References of "Desaive, Thomas"
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See detailTime-varying respiratory system elastance: a physiological model for patients who are spontaneously breathing.
Chiew, Yeong Shiong; Pretty, Christopher; Docherty, Paul D. et al

in PloS one (2015), 10(1), 0114847

BACKGROUND: Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no ... [more ▼]

BACKGROUND: Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (Edrs) model that can be used in spontaneously breathing patients to determine their respiratory mechanics. METHODS: A time-varying respiratory elastance model is developed with a negative elastic component (Edemand), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. Edrs of every breathing cycle for each patient at different ventilation modes are presented for comparison. RESULTS: At the start of every breathing cycle initiated by patient, Edrs is < 0. This negativity is attributed from the Edemand due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of Edrs trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of Edrs (AUCEdrs) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator. CONCLUSION: The Edrs model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes. [less ▲]

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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 detailEstimating Relative Change in Ventricular Stroke Work from Aortic Pressure Alone: Proof of Concept Study
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

in 48th DGBMT Biomedizinische Technik Conference (BMT 2014) (2014)

Continuous Ventricular Stroke Work (VSW) estimation requires accurate estimate of both stroke volume and aortic pressure. However, accurate beat-to-beat stroke volume measurement is highly invasive and ... [more ▼]

Continuous Ventricular Stroke Work (VSW) estimation requires accurate estimate of both stroke volume and aortic pressure. However, accurate beat-to-beat stroke volume measurement is highly invasive and thus typically unavailable in clinical practice. This study analyses the accuracy of a model-based method estimating relative change in VSW using only aortic pressure measurements. Using data from porcine experiment, the correlation coefficient was determined between the relative change of VSW from directly measured data and the model-based estimate of VSW. The result showed good agreement with, R=0.71. The model accurately captured the trend of VSW using only aortic pressure measurements and thus offers significant clinical value in early diagnosis and improving care for cardiovascular dysfunction. [less ▲]

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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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