Effects of Choice of Medical Imaging Modalities on a Non-invasive Diagnostic and Monitoring Computational Framework for Patients With Complex Valvular, Vascular, and Ventricular Diseases Who Undergo Transcatheter Aortic Valve Replacement.

computational model computed tomography diagnostic tool doppler echocardiography global hemodynamics local hemodynamics workload

Journal

Frontiers in bioengineering and biotechnology
ISSN: 2296-4185
Titre abrégé: Front Bioeng Biotechnol
Pays: Switzerland
ID NLM: 101632513

Informations de publication

Date de publication:
2021
Historique:
received: 18 12 2020
accepted: 19 04 2021
entrez: 26 7 2021
pubmed: 27 7 2021
medline: 27 7 2021
Statut: epublish

Résumé

Due to the high individual differences in the anatomy and pathophysiology of patients, planning individualized treatment requires patient-specific diagnosis. Indeed, hemodynamic quantification can be immensely valuable for accurate diagnosis, however, we still lack precise diagnostic methods for numerous cardiovascular diseases including complex (and mixed) valvular, vascular, and ventricular interactions (C3VI) which is a complicated situation made even more challenging in the face of other cardiovascular pathologies. Transcatheter aortic valve replacement (TAVR) is a new less invasive intervention and is a growing alternative for patients with aortic stenosis. In a recent paper, we developed a non-invasive and Doppler-based diagnostic and monitoring computational mechanics framework for C3VI, called C3VI-DE that uses input parameters measured reliably using Doppler echocardiography. In the present work, we have developed another computational-mechanics framework for C3VI (called C3VI-CT). C3VI-CT uses the same lumped-parameter model core as C3VI-DE but its input parameters are measured using computed tomography and a sphygmomanometer. Both frameworks can quantify: (1) global hemodynamics (metrics of cardiac function); (2) local hemodynamics (metrics of circulatory function). We compared accuracy of the results obtained using C3VI-DE and C3VI-CT against catheterization data (gold standard) using a C3VI dataset (

Identifiants

pubmed: 34307316
doi: 10.3389/fbioe.2021.643453
pmc: PMC8297508
doi:

Types de publication

Journal Article

Langues

eng

Pagination

643453

Informations de copyright

Copyright © 2021 Baiocchi, Barsoum, Khodaei, de la Torre Hernandez, Valentino, Dunford, MacDonald and Keshavarz-Motamed.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Melissa Baiocchi (M)

Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada.

Shirley Barsoum (S)

Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada.

Seyedvahid Khodaei (S)

Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada.

Jose M de la Torre Hernandez (JM)

Hospital Universitario Marques de Valdecilla, IDIVAL, Santander, Spain.

Sydney E Valentino (SE)

Department of Kinesiology, McMaster University, Hamilton, ON, Canada.

Emily C Dunford (EC)

Department of Kinesiology, McMaster University, Hamilton, ON, Canada.

Maureen J MacDonald (MJ)

Department of Kinesiology, McMaster University, Hamilton, ON, Canada.

Zahra Keshavarz-Motamed (Z)

Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada.
School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.
School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada.

Classifications MeSH