References of "Desaive, Thomas"
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See detailMinimal cardiovascular system model including a physiological description of progressive mitral valve orifice dynamics for studying valve dysfunction
Paeme, Sabine ULg; Moorhead, Katerine; Chase, J. Geoffrey et al

in XXIIIrd congress of the International Society of Biomechanics, July 3-7, 2011 (2011, July)

This research presents a new closed-loop cardiovascular system model including a description of the progressive opening and closing dynamic of the mitral valve. Furthermore, this model includes a ... [more ▼]

This research presents a new closed-loop cardiovascular system model including a description of the progressive opening and closing dynamic of the mitral valve. Furthermore, this model includes a mathematical description of the left atrium. This new CVS model enables the study of valve dysfunction in the appropriate clinical context of the overall cardiac and circulatory hemodynamics. [less ▲]

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See detailInfluence of thermoelectric coupling on pacemaker activity generated by mechano-electric feedback in a one-dimensional ring-shaped model of cardiac fiber
Collet, Arnaud ULg; Desaive, Thomas ULg; Pierard, Luc ULg et al

Poster (2011, June 01)

The mechano-electric feedback (MEF) in the heart consists in the influence of the tissue deformations on the cardiac electrical activity. Under certain conditions, tissue deformations can generate ... [more ▼]

The mechano-electric feedback (MEF) in the heart consists in the influence of the tissue deformations on the cardiac electrical activity. Under certain conditions, tissue deformations can generate electrical perturbations via stretch-activated channels, such that the membrane potential can exceed the threshold value needed in order to trigger cardiac action potentials (APs). In the present study, we have developed a one-dimensional ring-shaped model of cardiac fiber taking into account three different couplings: the excitation-contraction coupling (ECC), the MEF and the thermoelectric coupling (TEC). The main goal of this work is to examine the effects of the TEC on the different properties of the pacemaker activity generated by the MEF. [less ▲]

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See detailThe Parametrized Diastolic Filling Formalism: Application in the Asklepios Population
Claessens, Tom; Muhammad Waheed, Raja; Pironet, Antoine ULg et al

in Conference Program ASME 2011 Summer Bioengineering Conference (2011, June)

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See detailEnhanced insulin sensitivity variability in the first 3 days of ICU stay: Implications for TGC
Chase, J. Geoffrey; Le Compte, Aaron; Penning, Sophie ULg et al

in Critical Care (2011, March)

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See detailValidation of a virtual patient and virtual trials method for accurate prediction of TGC protocol performance
Suhaimi, Fatanah; Le Compte, Aaron; Penning, Sophie ULg et al

in Critical Care (2011, March)

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See detailValidation of a Virtual Patient and Virtual Trials Method for Accurate Prediction of TGC Protocol Performance
Suhaimi, Fatanah; Le Compte, Aaron; Penning, Sophie ULg et al

Poster (2011, March)

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See detailEnhanced insulin sensitivity variability in the first 3 days of ICU stay: Implications for TGC
Chase, Geoffrey; Le Compte, Aaron; Penning, Sophie ULg et al

Poster (2011, March)

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See detailRespiratory variability in mechanically ventilated patients
Desaive, Thomas ULg; Piquilloud, L.; Moorhead, KT et al

in Critical Care (2011), 15 (Suppl 1)

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See detailPulmonary embolism diagnostics from the driver function
Stevenson, DJ; Revie; Chase, JG et al

in Critical Care (2011), 15 (Suppl 1)

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See detailModel-based cardiovascular monitoring of acute pulmonary embolism in porcine trials
Revie, JA; Stevenson, DJ; Chase, JG et al

in Critical Care (2011), 15 (Suppl 1)

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See detailModel-based cardiovascular monitoring of large pore hemofiltration during endotoxic shock in pigs
Revie, JA; Stevenson, DJ; Chase, JG et al

in Critical Care (2011), 15 (Suppl 1)

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See detailPatient specific identification of the cardiac driver function in a cardiovascular system model.
Hann, C. E.; Revie, J.; Stevenson, D. et al

in Computer Methods & Programs in Biomedicine (2011)

The cardiac muscle activation or driver function, is a major determinant of cardiovascular dynamics, and is often approximated by the ratio of the left ventricle pressure to the left ventricle volume. In ... [more ▼]

The cardiac muscle activation or driver function, is a major determinant of cardiovascular dynamics, and is often approximated by the ratio of the left ventricle pressure to the left ventricle volume. In an intensive care unit, the left ventricle pressure is usually never measured, and the left ventricle volume is only measured occasionally by echocardiography, so is not available real-time. This paper develops a method for identifying the driver function based on correlates with geometrical features in the aortic pressure waveform. The method is included in an overall cardiovascular modelling approach, and is clinically validated on a porcine model of pulmonary embolism. For validation a comparison is done between the optimized parameters for a baseline model, which uses the direct measurements of the left ventricle pressure and volume, and the optimized parameters from the approximated driver function. The parameters do not significantly change between the two approaches thus showing that the patient specific approach to identifying the driver function is valid, and has potential clinically. [less ▲]

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See detailTight glycemic control in critical care - The leading role of insulin sensitivity and patient variability: A review and model-based analysis.
Chase, J. G.; Le Compte, A. J.; Suhaimi, F. et al

in Computer Methods & Programs in Biomedicine (2011)

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials ... [more ▼]

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S(I)). Variation in S(I) provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S(I)) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration. [less ▲]

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See detailPorcine trial validation of model-based cardiovascular monitoring of acute pulmonary embolism
Revie, JA; Stevenson, DJ; Shaw, GM et al

in Proceedings of ANZICS 2011 (2011)

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See detailDiagnosing pulmonary embolism from a model-based cardiac driver function
Stevenson, D; Revie, JA; Chase, JG et al

in Proceedings of ANZICS 2011 (2011)

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See detailProcessing aortic and pulmonary artery waveforms to derive the ventricle time-varying elastance
Stevenson, D; Chase, JG; Hann, CE et al

in Proceedings of the 18th IFAC World Congress, 2011 (2011)

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See detailNeurally Adjusted Ventilatory Assist (NAVA) improves the matching of diaphragmatic electrical activity and tidal volume in comparison to pressure support (PS)
Piquilloud, L; Chiew, YS; Bialais, E et al

in Intensive Care Medicine (2011), 37 (Suppl 1)

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See detailModel-based PEEP optimisation in mechanical ventilation
Chiew, Y. S.; Chase, J. G.; Shaw, G. M. et al

in BioMedical Engineering OnLine (2011), 10

Background: Acute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is ... [more ▼]

Background: Acute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is no well established method in optimal PEEP selection.Methods: A study of 10 patients diagnosed with ALI/ARDS whom underwent recruitment manoeuvre is carried out. Airway pressure and flow data are used to identify patient-specific constant lung elastance (E <br /> lung) and time-variant dynamic lung elastance (E <br /> drs) at each PEEP level (increments of 5cmH <br /> 2O), for a single compartment linear lung model using integral-based methods. Optimal PEEP is estimated using E <br /> lungversus PEEP, E <br /> drs-Pressure curve and E <br /> drsArea at minimum elastance (maximum compliance) and the inflection of the curves (diminishing return). Results are compared to clinically selected PEEP values. The trials and use of the data were approved by the New Zealand South Island Regional Ethics Committee.Results: Median absolute percentage fitting error to the data when estimating time-variant E <br /> drsis 0.9% (IQR = 0.5-2.4) and 5.6% [IQR: 1.8-11.3] when estimating constant E <br /> lung. Both E <br /> lungand E <br /> drsdecrease with PEEP to a minimum, before rising, and indicating potential over-inflation. Median E <br /> drsover all patients across all PEEP values was 32.2 cmH <br /> 2O/l [IQR: 26.1-46.6], reflecting the heterogeneity of ALI/ARDS patients, and their response to PEEP, that complicates standard approaches to PEEP selection. All E <br /> drs-Pressure curves have a clear inflection point before minimum E <br /> drs, making PEEP selection straightforward. Model-based selected PEEP using the proposed metrics were higher than clinically selected values in 7/10 cases.Conclusion: Continuous monitoring of the patient-specific E <br /> lungand E <br /> drsand minimally invasive PEEP titration provide a unique, patient-specific and physiologically relevant metric to optimize PEEP selection with minimal disruption of MV therapy. © 2011 Chiew et al; licensee BioMed Central Ltd. [less ▲]

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See detailInsulin Sensitivity, Its Variability and Glycemic Outcome: A model-based analysis of the difficulty in achieving tight glycemic control in critical care
Chase, J. Geoffrey; Le Compte, Aaron J.; Preiser, Jean-Charles et al

in 18th World Congress of the International Federation of Automatic Control (IFAC) (2011)

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a ... [more ▼]

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a <br />patient’s stay, are a function of variability in insulin sensitivity/resistance resulting from the level and <br />evolution of stress response, and are independently associated with mortality. This study examines the <br />daily evolution of variability of insulin sensitivity in ICU patients using patient data (N = 394 patients, <br />54019 hours) from the SPRINT TGC study. Model-based insulin sensitivity (SI) was identified each hour <br />and hour-to-hour percent changes in SI were assessed for Days 1-3 individually and Day 4 Onward, as <br />well as over all days. Cumulative distribution functions (CDFs), median values, and inter-quartile points <br />(25th and 75th percentiles) are used to assess differences between groups and their evolution over time. <br />Compared to the overall (all days) distributions, ICU patients are more variable on Days 1 and 2 (p < <br />0.0001), and less variable on Days 4 Onward (p < 0.0001). Day 3 is similar to the overall cohort (p = 0.74). <br />Absolute values of SI start lower and rise for Days 1 and 2, compared to the overall cohort (all days), (p < <br />0.0001), are similar on Day 3 (p = .72) and are higher on Days 4 Onward (p < 0.0001). ICU patients have <br />lower insulin sensitivity (greater insulin resistance) and it is more variable on Days 1 and 2, compared to <br />an overall cohort on all days. This is the first such model-based analysis of its kind. Greater variability <br />with lower SI early in a patient’s stay greatly increases the difficulty in achieving and safely maintaining <br />glycemic control, reducing potential positive outcomes. Clinically, the results imply that TGC patients will <br />require greater measurement frequency, reduced reliance on insulin, and more explicit specification of <br />carbohydrate nutrition in Days 1-3 to safely minimise glycemic variability for best outcome. [less ▲]

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See detailSafety and Performance of Stochastic Targeted (STAR) TGC of Insulin and Nutrition
Shaw, GM; Le Compte, Aaron; Evans, A et al

in Proceedings of SQAO 2011 (2011)

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