Bailoor Shantanu, Seo Jung-Hee, Schena Stefano, Mittal Rajat
Department of Mechanical Engineering, The Johns Hopkins University, Baltimore, MD, United States.
Division of Cardiac Surgery, Johns Hopkins Medical Institute, Baltimore, MD, United States.
Front Physiol. 2021 Oct 6;12:734224. doi: 10.3389/fphys.2021.734224. eCollection 2021.
Patients who receive transcatheter aortic valve replacement are at risk for leaflet thrombosis-related complications, and can benefit from continuous, longitudinal monitoring of the prosthesis. Conventional angiography modalities are expensive, hospital-centric and either invasive or employ potentially nephrotoxic contrast agents, which preclude their routine use. Heart sounds have been long recognized to contain valuable information about individual valve function, but the skill of auscultation is in decline due to its heavy reliance on the physician's proficiency leading to poor diagnostic repeatability. This subjectivity in diagnosis can be alleviated using machine learning techniques for anomaly detection. We present a computational and data-driven proof-of-concept analysis of a novel, auscultation-based technique for monitoring aortic valve, which is practical, non-invasive, and non-toxic. However, the underlying mechanisms leading to physiological and pathological heart sounds are not well-understood, which hinders development of such a technique. We first address this by performing direct numerical simulations of the complex interactions between turbulent blood flow in a canonical ascending aorta model and dynamic valve motion in 29 cases with healthy and stenotic valves. Using the turbulent pressure fluctuations on the aorta lumen boundary, we model the propagation of heart sounds, as elastic waves, through the patient's thorax. The heart sound may be recorded on the epidermal surface using a stethoscope/phonocardiograph. This approach allows us to correlate instantaneous hemodynamic phenomena and valve motion with the acoustic response. From this dataset we extract "acoustic signatures" of healthy and stenotic valves based on principal components of the recorded sound. These signatures are used to train a linear discriminant classifier by maximizing correlation between recorded heart sounds and valve status. We demonstrate that this classifier is capable of accurate prospective detection of anomalous valve function and that the principal component-based signatures capture prominent audible features of heart sounds, which have been historically used by physicians for diagnosis. Further development of such technology can enable inexpensive, safe and patient-centric at-home monitoring, and can extend beyond transcatheter valves to surgical as well as native valves.
接受经导管主动脉瓣置换术的患者存在与瓣叶血栓形成相关并发症的风险，并且可以从对人工瓣膜进行持续、纵向监测中获益。传统的血管造影方法成本高昂、以医院为中心，要么具有侵入性，要么使用可能具有肾毒性的造影剂，这使得它们无法常规使用。长期以来，人们一直认识到心音包含有关个体瓣膜功能的有价值信息，但由于听诊严重依赖医生的专业水平，导致诊断重复性差，听诊技能正在下降。使用机器学习技术进行异常检测可以减轻诊断中的这种主观性。我们提出了一种基于听诊的新型主动脉瓣监测技术的计算和数据驱动的概念验证分析，该技术实用、无创且无毒。然而，导致生理和病理心音的潜在机制尚未得到很好的理解，这阻碍了这种技术的发展。我们首先通过对一个典型升主动脉模型中的湍流血液流动与29例健康和狭窄瓣膜的动态瓣膜运动之间的复杂相互作用进行直接数值模拟来解决这个问题。利用主动脉腔边界上的湍流压力波动，我们将心音作为弹性波通过患者胸部的传播进行建模。心音可以使用听诊器/心音图仪记录在表皮表面。这种方法使我们能够将瞬时血流动力学现象和瓣膜运动与声学响应相关联。从这个数据集中，我们基于记录声音的主成分提取健康和狭窄瓣膜的“声学特征”。这些特征用于通过最大化记录的心音与瓣膜状态之间的相关性来训练线性判别分类器。我们证明该分类器能够准确地前瞻性检测异常瓣膜功能，并且基于主成分的特征捕获了心音的突出可听特征，这些特征在历史上一直被医生用于诊断。这种技术的进一步发展可以实现廉价、安全且以患者为中心的家庭监测，并且可以从经导管瓣膜扩展到外科瓣膜以及天然瓣膜。
