Mechanistic modelling of Virchows triad to assess thrombogenicity and stroke risk in atrial fibrillation patients
Mechanistic modelling of Virchows triad to assess thrombogenicity and stroke risk in atrial fibrillation patients
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DOI：
10.1093/ehjdh/ztac076.2788
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发表时间：
2022-12-22
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房颤（AF）几乎占所有卒中的三分之一，左心耳（LAA）是主要的血栓栓塞源，这是由于促血栓形成机制的局部刺激;血瘀、高凝状态和内皮损伤，称为Virchow三联征。我们提出了一种计算机模拟建模管道，该管道利用临床成像数据来机械评估Virchow三联征各个方面的患者血栓形成性，以改善AF相关卒中的预测和预防。选择2名接受Cine磁共振成像的AF患者（成像期间窦性心律（SR）N=1或AF N=1）进行3D左心房（LA）建模，根据图像衍生的室壁运动规定患者特异性心肌变形。通过5个心动周期的计算流体动力学（CFD）模拟对血瘀进行量化[1]。对三种关键凝血蛋白（凝血酶、纤维蛋白原和纤维蛋白）的生成进行建模，以代表血栓生长和高凝状态[2]。通过LAA中的振荡剪切指数（OSI）、时间平均壁面剪切应力（TAWSS）和内皮细胞活化电位（ECAP）指标确定了内皮损伤导致血栓形成的区域[3]。患者特异性LA模拟能够评估SR和AF状况之间的差异，量化为Virchow三联征每个方面的数字特征。在SR中，LA腔中的血流速度范围为0-2.6 m/s，平均值为0.85 m/s，而AF的范围为0-1.6 m/s，平均值为0.55 m/s。SR中的LAA峰值和平均速度分别为0.85 m/s和0.14 m/s，而AF中的LAA峰值速度为0.32 m/s，平均速度为0.09 m/s，显示AF期间降低了38%。AF病例中凝血酶浓度在4.7秒后达到稳定状态，为1.26 mmol/m3，5个心动周期后，左心耳纤维蛋白峰值浓度SR为1.3mmol/m3，AF为3.8mmol/m3，AF血栓面积比SR大40%。纤维蛋白原浓度下降的速度等于纤维蛋白生成的SR和AF仅在血栓形成的领域。左心耳的ECAP在SR中的峰值为2.9，在AF中的峰值为3.7，左心耳入口上方血栓形成风险最高的位置。AF中LAA OSI的平均值为0.45，SR中为0.36，显示增加了26%。同样，AF患者LAA的TAWSS平均值为3.5x10-3 Pa，SR患者为1.4x10-3 Pa。结合这三个定量特征的患者特异性LA模型可用于预测AF患者较高的血栓形成风险。经过进一步验证，这种基于模拟Virchow三联征中所有因素的定量评估AF患者血栓形成性的新方法可以个性化和改善管理有卒中风险的房颤患者。资金来源类型：公共赠款-仅限于国家预算。主要资金来源：英国工程和物理科学研究理事会图1。LAA伴血栓形成量化
Atrial fibrillation (AF) is responsible for almost one third of all strokes, with the left atrial appendage (LAA) being the primary thromboembolic source due to localised stimulation of prothrombotic mechanisms; blood stasis, hypercoagulability and endothelial damage, known as Virchow's triad. We propose an in-silico modelling pipeline that leverages clinical imaging data to mechanistically assess patient thrombogenicity for all aspects of Virchow's triad to improve the prediction and prevention of AF-related stroke. Two AF patients undergoing Cine magnetic resonance imaging (sinus rhythm (SR) N=1 or AF N=1 during imaging) were selected for 3D left atrial (LA) modelling with patient-specific myocardial deformation prescribed from image-derived wall motion. Blood stasis was quantified by computational fluid dynamics (CFD) simulations of 5 cardiac cycles [1]. Generation of three key coagulation proteins; thrombin, fibrinogen and fibrin, were modelled to represent thrombus growth and hypercoagulability [2]. Regions prone to thrombogenesis by endothelial damage were identified by the oscillatory shear index (OSI), time averaged wall shear stress (TAWSS) and endothelial cell activation potential (ECAP) metrics in the LAA [3]. Patient-specific LA simulations enabled the assessment of differences between SR and AF conditions, quantified as numerical characteristics of each aspect of Virchow's triad. In SR, blood flow velocities were in the range 0–2.6 m/s with mean of 0.85 m/s in the LA cavity, while AF had a range between 0–1.6 m/s with mean of 0.55 m/s. The peak and mean LAA velocities in SR were 0.85 m/s and 0.14 m/s, while AF had a peak LAA velocity of 0.32 m/s and mean of 0.09 m/s, showing a 38% decrease during AF. The thrombin concentration reached its steady state at 1.26 mmol/m3 in the AF case after 4.7 seconds, while thrombin was washed away from the initial injury site in SR. After 5 cardiac cycles of thrombus growth dynamics, the peak fibrin concentration in the LAA was 1.3 mmol/m3 in SR and 3.8 mmol/m3 in AF, with the thrombus area in AF being 40% larger. Fibrinogen concentration decreased at a rate equal to fibrin generation in both SR and AF solely in the area of thrombus formation. ECAP in the LAA had peak values of 2.9 in SR and 3.7 in AF, with the location at highest risk of thrombogenesis above the LAA entrance. LAA OSI had an average value of 0.45 in AF versus 0.36 in SR, showing a 26% increase. Similarly, the TAWSS was 3.5x10–3 Pa on average over the LAA in AF compared to 1.4x10–3 Pa in SR. Patient-specific LA models combining these three quantitative characteristics can be used to predict the higher thrombogenic risk in AF. After further validation, this novel approach for quantitative assessment of AF patient thrombogenicity based on modelling all factors in Virchow's triad can personalise and improve management of AF patients with a risk of stroke. Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Engineering and Physical Sciences Research Council Figure 1. LAA with thrombogenicity quantification