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A soft robotic sleeve mimicking the haemodynamics and biomechanics of left ventricular pressure overload and aortic stenosis

A soft robotic sleeve mimicking the haemodynamics and biomechanics of left ventricular pressure overload and aortic stenosis
A soft robotic sleeve mimicking the haemodynamics and biomechanics of left ventricular pressure overload and aortic stenosis

Preclinical models of aortic stenosis can induce left ventricular pressure overload and coarsely control the severity of aortic constriction. However, they do not recapitulate the haemodynamics and flow patterns associated with the disease. Here we report the development of a customizable soft robotic aortic sleeve that can mimic the haemodynamics and biomechanics of aortic stenosis. By allowing for the adjustment of actuation patterns and blood-flow dynamics, the robotic sleeve recapitulates clinically relevant haemodynamics in a porcine model of aortic stenosis, as we show via in vivo echocardiography and catheterization studies, and a combination of in vitro and computational analyses. Using in vivo and in vitro magnetic resonance imaging, we also quantified the four-dimensional blood-flow velocity profiles associated with the disease and with bicommissural and unicommissural defects re-created by the robotic sleeve. The design of the sleeve, which can be adjusted on the basis of computed tomography data, allows for the design of patient-specific devices that may guide clinical decisions and improve the management and treatment of patients with aortic stenosis.

2157-846X
1134-1147
Rosalia, Luca
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Ozturk, Caglar
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Coll-Font, Jaume
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Fan, Yiling
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Nagata, Yasufumi
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Singh, Manisha
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Goswami, Debkalpa
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Mauskapf, Adam
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Chen, Shi
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Eder, Robert A.
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Goffer, Efrat M.
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Kim, Jo H.
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Yurista, Salva
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Bonner, Benjamin P.
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Foster, Anna N.
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Levine, Robert A.
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Edelman, Elazer R.
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Panagia, Marcello
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Guerrero, Jose L.
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Roche, Ellen T.
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Nguyen, Christopher T.
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Rosalia, Luca
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Ozturk, Caglar
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Coll-Font, Jaume
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Fan, Yiling
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Nagata, Yasufumi
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Singh, Manisha
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Goswami, Debkalpa
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Mauskapf, Adam
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Chen, Shi
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Eder, Robert A.
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Goffer, Efrat M.
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Kim, Jo H.
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Yurista, Salva
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Bonner, Benjamin P.
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Foster, Anna N.
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Levine, Robert A.
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Edelman, Elazer R.
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Panagia, Marcello
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Guerrero, Jose L.
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Roche, Ellen T.
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Nguyen, Christopher T.
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Rosalia, Luca, Ozturk, Caglar, Coll-Font, Jaume, Fan, Yiling, Nagata, Yasufumi, Singh, Manisha, Goswami, Debkalpa, Mauskapf, Adam, Chen, Shi, Eder, Robert A., Goffer, Efrat M., Kim, Jo H., Yurista, Salva, Bonner, Benjamin P., Foster, Anna N., Levine, Robert A., Edelman, Elazer R., Panagia, Marcello, Guerrero, Jose L., Roche, Ellen T. and Nguyen, Christopher T. (2022) A soft robotic sleeve mimicking the haemodynamics and biomechanics of left ventricular pressure overload and aortic stenosis. Nature Biomedical Engineering, 6 (10), 1134-1147. (doi:10.1038/s41551-022-00937-8).

Record type: Article

Abstract

Preclinical models of aortic stenosis can induce left ventricular pressure overload and coarsely control the severity of aortic constriction. However, they do not recapitulate the haemodynamics and flow patterns associated with the disease. Here we report the development of a customizable soft robotic aortic sleeve that can mimic the haemodynamics and biomechanics of aortic stenosis. By allowing for the adjustment of actuation patterns and blood-flow dynamics, the robotic sleeve recapitulates clinically relevant haemodynamics in a porcine model of aortic stenosis, as we show via in vivo echocardiography and catheterization studies, and a combination of in vitro and computational analyses. Using in vivo and in vitro magnetic resonance imaging, we also quantified the four-dimensional blood-flow velocity profiles associated with the disease and with bicommissural and unicommissural defects re-created by the robotic sleeve. The design of the sleeve, which can be adjusted on the basis of computed tomography data, allows for the design of patient-specific devices that may guide clinical decisions and improve the management and treatment of patients with aortic stenosis.

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nihms-1833655 - Accepted Manuscript
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Accepted/In Press date: 12 August 2022
Published date: 26 September 2022

Identifiers

Local EPrints ID: 490858
URI: http://eprints.soton.ac.uk/id/eprint/490858
ISSN: 2157-846X
PURE UUID: 77d7c0ef-4e4a-4856-abf9-1ac1309614d6
ORCID for Caglar Ozturk: ORCID iD orcid.org/0000-0002-3688-0148

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Date deposited: 07 Jun 2024 16:35
Last modified: 07 Sep 2024 02:11

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Contributors

Author: Luca Rosalia
Author: Caglar Ozturk ORCID iD
Author: Jaume Coll-Font
Author: Yiling Fan
Author: Yasufumi Nagata
Author: Manisha Singh
Author: Debkalpa Goswami
Author: Adam Mauskapf
Author: Shi Chen
Author: Robert A. Eder
Author: Efrat M. Goffer
Author: Jo H. Kim
Author: Salva Yurista
Author: Benjamin P. Bonner
Author: Anna N. Foster
Author: Robert A. Levine
Author: Elazer R. Edelman
Author: Marcello Panagia
Author: Jose L. Guerrero
Author: Ellen T. Roche
Author: Christopher T. Nguyen

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