Erica Doutel
University of Porto,
Portugal
Abstract Title: HARMONY - Hemodynamic Analysis and Real-World Observation of Atherosclerosis Disease Progression Using Computational Fluid Dynamics
Biography:
E. Doutel, earned her PhD in Chemical Engineering from the University of Porto in 2016 with a focus on hemodynamics in the left coronary artery. Her research combines computational fluid dynamics, in vitro studies, and innovative methods for artificial artery production. She has established key collaborations with clinical teams, particularly the Cath Lab at Hospital de Braga, advancing knowledge transfer in cardiovascular research. With a robust publication record and experience leading multidisciplinary projects, she has also introduced innovative coursework at FEUP, bridging chemical engineering and health sciences. Currently, she supervises PhD and MSc theses in applied fluid mechanics.
Research Interests:
Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide [1], and understanding its progression is critical for improving patient outcomes. This study leverages real patient data from the Hemodynamics Service of Braga Hospital, to investigate the hemodynamic and morphological factors influencing the progression of atherosclerosis in the left main (LM) coronary artery. Two patients with lesions in the LM coronary artery were selected for analysis. Both underwent repeated invasive coronary angiographies (ICA), providing a unique longitudinal dataset. Patient A exhibited stable disease, with a stenosis at the bifurcation of 40-50% identified in 2018, which remained unchanged by 2024. In contrast, Patient B showed significant disease progression, with a distal stenosis increasing from 10-20% in 2012 to 70% in 2023. To analyze local hemodynamics, 3D reconstructions of the coronary bifurcations were performed and numerical simulations of local hemodynamics were analyzed using ANSYS Fluent®. Key hemodynamic and geometric parameters were extracted to identify factors associated with disease progression. This study’s novelty lies in its use of real-world, longitudinal data to explore atherosclerosis evolution over a decade. By analyzing changes in hemodynamic and morphological conditions, it aims to provide insights into the mechanisms driving coronary artery disease progression. The findings will inform the development of predictive models and algorithms, contributing to early detection and targeted interventions for a disease that poses a significant global health challenge.
