References of "Desaive, Thomas"
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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 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 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 detailTime varying elastance estimation in an 8 camber cardiovascular system model
Desaive, Thomas ULg; Chase, J. G.; Hann, C. E. et al

in Intensive Care Medicine (2010), 36(2), 151-151

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See detailWhat makes tight glycemic control tight? The impact of variability and nutrition in two clinical studies.
Suhaimi, Fatanah; Le Compte, Aaron; Preiser, Jean-Charles ULg et al

in Journal of Diabetes Science and Technology (2010), 4(2), 284-98

INTRODUCTION: Tight glycemic control (TGC) remains controversial while successful, consistent, and effective protocols remain elusive. This research analyzes data from two TGC trials for root causes of ... [more ▼]

INTRODUCTION: Tight glycemic control (TGC) remains controversial while successful, consistent, and effective protocols remain elusive. This research analyzes data from two TGC trials for root causes of the differences achieved in control and thus potentially in glycemic and other outcomes. The goal is to uncover aspects of successful TGC and delineate the impact of differences in cohorts. METHODS: A retrospective analysis was conducted using records from a 211-patient subset of the GluControl trial taken in Liege, Belgium, and 393 patients from Specialized Relative Insulin Nutrition Titration (SPRINT) in New Zealand. Specialized Relative Insulin Nutrition Titration targeted 4.0-6.0 mmol/liter, similar to the GluControl A (N = 142) target of 4.4-6.1 mmol/liter. The GluControl B (N = 69) target was 7.8-10.0 mmol/liter. Cohorts were matched by Acute Physiology and Chronic Health Evaluation II score and percentage males (p > .35); however, the GluControl cohort was slightly older (p = .011). Overall cohort and per-patient comparisons (median, interquartile range) are shown for (a) glycemic levels achieved, (b) nutrition from carbohydrate (all sources), and (c) insulin dosing for this analysis. Intra- and interpatient variability were examined using clinically validated model-based insulin sensitivity metric and its hour-to-hour variation. RESULTS: Cohort blood glucose were as follows: SPRINT, 5.7 (5.0-6.6) mmol/liter; GluControl A, 6.3 (5.3-7.6) mmol/liter; and GluControl B, 8.2 (6.9-9.4) mmol/liter. Insulin dosing was 3.0 (1.0-3.0), 1.5 (0.5-3), and 0.7 (0.0-1.7) U/h, respectively. Nutrition from carbohydrate (all sources) was 435.5 (259.2-539.1), 311.0 (0.0-933.1), and 622.1 (103.7-1036.8) kcal/day, respectively. Median per-patient results for blood glucose were 5.8 (5.3-6.4), 6.4 (5.9-6.9), and 8.3 (7.6-8.8) mmol/liter. Insulin doses were 3.0 (2.0-3.0), 1.5 (0.8-2.0), and 0.5 (0.0-1.0) U/h. Carbohydrate administration was 383.6 (207.4-497.7), 103.7 (0.0-829.4), and 207.4 (0.0-725.8) kcal/day. Overall, SPRINT gave approximately 2x more insulin with a 3-4x narrower, but generally non-zero, range of nutritional input to achieve equally TGC with less hypoglycemia. Specialized Relative Insulin Nutrition Titration had much less hypoglycemia (<2.2 mmol/liter), with 2% of patients, compared to GluControl A (7.7%) and GluControl B (2.9%), indicating much lower variability, with similar results for glucose levels <3.0 mmol/liter. Specialized Relative Insulin Nutrition Titration also had less hyperglycemia (>8.0 mmol/liter) than groups A and B. GluControl patients (A+B) had a approximately 2x wider range of insulin sensitivity than SPRINT. Hour-to-hour variation was similar. Hence GluControl had greater interpatient variability but similar intrapatient variability. CONCLUSION: Protocols that dose insulin blind to carbohydrate administration can suffer greater outcome glycemic variability, even if average cohort glycemic targets are met. While the cohorts varied significantly in model-assessed insulin resistance, their variability was similar. Such significant intra- and interpatient variability is a further significant cause and marker of glycemic variability in TGC. The results strongly recommended that TGC protocols be explicitly designed to account for significant intra- and interpatient variability in insulin resistance, as well as specifying or having knowledge of carbohydrate administration to minimize variability in glycemic outcomes across diverse cohorts and/or centers. [less ▲]

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See detailValidation of a model-based virtual trials method for tight glycemic control in intensive care.
Chase, J Geoffrey; Suhaimi, Fatanah; Penning, Sophie ULg et al

in BioMedical Engineering OnLine (2010), 9

BACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully ... [more ▼]

BACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods. METHODS: Data from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25). Virtual patients are created by fitting a clinically validated model to clinical data, yielding time varying insulin sensitivity profiles (SI(t)) that drives in-silico patients.Model fit and intra-patient (forward) prediction errors are used to validate individual in-silico virtual patients. Self-validation (tests A protocol on Group-A virtual patients; and B protocol on B virtual patients) and cross-validation (tests A protocol on Group-B virtual patients; and B protocol on A virtual patients) are used in comparison to clinical data to assess ability to predict clinical trial results. RESULTS: Model fit errors were small (<0.25%) for all patients, indicating model fitness. Median forward prediction errors were: 4.3, 2.8 and 3.5% for Group-A, Group-B and Overall (A+B), indicating individual virtual patients were accurate representations of real patients. SI and its variability were similar between cohorts indicating they were metabolically similar.Self and cross validation results were within 1-10% of the clinical data for both Group-A and Group-B. Self-validation indicated clinically insignificant errors due to model and/or clinical compliance. Cross-validation clearly showed that virtual patients enabled by identified patient-specific SI(t) profiles can accurately predict the performance of independent and different TGC protocols. CONCLUSIONS: This study fully validates these virtual patients and in silico virtual trial methods, and clearly shows they can accurately simulate, in advance, the clinical results of a TGC protocol, enabling rapid in silico protocol design and optimization. These outcomes provide the first rigorous validation of a virtual in-silico patient and virtual trials methodology. [less ▲]

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See detailUnique parameter identification for cardiac diagnosis in critical care using minimal data sets.
Hann, C. E.; Chase, J. G.; Desaive, Thomas ULg et al

in Computer Methods & Programs in Biomedicine (2010)

Lumped parameter approaches for modelling the cardiovascular system typically have many parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant ... [more ▼]

Lumped parameter approaches for modelling the cardiovascular system typically have many parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found. By utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care. [less ▲]

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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 detailMitral valve dynamics in a closed-loop model of the cardiovascular system
Paeme, Sabine ULg; Chase, J. Geoffrey; Hann, Christopher et al

Poster (2009, December 17)

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics by means of Heaviside function to simulate “open on ... [more ▼]

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics by means of Heaviside function to simulate “open on pressure, close on flow” law. Thus, it does not consider the real time scale of the valve aperture and thus doesn’t fully capture valve dysfunction. This research couples the CVS model with a model describing the progressive aperture of the mitral valve. [less ▲]

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See detailMitral valve dynamics in a closed-loop model of the cardiovascular system
Paeme, Sabine ULg; Chase, J. Geoffrey; Hann, christopher et al

in Archives des Maladies du Coeur et des Vaisseaux. Pratique (2009, December), hors série 1

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics by means of Heaviside function to simulate “open on ... [more ▼]

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics by means of Heaviside function to simulate “open on pressure, close on flow” law. Thus, it does not consider the real time scale of the valve aperture and thus doesn’t fully capture valve dysfunction. This work describes a new coupled model of the cardiovascular system that accounts for progressive mitral valve aperture. Simulations show good correlation with physiologically expected results for healthy or diseased valves. The large number of valve model parameters indicates a need for emerging, lighter and minimal mitral valve models that are readily identifiable to achieve full benefit in real-time use. These results suggest a further use of this model to track, diagnose and control valves pathologies. [less ▲]

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See detailUnique parameter identification of a cardiovascular system model using feedback control
Hann, C. E.; Chase, J. G.; Desaive, Thomas ULg et al

in Proc. 7th Intl Conf on Control and Automation (ICCA09) (2009, December)

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