Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.
Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.
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DOI：
10.1371/journal.pcbi.1011257
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发表时间：
2023-06
影响因子：
4.3
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中科院分区：
文献类型：
--
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心脏泵功能源于一系列跨越多个尺度的高度协调的事件。计算机电学可以在物理约束模型中对这些事件进行编码。然而，这些模型中的大量参数使得细胞、组织和器官尺度参数与整个心脏生理学之间的联系的系统研究具有挑战性。创建了患者特定的解剖心脏模型或数字孪生模型。分别用Courtemanche-Land和ToR-ORd-Land模型模拟心房和心室的细胞离子动力学和收缩。整个心脏收缩与循环系统耦合，用CircAdapt模拟，同时考虑心包对心脏运动的影响。四腔机电框架产生了117个感兴趣的参数。该模型分为五个层次的子模型：组织电生理学，ToR-ORd-Land模型，Courtemanche-Land模型，被动力学和CircAdapt。对于每个子模型，我们训练了高斯过程模拟器（GPE），然后用于执行全局敏感性分析（GSA），以保留解释90%总敏感性的参数用于后续分析。我们确定了117个参数中的45个对整个心脏功能很重要。我们对这45个参数进行了GSA，并将全身和肺外周阻力确定为所有四个腔室中广泛的体积和血液动力学心脏指数的关键参数。我们已经表明，GPE提供了一个强大的方法，细胞特性和临床测量之间的映射。这可以用于识别可以在患者特定模型或数字双胞胎中校准的参数，并将细胞功能与临床指标联系起来。心脏功能依赖于单个细胞和整个器官之间的复杂联系。数字双胞胎或患者特定模型，例如复制患者心脏的计算机模型，可以帮助理解健康或疾病状态下的这些联系，并改善心脏病患者的护理。为了建立患者特异性模型，首先我们需要量化哪些模型参数影响模型输出，丢弃那些影响不大的参数，并了解输入-输出相互作用。这通常需要大量昂贵的模型评估，使得这种类型的分析非常具有挑战性。我们使用高斯过程模拟器（GPE）来降低模型的计算成本。我们近似的心脏模拟器有117个初始参数，能够模拟整个心脏的电兴奋和收缩，细胞动力学，以及循环系统和心脏与心包的相互作用。多亏了GPE，我们能够以可行的成本确定最重要的45个参数，并研究它们对广泛的临床测量的心脏功能生物标志物的影响。我们的分析提供了细胞功能如何影响整个心脏的全面分析，并可用于研究广泛的心脏病理和治疗。
Cardiac pump function arises from a series of highly orchestrated events across multiple scales. Computational electromechanics can encode these events in physics-constrained models. However, the large number of parameters in these models has made the systematic study of the link between cellular, tissue, and organ scale parameters to whole heart physiology challenging. A patient-specific anatomical heart model, or digital twin, was created. Cellular ionic dynamics and contraction were simulated with the Courtemanche-Land and the ToR-ORd-Land models for the atria and the ventricles, respectively. Whole heart contraction was coupled with the circulatory system, simulated with CircAdapt, while accounting for the effect of the pericardium on cardiac motion. The four-chamber electromechanics framework resulted in 117 parameters of interest. The model was broken into five hierarchical sub-models: tissue electrophysiology, ToR-ORd-Land model, Courtemanche-Land model, passive mechanics and CircAdapt. For each sub-model, we trained Gaussian processes emulators (GPEs) that were then used to perform a global sensitivity analysis (GSA) to retain parameters explaining 90% of the total sensitivity for subsequent analysis. We identified 45 out of 117 parameters that were important for whole heart function. We performed a GSA over these 45 parameters and identified the systemic and pulmonary peripheral resistance as being critical parameters for a wide range of volumetric and hemodynamic cardiac indexes across all four chambers. We have shown that GPEs provide a robust method for mapping between cellular properties and clinical measurements. This could be applied to identify parameters that can be calibrated in patient-specific models or digital twins, and to link cellular function to clinical indexes. Cardiac function relies on complex links between the single cell and the whole organ. Digital twins or patient-specific models, e.g. computer models that replicate a patient’s heart, can help understanding these links in healthy or diseased states, and improving cardiac patient care. To build a patient-specific model, first we need to quantify which model parameters affect model outputs, to discard those that have little effect and to understand input-output interactions. This normally requires a lot of expensive model evaluations, making this type of analysis very challenging. We used Gaussian processes emulators (GPEs) to reduce the computational costs of our model. The heart simulator we approximated had 117 initial parameters, and was able to simulate whole heart electrical excitation and contraction, cellular dynamics, as well as the circulatory system and the interaction of the heart with the pericardium. Thanks to the GPEs, we were able to identify the most important 45 parameters at a feasible cost, and to study their effect on a wide range of clinically-measured biomarkers for cardiac function. Our analysis provides a comprehensive assay of how cellular function can impact the whole heart, and can be used to investigate a wide range of cardiac pathologies and treatment.
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影响因子：
3.8
作者：
Bayer, J. D.;Blake, R. C.;Plank, G.;Trayanova, N. A.
通讯作者：
Trayanova, N. A.
影响因子：
5.4
作者：
Claessen, Guido;La Gerche, Andre;Heidbuchel, Hein
通讯作者：
Heidbuchel, Hein
影响因子：
4.5
作者：
Ebus, JP;Papp, Z;Stienen, GJM
通讯作者：
Stienen, GJM
影响因子：
39.2
作者：
HARSHAW, CW;GROSSMAN, W;MCLAURIN, LP
通讯作者：
MCLAURIN, LP
影响因子：
4.1
作者：
Augustin CM;Neic A;Liebmann M;Prassl AJ;Niederer SA;Haase G;Plank G
通讯作者：
Plank G