References of "Chase, J. Geoffrey"
     in
Bookmark and Share    
Full Text
See detailEstimating Ventricular Stroke Work from Aortic Pressure Waveform
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

in 13th Belgian Day on Biomedical Engineering (2014, November 28)

Detailed reference viewed: 11 (1 ULg)
Full Text
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 ▲]

Detailed reference viewed: 14 (2 ULg)
Full Text
See detailEstimating Ventricular Stroke Work from Aortic Pressure Waveform
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

Poster (2014, November 28)

Detailed reference viewed: 25 (2 ULg)
Full Text
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 ▲]

Detailed reference viewed: 18 (4 ULg)
Full Text
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 ▲]

Detailed reference viewed: 11 (2 ULg)
Full Text
Peer Reviewed
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 ▲]

Detailed reference viewed: 20 (9 ULg)
Peer Reviewed
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 ▲]

Detailed reference viewed: 17 (8 ULg)
Full Text
Peer Reviewed
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)

Detailed reference viewed: 6 (1 ULg)
Peer Reviewed
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)

Detailed reference viewed: 15 (7 ULg)
Peer Reviewed
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)

Detailed reference viewed: 6 (1 ULg)
Full Text
Peer Reviewed
See detailDoes the achievement of an intermediate glycemic target reduce organ failure and mortality? A post-hoc analysis of the Glucontrol Trial
Penning, Sophie ULg; Chase, J. Geoffrey; Preiser, Jean-Charles et al

in Journal of Critical Care (2014)

Detailed reference viewed: 21 (5 ULg)
Full Text
See detailEstimating Relative Change in Ventricular Stroke Work from Aortic Pressure
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

Conference (2014)

Detailed reference viewed: 15 (0 ULg)
Full Text
Peer Reviewed
See detailVisualisation of time-varying respiratory system elastance in experimental ARDS animal models.
van Drunen, Erwin J.; Chiew, Yeong Shiong; Pretty, Christopher et al

in BMC pulmonary medicine (2014), 14

BACKGROUND: Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics ... [more ▼]

BACKGROUND: Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection. METHODS: The single compartment lung model was extended to monitor dynamic time-varying respiratory system elastance, Edrs, within each breathing cycle. Two separate animal models were considered, each consisting of three fully sedated pure pietrain piglets (oleic acid ARDS and lavage ARDS). A staircase recruitment manoeuvre was performed on all six subjects after ARDS was induced. The Edrs was mapped across each breathing cycle for each subject. RESULTS: Six time-varying, breath-specific Edrs maps were generated, one for each subject. Each Edrs map shows the subject-specific response to mechanical ventilation (MV), indicating the need for a model-based approach to guide MV. This method of visualisation provides high resolution insight into the time-varying respiratory mechanics to aid clinical decision making. Using the Edrs maps, minimal time-varying elastance was identified, which can be used to select optimal PEEP. CONCLUSIONS: Real-time continuous monitoring of in-breath mechanics provides further insight into lung physiology. Therefore, there is potential for this new monitoring method to aid clinicians in guiding MV treatment. These are the first such maps generated and they thus show unique results in high resolution. The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases. However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability. [less ▲]

Detailed reference viewed: 6 (0 ULg)
Full Text
Peer Reviewed
See detailThe Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management.
Szlavecz, Akos; Chiew, Yeong Shiong; Redmond, Daniel et al

in Biomedical engineering online (2014), 13(1), 140

BACKGROUND: Real-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software ... [more ▼]

BACKGROUND: Real-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software, Clinical Utilisation of Respiratory Elastance (CURE Soft), using a time-varying respiratory elastance model to offer this ability to aid in mechanical ventilation treatment. IMPLEMENTATION: CURE Soft is a desktop application developed in JAVA. It has two modes of operation, 1) Online real-time monitoring decision support and, 2) Offline for user education purposes, auditing, or reviewing patient care. The CURE Soft has been tested in mechanically ventilated patients with respiratory failure. The clinical protocol, software testing and use of the data were approved by the New Zealand South Regional Ethics Committee. RESULTS AND DISCUSSION: Using CURE Soft, patient's respiratory mechanics response to treatment and clinical protocol were monitored. Results showed that the patient's respiratory elastance (Stiffness) changed with the use of muscle relaxants, and responded differently to ventilator settings. This information can be used to guide mechanical ventilation therapy and titrate optimal ventilator PEEP. CONCLUSION: CURE Soft enables real-time calculation of model-based respiratory mechanics for mechanically ventilated patients. Results showed that the system is able to provide detailed, previously unavailable information on patient-specific respiratory mechanics and response to therapy in real-time. The additional insight available to clinicians provides the potential for improved decision-making, and thus improved patient care and outcomes. [less ▲]

Detailed reference viewed: 5 (0 ULg)
Full Text
Peer Reviewed
See detailReducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol.
Thomas, Felicity; Pretty, Christopher G.; Fisk, Liam et al

in Biomedical engineering online (2014), 13

BACKGROUND: The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the ... [more ▼]

BACKGROUND: The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12-48 hours in the intensive care unit (ICU). These hormones have a direct physiological impact on insulin sensitivity (SI). Understanding the variability of SI is important for safely managing glycaemic levels and understanding the evolution of patient condition. The objective of this study is to assess the evolution of SI over the first two days of ICU stay, and using this data, propose a separate stochastic model to reduce the impact of SI variability during glycaemic control using the STAR glycaemic control protocol. METHODS: The value of SI was identified hourly for each patient using a validated physiological model. Variability of SI was then calculated as the hour-to-hour percentage change in SI. SI was examined using 6 hour blocks of SI to display trends while mitigating the effects of noise. To reduce the impact of SI variability on achieving glycaemic control a new stochastic model for the most variable period, 0-18 hours, was generated. Virtual simulations were conducted using an existing glycaemic control protocol (STAR) to investigate the clinical impact of using this separate stochastic model during this period of increased metabolic variability. RESULTS: For the first 18 hours, over 80% of all SI values were less than 0.5 x 10(-3) L/mU x min, compared to 65% for >18 hours. Using the new stochastic model for the first 18 hours of ICU stay reduced the number of hypoglycaemic measurements during virtual trials. For time spent below 4.4, 4.0, and 3.0 mmol/L absolute reductions of 1.1%, 0.8% and 0.1% were achieved, respectively. No severe hypoglycaemic events (BG < 2.2 mmol/L) occurred for either case. CONCLUSIONS: SI levels increase significantly, while variability decreases during the first 18 hours of a patients stay in ICU. Virtual trials, using a separate stochastic model for this period, demonstrated a reduction in variability and hypoglycaemia during the first 18 hours without adversely affecting the overall level of control. Thus, use of multiple models can reduce the impact of SI variability during model-based glycaemic control. [less ▲]

Detailed reference viewed: 4 (0 ULg)
Full Text
Peer Reviewed
See detailWhen the value of gold is zero.
Chase, J. Geoffrey; Moeller, Knut; Shaw, Geoffrey M. et al

in BMC research notes (2014), 7

This manuscript presents the concerns around the increasingly common problem of not having readily available or useful "gold standard" measurements. This issue is particularly important in critical care ... [more ▼]

This manuscript presents the concerns around the increasingly common problem of not having readily available or useful "gold standard" measurements. This issue is particularly important in critical care where many measurements used in decision making are surrogates of what we would truly wish to use. However, the question is broad, important and applicable in many other areas.In particular, a gold standard measurement often exists, but is not clinically (or ethically in some cases) feasible. The question is how does one even begin to develop new measurements or surrogates if one has no gold standard to compare with?We raise this issue concisely with a specific example from mechanical ventilation, a core bread and butter therapy in critical care that is also a leading cause of length of stay and cost of care. Our proposed solution centers around a hierarchical validation approach that we believe would ameliorate ethics issues around radiation exposure that make current gold standard measures clinically infeasible, and thus provide a pathway to create a (new) gold standard. [less ▲]

Detailed reference viewed: 6 (1 ULg)
Full Text
Peer Reviewed
See detailContinuous stroke volume estimation from aortic pressure using zero dimensional cardiovascular model: proof of concept study from porcine experiments.
Kamoi, Shun; Pretty, Christopher; Docherty, Paul et al

in PloS one (2014), 9(7), 102476

INTRODUCTION: Accurate, continuous, left ventricular stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status and response to therapy. However, direct ... [more ▼]

INTRODUCTION: Accurate, continuous, left ventricular stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status and response to therapy. However, direct measurements are highly invasive in clinical practice, and current procedures for estimating SV require specialized devices and significant approximation. METHOD: This study investigates the accuracy of a three element Windkessel model combined with an aortic pressure waveform to estimate SV. Aortic pressure is separated into two components capturing; 1) resistance and compliance, 2) characteristic impedance. This separation provides model-element relationships enabling SV to be estimated while requiring only one of the three element values to be known or estimated. Beat-to-beat SV estimation was performed using population-representative optimal values for each model element. This method was validated using measured SV data from porcine experiments (N = 3 female Pietrain pigs, 29-37 kg) in which both ventricular volume and aortic pressure waveforms were measured simultaneously. RESULTS: The median difference between measured SV from left ventricle (LV) output and estimated SV was 0.6 ml with a 90% range (5th-95th percentile) -12.4 ml-14.3 ml. During periods when changes in SV were induced, cross correlations in between estimated and measured SV were above R = 0.65 for all cases. CONCLUSION: The method presented demonstrates that the magnitude and trends of SV can be accurately estimated from pressure waveforms alone, without the need for identification of complex physiological metrics where strength of correlations may vary significantly from patient to patient. [less ▲]

Detailed reference viewed: 6 (0 ULg)
Full Text
Peer Reviewed
See detailA patient-specific airway branching model for mechanically ventilated patients.
Damanhuri, Nor Salwa; Docherty, Paul D.; Chiew, Yeong Shiong et al

in Computational and mathematical methods in medicine (2014), 2014

Background. Respiratory mechanics models have the potential to guide mechanical ventilation. Airway branching models (ABMs) were developed from classical fluid mechanics models but do not provide accurate ... [more ▼]

Background. Respiratory mechanics models have the potential to guide mechanical ventilation. Airway branching models (ABMs) were developed from classical fluid mechanics models but do not provide accurate models of in vivo behaviour. Hence, the ABM was improved to include patient-specific parameters and better model observed behaviour (ABMps). Methods. The airway pressure drop of the ABMps was compared with the well-accepted dynostatic algorithm (DSA) in patients diagnosed with acute respiratory distress syndrome (ARDS). A scaling factor (alpha) was used to equate the area under the pressure curve (AUC) from the ABMps to the AUC of the DSA and was linked to patient state. Results. The ABMps recorded a median alpha value of 0.58 (IQR: 0.54-0.63; range: 0.45-0.66) for these ARDS patients. Significantly lower alpha values were found for individuals with chronic obstructive pulmonary disease (P < 0.001). Conclusion. The ABMps model allows the estimation of airway pressure drop at each bronchial generation with patient-specific physiological measurements and can be generated from data measured at the bedside. The distribution of patient-specific alpha values indicates that the overall ABM can be readily improved to better match observed data and capture patient condition. [less ▲]

Detailed reference viewed: 4 (0 ULg)