References of "chase, J. Geoffrey"
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See detailTime-varying respiratory system elastance: a physiological model for patients who are spontaneously breathing.
Chiew, Yeong Shiong; Pretty, Christopher; Docherty, Paul D. et al

in PloS one (2015), 10(1), 0114847

BACKGROUND: Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no ... [more ▼]

BACKGROUND: Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (Edrs) model that can be used in spontaneously breathing patients to determine their respiratory mechanics. METHODS: A time-varying respiratory elastance model is developed with a negative elastic component (Edemand), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. Edrs of every breathing cycle for each patient at different ventilation modes are presented for comparison. RESULTS: At the start of every breathing cycle initiated by patient, Edrs is < 0. This negativity is attributed from the Edemand due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of Edrs trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of Edrs (AUCEdrs) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator. CONCLUSION: The Edrs model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes. [less ▲]

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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 ▲]

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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)

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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 ▲]

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See detailEstimating Ventricular Stroke Work from Aortic Pressure Waveform
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

Poster (2014, November 28)

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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 ▲]

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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 ▲]

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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 ▲]

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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)

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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)

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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)

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See detailEstimating Relative Change in Ventricular Stroke Work from Aortic Pressure Alone: Proof of Concept Study
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

in 48th DGBMT Biomedizinische Technik Conference (BMT 2014) (2014)

Continuous Ventricular Stroke Work (VSW) estimation requires accurate estimate of both stroke volume and aortic pressure. However, accurate beat-to-beat stroke volume measurement is highly invasive and ... [more ▼]

Continuous Ventricular Stroke Work (VSW) estimation requires accurate estimate of both stroke volume and aortic pressure. However, accurate beat-to-beat stroke volume measurement is highly invasive and thus typically unavailable in clinical practice. This study analyses the accuracy of a model-based method estimating relative change in VSW using only aortic pressure measurements. Using data from porcine experiment, the correlation coefficient was determined between the relative change of VSW from directly measured data and the model-based estimate of VSW. The result showed good agreement with, R=0.71. The model accurately captured the trend of VSW using only aortic pressure measurements and thus offers significant clinical value in early diagnosis and improving care for cardiovascular dysfunction. [less ▲]

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See detailEstimating Relative Change in Ventricular Stroke Work from Aortic Pressure
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

Conference (2014)

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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 ▲]

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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 ▲]

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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 ▲]

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