학술논문

RF02 ANOMALOUS AORTIC CORONARY ARTERY ORIGIN: A PARAMETRIC STRUCTURAL FINITE ELEMENT ANALYSIS AND COMPUTATIONAL SIMULATION.
Document Type
Academic Journal
Source
Journal of Cardiovascular Medicine. Nov 01, 2018 19 Suppl 2: Abstracts of the XIX Meeting of the Società Italiana di Chirurgia Cardiaca, Rome, November 23rd - 25th, 2018:e74-e75
Subject
Language
English
ISSN
1558-2027
Abstract
BACKGROUND AND AIM:: In anomalous aortic origin of coronary arteries (AAOCA) underlying ischemic mechanism during high stress conditions have still to be elucidated. The study aims to understand the biomechanics of such a condition and in particular how the lumen of the AAOCA may narrow during aortic expansion. METHODS:: We create a parametric computer-aided designed (CAD) model of the aortic root and AAOCA origin and perform a static finite element analysis (FEA) on 10 models with different angles of take-off and intramural penetration at three different loading conditions (i.e. stress condition). The model is defined by twenty-three parameters describing aortic root geometry and allows to vary coronary position, take-off angle, degree of intramural penetration, and length of the intramural course to simulate the pathological conditions of AAOCA. RESULTS:: We show that the AAOCAs experience a reduced luminal expansion (LE) (LE = 1.85%, 5.98%, 10.42%) compared to the healthy ones (LE = 11.9%, 22.43%, 36.66%,) at 120-150-180 mmHg of aortic root pressure respectively. Acute angles of take-off lead to elongated ostia, with an eccentricity (e) that increases with aortic expansion (i.e. e = 0.72-0.69-0.66 at 120-150-180 mmHg, take-off angle = 35’ and 50% penetration). CONCLUSIONS:: The study describes for the first time an idealized geometrical model as a proof of concept for static structural finite element simulations to assess the role of specific geometrical features on AAOCA physiopathology. The possible mechanism of lumen reduction in AAOCA can rely on biomechanical reasons and computational simulations may allow patient specific predictive model in future.