Department of Biomedical Engineering, Georgia Institute of Technology, 387 Technology Circle, Atlanta, GA, 30313, USA.
Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA.
Cardiovasc Eng Technol. 2021 Dec;12(6):576-588. doi: 10.1007/s13239-021-00596-x. Epub 2021 Dec 2.
Leaflet thrombosis is a significant adverse event after transcatheter aortic valve (TAV) replacement (TAVR). The purpose of our study was to present a semi-empirical, mathematical model that links patient-specific anatomic, valve, and flow parameters to predict likelihood of leaflet thrombosis.
The two main energy sources of neo-sinus (NS) washout after TAVR include the jet flow downstream of the TAV and NS geometric change in volume due to the leaflets opening and closing. Both are highly dependent on patient anatomic and hemodynamic factors. As rotation of blood flow is prevalent in both the sinus of Valsalva and then the NS, we adopted the vorticity flux or circulation (Г) as a metric quantifying overall washout. Leaflet thrombus volumes were segmented based on hypo-attenuating leaflet thickening (HALT) in post-TAVR patient's gated computed tomography. Г was assessed using dimensional scaling as well as computational fluid dynamics (CFD) respectively and correlated to the thrombosis volumes using sensitivity and specificity analysis.
Г in the NS, that accounted for patient flow and anatomic conditions derived from scaling arguments significantly better predicted the occurrence of leaflet thrombus than CFD derived measures such as stasis volumes or wall shear stress. Given results from the six patient datasets considered herein, a threshold Г value of 28.0 yielded a sensitivity and specificity of 100% where patients with Gamma < 28 developed valve thrombosis. A 10% error in measurements of all variables can bring the sensitivity specificity down to 87%.
A predictive model relating likelihood of valve thrombosis using Г in the NS was developed with promising sensitivity and specificity. With further studies and improvements, this predictive technology may lead to alerting physicians on the risk for thrombus formation following TAVR.
经导管主动脉瓣置换术(TAVR)后出现的瓣叶血栓形成是一种严重的不良事件。本研究的目的是提出一种半经验的数学模型,将患者特定的解剖、瓣膜和血流参数联系起来,以预测瓣叶血栓形成的可能性。
TAVR 后 neo-sinus(NS)冲洗的两个主要能量来源包括 TAV 下游的射流和由于瓣叶开启和关闭导致的 NS 容积的几何变化。这两者都高度依赖于患者的解剖和血流动力学因素。由于血流在主动脉窦和 NS 中都存在旋转,我们采用涡通量或环流(Г)作为量化整体冲洗的指标。基于 TAVR 后患者门控计算机断层扫描中的低衰减瓣叶增厚(HALT),对瓣叶血栓体积进行分割。Г 通过尺寸缩放以及计算流体动力学(CFD)分别进行评估,并通过敏感性和特异性分析与血栓体积相关联。
NS 中的 Г 能够更好地预测瓣叶血栓的发生,这归因于来自尺度分析的患者流量和解剖条件,而不是 CFD 衍生的测量值,如停滞体积或壁面剪切应力。考虑到本文中考虑的六个患者数据集的结果,Г 值为 28.0 的阈值产生了 100%的敏感性和特异性,其中Г < 28 的患者发生了瓣膜血栓形成。所有变量测量值的 10%误差会将敏感性特异性降低至 87%。
使用 NS 中的 Г 开发了一种与瓣膜血栓形成可能性相关的预测模型,具有良好的敏感性和特异性。随着进一步的研究和改进,这种预测技术可能会提醒医生在 TAVR 后血栓形成的风险。