A dynamic model of ventricular interaction and pericardial influence

Computer Methods and Programs in Biomedicine

Hann

et al. 2008

(stroke volume). These results validate the general applicability of such a rule, which is not pig-specific, but holds over for all analyzed pigs. This rule enables physiological simulation and prediction of patient response. Overall, the prediction accuracy achieved represents a further clinical validation of these models, methods and overall approach to cardiovascular diagnosis and therapy guidance.

Section: Cvs Modelmentioning

confidence: 99%

Prediction of hemodynamic changes towards PEEP titrations at different volemic levels using a minimal cardiovascular model

Starfinger

Computer Methods and Programs in Biomedicine

Hann

et al. 2008

“…Problems with long-term divergence and dependence on accurate initial conditions, caused by too many governing equations over-defining the model, were encountered with other similar models (e.g. Chung, 1997). The minimal model is developed starting with a simple single ventricle system and progressively adding complexity.…”

Section: • •mentioning

confidence: 99%

A minimal cardiovascular system haemodynamic model for rapid diagnostic assistance

Smith

IFAC Proceedings Volumes

Nokes

2003

Characterising circulatory dysfunction and choosing a suitable treatment is often difficult, and time consuming. This paper outlines a numerically stable minimal model of the human cardiovascular system (CVS) specifically aimed for rapid, on site modelling to assist in diagnosis and treatment. A minimal number of governing equations and a realistic valve law are used to accurately capture trends in CVS dynamics. The model is shown to have long-term stability and consistency with non-specific initial conditions. Results show that the model adequately provides appropriate magnitudes and trends for a variety of physiologically verified test cases.

“…Lumped parameter models (LPM) are a common approach to minimizing complexity in the cardiovascular system [12][13][14][15][16] but there are still many parameters involved. Thus typically, only small subsets of the parameter set can be identified (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…A common method of modelling the CVS is by a series of elastic chambers connected by resistors with a time varying elastance. This approach has been well developed and validated in the literature [12][13][14][15][16] so it serves as a good starting point to test the methods and concepts in this paper. The specific baseline model structure considered is a six chamber cardiovascular model including ventricular interaction and inertial effects that has been previously developed [16] and validated in clinical animal trials [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

Hann

Computer Methods and Programs in Biomedicine

Desaive

et al. 2010

Lumped parameter approaches for modelling the cardiovascular system typically have many parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found.By utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care. Keywords :Model-based cardiac diagnosis ; Cardiovascular system ; Integral-based parameter identification ; Pressure waveform ; ECG ; Intensive care unit IntroductionIn critical care, cardiovascular dysfunction can be easily misdiagnosed due to incomplete information and the complexities involved, leading to premature discharge or non-optimal treatment [1][2][3]. It is also a major cause of increased length of stay and death [4,5]. Demand for critical care is growing dramatically severely affecting healthcare delivery [6][7][8]. The overall goal of this research is to use computational cardiac models to better aggregate available clinical data in an intensive care unit (ICU) into a more readily understood physiological context for clinicians. The computational models can be used to reveal non-linear dynamics and interactions that are not readily apparent in the data.A major difficulty faced with cardiovascular modelling in general, is the level of detail these models typically include. For example multi-scale modelling approaches utilizing finite elements have successfully expl...