A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.
A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data.
复制标题
通过结合心房计算流体动力学模拟和功能性患者数据进行全面的中风风险评估。
DOI：
10.1101/2024.01.11.575156
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Trayanova,NataliaA
中科院分区：
文献类型：
--
作者：
Zingaro,Alberto;Ahmad,Zan;Kholmovski,Eugene;Sakata,Kensuke;Dede',Luca;Morris,AlanK;Quarteroni,Alfio;Trayanova,NataliaA
卒中是一个主要的全球健康问题，通常植根于心脏动力学，需要准确的风险评估来进行有针对性的干预。当前的风险模型，如Score，往往缺乏个性化预测所需的粒度。在这项研究中，我们通过将心脏磁共振(CMR)的功能洞察与患者特定的计算流体动力学(CFD)模拟相结合，提出了一种细微而彻底的中风风险评估。我们的队列包括8名患者，平均分为对照组和中风组。利用电影CMR，我们计算了运动学特征，揭示了中风患者较小的左房体积。在我们的血流动力学模拟中加入了患者特定的心房位移，揭示了心房顺应性对流场的影响，强调了LA运动在CFD模拟中的重要性，并挑战了血流动力学模型中传统的刚性壁假设。用功能指标标准化血流动力学特征增强了卒中和对照病例之间的区别。虽然单独的评估提供的清晰度有限，但CMR衍生的功能数据和患者信息的CFD模拟的协同融合提供了个性化和机械性的理解，将卒中与对照病例明显分离。具体地说，我们的研究揭示了一个重要的临床洞察力：基于射血分数的血流动力学特征正常化未能区分中风患者和对照组患者。不同的是，当与每搏量进行标准化时，出现了明显的临床显著差异，这适用于左心房及其附件，为临床环境中准确的中风风险评估提供了有价值的启示。这项工作介绍了一种无缝集成血流动力学和功能指标的新框架，为改进预测模型奠定了基础，并突出了了解运动情况的个性化风险评估的重要性。
Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like thescore, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.