References of "Shaw, Geoffrey M"
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See detailUntangling glycaemia and mortality in critical care
Uyttendaele, Vincent ULg; Dickson, Jennifer L.; Shaw, Geoffrey M. et al

in Critical Care (2017), 21(1), 152

Background: Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often ... [more ▼]

Background: Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided. Methods: Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant. Results: SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed. Conclusions: Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition. [less ▲]

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See detailGeneralisability of a Virtual Trials Method for Glycaemic Control in Intensive Care.
Dickson, Jennifer L.; Stewart, Kent W.; Pretty, Christopher G. et al

in IEEE transactions on bio-medical engineering (2017)

BACKGROUND: Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic ... [more ▼]

BACKGROUND: Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia). OBJECTIVE: This paper presents a model-based virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations. METHODS: Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross-simulation) were compared. RESULTS: Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and cross-simulation results were found to have overall similarity and consistency, though results may differ in the first 24-48 hours due to different cohort starting BG and underlying SI. CONCLUSIONS AND SIGNIFICANCE: Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method. [less ▲]

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See detailImproved pressure contour analysis for estimating cardiac stroke volume using pulse wave velocity measurement.
Kamoi, Shun; Pretty, Christopher; Balmer, Joel et al

in BioMedical Engineering OnLine (2017), 16(1), 51

BACKGROUND: Pressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques ... [more ▼]

BACKGROUND: Pressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques for continuous estimation of stroke volume (SV) from pressure measurement can be unreliable during hemodynamic instability, which is inevitable for patients requiring significant treatment. For this reason, pressure contour methods must be improved to capture changes in vascular properties and thus provide accurate conversion from pressure to flow. METHODS: This paper presents a novel pressure contour method utilizing pulse wave velocity (PWV) measurement to capture vascular properties. A three-element Windkessel model combined with the reservoir-wave concept are used to decompose the pressure contour into components related to storage and flow. The model parameters are identified beat-to-beat from the water-hammer equation using measured PWV, wave component of the pressure, and an estimate of subject-specific aortic dimension. SV is then calculated by converting pressure to flow using identified model parameters. The accuracy of this novel method is investigated using data from porcine experiments (N = 4 Pietrain pigs, 20-24.5 kg), where hemodynamic properties were significantly altered using dobutamine, fluid administration, and mechanical ventilation. In the experiment, left ventricular volume was measured using admittance catheter, and aortic pressure waveforms were measured at two locations, the aortic arch and abdominal aorta. RESULTS: Bland-Altman analysis comparing gold-standard SV measured by the admittance catheter and estimated SV from the novel method showed average limits of agreement of +/-26% across significant hemodynamic alterations. This result shows the method is capable of estimating clinically acceptable absolute SV values according to Critchely and Critchely. CONCLUSION: The novel pressure contour method presented can accurately estimate and track SV even when hemodynamic properties are significantly altered. Integrating PWV measurements into pressure contour analysis improves identification of beat-to-beat changes in Windkessel model parameters, and thus, provides accurate estimate of blood flow from measured pressure contour. The method has great potential for overcoming weaknesses associated with current pressure contour methods for estimating SV. [less ▲]

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See detailAssessing respiratory mechanics using pressure reconstruction method in mechanically ventilated spontaneous breathing patient.
Damanhuri, Nor Salwa; Chiew, Yeong Shiong; Othman, Nor Azlan et al

in Computer Methods & Programs in Biomedicine (2016), 130

BACKGROUND: Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave ... [more ▼]

BACKGROUND: Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care. METHODS: Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort. RESULTS: The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort. CONCLUSION: A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care. [less ▲]

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See detailRelationship between Stroke Volume and Pulse Wave Velocity
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

Conference (2015, September 01)

Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or non- invasive ... [more ▼]

Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or non- invasive additional measurements. This study investigates the relationship between SV and Pulse Wave Velocity (PWV) to examine whether the value of PWV can capture the changes in SV. The analysis was performed using data from six porcine experiments (N=6 Pietrain Pigs, 20-29 kg) in which left ventricular volume, aortic arc pressure, and descending aortic pressure waveforms were measured simultaneously. From the measured data, correlation coefficients were determined between absolute value of aortic PWV, SV and trend value ‘PWV – mean PWV’, ‘SV – mean SV’ during periods when changes in SV were induced from preload changes, as well as infusion of dobutamine. The results showed good correlation (r = 0.59) for trend value, however, the correlation coefficient were poor with r = 0.028 for absolute value across all pigs. The analysis showed that value of PWV is reliable for capturing trend value of SV in preload changes. However, it is unreliable for capturing absolute value of SV or changes in SV made from dobutamine. [less ▲]

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See detailRelationship between Stroke Volume and Pulse Wave Velocity
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

in Preprints of the 9th IFAC Symposium on Biological and Medical Systems (2015, September)

Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or non- invasive ... [more ▼]

Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or non- invasive additional measurements. This study investigates the relationship between SV and Pulse Wave Velocity (PWV) to examine whether the value of PWV can capture the changes in SV. The analysis was performed using data from six porcine experiments (N=6 Pietrain Pigs, 20-29 kg) in which left ventricular volume, aortic arc pressure, and descending aortic pressure waveforms were measured simultaneously. From the measured data, correlation coefficients were determined between absolute value of aortic PWV, SV and trend value ‘PWV – mean PWV’, ‘SV – mean SV’ during periods when changes in SV were induced from preload changes, as well as infusion of dobutamine. The results showed good correlation (r = 0.59) for trend value, however, the correlation coefficient were poor with r = 0.028 for absolute value across all pigs. The analysis showed that value of PWV is reliable for capturing trend value of SV in preload changes. However, it is unreliable for capturing absolute value of SV or changes in SV made from dobutamine. [less ▲]

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See detailStroke Volume Estimation using Aortic Pressure Measurements and Aortic Cross Sectional Area: Proof of Concept
Kamoi, Shun; Pretty, Christopher G.; Chiew, Yeong Shiong et al

in Proceedings of the 37th International Conference of the IEEE Engineering in Medicine and Biology Society (2015, August)

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of ... [more ▼]

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of measurement devices. Current devices for indirect monitoring of SV are shown to be inaccurate during sudden hemodynamic changes. This paper presents a novel SV estimation using readily available aortic pressure measurements and aortic cross sectional area, using data from a porcine experiment where medical interventions such as fluid replacement, dobutamine infusions, and recruitment maneuvers induced SV changes in a pig with circulatory shock. Measurement of left ventricular volume, proximal aortic pressure, and descending aortic pressure waveforms were made simultaneously during the experiment. From measured data, proximal aortic pressure was separated into reservoir and excess pressures. Beat-to-beat aortic characteristic impedance values were calculated using both aortic pressure measurements and an estimate of the aortic cross sectional area. SV was estimated using the calculated aortic characteristic impedance and excess component of the proximal aorta. The median difference between directly measured SV and estimated SV was -1.4ml with 95% limit of agreement +/- 6.6ml. This method demonstrates that SV can be accurately captured beat-to-beat during sudden changes in hemodynamic state. This novel SV estimation could enable improved cardiac and circulatory treatment in the critical care environment by titrating treatment to the effect on SV. [less ▲]

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See detailStroke Volume Estimation using Aortic Pressure Measurements and Aortic Cross Sectional Area
Kamoi, Shun; Pretty, Christopher G.; Chiew, Yeong Shiong et al

Poster (2015, August)

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of ... [more ▼]

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of measurement devices. Current devices for indirect monitoring of SV are shown to be inaccurate during sudden hemodynamic changes. This paper presents a novel SV estimation using readily available aortic pressure measurements and aortic cross sectional area, using data from a porcine experiment where medical interventions such as fluid replacement, dobutamine infusions, and recruitment maneuvers induced SV changes in a pig with circulatory shock. Measurement of left ventricular volume, proximal aortic pressure, and descending aortic pressure waveforms were made simultaneously during the experiment. From measured data, proximal aortic pressure was separated into reservoir and excess pressures. Beat-to-beat aortic characteristic impedance values were calculated using both aortic pressure measurements and an estimate of the aortic cross sectional area. SV was estimated using the calculated aortic characteristic impedance and excess component of the proximal aorta. The median difference between directly measured SV and estimated SV was -1.4ml with 95% limit of agreement +/- 6.6ml. This method demonstrates that SV can be accurately captured beat-to-beat during sudden changes in hemodynamic state. This novel SV estimation could enable improved cardiac and circulatory treatment in the critical care environment by titrating treatment to the effect on SV. [less ▲]

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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 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 detailContinuous glucose monitoring in newborn infants: how do errors in calibration measurements affect detected hypoglycemia?
Thomas, Felicity Louise ULg; Signal, Mathew; Harris, Deborah L. et al

in Journal of diabetes science and technology (2014), 8(3), 543-50

Neonatal hypoglycemia is common and can cause serious brain injury. Continuous glucose monitoring (CGM) could improve hypoglycemia detection, while reducing blood glucose (BG) measurements. Calibration ... [more ▼]

Neonatal hypoglycemia is common and can cause serious brain injury. Continuous glucose monitoring (CGM) could improve hypoglycemia detection, while reducing blood glucose (BG) measurements. Calibration algorithms use BG measurements to convert sensor signals into CGM data. Thus, inaccuracies in calibration BG measurements directly affect CGM values and any metrics calculated from them. The aim was to quantify the effect of timing delays and calibration BG measurement errors on hypoglycemia metrics in newborn infants. Data from 155 babies were used. Two timing and 3 BG meter error models (Abbott Optium Xceed, Roche Accu-Chek Inform II, Nova Statstrip) were created using empirical data. Monte-Carlo methods were employed, and each simulation was run 1000 times. Each set of patient data in each simulation had randomly selected timing and/or measurement error added to BG measurements before CGM data were calibrated. The number of hypoglycemic events, duration of hypoglycemia, and hypoglycemic index were then calculated using the CGM data and compared to baseline values. Timing error alone had little effect on hypoglycemia metrics, but measurement error caused substantial variation. Abbott results underreported the number of hypoglycemic events by up to 8 and Roche overreported by up to 4 where the original number reported was 2. Nova results were closest to baseline. Similar trends were observed in the other hypoglycemia metrics. Errors in blood glucose concentration measurements used for calibration of CGM devices can have a clinically important impact on detection of hypoglycemia. If CGM devices are going to be used for assessing hypoglycemia it is important to understand of the impact of these errors on CGM data. [less ▲]

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See detailEvolution of insulin sensitivity and its variability in out of hospital cardiac arrest (OHCA) patients treated with hypothermia.
Sah Pri, Azurahisham; Chase, James G.; Pretty, Christopher G. et al

in Critical care (London, England) (2014), 18(5), 586

IntroductionTherapeutic hypothermia (TH) is often used to treat out of hospital cardiac arrest (OHCA) patients who also often simultaneously receive insulin for stress-induced hyperglycaemia. However, the ... [more ▼]

IntroductionTherapeutic hypothermia (TH) is often used to treat out of hospital cardiac arrest (OHCA) patients who also often simultaneously receive insulin for stress-induced hyperglycaemia. However, the impact of TH on systemic metabolism and insulin resistance in critical illness is unknown. This study analyses the impact of TH on metabolism, including the evolution of insulin sensitivity (SI) and its variability, in patients with coma after OHCA.MethodsThis study uses a clinically validated, model-based measure of SI. Insulin sensitivity was identified hourly using retrospective data from 200 post-cardiac arrest patients (8,522 hours) treated with TH, shortly after admission to the Intensive Care Unit (ICU). Blood glucose and body temperature readings were taken every one to two hours. Data were divided into three periods: 1) cool (T <35 degrees C); 2) an idle period of two hours as normothermia was re-established; and 3) warm (T >37 degrees C). A maximum of 24 hours each for the cool and warm periods were considered. The impact of each condition on SI is analysed per cohort and per patient for both level and hour-to-hour variability, between periods and in 6-hour blocks.ResultsCohort and per patient median SI levels increase consistently by 35% to 70% and 26% to 59% (P <0.001) respectively from cool to warm. Conversely, cohort and per patient SI variability decreased by 11.1% to 33.6% (P <0.001) for the first 12 hours of treatment. However, SI variability increases between the 18th and 30th hours over the cool-warm transition, before continuing to decrease afterward.ConclusionsOCHA patients treated with TH have significantly lower and more variable SI during the cool period, compared to the later warm period. As treatment continues, SI level rises, and variability decreases consistently except for a large, significant increase during the cool-warm transition. These results demonstrate increased resistance to insulin during mild induced hypothermia. Our study might have important implications for glycaemic control during targeted temperature management. [less ▲]

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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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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 ▲]

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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 ▲]

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See detailInterstitial insulin kinetic parameters for a 2-compartment insulin model with saturable clearance
Pretty, Christopher G.; Le Compte, Aaron; Penning, Sophie ULg et al

in Computer Methods & Programs in Biomedicine (2014)

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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 detailInsulin Sensitivity during Hypothermia in Critically Ill Patients
Sah Pri, Azurahisham; Chase, J. Geoffrey; Le Compte, Aaron J. et al

Poster (2013, September)

Detailed reference viewed: 29 (4 ULg)