References of "Pretty, Christopher"
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See detailImpact of sensor and measurement timing errors on model-based insulin sensitivity
Pretty, Christopher ULg; Signal, Matthew; Fisk, Liam et al

in Computer Methods & Programs in Biomedicine (2013)

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See detailEvaluation of a Model-Based Hemodynamic Monitoring Method in a Porcine Study of Septic Shock
Revie, James; Stevenson, David; Chase, J. Geoffrey et al

in Computational and Mathematical Methods in Medicine (2013)

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See detailA multi-scale cardiovascular system model can account for the load-dependence of the end-systolic pressure-volume relationship.
Pironet, Antoine ULg; Desaive, Thomas ULg; Kosta, Sarah ULg et al

in BioMedical Engineering OnLine (2013), 12(1), 8

ABSTRACT: BACKGROUND: The end-systolic pressure-volume relationship is often considered as a load-independent property of the heart and, for this reason, is widely used as an index of ventricular ... [more ▼]

ABSTRACT: BACKGROUND: The end-systolic pressure-volume relationship is often considered as a load-independent property of the heart and, for this reason, is widely used as an index of ventricular contractility. However, many criticisms have been expressed against this index and the underlying time-varying elastance theory: first, it does not consider the phenomena underlying contraction and second, the end-systolic pressure volume relationship has been experimentally shown to be load-dependent. METHODS: In place of the time-varying elastance theory, a microscopic model of sarcomere contraction is used to infer the pressure generated by the contraction of the left ventricle, considered as a spherical assembling of sarcomere units. The left ventricle model is inserted into a closed-loop model of the cardiovascular system. Finally, parameters of the modified cardiovascular system model are identified to reproduce the hemodynamics of a normal dog. RESULTS: Experiments that have proven the limitations of the time-varying elastance theory are reproduced with our model: (1) preload reductions, (2) afterload increases, (3) the same experiments with increased ventricular contractility, (4) isovolumic contractions and (5) flow-clamps. All experiments simulated with the model generate different end-systolic pressure-volume relationships, showing that this relationship is actually load-dependent. Furthermore, we show that the results of our simulations are in good agreement with experiments. CONCLUSIONS: We implemented a multi-scale model of the cardiovascular system, in which ventricular contraction is described by a detailed sarcomere model. Using this model, we successfully reproduced a number of experiments that have shown the failing points of the time-varying elastance theory. In particular, the developed multi-scale model of the cardiovascular system can capture the load-dependence of the end-systolic pressure-volume relationship. [less ▲]

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See detailModel-based glycemic control in critical care
Pretty, Christopher ULg; Penning, Sophie ULg; Le Compte, Aaron J. et al

Poster (2012, December)

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See detailModel-based glycemic control in critical care
Pretty, Christopher ULg; Penning, Sophie ULg; Le Compte, Aaron J. et al

in Proceedings of the 11th Belgian Day on Biomedical Engineering (2012, December)

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See detailInsulin clearance during hyper-insulinemia euglycemia therapy
Penning, Sophie ULg; MASSION, Paul ULg; Pretty, Christopher ULg et al

in Proceedings of the 11th Belgian Day on Biomedical Engineering (2012, December)

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See detailInsulin clearance during hyper-insulinemia euglycemia therapy
Penning, Sophie ULg; MASSION, Paul ULg; Pretty, Christopher ULg et al

Poster (2012, December)

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See detailCumulative time in band (cTIB): glycemic level, variability and patient outcome all in one
Penning, Sophie ULg; Signal, Matthew; Preiser, Jean-Charles et al

Conference (2012, October 15)

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See detailCumulative time in band: glycemic level, variability and patient outcome vs. mortality
Penning, Sophie ULg; Signal, Matthew; Preiser, Jean-Charles et al

Poster (2012, October)

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See detailCumulative Time in Band (cTIB): Glycemic Level, Variability and Patient Outcome All in 1
Penning, Sophie ULg; Signal, Matthew; Preiser, Jean-Charles et al

in Intensive Care Medicine (2012, October), 38 (Suppl 1)

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See detailSecond pilot trials of the STAR-Liege protocol for tight glycemic control in critically ill patients
Penning, Sophie ULg; Le Compte, Aaron J.; MASSION, Paul ULg et al

in BioMedical Engineering OnLine (2012)

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See detailInterstitial kinetic parameters for a 2-compartment insulin model with saturable clearance
Pretty, Christopher ULg; Le Compte, Aaron J.; Shaw, Geoffrey M. et al

Conference (2012, August)

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See detailImpact of sensor and measurement timing errors on model-based insulin sensitivity
Pretty, Christopher ULg; Le Compte, Aaron J.; Shaw, Geoffrey M. et al

Conference (2012, August)

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See detailInterstitial kinetic parameters for a 2-compartment insulin model with saturable clearance
Pretty, Christopher ULg; Le Compte, Aaron J.; Shaw, Geoffrey M. et al

in Proceedings of the 8th IFAC Symposium on Biological and Medical Systems (2012, August)

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See detailImpact of sensor and measurement timing errors on model-based insulin sensitivity
Pretty, Christopher ULg; Le Compte, Aaron J.; Shaw, Geoffrey M. et al

in Proceedings of the 8th IFAC Symposium on Biological and Medical Systems (2012, August)

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See detailVariability of insulin sensitivity during the first 4 days of critical illness: implications for tight glycemic control
Pretty, Christopher ULg; Le Compte, Aaron J.; Chase, J. Geoffrey et al

in Annals of Intensive Care (2012)

Introduction: Effective tight glycaemic control (TGC) can improve outcomes in critical care patients, but is difficult to achieve consistently. Insulin sensitivity defines the metabolic balance between ... [more ▼]

Introduction: Effective tight glycaemic control (TGC) can improve outcomes in critical care patients, but is difficult to achieve consistently. Insulin sensitivity defines the metabolic balance between insulin concentration and insulin mediated glucose disposal. Hence, variability of insulin sensitivity can cause variable glycaemia. This study quantifies and compares the daily evolution of insulin sensitivity level and variability for critical care patients receiving TGC. <br /> <br />Methods: A retrospective analysis of data from the SPRINT TGC study involving patients admitted to a mixed medical-surgical ICU between August 2005 and May 2007. Only patients who commenced TGC within 12 hours of ICU admission and spent at least 24 hours on the SPRINT protocol were included (N=164). Model-based insulin sensitivity (SI) was identified each hour. Absolute level and hour-to-hour percent changes in SI were assessed on cohort and per-patient bases. Levels and variability of SI were compared over time on 24-hour and 6-hour timescales for the first 4 days of ICU stay. <br /> <br />Results: Cohort and per-patient median SI levels increased by 34% and 33% (p<0.001) between days 1 and 2 of ICU stay. Concomitantly, cohort and per-patient SI variability decreased by 32% and 36% (p<0.001). For 72% of the cohort, median SI on day 2 was higher than on day 1. The day 1-2 results are the only clear, statistically significant trends across both analyses. <br /> <br />Analysis of the first 24 hours using 6-hour blocks of SI data showed that most of the improvement in insulin sensitivity level and variability seen between days 1 and 2 occurred during the first 12-18 hours of day 1. <br /> <br />Conclusions: Critically ill patients have significantly lower and more variable insulin sensitivity on day 1 than later in their ICU stay and particularly during the first 12 hours. This rapid improvement is likely due to the decline of counter-regulatory hormones as the acute phase of critical illness progresses. Clinically, these results suggest that while using TGC protocols with patients during their first few days of ICU stay, extra care should be afforded. Increased measurement frequency, higher target glycaemic bands, conservative insulin dosing and modulation of carbohydrate nutrition should be considered to safely minimize outcome glycaemic variability and reduce the risk of hypoglycaemia. [less ▲]

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See detailVariability of insulin sensitivity during the first 4 days of critical illness
Pretty, Christopher ULg; Le Compte, A; Chase, JG et al

in Critical Care (2012), 16 (Suppl 1)

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See detailCumulative time in band (cTIB): glycemic level, variability and patient outcome vs mortality
Penning, Sophie ULg; Signal, M; Preiser, JC et al

in Proceedings of ANZICS 2012 (2012)

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