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Heart failure with preserved ejection fraction (HFpEF) is a major challenge in cardiovascular medicine, accounting for ≈50% of all cases of heart failure. Despite the ongoing efforts, no medical device has yet received FDA approval. This is largely due to the lack of an in vivo model of the HFpEF hemodynamics, resulting in the inability to evaluate device effectiveness in vivo prior to clinical trials. Here, the development of a highly tunable porcine model of HFpEF hemodynamics is described using implantable soft robotic sleeves, where controlled actuation of a left ventricular and an aortic sleeve can recapitulate changes in ventricular compliance and afterload associated with a broad spectrum of HFpEF hemodynamic phenotypes. The feasibility of the proposed model in preclinical testing is demonstrated by evaluating the hemodynamic response of the model post‐implantation of an interatrial shunt device, which is found to be consistent with findings from in silico studies and clinical trials. This work overcomes limitations of prior HFpEF models, such as low hemodynamic accuracy, high costs, and long development phases. The versatile and adjustable platform introduced can transform HFpEF device development, aiming to enhance the lives of the 32 million people affected globally.

 

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.