Melidoro Paolo, Sultan Abdel Rahman Amr, Qureshi Ahmed, Yacoub Magdi H, Elkhodary Khalil L, Lip Gregory Y H, Montarello Natalie, Lahoti Nishant, Rajani Ronak, Klis Magdalena, Williams Steven E, Aslanidi Oleg, De Vecchi Adelaide
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Department of Mechanical Engineering, The American University in Cairo, New Cairo, Egypt.
J Physiol. 2024 Dec 17. doi: 10.1113/JP287283.
Atrial fibrillation (AF) is the most common heart arrhythmia, linked to a five-fold increase in stroke risk. The left atrial appendage (LAA), prone to blood stasis, is a common thrombus formation site in AF patients. The LAA can be classified into four morphologies: broccoli, cactus, chicken wing and windsock. Stroke risk prediction in AF typically relies on demographic characteristics and comorbidities, often overlooking blood flow dynamics. We developed patient-specific non-Newtonian models of blood flow, dependent on fibrinogen and haematocrit, to predict changes in LAA viscosity, aiming to predict stroke in AF patients. We conducted 480 computational fluid dynamics (CFD) simulations using the non-Newtonian model across the four LAA morphologies for four virtual patient cohorts: AF + Covid-19, AF + pathological fibrinogen, AF + normal fibrinogen, and healthy controls. Gaussian process emulators (GPEs) were trained on this in silico cohort to predict average LAA viscosity at near-zero computational cost. GPEs demonstrated high accuracy in AF cohorts but lower accuracy when the chicken wing GPE was applied to other morphologies. Global sensitivity analysis showed fibrinogen significantly influenced blood viscosity in all AF cohorts. The chicken wing morphology exhibited the highest viscosity, while the AF + Covid-19 cohort had the highest viscosity. Our non-Newtonian model in CFD simulations confirmed fibrinogen's substantial impact on blood viscosity at low shear rates in the LAA, suggesting that combining blood values and geometric parameters of the LAA into GPE training could enhance stroke risk stratification accuracy. KEY POINTS: Fibrinogen has a significant effect on blood viscosity in the left atrial appendage (LAA) at low shear rates. Gaussian process emulators (GPEs) can predict the viscosity of blood in the LAA at near-zero computational cost. Out of all LAA morphologies, the chicken wing morphology exhibited the highest average blood viscosity. High average blood viscosity in the LAA of atrial fibrilation + Covid-19 patients was observed due to high fibrinogen levels in this cohort. Combining blood values and geometric parameters of the LAA into GPE training could enhance stroke risk stratification accuracy.
心房颤动（AF）是最常见的心律失常，与中风风险增加五倍有关。左心耳（LAA）容易发生血液淤滞，是房颤患者常见的血栓形成部位。LAA可分为四种形态：西兰花型、仙人掌型、鸡翅型和风向袋型。房颤的中风风险预测通常依赖于人口统计学特征和合并症，常常忽略血流动力学。我们开发了依赖于纤维蛋白原和血细胞比容的患者特异性非牛顿血流模型，以预测LAA粘度的变化，旨在预测房颤患者的中风。我们使用非牛顿模型对四个虚拟患者队列（房颤+新冠病毒-19、房颤+病理性纤维蛋白原、房颤+正常纤维蛋白原和健康对照）的四种LAA形态进行了480次计算流体动力学（CFD）模拟。高斯过程模拟器（GPE）在此计算机模拟队列上进行训练，以近乎零的计算成本预测平均LAA粘度。GPE在房颤队列中显示出高精度，但当鸡翅型GPE应用于其他形态时精度较低。全局敏感性分析表明，纤维蛋白原在所有房颤队列中对血液粘度有显著影响。鸡翅型形态表现出最高的粘度，而房颤+新冠病毒-19队列的粘度最高。我们在CFD模拟中的非牛顿模型证实了纤维蛋白原在低剪切速率下对LAA血液粘度有重大影响，这表明将LAA的血液值和几何参数纳入GPE训练可以提高中风风险分层的准确性。要点：纤维蛋白原在低剪切速率下对左心耳（LAA）的血液粘度有显著影响。高斯过程模拟器（GPE）可以以近乎零的计算成本预测LAA中的血液粘度。在所有LAA形态中，鸡翅型形态表现出最高的平均血液粘度。由于该队列中纤维蛋白原水平较高，观察到房颤合并新冠病毒-19患者的LAA平均血液粘度较高。将LAA的血液值和几何参数纳入GPE训练可以提高中风风险分层的准确性。
