Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling
Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling
Aortic stenosis (AS) affects about 1.5 million people in the United States and is associated with a 5-year survival rate of 20% if untreated. In these patients, aortic valve replacement is performed to restore adequate hemodynamics and alleviate symptoms. The development of next-generation prosthetic aortic valves seeks to provide enhanced hemodynamic performance, durability, and long-term safety, emphasizing the need for high-fidelity testing platforms for these devices. We propose a soft robotic model that recapitulates patient-specific hemodynamics of AS and secondary ventricular remodeling which we validated against clinical data. The model leverages 3D-printed replicas of each patient’s cardiac anatomy and patient-specific soft robotic sleeves to recreate the patients’ hemodynamics. An aortic sleeve allows mimicry of AS lesions due to degenerative or congenital disease, whereas a left ventricular sleeve recapitulates loss of ventricular compliance and diastolic dysfunction (DD) associated with AS. Through a combination of echocardiographic and catheterization techniques, this system is shown to recreate clinical metrics of AS with greater controllability compared with methods based on image-guided aortic root reconstruction and parameters of cardiac function that rigid systems fail to mimic physiologically. Last, we leverage this model to evaluate the hemodynamic benefit of transcatheter aortic valves in a subset of patients with diverse anatomies, etiologies, and disease states. Through the development of a high-fidelity model of AS and DD, this work demonstrates the use of soft robotics to recreate cardiovascular disease, with potential applications in device development, procedural planning, and outcome prediction in industrial and clinical settings.
Rosalia, Luca
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Ozturk, Caglar
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Goswami, Debkalpa
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Bonnemain, Jean
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Wang, Sophie X.
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Bonner, Benjamin
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Weaver, James C.
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Puri, Rishi
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Kapadia, Samir
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Nguyen, Christopher T.
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Roche, Ellen T.
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22 February 2023
Rosalia, Luca
e3f00c11-aa4f-4454-ba25-cd0fd5cfb20a
Ozturk, Caglar
70bbd3bd-fc56-48e8-8b5e-00d5270c1526
Goswami, Debkalpa
ff3ce96a-0cb5-427b-bdc9-5408965b4a37
Bonnemain, Jean
3af327cb-2132-4569-abba-6347a3f8b3f7
Wang, Sophie X.
f9873600-3507-4a6f-93b0-a96069aba629
Bonner, Benjamin
803cfba9-f63e-4e25-8f36-81ff89c9ef72
Weaver, James C.
a8234238-bb1c-4bc2-8a1b-5484a8bbd5bd
Puri, Rishi
3eae95a7-e8ab-4713-bef0-5ae9ce4a13d1
Kapadia, Samir
a7b0f41f-8477-4fe0-991f-2763bd72d5a6
Nguyen, Christopher T.
bd447bb3-25fa-4e85-a4b5-2b291bfa2b61
Roche, Ellen T.
63e632c8-d821-4c2f-a728-aaf331a5c2a1
Rosalia, Luca, Ozturk, Caglar, Goswami, Debkalpa, Bonnemain, Jean, Wang, Sophie X., Bonner, Benjamin, Weaver, James C., Puri, Rishi, Kapadia, Samir, Nguyen, Christopher T. and Roche, Ellen T.
(2023)
Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling.
Science Robotics, 8 (75), [eade2184].
(doi:10.1126/SCIROBOTICS.ADE2184).
Abstract
Aortic stenosis (AS) affects about 1.5 million people in the United States and is associated with a 5-year survival rate of 20% if untreated. In these patients, aortic valve replacement is performed to restore adequate hemodynamics and alleviate symptoms. The development of next-generation prosthetic aortic valves seeks to provide enhanced hemodynamic performance, durability, and long-term safety, emphasizing the need for high-fidelity testing platforms for these devices. We propose a soft robotic model that recapitulates patient-specific hemodynamics of AS and secondary ventricular remodeling which we validated against clinical data. The model leverages 3D-printed replicas of each patient’s cardiac anatomy and patient-specific soft robotic sleeves to recreate the patients’ hemodynamics. An aortic sleeve allows mimicry of AS lesions due to degenerative or congenital disease, whereas a left ventricular sleeve recapitulates loss of ventricular compliance and diastolic dysfunction (DD) associated with AS. Through a combination of echocardiographic and catheterization techniques, this system is shown to recreate clinical metrics of AS with greater controllability compared with methods based on image-guided aortic root reconstruction and parameters of cardiac function that rigid systems fail to mimic physiologically. Last, we leverage this model to evaluate the hemodynamic benefit of transcatheter aortic valves in a subset of patients with diverse anatomies, etiologies, and disease states. Through the development of a high-fidelity model of AS and DD, this work demonstrates the use of soft robotics to recreate cardiovascular disease, with potential applications in device development, procedural planning, and outcome prediction in industrial and clinical settings.
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Accepted/In Press date: 30 January 2023
Published date: 22 February 2023
Identifiers
Local EPrints ID: 490918
URI: http://eprints.soton.ac.uk/id/eprint/490918
ISSN: 2470-9476
PURE UUID: 85502699-d221-4ec3-8e1d-cfc1140d3427
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Date deposited: 07 Jun 2024 17:52
Last modified: 08 Jun 2024 02:11
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Contributors
Author:
Luca Rosalia
Author:
Caglar Ozturk
Author:
Debkalpa Goswami
Author:
Jean Bonnemain
Author:
Sophie X. Wang
Author:
Benjamin Bonner
Author:
James C. Weaver
Author:
Rishi Puri
Author:
Samir Kapadia
Author:
Christopher T. Nguyen
Author:
Ellen T. Roche
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