Tunable, soft robotics-enabled patient-specific hydrodynamic model of aortic stenosis and secondary ventricular remodeling

Aortic stenosis (AS) affects approximately 1.5 million people in the US 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 of high-fidelity testing platforms for these devices. We propose a soft robotic model of AS capable of recapitulating patient-specific hemodynamics of AS and secondary ventricular remodeling, validated against clinical data. The model leverages 3D printed replicas of each patients 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, while a left ventricular sleeve recapitulates loss of ventricular compliance, and impaired filling 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 to methods based on image-guided aortic root reconstruction, and parameters of cardiac function which rigid systems fail to mimic physiologically. Finally, we demonstrate the use of this model for the evaluation 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 secondary remodeling, this work pioneers the use of patient-specific soft robotic platforms of cardiovascular disease, with potential application in device development, procedural planning, and outcome prediction in industrial and clinical settings. One Sentence SummaryA high-fidelity, soft robotics-driven model recreates patient-specific biomechanics and hemodynamics of cardiovascular disease.