References of "Desaive, Thomas"
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See detailTracking stressed blood volume during vascular filling experiments
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

Poster (2014, November 28)

A three-chamber cardiovascular system model is used to compute stressed blood volume from filling experiments. As previously observed, stressed blood volume is a good predictor of the change in cardiac ... [more ▼]

A three-chamber cardiovascular system model is used to compute stressed blood volume from filling experiments. As previously observed, stressed blood volume is a good predictor of the change in cardiac output after fluid infusion. [less ▲]

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See detailEstimating Ventricular Stroke Work from Aortic Pressure Waveform
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

Poster (2014, November 28)

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See detailTracking stressed blood volume during vascular filling experiments
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

in 13th Belgian Day on Biomedical Engineering (2014, November 28)

A three-chamber cardiovascular system model is used to compute stressed blood volume from filling experiments. As previously observed, stressed blood volume is a good predictor of the change in cardiac ... [more ▼]

A three-chamber cardiovascular system model is used to compute stressed blood volume from filling experiments. As previously observed, stressed blood volume is a good predictor of the change in cardiac output after fluid infusion. [less ▲]

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See detailModeling of the cardio-pulmonary system assisted by ECMO
Habran, Simon ULg; Dauby, Pierre ULg; Desaive, Thomas ULg et al

in National Day on Biomedical Engineering 2014 (2014, October)

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See detailModel-Based Computation of Total Stressed Blood Volume from a Preload Reduction Experiment
Pironet, Antoine ULg; Desaive, Thomas ULg; Chase, J. Geoffrey et al

Conference (2014, August)

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 resuscitation therapy, which ... [more ▼]

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 resuscitation therapy, which is a treatment for cardiac failure. From an engineering point of view, this parameter dictates the cardiovascular system's dynamic behavior. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a method is developed to compute stressed blood volume from preload reduction experiments. 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 most sensitive parameters; and (3) adjust only these sensitive parameters. Stressed blood volume was determined sensitive for all datasets, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal mean squared error of 11.77 %. Stressed blood volume has been computed to range between 450 and 963 ml, or 15 to 28 ml/kg, which matches previous independent experiments on pigs, dogs and humans. Consequently, the method proposed in this work provides a simple way to compute total stressed blood volume from usual hemodynamic data. [less ▲]

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See detailStructural identifiability analysis of a cardiovascular system model
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

Conference (2014, August)

A simple experimentally validated cardiovascular system model has been shown to be able to track the evolution of various diseases. The model has previously been made patient-specific by adjustment of its ... [more ▼]

A simple experimentally validated cardiovascular system model has been shown to be able to track the evolution of various diseases. The model has previously been made patient-specific by adjustment of its parameters on the basis of a minimal set of hemodynamic measurements. However, this model has not yet been shown to be structurally identifiable, which means that the adjusted model parameters may not be unique. The model equations were manipulated to show that, from a theoretical point of view, all of their parameters can be exactly retrieved from a restricted set of model outputs. However, this set of model outputs is still too large for a clinical application, because it includes left and right ventricular pressures. Consequently, further hypotheses that determine some model parameter values have to be made for the model to be clinically applicable. [less ▲]

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See detailStructural Identifiability Analysis of a Cardiovascular System Model
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

in Preprints of the 19th World Congress (2014, August)

A simple experimentally validated cardiovascular system model has been shown to be able to track the evolution of various diseases. The model has previously been made patient-specific by adjustment of its ... [more ▼]

A simple experimentally validated cardiovascular system model has been shown to be able to track the evolution of various diseases. The model has previously been made patient-specific by adjustment of its parameters on the basis of a minimal set of hemodynamic measurements. However, this model has not yet been shown to be structurally identifiable, which means that the adjusted model parameters may not be unique. The model equations were manipulated to show that, from a theoretical point of view, all of their parameters can be exactly retrieved from a restricted set of model outputs. However, this set of model outputs is still too large for a clinical application, because it includes left and right ventricular pressures. Consequently, further hypotheses that determine some model parameter values have to be made for the model to be clinically applicable. [less ▲]

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See detailModel-Based Computation of Total Stressed Blood Volume from a Preload Reduction Experiment
Pironet, Antoine ULg; Desaive, Thomas ULg; Chase, J. Geofrrey et al

in Preprints of the 19th World Congress (2014, August)

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 resuscitation therapy, which ... [more ▼]

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 resuscitation therapy, which is a treatment for cardiac failure. From an engineering point of view, this parameter dictates the cardiovascular system’s dynamic behavior. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a method is developed to compute stressed blood volume from preload reduction experiments. 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 most sensitive parameters; and (3) adjust only these sensitive parameters. Stressed blood volume was determined sensitive for all datasets, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal mean squared error of 11.77 %. Stressed blood volume has been computed to range between 450 and 963 ml, or 15 to 28 ml/kg, which matches previous independent experiments on pigs, dogs and humans. Consequently, the method proposed in this work provides a simple way to compute total stressed blood volume from usual hemodynamic data. [less ▲]

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See detailSurvey about diffusion and adoption of glycaemic controller in European intensive care units
Penning, Sophie ULg; Pironet, Antoine ULg; Chase, J. Geoffrey et al

Conference (2014, August)

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See detailSurvey about diffusion and adoption of glycaemic controller in European intensive care units
Penning, Sophie ULg; Pironet, Antoine ULg; Chase, J. Geoffrey et al

in Proceedings of the 19th IFAC Conference (2014, August)

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See detailInsulin Sensitivity Variability during Hypothermia
Sah Pri, Azurahisham; Chase, J. Geoffrey; Pretty, Christopher et al

in Proceedings of the 19th IFAC Conference (2014, August)

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See detailDoes the achievement of an intermediate glycemic target reduce organ failure and mortality? A post-hoc analysis of the Glucontrol Trial
Penning, Sophie ULg; Chase, J. Geoffrey; Preiser, Jean-Charles et al

in Journal of Critical Care (2014)

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See detailEstimating Relative Change in Ventricular Stroke Work from Aortic Pressure
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

Conference (2014)

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See detailModelling in anaesthesia and intensive care: a special section including papers from IFAC's 8. Symposium on Medical and Biological Systems in Budapest 2012.
Andreassen, Steen; Desaive, Thomas ULg; Karbing, Dan S.

in Journal of clinical monitoring and computing (2014)

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See detailEvolution of insulin sensitivity and its variability in out of hospital cardiac arrest (OHCA) patients treated with hypothermia.
Sah Pri, Azurahisham; Chase, James G.; Pretty, Christopher G. et al

in Critical care (London, England) (2014), 18(5), 586

IntroductionTherapeutic hypothermia (TH) is often used to treat out of hospital cardiac arrest (OHCA) patients who also often simultaneously receive insulin for stress-induced hyperglycaemia. However, the ... [more ▼]

IntroductionTherapeutic hypothermia (TH) is often used to treat out of hospital cardiac arrest (OHCA) patients who also often simultaneously receive insulin for stress-induced hyperglycaemia. However, the impact of TH on systemic metabolism and insulin resistance in critical illness is unknown. This study analyses the impact of TH on metabolism, including the evolution of insulin sensitivity (SI) and its variability, in patients with coma after OHCA.MethodsThis study uses a clinically validated, model-based measure of SI. Insulin sensitivity was identified hourly using retrospective data from 200 post-cardiac arrest patients (8,522 hours) treated with TH, shortly after admission to the Intensive Care Unit (ICU). Blood glucose and body temperature readings were taken every one to two hours. Data were divided into three periods: 1) cool (T <35 degrees C); 2) an idle period of two hours as normothermia was re-established; and 3) warm (T >37 degrees C). A maximum of 24 hours each for the cool and warm periods were considered. The impact of each condition on SI is analysed per cohort and per patient for both level and hour-to-hour variability, between periods and in 6-hour blocks.ResultsCohort and per patient median SI levels increase consistently by 35% to 70% and 26% to 59% (P <0.001) respectively from cool to warm. Conversely, cohort and per patient SI variability decreased by 11.1% to 33.6% (P <0.001) for the first 12 hours of treatment. However, SI variability increases between the 18th and 30th hours over the cool-warm transition, before continuing to decrease afterward.ConclusionsOCHA patients treated with TH have significantly lower and more variable SI during the cool period, compared to the later warm period. As treatment continues, SI level rises, and variability decreases consistently except for a large, significant increase during the cool-warm transition. These results demonstrate increased resistance to insulin during mild induced hypothermia. Our study might have important implications for glycaemic control during targeted temperature management. [less ▲]

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See detailVisualisation of time-varying respiratory system elastance in experimental ARDS animal models.
van Drunen, Erwin J.; Chiew, Yeong Shiong; Pretty, Christopher et al

in BMC pulmonary medicine (2014), 14

BACKGROUND: Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics ... [more ▼]

BACKGROUND: Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection. METHODS: The single compartment lung model was extended to monitor dynamic time-varying respiratory system elastance, Edrs, within each breathing cycle. Two separate animal models were considered, each consisting of three fully sedated pure pietrain piglets (oleic acid ARDS and lavage ARDS). A staircase recruitment manoeuvre was performed on all six subjects after ARDS was induced. The Edrs was mapped across each breathing cycle for each subject. RESULTS: Six time-varying, breath-specific Edrs maps were generated, one for each subject. Each Edrs map shows the subject-specific response to mechanical ventilation (MV), indicating the need for a model-based approach to guide MV. This method of visualisation provides high resolution insight into the time-varying respiratory mechanics to aid clinical decision making. Using the Edrs maps, minimal time-varying elastance was identified, which can be used to select optimal PEEP. CONCLUSIONS: Real-time continuous monitoring of in-breath mechanics provides further insight into lung physiology. Therefore, there is potential for this new monitoring method to aid clinicians in guiding MV treatment. These are the first such maps generated and they thus show unique results in high resolution. The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases. However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability. [less ▲]

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See detailThe Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management.
Szlavecz, Akos; Chiew, Yeong Shiong; Redmond, Daniel et al

in Biomedical engineering online (2014), 13(1), 140

BACKGROUND: Real-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software ... [more ▼]

BACKGROUND: Real-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software, Clinical Utilisation of Respiratory Elastance (CURE Soft), using a time-varying respiratory elastance model to offer this ability to aid in mechanical ventilation treatment. IMPLEMENTATION: CURE Soft is a desktop application developed in JAVA. It has two modes of operation, 1) Online real-time monitoring decision support and, 2) Offline for user education purposes, auditing, or reviewing patient care. The CURE Soft has been tested in mechanically ventilated patients with respiratory failure. The clinical protocol, software testing and use of the data were approved by the New Zealand South Regional Ethics Committee. RESULTS AND DISCUSSION: Using CURE Soft, patient's respiratory mechanics response to treatment and clinical protocol were monitored. Results showed that the patient's respiratory elastance (Stiffness) changed with the use of muscle relaxants, and responded differently to ventilator settings. This information can be used to guide mechanical ventilation therapy and titrate optimal ventilator PEEP. CONCLUSION: CURE Soft enables real-time calculation of model-based respiratory mechanics for mechanically ventilated patients. Results showed that the system is able to provide detailed, previously unavailable information on patient-specific respiratory mechanics and response to therapy in real-time. The additional insight available to clinicians provides the potential for improved decision-making, and thus improved patient care and outcomes. [less ▲]

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