296 results for Shaw, G.M.

  • 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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  • 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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  • Stochastic Modelling of Insulin Sensitivity for Out of Hospital Cardiac Arrest Patients treated with Hypothermia

    Sah Pri, A.; Chase, J.G.; Pretty, C.G.; Shaw, G.M.; Preiser, J.G.; Taccone, F.; Penning, S.; Desaive, T. (2015)

    Conference Contributions - Published
    University of Canterbury Library

    Hypothermia is often used to treat out of hospital cardiac arrest (OHCA) patients who often simultaneously receive insulin for stress induced hyperglycaemia. Variations in response to insulin reflect dynamic changes in insulin sensitivity (SI), defined by the overall metabolic response to stress and therapy. Thus, tracking and forecasting this parameter is important to provide safe glycaemic control in highly dynamic patients. This study examines stochastic forecasting models of model-based SI variability in OHCA patients to assess the resulting potential impact of this therapy on glycaemic control quality and safety. A retrospective analysis of clinically validated model-based SI profiles identified using data from 240 post-cardiac arrest patients (9988 hours) treated with hypothermia, shortly after admission in the Intensive Care Unit (ICU). Data were divided into three periods: 1) cool (T ≥ 35oC); 2) idle period of 2 hours as hypothermia was removed; and 3) warm (T ≥ 37oC). The stochastic model captured 60.7% and 90.2% of SI predictions within the (25th–75th) and (5th–95th) probability forecast intervals during cool period. Equally, it is also recorded 62.8% and 92.1% of SI predictions respectively during the warm period. Maintaining the kernel density variance estimator to c = 1.0 yielded 60.7% and 90.2% for the cool period. Similarly, adjusting a variance estimator of c = 2.0 yields 60.4% and 90.1% for the warm period. A cohort-specific stochastic model of SI provided a conservative forecast for the inter-quartile range and was relatively exact for the 90% range. Adjusting the variance estimator provides a more accurate, cohort-specific stochastic model of SI dynamics for the 90% range. These latter results show clearly different levels and distribution of forecasted SI variability between the cold and warm periods.

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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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  • Clinical Glycaemic Performance of the STAR Protocol

    Stewart, K.W.; Tomlinson, H.; Pretty, C.G.; Fisk, L.; Thomas, F.; Shaw, G.M.; Benyo, B.; Illyes, A.; Szabo-Nemedi, N.; Chase, J.G. (2015)

    Conference Contributions - Other
    University of Canterbury Library

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  • Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis

    Stewart, K.W.; Pretty, C.G.; Tomlinson, H.; Thomas, F.L.; Homlok, J.; Nemedi Noemi, S.; Illyes, A.; Shaw, G.M.; Benyo, B.; Chase, J.G. (2016)

    Journal Articles
    University of Canterbury Library

    Background: The changes in metabolic pathways and metabolites due to critical illness result in a highly complex and dynamic metabolic state, making safe, effective management of hyperglycemia and hypoglycemia difficult. In addition, clinical practices can vary significantly, thus making GC protocols difficult to generalize across units.The aim of this study was to provide a retrospective analysis of the safety, performance and workload of the stochastic targeted (STAR) glycemic control (GC) protocol to demonstrate that patient-specific, safe, effective GC is possible with the STAR protocol and that it is also generalizable across/over different units and clinical practices. Methods: Retrospective analysis of STAR GC in the Christchurch Hospital Intensive Care Unit (ICU), New Zealand (267 patients), and the Gyula Hospital, Hungary (47 patients), is analyzed (2011–2015). STAR Christchurch (BG target 4.4–8.0 mmol/L) is also compared to the Specialized Relative Insulin and Nutrition Tables (SPRINT) protocol (BG target 4.4–6.1 mmol/L) implemented in the Christchurch Hospital ICU, New Zealand (292 patients, 2005–2007). Cohort mortality, effectiveness and safety of glycemic control and nutrition delivered are compared using nonparametric statistics. Results: Both STAR implementations and SPRINT resulted in over 86 % of time per episode in the blood glucose (BG) band of 4.4–8.0 mmol/L. Patients treated using STAR in Christchurch ICU spent 36.7 % less time on protocol and were fed significantly more than those treated with SPRINT (73 vs. 86 % of caloric target). The results from STAR in both Christchurch and Gyula were very similar, with the BG distributions being almost identical. STAR provided safe GC with very few patients experiencing severe hypoglycemia (BG < 2.2 mmol/L, <5 patients, 1.5 %). Conclusions: STAR outperformed its predecessor, SPRINT, by providing higher nutrition and equally safe, effective control for all the days of patient stay, while lowering the number of measurements and interventions required. The STAR protocol has the ability to deliver high performance and high safety across patient types, time, clinical practice culture (Christchurch and Gyula) and clinical resources.

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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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  • 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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  • Analysing the effects of cold, normal, and warm digits on transmittance pulse oximetry

    Khan, M.; Pretty, C.G.; Amies, A.C.; Elliott, R.; Chiew, Y.S.; Shaw, G.M.; Chase, J.G. (2016)

    Journal Articles
    University of Canterbury Library

    Non-invasive estimation of arterial oxygen saturation (SpO2) and heart rate using pulse oximeters is widely used in hospitals. Pulse oximeters rely on photoplethysmographic (PPG) signals from a peripherally placed optical sensor. However, pulse oximeters can be less accurate if the sensor site is relatively cold. This research investigates the effects on PPG signal quality of local site temperatures for 20 healthy adult volunteers (24.5 ±4.1 years of age). Raw PPG data, composed of Infrared (IR) and Red (RD) signals, was obtained from a transmittance finger probe using a custom pulse oximeter (PO) system. Three tests were performed with the subject’s hand surface temperature maintained at baseline (29 ±2°C), cold (19 ±2°C), and warm (33 ±2°C) conditions. Median root mean square (RMS) of PPG signal during the Cold test dropped by 54.0% for IR and 30.6% for RD from the baseline values. In contrast, the PPG RMS increased by 64.4% and 60.2% for RD and IR, respectively, during the Warm test. Mean PPG pulse amplitudes decreased by 59.5% for IR and 46.1% for RD in the cold test when compared to baseline, but improved by 70.1% for IR and 59.0% for RD in the warm test. This improvement of up to 4x in signal quality during the warm condition was associated with a closer match (median difference of 1.5%) between the SpO2 values estimated by the PO system and a commercial pulse oximeter. The differences measured in RMS and mean amplitudes for the three tests were statistically significant (p < 0.001). Overall, warm temperatures significantly improve PPG signal quality and SpO2 estimation accuracy. Sensor site temperature is recommended to be maintained near 33°C for reliable transmittance pulse oximetry.

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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

    Hyperglycaemia is a common complication of prematurity and stress in neonatal intensive care units (NICUs). It has been linked to worsened outcomes and mortality. There is currently no universally accepted best practice glycaemic control method, with many protocols lacking patient specificity or relying heavily on ad hoc clinical judgment from clinical staff who may be caring or overseeing care for several patients at once. The result is persistent hypoglycaemia and poor control. This research presents the virtual trial design and optimisation of a stochastic targeted (STAR) approach to improve performance and reduce hypoglycaemia. Clinically validated virtual trials based on NICU patient data (N = 61 patients, 7006 hours) are used to develop and optimise a STAR protocol that improves on current STAR-NICU performance and reduce hypoglycaemia. Five approaches are used to maximize the stochastic range of BG outcomes within 4.0-8.0mmol/L, and are designed based on an overall cohort risk to provide clinically specified risk (5%) of BG above or below a clinically specified level. The best protocol placed the 5th percentile BG outcome for an intervention on 4.0mmol/L band. The optimised protocol increased %BG in the 4.0-8.0mmol/L band by 3.5% and the incidence of BG<2.6mmol/L by 1 patient (50%). Significant intra- and inter- patient variability limited possible performance gains so that they are unlikely to be clinically substantial, indicating a need for a further increase patient-specific or sub-cohort specific approaches to manage variability.

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  • In-Breath Monitoring of Lung Recruitment and Distension using Pulmonary Mechanics in an Experimental ARDS Animal model

    Chiew, Y.S.; van Drunen, E.J.; Lambermont, B.; Morimont, P.; Janssen, N.; Pretty, C.G.; Desaive, T.; Shaw, G.M.; Chase, J.G. (2013)

    Conference Contributions - Other
    University of Canterbury Library

    Introduction: Positive end expiratory pressure (PEEP) during mechanical ventilation (MV) supports breathing and gas exchange. However, the best level of PEEP can be patient-specific and is thus subject to great debate. Objective: This study presents a non-invasive, model-based method to monitor breath-to-breath recruitment and distension during MV.

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  • Concurrent continuous glucose monitoring in critically ill patients: Interim results and observations

    Signal, M.; Fisk, L.; Shaw, G.M.; Chase, J.G. (2013)

    Journal Articles
    University of Canterbury Library

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  • Patient-Ventilator Interaction using Time-varying Pulmonary Mechanics - A Retrospective Comparison between Pressure Support and Neurally Adjusted Ventilatory Assist

    Chiew, Y.S.; Poole, S.; Van Drunen, E.J.; Lambermont, B.; Morimont, P.; Damanhuri, N.S.; Desaive, T.; Shaw, G.M.; Chase, J.G. (2013)

    Conference Contributions - Other
    University of Canterbury Library

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  • Can the STAR protocol generalize effectively? Initial clinical results in New Zealand and Hungarian ICUs

    Benyo, B.; Ilyes, A.; Havas, A.; Szabo-Noemi, N.; Le Compte, A.J.; Fisk, L.; Shaw, G.M.; Chase, J.G. (2012)

    Conference Contributions - Other
    University of Canterbury Library

    http://diabetestechnology.org/Submitted.pdf for list of accepted abstracts will also appear 3 months later at www.journalofdst.org in journal of diabetes science and technology

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  • Glucometer Performance in the Intensive Care Unit

    Thomas, F.; Pretty, C.G.; Shaw, G.M.; Chase, J.G. (2014)

    Conference Contributions - Other
    University of Canterbury Library

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  • Initial ICU Clinical Results Using SPRINT to Guide Insulin Infusions in a Hungarian Medical ICU

    Ilyes, A.; Havas, A.; Nemedi, N.S.; Benyo, B.; Kovacs, L.; LeCompte, A.J.; Shaw, G.M.; Chase, J.G. (2011)

    Conference Contributions - Other
    University of Canterbury Library

    Objective: To report the initial clinical results and glycemic control using the SPRINT protocol at an independent intensive care unit (ICU), with modifications to modulate only insulin infusions. Method: The SPRINT (Specialised Relative Insulin-Nutrition Titration) protocol was used for 10 adult ICU patients (615 hours) at Kálmán Pándy Hospital (Gyula, Hungary) as part of a clinical practice assessment. SPRINT insulin recommendations were administered via constant infusion rather than bolus delivery. Nutrition was administered per local standard protocol weaning parenteral to enteral nutrition, and reduced per SPRINT when required and clinically approved. Measurement was 1-2 hourly per protocol. Glycemic performance is assessed by percentage of (hourly resampled) blood glucose measurements in glycemic bands for the cohort and per-patient. Safety is assessed by numbers of patients with BG < 2.2 (severe) and 3.5 (moderate) mmol/L. Clinical effort is assessed by measurements per day. Results are median [IQR] as appropriate. Results: There were 428 measurements over 615 hours of control (16.7 measurements/day), which is similar to clinical SPRINT results (16/day). Per-patient hours of control were 56 [46-75] hours. Initial per-patient BG was 10.5 [8.6-11.5] mmol/L. All 10 patients (100%) reached 6.1 mmol/L in 7.5 [1.5-9.0] hours. Cohort BG was 6.6 [5.6-7.7] mmol/L with 48.8%, 61.8% and 81.0% of BG in the 4.0-6.5, 4.0-7.0 and 4.0-8.0 mol/L bands, respectively. Per-patient, the percentage time in these bands were 54.2 [32.5-68.5]%, 63.8 [42.6-83.7]% and 85.5 [70.0-92.6]%, respectively. No patients had BG < 2.2 mmol/L and 2 had one BG < 3.5 mmol/L. %BG < 4.0 mmol/L was 1.6%. These results were achieved using 3.0 [3.0-5.0] U/hour of insulin with 7.4 [4.0-10.8] g/hour of dextrose administration (all sources), for the cohort. Per-patient median insulin administration was 3.0 [3.0-3.0] U/hour, and 6.3 [1.3-9.7] g/hour dextrose. Higher carbohydrate nutrition than in SPRINT is offset by slightly higher insulin administration. Conclusion: The glycemic performance shows that the SPRINT protocol to guide insulin infusions provided very good glycemic control in initial pilot testing with no severe hypoglycemia. The overall design of the protocol was able generalize with good compliance and outcomes across geographically distinct clinical units, patients, and clinical practice.

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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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  • 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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  • 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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