Coupled models technology in multi-scale computational haemodynamics

Blanco, Pablo; Pivello, Márcio Ricardo; Urquiza, Santiago; Silva, Nelson Albuquerque de Souza e; Feijóo, Raúl A.

doi:10.1504/ijbet.2011.039195

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…The mathematical modeling of physiological systems on the cellular and organ scales has a long history (cf., [3] and [4] for a wonderful introduction), while the modeling of larger scale organ structures is just beginning [5]. Fundamental to mathematical modeling of physiology is a concrete connection to real data; as is the case for other basic sciences, mathematical physiological modeling is always tested against physiological data collected in rigorously controlled circumstances.…”

Section: Introductionmentioning

confidence: 99%

Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

2012

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Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use. For example, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population with a well understood health state, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a random population of humans. Moreover, in a population of continuously (tube) fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled.

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Section: Introductionmentioning

confidence: 99%

Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

2012

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“…To extent their use to non-periodic conditions, an approach for prescribing lumped parameter outflow boundary conditions that accommodate transient phenomena has been presented in [66]. Few groups have studied the coupling of 3D-1D-0D models to consider the interactions between the local and systemic circulation [7,10,20,40].…”

Section: Introductionmentioning

confidence: 99%

Modeling Hemodynamics in Vascular Networks Using a Geometrical Multiscale Approach: Numerical Aspects

et al. 2012

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On the one hand the heterogeneity of the circulatory system requires the use of different models in its different compartments, featuring different assumptions on the spatial degrees of freedom. On the other hand, the mutual interactions between its compartments imply that these models should preferably not be considered separately. These requirements have led to the concept of geometrical multiscale modeling, where the main idea is to couple 3D models with reduced 1D and/or 0D models. As such detailed information on the flow field in a specific region of interest can be obtained while accounting for the global circulation. However, the combination of models with different mathematical features gives rise to many difficulties such as the assignment of boundary conditions at the interface between two models and the development of robust coupling algorithms, as the subproblems are usually solved in a partitioned way. This review aims to give an overview of the most important aspects concerning 3D-1D-0D coupled models. In addition, some applications are presented in order to illustrate the potentialities of these coupled models.

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“…O foco principal é gerar malhas de qualidade para serem usadas em simulações de artérias do corpo humano. Estas técnicas devem ser integradas a uma ferramenta para simulação computacional do Sistema Cardiovascular Humano [3] desenvolvido no projeto HemoLab do LNCC -Laboratório Nacional de Computação…”

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Geração e melhoramento de malhas adaptativas

Lacassa¹

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Agradeço à Deus, meu Senhor e Rei, à Jesus meu Salvador e amigo íntimo, razão do meu viver e de toda força que tenho. Aos meus pais, Miguel e Nilma por simplesmente serem pais, vocês me fortalecem com seu amor. Às minhas irmãs Juliana, Michelle, Daniele e Gisele, e aos meus sobrinhos, pelo imenso carinho com que sempre me presenteiam quando vou visitá-los. Aos pastores Lael e Tânia pelas orações e amor que sempre demonstraram e por me acolherem em sua casa. Ao meu amigo e discipulador Mateus e seu irmão Jônatas, com seus conselhos e apoio vocês me ajudaram a suportar os momentos mais difíceis que tive durante essa jornada. Aos amigos Mario e sua esposa Juliana pelo carinho e bons conselhos nos agradáveis almoços de domingo. A todos os irmãos da Comunidade Cristã de São Carlos, irmãos na fé. Aos amigos do LCAD, principalmente ao amigo Miguel Caro Candezano, pelas palavras de ânimo e pela companhia na torcida pelo timão. Aos amigos Mario Liziér e João Paulo Gois que me ajudaram com minhas dúvidas e dificuldades de programação. Ao meu orientador Prof. Castelo, pela compreensão e ajuda no desenvolvimento deste trabalho. À CAPES pelo suporte financeiro.

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Coupled models technology in multi-scale computational haemodynamics

Cited by 4 publications

References 22 publications

Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

Modeling Hemodynamics in Vascular Networks Using a Geometrical Multiscale Approach: Numerical Aspects

Geração e melhoramento de malhas adaptativas

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