通过非牛顿血液模型和高斯过程仿真加强心房颤动的中风风险分层。

IF 4.7 2区 医学 Q1 NEUROSCIENCES
Paolo Melidoro, Abdel Rahman Amr Sultan, Ahmed Qureshi, Magdi H Yacoub, Khalil L Elkhodary, Gregory Y H Lip, Natalie Montarello, Nishant Lahoti, Ronak Rajani, Magdalena Klis, Steven E Williams, Oleg Aslanidi, Adelaide De Vecchi
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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. 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引用次数: 0

摘要

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Enhancing stroke risk stratification in atrial fibrillation through non-Newtonian blood modelling and Gaussian process emulation.

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.

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来源期刊
Journal of Physiology-London
Journal of Physiology-London 医学-神经科学
CiteScore
9.70
自引率
7.30%
发文量
817
审稿时长
2 months
期刊介绍: The Journal of Physiology publishes full-length original Research Papers and Techniques for Physiology, which are short papers aimed at disseminating new techniques for physiological research. Articles solicited by the Editorial Board include Perspectives, Symposium Reports and Topical Reviews, which highlight areas of special physiological interest. CrossTalk articles are short editorial-style invited articles framing a debate between experts in the field on controversial topics. Letters to the Editor and Journal Club articles are also published. All categories of papers are subjected to peer reivew. The Journal of Physiology welcomes submitted research papers in all areas of physiology. Authors should present original work that illustrates new physiological principles or mechanisms. Papers on work at the molecular level, at the level of the cell membrane, single cells, tissues or organs and on systems physiology are all acceptable. Theoretical papers and papers that use computational models to further our understanding of physiological processes will be considered if based on experimentally derived data and if the hypothesis advanced is directly amenable to experimental testing. While emphasis is on human and mammalian physiology, work on lower vertebrate or invertebrate preparations may be suitable if it furthers the understanding of the functioning of other organisms including mammals.
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