Simulation, identification and statistical variation in cardiovascular analysis (SISCA) - A software framework for multi-compartment lumped modeling
Simulation, identification and statistical variation in cardiovascular analysis (SISCA) - A software framework for multi-compartment lumped modeling
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DOI：
10.1016/j.compbiomed.2017.05.021
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发表时间：
2017-08
影响因子：
7.7
通讯作者：
R. Huttary;L. Goubergrits;C. Schütte;S. Bernhard
中科院分区：
文献类型：
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作者：
R. Huttary;L. Goubergrits;C. Schütte;S. Bernhard
由于许多系统参数是不确定甚至未知的，目前还不可能获得适合于涵盖心血管生理学中广泛现实场景的建模方法。心血管系统参数在健康和疾病状态下的自然变异性和统计变异性是更详细地理解心血管疾病的特征，本文提出了一种新的心血管系统建模软件框架SISCA及其在MATLAB中的实现。该框架为降维、多间隔模型定义了一种多模型统计集成方法，重点关注基于临床数据的统计变异、系统识别和特定于患者的模拟。我们还讨论了一个数据驱动的建模场景作为用例。所关注的数据集来自常规临床检查，包括一名被诊断为主动脉缩窄的患者的典型手术前和手术后临床数据。我们采用了一个经过验证的标称多室模型，使用元数据和MRI几何技术对患者和疾病特定的手术前/后模型进行了建模。在这两个模型中，关于狭窄和支架治疗以及治疗前交叉狭窄脉搏波的相移，模拟很好地再现了测量的压力和流动。然而，随着治疗后数据显示不现实的相移和数据集中其他更明显的不一致，我们提出的方法和结果表明，常规临床数据集的条件反射和不确定性管理需要更多地关注，以在特定患者的心血管建模中获得合理的结果。
It has not yet been possible to obtain modeling approaches suitable for covering a wide range of real world scenarios in cardiovascular physiology because many of the system parameters are uncertain or even unknown. Natural variability and statistical variation of cardiovascular system parameters in healthy and diseased conditions are characteristic features for understanding cardiovascular diseases in more detail.This paper presents SISCA, a novel software framework for cardiovascular system modeling and its MATLAB implementation. The framework defines a multi-model statistical ensemble approach for dimension reduced, multi-compartment models and focuses on statistical variation, system identification and patient-specific simulation based on clinical data. We also discuss a data-driven modeling scenario as a use case example. The regarded dataset originated from routine clinical examinations and comprised typical pre and post surgery clinical data from a patient diagnosed with coarctation of aorta. We conducted patient and disease specific pre/post surgery modeling by adapting a validated nominal multi-compartment model with respect to structure and parametrization using metadata and MRI geometry.In both models, the simulation reproduced measured pressures and flows fairly well with respect to stenosis and stent treatment and by pre-treatment cross stenosis phase shift of the pulse wave. However, with post-treatment data showing unrealistic phase shifts and other more obvious inconsistencies within the dataset, the methods and results we present suggest that conditioning and uncertainty management of routine clinical data sets needs significantly more attention to obtain reasonable results in patient-specific cardiovascular modeling.