Detection of Coronary Artery Disease With Micro Electro-Mechanical Sensors

Heart diseases are the most common cause of morbidity and mortality in the western world. Coronary artery disease (CAD) alone is estimated to affect 110 million people globally and resulted in 8.9 million deaths in 2015. A hallmark phenomenon in the development of CAD is the formation of arterial stenoses eventually limiting the arterial circulation. Myocardial ischemia caused by the stenoses may present symptoms considered typical, i.e, angina pectoris chest pain, but the symptoms may vary up to an asymptomatic state. Estimating the pre-test likelihood of angiographically significant CAD (≥50% diameter stenotic CAD) is a fundamental component in the initial evaluation of symptomatic patients presenting with suspected CAD. This determination directly influences subsequent decisions for noninvasive diagnostic testing and treatment. However, studies have shown a relatively low prevalence of either ischemia or obstructive CAD on noninvasive imaging and invasive angiography (IA) in this population. Therefore, additional methods are needed to improve patient selection for such testing. Mechanocardiography (MCG) assesses the condition of the heart by measuring the mechanical activity (cardiac muscle motion) of the heart from the surface of thorax. MCG can be measured with accelerometer and gyroscope which react to recoil and vibration caused my cardiac contraction. This study aims to validate how the gyroscope and accelerometer derived parameters can identify patients with hemodynamically significant CAD in combined contrast computed tomography (CT) coronary angiography and positron emission tomography (PET) perfusion imaging in combination of high-sensitive troponin testing. The performance of different MCG algorithms will be tested offline as a head-to-head comparison with medical history, cardiovascular events, high-sensitive troponin values, coronary computed tomography angiography (CCTA) and PET scan results.