Purpose

Patients receiving transcatheter aortic valve replacement (TAVR) can benefit from continuous, longitudinal monitoring of valve prosthesis to prevent leaflet thrombosis-related complications. We present a computational proof-of-concept study of a novel, non-invasive and non-toxic valve monitoring technique for TAVs which uses pressure measurements from microsensors embedded on the valve stent. We perform a data-driven analysis to determine the signal processing and machine learning required to detect reduced mobility in individual leaflets.

Methods

We use direct numerical simulations to describe hemodynamic differences in transvalvular flow in ascending aorta models with healthy and stenotic valves. A Cartesian-grid flow solver and a reduced-order valve model simulate the complex dynamics of blood flow and leaflet motion, respectively. The two-way fluid-structure interaction coupling is achieved using a sharp interface immersed boundary method.

Results

From a dataset of 21 simulations, we show leaflets with reduced mobility result in large, asymmetric pressure fluctuations in their vicinity, particularly in the region extending from the aortic sinus to the sino-tubular junction (STJ). We train a linear classifier algorithm by correlating sinus and STJ pressure measurements on the stent surface to individual leaflet status. The algorithm was shown to have >90% accuracy for prospective detection of individual leaflet dysfunction.

Conclusions

We demonstrate that using only two discrete pressure measurements, per leaflet, on the TAV stent, individual leaflet status can be accurately predicted. Such a sensorized TAV system could enable safe and inexpensive detection of prosthetic valve dysfunction.

中文翻译：

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通过原位压力传感器监测人工瓣膜：使用监督学习进行计算机概念评估

目的

接受经导管主动脉瓣置换术 (TAVR) 的患者可以受益于对瓣膜的连续纵向监测，以预防与小叶血栓形成相关的并发症。我们提出了一种用于 TAV 的新型、非侵入性和无毒瓣膜监测技术的计算概念验证研究，该技术使用嵌入瓣膜支架上的微传感器的压力测量。我们执行数据驱动分析，以确定检测单个传单中活动性降低所需的信号处理和机器学习。

方法

我们使用直接数值模拟来描述具有健康和狭窄瓣膜的升主动脉模型中跨瓣流的血流动力学差异。笛卡尔网格流量求解器和降阶瓣膜模型分别模拟血流和瓣叶运动的复杂动力学。双向流固耦合耦合是使用锐界面浸没边界法实现的。

结果

从 21 次模拟的数据集中，我们显示活动性降低的传单会导致其附近出现较大的不对称压力波动，特别是在从主动脉窦延伸到窦管交界处 (STJ) 的区域。我们通过将支架表面上的窦和 STJ 压力测量值与单个瓣叶状态相关联来训练线性分类器算法。该算法被证明具有 > 90% 的准确性，用于前瞻性检测单个瓣叶功能障碍。

结论

我们证明，在 TAV 支架上，每个瓣叶仅使用两个离散的压力测量值，就可以准确预测单个瓣叶的状态。这种传感 TAV 系统可以安全且廉价地检测人工瓣膜功能障碍。