Model-based identification and diagnosis of a porcine model of induced endotoxic shock with hemofiltration

Starfinger, C.; Chase, J. Geoffrey; Hann, C.E.; Shaw, Geoffrey M.; Lambermont, Bernard; Ghuysen, Alexandre; Kolh, Philippe; Dauby, Pierre; Desaive, Thomas

doi:10.1016/j.mbs.2008.08.014

Cited by 36 publications

(50 citation statements)

References 17 publications

(4 reference statements)

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“…It has been validated in several animal experiments [28,32,33,34]. In this work, direct ventricular interaction is neglected for simplicity.…”

Section: Cardiovascular System Modelmentioning

confidence: 99%

“…However, this importance is often underestimated, because most studies focusing on CVS models aim at simulating situations in which the determinants of CO (inotropy, preload, afterload and heart rate [14]) are constant [1,5,6,27,28,29,31,32,33,34], while the effect of SBV only appears in simulations in which these determinants change. Nevertheless, this type of simulation is the most useful, because in real life, these determinants are constantly changing due to breathing, exercise, etc., and sudden larger changes occur in dysfunctions.…”

Section: Introductionmentioning

confidence: 99%

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Model-based computation of total stressed blood volume from a preload reduction manoeuvre

Pironet

Desaive

Chase

et al. 2015

Mathematical Biosciences

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Total stressed blood volume is an important parameter for both doctors and engineers. From a medical point of view, it has been associated with the success or failure of fluid therapy, a primary treatment to manage acute circulatory failure. From an engineering point of view, it dictates the cardiovascular system's behaviour in changing physiological situations. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a more straightforward method is developed using data from a preload reduction manoeuvre. A simple six-chamber cardiovascular system model is used and its parameters are adjusted to pig experimental data. The parameter adjustment process has three steps: (1) compute nominal values for all model parameters; (2) determine the five most sensitive parameters; and (3) adjust only these five parameters. Stressed blood volume was selected by the algorithm, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal root mean squared error of 29.2 %. Computed stressed blood volume equals 486 ± 117 ml or 15.7 ± 3.6 ml/kg, which matches previous independent experiments on pigs, dogs and humans. The method proposed in this work thus provides a simple way to compute total stressed blood volume from usual hemodynamic data.

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“…It has been validated in several animal experiments [28,32,33,34]. In this work, direct ventricular interaction is neglected for simplicity.…”

Section: Cardiovascular System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based computation of total stressed blood volume from a preload reduction manoeuvre

Pironet

Desaive

Chase

et al. 2015

Mathematical Biosciences

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“…This approach could in principal be applied to any complex lumped parameter CVS model. In particular, future work will utilize the simpler models to allow rapid identification of the eight chamber model [22] and any other added dynamics as required to diagnose cardiac disease states and characterize therapy response.…”

Section: Discussionmentioning

confidence: 99%

“…The method is to let the mean V lv be an extra unknown parameter and optimized along with the parameters in Eq. (22). To account for the change in unknown parameters, more information from the aortic pressure waveform is used.…”

Section: No Volume Measurementmentioning

confidence: 99%

“…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]. However, note that the approach is general and could be applied to any cardiac model structure.…”

Section: Introductionmentioning

confidence: 99%

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

Hann

Chase

Desaive

et al. 2010

Computer Methods and Programs in Biomedicine

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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...

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Improving model-based cardiac diagnosis with an ECG

et al. 2009

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Model-based identification and diagnosis of a porcine model of induced endotoxic shock with hemofiltration

Cited by 36 publications

References 17 publications

Model-based computation of total stressed blood volume from a preload reduction manoeuvre

Model-based computation of total stressed blood volume from a preload reduction manoeuvre

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

Improving model-based cardiac diagnosis with an ECG

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