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See detailMathematical model of the mitral valve and the cardiovascular system, application for studying, monitoring and in the diagnosis of valvular pathologies
Paeme, Sabine ULg; Moorhead, Katherine ULg; Chase, J. Geoffrey et al

in UKACC international Conference on Control 2010 : Coventry, 7-10 september 2010 (2010, September 07)

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics using Heaviside functions to simulate a physiological ... [more ▼]

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics using Heaviside functions to simulate a physiological accurate “open on pressure, close on flow” law. Thus, it does not consider the real time scale of the valve aperture dynamics and thus doesn’t fully capture valve dysfunction particularly where the dysfunction involves partial closure. This research describes a new closed-loop CVS model including a model describing the progressive aperture of the mitral valve and valid over the full cardiac cycle. This new model is solved for a healthy and diseased mitral valve. [less ▲]

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See detailAortic dP/dt_max accurately reflects left ventricular contractility when effective preload independence is achieved
MORIMONT, Philippe ULg; LAMBERMONT, Bernard ULg; Desaive, Thomas ULg et al

in Critical Care: the Official Journal of the Critical Care Forum (2010), 14 (Suppl 1)

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See detailPatient specific modelling of cardiac muscle activation
Stevenson, D; Hann, CE; Revie, JA et al

in Proceedings of the Health Research Society of Canterbury (HRSC) Clinical Meeting 2010 (2010)

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See detailModel-based cardiac disease diagnosis in critical care
Revie, JA; Hann, CE; Stevenson, D et al

in Proceedings of the Health Research Society of Canterbury (HRSC) Clinical Meeting 2010 (2010)

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See detailVentriculo-Arterial Coupling: an ideal problem for collaboration between clinicians and engineers
MORIMONT, Philippe ULg; Desaive, Thomas ULg; Chase et al

in Proceedings of CONTROL 2010 (2010)

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See detailEstimating the driver function of a cardiovascular system model
Stevenson, D; Hann, CE; Chase, JG et al

in Proceedings of CONTROL 2010 (2010)

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See detailA Model-based Approach to Cardiovascular Monitoring of Pulmonary Embolism
Revie, JA; Hann, CE; Stevenson, D et al

in Proceedings of CONTROL 2010 (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 detailPatient-specific modelling of cardiovascular dysfunction: Identifying models of pulmonary embolism in pigs
Desaive, Thomas ULg; Revie, J; Hann, CE et al

in Proceedings of the 19th International Conference of the Cardiovascular System Dynamics Society (2010)

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See detailNAVA enhances ventilatory variability and diaphragmatic activity/tidal volume coupling
Moorhead, KT; Piquilloud, L.; Desaive, Thomas ULg et al

in Intensive Care Medicine (2010), 36 (Suppl 2)

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See detailEffect of various Neurally adjusted ventilatory assist (NAVA) gains on the relationship between diaphragmatic activity (Eadi max) and tidal volume
Chiew, YS; Piquilloud, L.; Desaive, Thomas ULg et al

in Intensive Care Medicine (2010), 37 (Suppl 1)

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See detailThe future of glycaemic control in critically ill patients requires a close collaboration between bio-engineers and clinicians
Preiser, JC; Desaive, Thomas ULg; Chase, JG

in Proceedings of CONTROL 2010 (2010)

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See detailThe critical role of carbohydrate administration in safe, effective TGC
Preiser, J-C; Suhaimi, F; Chase, JG et al

in Clinical Nutrition (2010), 5 (Suppl 2):111

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See detailValidation of a virtual trial method for tight glycemic control in intensive care
Suhaimi, F; Chase, JG; Preiser, JC et al

in Proceedings of the Health Research Society of Canterbury (HRSC) Clinical Meeting 2010 (2010)

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See detailModel-based assessment of ventricular contractility
Desaive, Thomas ULg; Hann, CE; Chase, JG

in Proceedings of the 19th International Conference of the Cardiovascular System Dynamics Society (2010)

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See detailReduced Organ Failure with Effective Glycemic Control
Preiser, JC; Chase, JG; Pretty, CG et al

in Intensive Care Medicine (2010), 36 (Suppl 2)

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See detailTight Glycaemic control and nutrition: A comparison of two protocols
Suhaimi, F; Le Compte, AJ; Preiser, JC et al

in Proceedings of the Centre for Bio-Engineering One Day Conference 2010 (2010)

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See detailValidation of a Model-based Virtual Trials Method for Tight Glycaemic Control in Intensive Care
Suhaimi, F; Chase, JG; Le Compte, AJ et al

in Proceedings of the Centre for Bio-Engineering One Day Conference 2010 (2010)

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See detailWhy Protocolised care works in my unit?
Shaw, GM; Chase, JG; Pfeiffer, L et al

in Proceedings of the Australia-New Zealand Intensive Care Society Scientific Meeting (ANZICS 2010) (2010)

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See detailOrgan failure and tight glycemic control in the SPRINT study.
Chase, J Geoffrey; Pretty, Christopher G; Pfeifer, Leesa et al

in Critical Care: the Official Journal of the Critical Care Forum (2010), 14(4), 154

INTRODUCTION: Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is used to evaluate the impact of a successful ... [more ▼]

INTRODUCTION: Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is used to evaluate the impact of a successful tight glycemic control (TGC) intervention (SPRINT) on organ failure, morbidity, and thus mortality. METHODS: A retrospective analysis of 371 patients (3,356 days) on SPRINT (August 2005 - April 2007) and 413 retrospective patients (3,211 days) from two years prior, matched by Acute Physiology and Chronic Health Evaluation (APACHE) III. SOFA is calculated daily for each patient. The effect of the SPRINT TGC intervention is assessed by comparing the percentage of patients with SOFA </=5 each day and its trends over time and cohort/group. Organ-failure free days (all SOFA components </=2) and number of organ failures (SOFA components >2) are also compared. Cumulative time in 4.0 to 7.0 mmol/L band (cTIB) was evaluated daily to link tightness and consistency of TGC (cTIB >/=0.5) to SOFA </=5 using conditional and joint probabilities. RESULTS: Admission and maximum SOFA scores were similar (P = 0.20; P = 0.76), with similar time to maximum (median: one day; IQR: 13 days; P = 0.99). Median length of stay was similar (4.1 days SPRINT and 3.8 days Pre-SPRINT; P = 0.94). The percentage of patients with SOFA </=5 is different over the first 14 days (P = 0.016), rising to approximately 75% for Pre-SPRINT and approximately 85% for SPRINT, with clear separation after two days. Organ-failure-free days were different (SPRINT = 41.6%; Pre-SPRINT = 36.5%; P < 0.0001) as were the percent of total possible organ failures (SPRINT = 16.0%; Pre-SPRINT = 19.0%; P < 0.0001). By Day 3 over 90% of SPRINT patients had cTIB >/=0.5 (37% Pre-SPRINT) reaching 100% by Day 7 (50% Pre-SPRINT). Conditional and joint probabilities indicate tighter, more consistent TGC under SPRINT (cTIB >/=0.5) increased the likelihood SOFA </=5. CONCLUSIONS: SPRINT TGC resolved organ failure faster, and for more patients, from similar admission and maximum SOFA scores, than conventional control. These reductions mirror the reduced mortality with SPRINT. The cTIB >/=0.5 metric provides a first benchmark linking TGC quality to organ failure. These results support other physiological and clinical results indicating the role tight, consistent TGC can play in reducing organ failure, morbidity and mortality, and should be validated on data from randomised trials. [less ▲]

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