti-coagulant should be taken to avoid thromboembolic events. Despite the significant improvement in valve design resulting in minimizing prosthetic valve complications (thromboembolic events or pannus formation), these complications are still possible with MHV Implantation. It is worth noting that mechanical heart valve dysfunction is acute and lethal. Therefore, an accurate early detection and treatment of such problem is essential. Although Doppler echocardiography is the current method of choice for evaluating the performance of MHVs, the current clinical diagnosis technique using transthoracic echocardiography (TTE) has failed, in many cases, to detect prosthetic valve dysfunction (Aoyagi et al, 2000). The scope of the current study is to explore the impact of stenotic bileaflet mechanical heart valve on heart hemodynamics (i.e. pressure gradient, effective orifice area (EOA), left ventriclular afterload) and also, to clarify the reasons for TEE failure in detecting such problem. The problem was approached using in-silico, in-vitro and a lumped parameter model.