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See detailAssessment of ventricular contractility and ventricular-arterial coupling with a model-based sensor.
Desaive, Thomas ULg; LAMBERMONT, Bernard ULg; JANSSEN, Nathalie ULg et al

in Computer Methods & Programs in Biomedicine (2013), 109(2),

Estimation of ventricular contractility and ventricular arterial coupling is clinically important in diagnosing and treating cardiac dysfunction in the critically ill. However, experimental assessment of ... [more ▼]

Estimation of ventricular contractility and ventricular arterial coupling is clinically important in diagnosing and treating cardiac dysfunction in the critically ill. However, experimental assessment of indexes of ventricular contractility, such as the end-systolic pressure-volume relationship, requires a highly invasive maneuver and measurements that are not typical in an intensive care unit (ICU). This research describes the use of a previously validated cardiovascular system model and parameter identification process to evaluate the right ventricular arterial coupling in septic shock. Model-based ventricular arterial coupling is defined by the ratio of the end systolic right ventricular elastance (E(esrvf)) over the pulmonary artery elastance (E(pa)) or the mean pulmonary inflow resistance (R(pulin)). Results are compared to the clinical gold-standard assessment (conductance catheter method). Six anesthetized healthy pigs weighing 20-30kg received a 0.5mgkg(-1) endotoxin infusion over a period of 30min from T0 to T30, to induce septic shock and veno-venous hemofiltration was used from T60 onward. The results show good agreement with the gold-standard experimental assessment. In particular, the model-based right ventricular elastance (E(esrvf)) correlates well with the clinical gold standard (R(2)=0.69) and the model-based non-invasive coupling (E(esrvf)/R(pulin)) follow the same trends and dynamics (R(2)=0.37). The overall results show the potential to develop a model-based sensor to monitor ventricular-arterial coupling in clinical real-time. [less ▲]

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See detailPatient-specific modelling of the cardiovascular system – application to septic shock with a minimal data set
Desaive, Thomas ULg; Chase, J. G.; Starfinger, C. et al

in World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany (2010)

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See detailAssessment of ventricular-arterial coupling with a model-based sensor
Desaive, Thomas ULg; LAMBERMONT, Bernard ULg; GHUYSEN, Alexandre ULg et al

in Proceedings of CONTROL 2010 (2010)

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See detailModel-based prediction of the patient-specific response to adrenaline
Chase, J. G.; Starfinger, C.; Hann, C. E. et al

in The open medical informatics journal (2010), 4

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See detailModel-based Cardiovascular Therapeutics: Capturing the patient-specific impact of inotrope therapy
Desaive, Thomas ULg; Starfinger, C; Chase, JG et al

in Proceedings of the 3rd International Meeting of the French Society of Hypertension (2009)

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See detailPatient specific model of the cardiovascular system during septic shock
Desaive, Thomas ULg; Chase, J. G.; Lambermont, Bernard ULg et al

in Intensive Care Medicine (2009), 35(suppl. 1), 80

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See detailModel-based diagnosis of acute pulmonary embolism - results from a porcine model
Desaive, Thomas ULg; Ghuysen, Alexandre ULg; Kolh, Philippe ULg et al

in Intensive Care Medicine (2008), 34(suppl. 1), 78

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See detailModel-based identification and diagnosis of a porcine model of induced endotoxic shock with hemofiltration
Starfinger, C.; Chase, J. G.; Hann, C. E. et al

in Mathematical Biosciences (2008), 216(2), 132-139

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See detailDiagnosing Cardiac Dysfunction and Guiding Therapy in Critical Care
Starfinger, C.; Chase, J. G.; Hann, C. et al

in Proceedings of the NZBio 2008 Conference & Exposition, (2008)

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See detailMathematical modelling and parameter identification methods in systems
Hann, C. E.; Chase, J. G.; Shaw, G. M. et al

in Proceedings of the 7th joint Australia-New Zealand Mathematics Convention (ANZMC2008) (2008)

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See detailModel-based detection of pulmonary embolism using an extended physiologically relevant, cardiovascular model
Kok, K.; Starfinger, C.; Hann, C. E. et al

in Proceedings of Engineering & Physical Sciences in Medicine and Australian Biomedical Engineering Conference (EPSM ABEC 2008) (2008)

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See detailMaking sense of the Chaos: Model-based CVS monitoring and decision support in critical care
Shaw, G. M.; Chase, J. G.; Hann, C. E. et al

in Proceedings of the NZ Physiological Society 2008 Medical Science Congress (MedSci 2008) (2008)

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See detailModel-based analysis of induced endotoxic shock in pigs with and without hemofiltration,
Kok, K.; Starfinger, C.; Hann, C. E. et al

in Prodeedings of the Engineering & Physical Sciences in Medicine and Australian Biomedical Engineering Conference (EPSM ABEC 2008 (2008)

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See detailImproving model-based cardiac diagnosis with an ECG
Hann, C. E.; Chase, J. G.; Desaive, Thomas ULg et al

in Proceedings (CD) of the 4th European Congress for Medical and Biomedical Engineering (eMBEC 2008), Antwerp, Belgium, Nov 23-27, 2008 (2008)

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See detailModel-Based Assessment of Right Ventricular Arterial Coupling During Septic Shock – Results With a Porcine Model
Desaive, Thomas ULg; Lambermont, Bernard ULg; Janssen, N. et al

in Proceedings of the 21st European Society of Intensive Care Medicine (ESICM) Annual Congress, September 21-24, 2008, Lisbon, Portugal (2008)

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See detailModel-based cardiac diagnosis of pulmonary embolism
Starfinger, C.; Hann, C. E.; Chase, J. G. et al

in Computer Methods & Programs in Biomedicine (2007), 87(1), 46-60

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification ... [more ▼]

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification methods for this model are highly non-linear and non-convex, hindering real-time clinical application. An integral-based identification method that transforms the problem into a linear, convex problem, has been previously developed, but was only applied on continuous simulated data with random noise. This paper extends the method to handle discrete sets of clinical data, unmodelled dynamics, a significantly reduced data set theta requires only the minimum and maximum values of the pressure in the aorta, pulmonary artery and the volumes in the ventricles. The importance of integrals in the formulation for noise reduction is illustrated by demonstrating instability in the identification using simple derivative-based approaches. The cardiovascular system (CVS) model and parameter identification method are then clinically validated on porcine data for pulmonary embolism. Errors for the identified model are within 10% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents the first clinical validation of these models, methods and approach to cardiovascular diagnosis in critical care. (c) 2007 Elsevier Ireland Ltd. All rights reserved. [less ▲]

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See detailStudy of ventricular interaction during pulmonary embolism using clinical identification in a minimum cardiovascular system model.
Desaive, Thomas ULg; Ghuysen, Alexandre ULg; Lambermont, Bernard ULg et al

in Proceedings of the IEEE (2007)

Cardiovascular disturbances are difficult to diagnose and treat because of the large range of possible underlying dysfunctions combined with regulatory reflex mechanisms that can result in conflicting ... [more ▼]

Cardiovascular disturbances are difficult to diagnose and treat because of the large range of possible underlying dysfunctions combined with regulatory reflex mechanisms that can result in conflicting clinical data. Thus, medical professionals often rely on experience and intuition to optimize hemodynamics in the critically ill. This paper combines an existing minimal cardiovascular system model with an extended integral based parameter identification method to track the evolution of induced pulmonary embolism in porcine data. The model accounts for ventricular interaction dynamics and is shown to predict an increase in the right ventricle expansion index and a decrease in septum volume consistent with known physiological response to pulmonary embolism. The full range of hemodynamic responses was captured with mean prediction errors of 4.1% in the pressures and 3.1% in the volumes for 6 sets of clinical data. Pulmonary resistance increased significantly with the onset of embolism in all cases, as expected, with the percentage increase ranging from 89.98% to 261.44% of the initial state. These results are an important first step towards model-based cardiac diagnosis in the Intensive Care Unit. [less ▲]

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See detailModelling the cardiovascular system
Shaw, G. M.; Chase, J. G.; Starfinger, C. et al

in Critical Care and Resuscitation : Journal of the Australasian Academy of Critical Care Medicine (2007), 9(3), 264-269

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