298 results for Shaw, G.M.

  • Intenzív osztályon ápolt betegek szoros vércukorszabályozása

    Benyo, B.; Homlok, J.; Ilyes, A.; Szabo Nemedi, N.; Shaw, G.M.; Chase, J.G. (2014)

    Conference Contributions - Other
    University of Canterbury Library

    Invited presentation, abstract made of presentation notes by organisers - Thus, the presentation IS the abstract

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  • Technologies for semi-automated glucose control, and the impact of the human in the loop

    Chase, J.G.; Shaw, G.M. (2015)

    Conference Contributions - Other
    University of Canterbury Library

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  • Respiratory mechanics variation during partially supported ventilation

    Kim, K.T.; Chiew, Y.S.; Redmond, D.; Pretty, C.G.; Shaw, G.M.; Desaive, T.; Chase, J.G. (2015)

    Conference Contributions - Other
    University of Canterbury Library

    Respiratory mechanics vary breath-to-breath during mechanical ventilation (MV), and increases during partially supported ventilation modes when patients exhibit spontaneous breathing (SB). While increased variability is associated with patient recovery, SB efforts hinder the application of respiratory mechanics metrics to guide MV. This study quantifies the natural variability of respiratory mechanics during partially supported ventilation.

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  • Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction

    Major, V.; Corbett, S.; Redmond, D.; Beatson, A.; Glassenbury, D.; Chiew, Y.S.; Pretty, C.G.; Desaive, T.; Szlávecz, A.; Benyo, B.; Shaw, G.M.; Chase, J.G. (2016)

    Journal article
    University of Canterbury Library

    Monitoring patient-specific respiratory mechanics can be used to guide mechanical ventilation (MV) therapy in critically ill patients. However, many patients can exhibit spontaneous breathing (SB) efforts during ventilator supported breaths, altering airway pressure waveforms and hindering model-based (or other) identification of the true, underlying respiratory mechanics necessary to guide MV. This study aims to accurately assess respiratory mechanics for breathing cycles masked by SB efforts. A cumulative pressure reconstruction method is used to ameliorate SB by identifying SB affected waveforms and reconstructing unaffected pressure waveforms for respiratory mechanics identification using a single-compartment model. Performance is compared to conventional identification without reconstruction, where identified values from reconstructed waveforms should be less variable. Results are validated with 9485 breaths affected by SB, including periods of muscle paralysis that eliminates SB, as a validation test set where reconstruction should have no effect. In this analysis, the patients are their own control, with versus without reconstruction, as assessed by breath-to-breath variation using the non-parametric coefficient of variation (CV) of respiratory mechanics. Pressure reconstruction successfully estimates more consistent respiratory mechanics. CV of estimated respiratory elastance is reduced up to 78% compared to conventional identification (p < 0.05). Pressure reconstruction is comparable (p > 0.05) to conventional identification during paralysis, and generally performs better as paralysis weakens, validating the algorithm’s purpose. Pressure reconstruction provides less-affected pressure waveforms, ameliorating the effect of SB, resulting in more accurate respiratory mechanics identification. Thus providing the opportunity to use respiratory mechanics to guide mechanical ventilation without additional muscle relaxants, simplifying clinical care and reducing risk.

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  • External validation and sub-cohort analysis of stochastic forecasting models in NICU cohorts

    Dickson, J.L.; Floyd, R.P.; LeCompte, A.J.; Fisk, L.M.; Chase, J.G.; Lynn, A.; Shaw, G.M. (2013)

    Journal Articles
    University of Canterbury Library

    Hyperglycaemia is a prevalent complication in the neonatal intensive care unit (NICU) and is associated with worsened outcomes. It occurs as a result of prematurity, under-developed endogenous glucose regulatory systems, and clinical stress. The stochastic targeting (STAR) framework provides patient-specific, model-based glycaemic control with a clinically proven level of confidence on the outcome of treatment interventions, thus directly managing the risk of hypo- and hyper- glycaemia. However, stochastic models that are over conservative can limit control performance. Retrospective clinical data from 61 episodes (25 retrospective to STAR, and 36 from a prospective-STAR blood glucose control study) of insulin therapy in very-low birth weight (VLBW) and extremely-low birth weight (ELBW) neonates are used to create a new stochastic model of model-based insulin sensitivity (SI [L/mU/min]). Sub-cohort models based on gestational age (GA) and birth weight (BW) are also created. Performance is assessed by the percentage of patients who have 90% of actual intra-patient variability in SI captured by the 90% confidence bands of the cohort based (inter-patient) stochastic variability model created. This assessment measures per-patient accuracy for any given cohort model. Per-patient coverage trends were very similar between prospective and retrospective cohorts, providing a measure of external validation of cohort similarity. Per-patient coverage was improved though the use of BW and GA dependent stochastic models, which ensures that the stochastic models more accurately capture both inter- and intra- patient variability. Stochastic models based on insulin sensitivities during insulin treatment periods are tighter, and give better and safer glycaemic control. Overall it seems that inter-patient variation is more significant than intra-patient variation as a limiting factor in this stochastic forecasting model, and a small number of patients are essentially different in behaviour. More patient specific methods, particularly in the modelling of endogenous insulin and glucose production, will be required to further improve forecasting and glycaemic control.

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  • Development and optimisation of stochastic targeted (STAR) glycaemic control for pre-term infants in neonatal intensive care

    Dickson, J.L.; Le Compte, A.J.; Floyd, R.P.; Chase, J.G.; Lynn, A.; Shaw, G.M. (2013)

    Journal Articles
    University of Canterbury Library

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  • Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: CGM Devices, Sensor Locations, and DFA Methods

    Signal, M.; Thomas, F.; Shaw, G.M.; Chase, J.G. (2013)

    Journal Articles
    University of Canterbury Library

    BACKGROUND: Critically ill patients often experience high levels of insulin resistance and stress-induced hyperglycemia, which may negatively impact outcomes. However, evidence surrounding the causes of negative outcomes remains inconclusive. Continuous glucose monitoring (CGM) devices allow researchers to investigate glucose complexity, using detrended fluctuation analysis (DFA), to determine whether it is associated with negative outcomes. AIM: The aim of this study was to investigate the effects of CGM device type/calibration and CGM sensor location on results from DFA. METHODS: This study uses CGM data from critically ill patients who were each monitored concurrently using Medtronic iPro2’s on the thigh and abdomen, and a Medtronic Guardian Real-Time on the abdomen. This allowed inter-device/calibration type and inter-sensor site variation to be assessed. DFA is a technique that has previously been used to determine the complexity of CGM data in critically ill patients. Two variants of DFA, monofractal and multifractal, were used to assess the complexity of sensor glucose (SG) data, as well as the pre-calibration raw sensor current. Monofractal DFA produces a scaling exponent (H), where H is inversely related to complexity. The results of multifractal DFA are presented graphically, by the multifractal spectrum. RESULTS: From the 10 patients recruited, 26 CGM devices produced data suitable for analysis. The values of H from abdominal iPro2 data were 0.10 [0.03 – 0.20] higher than those from Guardian Real-Time data, indicating consistently lower complexities in iPro2 data. However, repeating the analysis on the raw sensor current showed little or no difference in complexity. Sensor site had little effect on the scaling exponents in this data set. Finally, multi-fractal DFA revealed no significant associations between the multifractal spectrums and CGM device type/calibration or sensor location. CONCLUSIONS: Monofractal DFA results are dependent on the device/calibration used to obtain CGM data, but sensor location has little impact. Future studies of glucose complexity should consider the findings presented here when designing their investigations.

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  • Evaluation of a Model-Based Hemodynamic Monitoring Method in a Porcine Study of Septic Shock

    Revie, J.A.; Stevenson, D.; Chase, J.G.; Pretty, C.G.; Lambermont, B.C.; Ghuysen, A.; Kolh, P.; Shaw, G.M.; Desaive, T. (2013)

    Journal Articles
    University of Canterbury Library

    Introduction. The accuracy and clinical applicability of an improved model-based system for tracking hemodynamic changes is assessed in an animal study on septic shock. Methods. This study used cardiovascular measurements recorded during a porcine trial studying the efficacy of large-pore hemofiltration for treating septic shock. Four Pietrain pigs were instrumented and induced with septic shock. A subset of the measured data, representing clinically available measurements, was used to identify subject-specific cardiovascular models. These models were then validated against the remaining measurements. Results. The system accurately matched independent measures of left and right ventricle end diastolic volumes and maximum left and right ventricular pressures to percentage errors less than 20% (except for the 95th percentile error in maximum right ventricular pressure) and all ?² > 0.76. An average decrease of 42% in systemic resistance, a main cardiovascular consequence of septic shock, was observed 120 minutes after the infusion of the endotoxin, consistent with experimentally measured trends. Moreover, modelled temporal trends in right ventricular end systolic elastance and afterload tracked changes in corresponding experimentally derived metrics. Conclusions. These results demonstrate that this model-based method can monitor disease-dependent changes in preload, afterload, and contractility in porcine study of septic shock.

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  • External validation and sub-cohort analysis of stochastic forecasting models in NICU cohorts

    Dickson, J.L.; Floyd, R.P.; LeCompte, A.J.; Fisk, L.M.; Chase, J.G.; Lynn, A.; Shaw, G.M. (2013)

    Journal Articles
    University of Canterbury Library

    Hyperglycaemia is a prevalent complication in the neonatal intensive care unit (NICU) and is associated with worsened outcomes. It occurs as a result of prematurity, under developed endogenous glucose regulatory systems and clinical stress. The stochastic targeting (STAR) framework provides patient-specific, model-based glycaemic control with a clinically proven level of confidence on the outcome of treatment interventions, thus directly managing the risk of hypo- and hyper- glycaemia. However, stochastic models that are over conservative can limit control performance. Retrospective clinical data from 61 episodes (25 retrospective and 36 from a prospective blood glucose control study) of insulin therapy in very-low birth weight (VLBW) and extremely-low birth weight (ELBW) neonates are used to create a new stochastic model of model-based insulin sensitivity (SI [L/mU/min]). Sub-cohort models based on gestational age (GA) and birth weight (BW) are also created. Performance is assessed by the percentage of patients who have 90% of actual intra-patient variability in SI captured by the 90% confidence bands of the cohort based (inter-patient) stochastic variability model created. This assessment measures per-patient accuracy for any given cohort model. Per-patient coverage trends were very similar between prospective and retrospective cohorts, providing a measure of external validation of cohort similarity. Per-patient coverage was improved though the use of BW and GA dependent stochastic models, which ensures that the stochastic models more accurately capture both inter- and intra- patient variability. Stochastic models based on insulin sensitivities during insulin treatment periods are tighter and give better and safer glycaemic control. More patient specific methods, particularly in the modeling of endogenous insulin and glucose production, will be required to further improve forecasting and glycaemic control.

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  • Insulin Glargine in the Intensive Care Unit: A Model-Based Clinical Trial Design

    Willis, J.G.; Fisk, L.; Razak, N.; Le Compte, A.J.; Shaw, G.M.; Chase, J.G. (2013)

    Journal Articles
    University of Canterbury Library

    Online 4 Oct 2012

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  • Analysis of different model-based approaches for estimating dFRC for real-time aplication

    Van Drunen, E.J.; Chase, J.G.; Chiew, Y.S.; Shaw, G.M.; Desaive, T. (2013)

    Journal Articles
    University of Canterbury Library

    Background: Acute Respiratory Distress Syndrome (ARDS) is characterized by inflammation, filling of the lung with fluid and the collapse of lung units. Mechanical ventilation (MV) is used to treat ARDS using positive end expiratory pressure (PEEP) to recruit and retain lung units, thus increasing pulmonary volume and dynamic functional residual capacity (dFRC) at the end of expiration. However, simple, non-invasive methods to estimate dFRC do not exist. Methods: Four model-based methods for estimating dFRC are compared based on their performance on two separate clinical data cohorts. The methods are derived from either stress-strain theory or a single compartment lung model, and use commonly controlled or measured parameters (lung compliance, plateau airway pressure, pressure-volume (PV) data). Population constants are determined for the stress-strain approach, which is implemented using data at both single and multiple PEEP levels. Estimated values are compared to clinically measured values to assess the reliability of each method for each cohort individually and combined. Results: The stress-strain multiple breath (at multiple PEEP levels) method produced an overall correlation coefficient R2 = 0.966. The stress-strain single breath method produced R2 = 0.530. The single compartment single breath method produced R2 = 0.415. A combined method at single and multiple PEEP levels produced R2 = 0.963. Conclusions: The results suggest that model-based, single breath and non-invasive approaches to estimating dFRC may be viable in a clinical scenario, ensuring no interruption to MV. The models provide a means of estimating dFRC at any PEEP level. However, model limitations and large estimation errors limit the use of the methods at very low PEEP.

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  • Second Pilot Trials of the STAR-Liege Protocol for Tight Glycemic Control in Critically Ill Patients

    Penning, S.; Le Compte, A.J.; Massion, P.; Moorhead, K.T.; Pretty, C.G.; Preiser, J.C.; Shaw, G.M.; Suhaimi, F.; Desaive, T.; Chase, J.G. (2012)

    Journal Articles
    University of Canterbury Library

    Critically ill patients often present increased insulin resistance and stress-induced hyperglycemia. Tight glycemic control aims to reduce blood glucose (BG) levels and variability while ensuring safety from hypoglycemia. This paper presents the results of the second Belgian clinical trial using the customizable STAR framework in a target-to-range control approach. The main objective is reducing measurement frequency while maintaining performance and safety of the glycemic control.

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  • Teaching old electronics, new tricks: medical applications of well-known industrial electronics

    Chase, J.G.; Shaw, G.M.; Signal, M.K.; Pretty, C.G.; Rodgers, G.W.; Benyo, B.; Moeller, K.; Desaive, T. (2015)

    Conference Contributions - Other
    University of Canterbury Library

    (invited, Distinguished Invited Lecture).

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  • Glycemic and Nutrition Delivery: Performance of the STAR Protocol

    Tomlinson, H.; Shaw, G.M.; Fisk, L.; Chase, J.G.; Pretty, C.G. (2014)

    Conference Contributions - Other
    University of Canterbury Library

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  • A better way to determine sample size to detect changes in length of mechanical ventilation?

    Chiew, Y.S.; Pretty, C.G.; Redmond, D.; Shaw, G.M.; Desaive, T.; Chase, J.G. (2015)

    Conference Contributions - Other
    University of Canterbury Library

    Introduction: Estimation of effective sample size (N/arm) is important to ensure power to detect significant treatment effects. However, traditional parametric sample size estimations depend upon restrictive assumptions that often do not hold in real data. This study estimates N to detect changes in length of mechanical ventilation (LoMV) using Monte-Carlo Simulation (MCS) and mechanical ventilation (MV) data to better simulate the cohort. Methods: Data from 2534 MV patients admitted to Christchurch Hospital ICU from 2011-13 were used. N was estimated using MCS to determine a sample size with power of 80%, and compared to the Altman’s nomogram for two patients groups, 1)all patients and 2)targeted patients with 11000 to detect a 25% LoMV change. Panels (1-2) show N for 80% power if all patients were included, and Panels (3-4) when for the targeted patient group. Panels (1) and (3) show that it is impossible to achieve 80% power for a 10% intervention effect. For 25% effect, MSC found N=400/arm (all patients) and N=150/arm (targeted cohort). Conclusions: Traditional parametric sample size estimation may over-estimate the required patients. MCS can estimate effective N/arm and evaluate specific patient groups objectively, capturing local clinical practice and its impact on LoMV. It is important to consider targeting specific patient groups by applying patient selection criteria that can be easily translated into trial design.

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  • A pressure reconstruction method for spontaneous breathing effort monitoring

    Damanhuri, N.S.; Chiew, Y.S.; Othman, N.A.; Docherty, P.D.; Shaw, G.M.; Chase, J.G. (2015)

    Conference Contributions - Other
    University of Canterbury Library

    Introduction: Estimating respiratory mechanics of mechanically ventilated (MV) patients is unreliable when patients exhibit spontaneous breathing (SB) efforts on top of ventilator support. This reverse triggering effect [1] results in an M-wave shaped pressure wave. A model-based method to reconstruct the affected airway pressure curve is presented to enable estimation of the true underlying respiratory mechanics of these patients. Methods: Airway pressure and flow data from 72 breaths of a pneumonia patient were used for proof of concept. A pressure wave reconstruction method ‘fills’ parts of the missing area caused by SB efforts and reverse triggering by connecting the peak pressure and end inspiration slope (Figure 1). A time-varying elastance model [2] is then used to identify underlying respiratory elastance (AUCEdrs). The area of the unreconstructed M-wave has less pressure, resulting in a lower overall AUCEdrs without reconstruction. The missing area of the airway pressure or AUCEdrs is hypothesized to be a surrogate of patient-specific inspiratory to assess the strength of SB efforts. AUCEdrs and missing area A2 are compared with/without reconstruction. Results: Median AUCEdrs and breath-specific effort using reconstruction were 24.99[IQR:22.90-25.98] cmH2O/l and 3.64 [IQR:0.00-3.87] % versus AUCEdrs of 20.87[IQR:15.24-27.48] cmH2O/l for unreconstructed M-wave data, indicating significant patient and breath specific SB effort, and the expected higher elastance (p < 0.05). Conclusions: A simple reconstruction method enables the real-time measurements respiratory system properties of a SB patient and measure the surrogate of the SB effort, that latter of which has clinical useful in deciding whether to extubate or re-sedate the patient.

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  • Intenzív ápolás során a vércukorszint szabályozására alkalmazott STAR protokoll hatékonyságának elemzése különböz

    Benyó, B.; Homlok, J.; Illyés, A.; Havas, A.; Szabó Némedi, N.; Fisk, L.; Shaw, G.M.; Chase, J.G. (2013)

    Conference Contributions - Other
    University of Canterbury Library

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  • Breath-to-breath respiratory mechanics variation: how much variation should we expect?

    Chiew, Y.S.; Kim, K.T.; Pretty, C.G.; Shaw, G.M.; Desaive, T.; Chase, J.G. (2015)

    Conference Contributions - Other
    University of Canterbury Library

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  • Assessment of ventricular contractility and ventricular-arterial coupling with a model-based sensor

    Desaive, T.; Lambermont, B.; Janssen, N.; Ghuysen, A.; Kolh, P.; Morimont, P.; Dauby, P.C.; Starfinger, C.; Shaw, G.M.; Chase, J.G. (2013)

    Journal Articles
    University of Canterbury Library

    Invited

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  • Insulin Sensitivity in Out of Hospital Cardiac Arrest Patients Treated with Hypothermia

    Taccone, F.S.; Preiser, J.C.; Sah-Pri, A.; Shaw, G.M.; Chase, J.G.; Desaive, T. (2013)

    Conference Contributions - Other
    University of Canterbury Library

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