Soft Robotic Platforms for the Simulation of Cardiovascular Disease and Device Development
The advancement of safe and effective medical devices critically hinges on the utilization of high-fidelity models of human disease. The closer these models emulate the intricacy of pathophysiological phenomena driving diseases and their effects, the greater their potential impact on clinical medici...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/152765 https://orcid.org/0000-0002-7214-7859 |
| Summary: | The advancement of safe and effective medical devices critically hinges on the utilization of high-fidelity models of human disease. The closer these models emulate the intricacy of pathophysiological phenomena driving diseases and their effects, the greater their potential impact on clinical medicine. This thesis delves into the exploration of soft robotics in disease modeling, a promising avenue for faithfully replicating the biomechanical aspects of human disease in a patient-specific manner. In addition to supporting the development and evaluation of medical devices, high-fidelity models have the potential to elucidate pathophysiological mechanisms of disease onset that are not yet fully understood, support personalized clinical decisions and interventions, and be utilized as pedagogical tools for medical education and training. This thesis will demonstrate the efficacy of soft robotics in recapitulating cardiovascular diseases, specifically aortic stenosis (AS) and heart failure with preserved ejection fraction (HFpEF), affecting over 40 million people worldwide.
This work first describes the development of a tunable and biomimetic soft robotic aortic sleeve that can re-create the anatomy and hemodynamics of AS with high fidelity. In a preclinical swine model of AS and through a combination of invasive monitoring and 4-dimensional flow imaging techniques, this thesis demonstrates the ability of the aortic sleeve to recapitulate clinically relevant hemodynamics of AS and mimic the complex transvalvular blood flow patterns associated with this disease. The utility of the aortic sleeve in the re-creation of patient-specific AS hemodynamics is showcased using a 3D-printed hemodynamic in vitro model of AS. Through the miniaturization of the soft robotic aortic sleeve, this work then describes the development of a chronic small animal model of AS, which was leveraged to investigate ventricular remodeling and plasticity due to the cessation of the biomechanical stimuli induced by the aortic sleeve.
This thesis then presents the design and development of a soft robotic cardiac sleeve that can tunably recapitulate loss of cardiac compliance associated with HFpEF. In a patient-specific in vitro model of ventricular remodeling and, separately, in an acute porcine model, this work demonstrates that cardiac filling capacity can be finely modulated by varying the actuation level of the cardiac sleeve, enabling the recapitulation of the hemodynamic aberrations of HFpEF. The computational platforms, including lumped-parameter, finite element, and computational fluid dynamic models leveraged for the design and optimization of these soft robotics-driven models of AS and HFpEF will be described.
The final part of this thesis demonstrates the utility of these platforms for the design and evaluation of device-based solutions for AS and HFpEF. Specifically, it describes an in silico study focused on designing a pulsatile-flow mechanical circulatory support device for HFpEF, an in vitro investigation of patient-specific hemodynamics following TAVR implantation, as well as a demonstration of the use of the soft robotic porcine model of HFpEF hemodynamics for medical device testing.
By elucidating the first applications of soft robotics in disease modeling, this dissertation holds the potential for profound impact by enhancing our understanding of disease mechanisms, aiding in medical education, and offering potential for innovative and personalized therapeutic solutions for patients with AS and HFpEF. In the future, it may propel the evolution of soft robotic models for other cardiovascular conditions and beyond the cardiovascular field, catalyzing further advancements in preclinical and translational research. |
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