Non-invasive diagnostics of blockage growth in the descending aorta-computational approach

Med Biol Eng Comput. 2022 Nov;60(11):3265-3279. doi: 10.1007/s11517-022-02665-2. Epub 2022 Sep 27.

Abstract

Atherosclerosis causes blockages to the main arteries such as the aorta preventing blood flow from delivering oxygen to the organs. Non-invasive diagnosis of these blockages is difficult, particularly in primary healthcare. In this paper, the effect of arterial blockage development and growth is investigated at the descending aorta on some possible non-invasive assessment parameters including the blood pressure waveform, wall shear stress (WSS), time-average WSS (TAWSS) and the oscillation shear index (OSI). Blockage severity growth is introduced in a simulation model as 25%, 35%, 50% and 65% stenosis at the descending aorta based on specific healthy control aorta data clinically obtained. A 3D aorta model with invasive pulsatile waveforms (blood flow and pressure) is used in the CFD simulation. Blockage severity is assessed by using blood pressure measurements at the left subclavian artery. An arterial blockage growth more than 35% of the lumen diameter significantly affects the pressure. A strong correlation is also observed between the ascending aorta pressure values, pressure at the left subclavian artery and the relative residence time (RRT). An increase of RRT downstream from the stenosis indicates a 35% stenosis at the descending aorta which results in high systolic and diastolic pressure readings. The findings of this study could be further extended by transferring the waveform reading from the left subclavian artery to the brachial artery.

Keywords: Atherosclerosis; CFD; Carreau-Yasuda model; Oscillatory shear index; Relative residence time.

MeSH terms

  • Aorta, Thoracic* / diagnostic imaging
  • Blood Flow Velocity / physiology
  • Computer Simulation
  • Constriction, Pathologic
  • Hemodynamics* / physiology
  • Humans
  • Models, Cardiovascular
  • Oxygen
  • Stress, Mechanical

Substances

  • Oxygen