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Rosalia L, Ozturk C, Goswami D, Bonnemain J, Wang SX, Bonner B, Weaver JC, Puri R, Kapadia S, Nguyen CT, Roche ET. Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling. Sci Robot 2023; 8:eade2184. [PMID: 36812335 PMCID: PMC10280738 DOI: 10.1126/scirobotics.ade2184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 01/30/2023] [Indexed: 02/24/2023]
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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Affiliation(s)
- Luca Rosalia
- Health Sciences and Technology Program, Harvard–Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA, USA
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Caglar Ozturk
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Debkalpa Goswami
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Health Sciences and Technology, ETH-Zürich, Zürich, Switzerland
- Institute of Robotics and Intelligent Systems, ETH-Zürich, Zürich, Switzerland
| | - Jean Bonnemain
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Adult Intensive Care Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sophie X. Wang
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Benjamin Bonner
- Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA, USA
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - James C. Weaver
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Rishi Puri
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Samir Kapadia
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Christopher T. Nguyen
- Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA, USA
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
- Cardiovascular Innovation Research Center, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ellen T. Roche
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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