Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling
Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling
复制标题
DOI：
10.1126/scirobotics.ade2184
复制
发表时间：
2023-02-22
期刊：
影响因子：
25
通讯作者：
Roche，Ellen T.
中科院分区：
文献类型：
--
作者：
Rosalia，Luca;Ozturk，Caglar;Roche，Ellen T.
主动脉瓣狭窄（AS）影响美国约150万人，如果不治疗，5年生存率为20%。在这些患者中，进行主动脉瓣置换术以恢复足够的血流动力学并缓解症状。下一代人工主动脉瓣的开发旨在提供增强的血流动力学性能、耐用性和长期安全性，强调这些器械需要高保真测试平台。我们提出了一个软机器人模型，概括了患者特定的血流动力学的AS和继发性心室重构，我们对临床数据进行了验证。该模型利用每个患者心脏解剖结构的3D打印复制品和患者特定的软机器人袖子来重建患者的血液动力学。主动脉袖可以模拟退行性或先天性疾病引起的AS病变，而左心室袖则重现了与AS相关的心室顺应性丧失和舒张功能障碍（DD）。通过结合超声心动图和导管插入技术，该系统被证明是重建AS的临床指标具有更大的可控性相比，基于图像引导的主动脉根重建和心脏功能参数的刚性系统无法模仿生理的方法。最后，我们利用该模型评价经导管主动脉瓣在具有不同解剖结构、病因和疾病状态的患者子集中的血流动力学受益。通过开发AS和DD的高保真模型，这项工作展示了使用软机器人技术重建心血管疾病，在工业和临床环境中的设备开发，程序规划和结果预测中具有潜在的应用。
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.