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Simulating impaired left ventricular–arterial coupling in aging and disease: a systematic review.

Authors :
Ding, Corina Cheng Ai
Dokos, Socrates
Bakir, Azam Ahmad
Zamberi, Nurul Jannah
Liew, Yih Miin
Chan, Bee Ting
Md Sari, Nor Ashikin
Avolio, Alberto
Lim, Einly
Source :
BioMedical Engineering OnLine; 2/22/2024, Vol. 23 Issue 1, p1-36, 36p
Publication Year :
2024

Abstract

Aortic stenosis, hypertension, and left ventricular hypertrophy often coexist in the elderly, causing a detrimental mismatch in coupling between the heart and vasculature known as ventricular−vascular (VA) coupling. Impaired left VA coupling, a critical aspect of cardiovascular dysfunction in aging and disease, poses significant challenges for optimal cardiovascular performance. This systematic review aims to assess the impact of simulating and studying this coupling through computational models. By conducting a comprehensive analysis of 34 relevant articles obtained from esteemed databases such as Web of Science, Scopus, and PubMed until July 14, 2022, we explore various modeling techniques and simulation approaches employed to unravel the complex mechanisms underlying this impairment. Our review highlights the essential role of computational models in providing detailed insights beyond clinical observations, enabling a deeper understanding of the cardiovascular system. By elucidating the existing models of the heart (3D, 2D, and 0D), cardiac valves, and blood vessels (3D, 1D, and 0D), as well as discussing mechanical boundary conditions, model parameterization and validation, coupling approaches, computer resources and diverse applications, we establish a comprehensive overview of the field. The descriptions as well as the pros and cons on the choices of different dimensionality in heart, valve, and circulation are provided. Crucially, we emphasize the significance of evaluating heart−vessel interaction in pathological conditions and propose future research directions, such as the development of fully coupled personalized multidimensional models, integration of deep learning techniques, and comprehensive assessment of confounding effects on biomarkers. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1475925X
Volume :
23
Issue :
1
Database :
Complementary Index
Journal :
BioMedical Engineering OnLine
Publication Type :
Academic Journal
Accession number :
175633785
Full Text :
https://doi.org/10.1186/s12938-024-01206-2