296 results for Shaw, G.M.

  • Continuous Glucose Monitors and Tight Glycaemic Control in Intensive Care: An In-Silico Proof of Concept Analysis

    Signal, M.; Pretty, C.G.; LeCompte, A.J.; Shaw, G.M.; Chase, J.G. (2010)


    University of Canterbury Library

    Tight glycaemic control (TGC) in critical care has shown distinct benefits, but has also proven difficult to obtain. The risk of severe hypoglycaemia (< 2.2mmol/L) raises significant concerns for safety. Continuous Glucose Monitors (CGMs) offer frequent, though potentially noisy, automated measurement and thus the possibility of using them for early detection and intervention of hypoglycaemic events. This in-silico study investigates the potential of CGM devices to maintain control, prevent hypoglycaemia and reduce clinical effort. Retrospective clinical data from the SPRINT TGC study covering 26 patients was used with clinically validated metabolic system models and 3 different stochastic noise models (two Gaussian and one first-order autoregressive.) The noisy, virtual CGM blood glucose (BG) values were filtered and used to drive the SPRINT TGC protocol. A simple threshold alarm was used to trigger glucose interventions to avert potential hypoglycaemia. Monte Carlo analysis was used to get robust results from the stochastic noise models. Using SPRINT with simulated CGM noise, the BG time in the 4.4-6.1mmol/L band was reduced no more than 3% from 45.2% obtained with glucometer sensors. The number of patients experiencing severe hypoglycaemia was reduced by 0-30%. Duration of hypoglycaemic events was reduced by 19-65%. Finally, nurse workload was reduced by approximately 20 minutes per patient, per day. The results of this proof of concept study justify a pilot clinical study for verification in a clinical setting.

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  • Modeled Insulin Sensitivity and Interstitial Insulin Action from a Pilot Study of Dynamic Insulin Sensitivity Tests

    Lin, J.; Jamaludin, U.; Docherty, P.D.; Razak, N.N.; Le Compte, A.J.; Pretty, C.G.; Hann, C.E.; Shaw, G.M.; Chase, J.G. (2010)


    University of Canterbury Library

    An accurate test for insulin resistance can delay or prevent the development of Type 2 diabetes and its complications. The current gold standard test, CLAMP, is too labor intensive to be used in general practice. A recently developed dynamic insulin sensitivity test, DIST, uses a glucose-insulin-C-peptide model to calculate model-based insulin sensitivity, SI. Preliminary results show good correlation to CLAMP. However both CLAMP and DIST ignore saturation in insulin-mediated glucose removal. This study uses the data from 17 patients who underwent multiple DISTs to investigate interstitial insulin action and its influence on modeled insulin sensitivity. The critical parameters influencing interstitial insulin action are saturation in insulin receptor binding, αG, and plasma-interstitial difiusion rate, nI . Very low values of αG and very low values of nI produced the most intra-patient variability in SI. Repeatability in SI is enhanced with modeled insulin receptor saturation. Future parameter study on subjects with varying degree of insulin resistance may provide a better understanding of different contributing factors of insulin resistance.

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  • Glargine as a Basal Insulin Supplement in Recovering Critically Ill Patients - An In Silico Study

    Razak, N.N.; Lin, J.; Chase, J.G.; Shaw, G.M.; Pretty, C.G.; Le Compte, A.J.; Suhaimi, F.M.; Jamaludin, U. (2010)


    University of Canterbury Library

    Tight glycaemic control is now benefiting medical and surgical intensive care patients by reducing complications associated with hyperglycaemia. Once patients leave this intensive care environment, less acute wards do not continue to provide the same level of glycaemic control. Main reason is that these less acute wards do not have the high levels of nursing resources to provide the same level of glycaemic control. Therefore developments in protocols that are less labour intensive are necessary. This study examines the use of insulin glargine for basal supplement in recovering critically ill patients. These patients represent a group who may benefit from such basal support therapy. In silico study results showed the potential in reducing nursing effort with the use of glargine. However, a protocol using only glargine for glucose control did not show to be effective in the simulated patients. This may be an indication that a protocol using only glargine is more suitable after discharge from critical care.

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  • Impact of variation in patient response on model-based control of glycaemia in critically ill patients

    LeCompte, A.J.; Chase, J.G.; Shaw, G.M.; Lin, J.; Lynn, A.; Pretty, C.G. (2010)


    University of Canterbury Library

    Critically ill patients commonly experience stress-induced hyperglycaemia, and several studies have shown tight glycaemic control (TGC) can reduce patient mortality. However, tight control is often difficult to achieve due to conflicting drug therapies and evolving patient condition. Thus, a number of studies have failed to achieve TGC possibly due to use of fixed insulin dosing protocols over adaptive patient-specific methods. Model-based targeted glucose control can adapt insulin and dextrose interventions to match identified patient sensitivity. This study explores the impact on control of assuming patient response to insulin is constant versus time-varying. Simulated trials of glucose control were performed on adult and neonatal virtual patient cohorts. Results indicate assumptions of constant insulin sensitivity can lead to significantly increased rates of hypoglycaemia, a commonly cited issue preventing increased adoption of tight glycaemic control in critical care. It is clear that adaptive, patientspecific, approaches are better able to manage inter- and intra- patient variability than typical, fixed protocols.

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  • Evaluation of the Performances and Costs of a Spectrum of DIST Protocols

    Docherty, P.D.; Chase, J.G.; Lotz, T.F.; Hann, C.E.; TeMorenaga, L.; McAuley, K.A.; Shaw, G.M.; Berkeley, J.E,; Mann, J.I. (2010)


    University of Canterbury Library

    The strategic design of most insulin sensitivity (SI) tests maximises either accuracy or economy, but not both. Hence, accurate, large-scale screening isn’t feasible. The DIST was developed to better optimize both important metrics. The highly flexible DIST protocol samples insulin, glucose and C-peptide during a comparatively short test. Varying the sampling periods and assays, and utilising alternative computational methods enables a wide range of tests with different accuracy and economy tradeoffs. The result is a hierarchy of tests to facilitate low-cost screening. Eight variations of the DIST are evaluated against the fully-sampled test by correlating the SI and endogenous insulin production (Uen(t)) metrics. Five variations include sample and assay reductions and three utilise DISTq parameter estimations. The DISTq identification methods only require glucose assays and thus enable real-time analysis. Three DISTq methods were tested; the fully-sampled, the Short, and the 30 minute two-sample protocol. 218 DIST tests were completed on 84 participants to provide the data for this study. Methods that assayed insulin replicated the findings of the full DIST particularly well (R=0.89~0.92) while those that assayed C-peptide managed to best replicate endogenous insulin metrics (R=0.72~1.0). The three DISTq protocols correlated to the fully-sampled DIST at R=0.83, 0.77 and 0.71 respectively. As expected, test resolution increased with rising protocol cost and intensity. The ability of significantly less expensive tests to replicate the values of the fully-sampled DIST was relatively high (R=0.92 with four glucose and two insulin assays and 0.71 with only two glucose assays). Thus, an SI screening programme could achieve high resolution at a low cost by using a lower resolution DIST test. When an individual’s result is close to a diagnostic threshold stored test samples could be re-assayed for more species to allow a higher resolution analysis without the need for a second invasive clinical test. Hence, a single test can lead to several outcomes with this hierarchy approach, enabling large scale screening with high resolution only where required with minimal and feasible economic cost and only a single invasive clinical procedure.

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  • A Fast and Accurate Diagnostic Test for Severe Sepsis Using Kernel Classifiers

    Parente, J.D.; Lee, D.S.; Lin, J.; Chase, J.G.; Shaw, G.M. (2010)


    University of Canterbury Library

    Severe sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however gold standard blood culture test results may return in up to 48 hours. Insulin sensitivity (SI) is known to decrease with worsening condition and inflammatory response, and could thus be used to aid clinical treatment decisions. Some glycemic control protocols are able to accurately identify SI in real-time. A biomarker for severe sepsis was developed from retrospective SI and concurrent temperature, heart rate, respiratory rate, blood pressure, and SIRS score from 36 adult patients with sepsis. Patients were identified as having sepsis based on a clinically validated sepsis score (ss) of 2 or higher (ss = 0–4 for increasing severity). Kernel density estimates were used for the development of joint probability density profiles for ss = 2 and ss < 2 data hours (213 and 5858 respectively of 6071 total hours) and for classification. From the receiver operator characteristic (ROC) curve, the optimal probability cutoff values for classification were determined for in-sample and out-of-sample estimates. A biomarker including concurrent insulin sensitivity and clinical data for the diagnosis of severe sepsis (ss = 2) achieves 69–94% sensitivity, 75–94% specificity, 0.78–0.99 AUC, 3–17 LHR+, 0.06–0.4 LHR-, 9–38% PPV, 99–100% NPV, and a diagnostic odds ratio of 7–260 for optimal probability cutoff values of 0.32 and 0.27 for in-sample and out-of-sample data, respectively. The overall result lies between these minimum and maximum error bounds. Thus, the clinical biomarker shows good to high accuracy and may provide useful information as a real-time diagnostic test for severe sepsis.

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  • Intensive Control Insulin-Nutrition-Glucose Model Validated in Critically Ill Patients

    Lin, J.; Razak, N.N.; Pretty, C.G.; LeCompte, A.J.; Docherty, P.D.; Parente, J.D.; Shaw, G.M.; Hann, C.E.; Chase, J.G. (2010)


    University of Canterbury Library

    A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition- Glucose (ICING) Model is presented and validated using data from critically ill patients. Glucose utilisation and its endogenous production in particular, are more distinctly expressed. A more robust glucose absorption model through ingestion is also added. Finally, this model also includes explicit pathways of insulin kinetics, clearance and utilisation. Identification of critical constant population parameters is carried out parametrically, optimising one hour forward prediction errors, while avoiding model identifiability issues. The identified population values are pG = 0.006 min-1, EGPb = 1.16 mmol/min and nI = 0.003 min-1, all of which are within reported physiological ranges. Insulin sensitivity, SI , is identified hourly for each individual. All other model parameters are kept at well-known population values or functions of body weight or surface area. A sensitivity study confirms the validity of limiting time-varying parameters to SI only. The model achieves median fitting error <1% in data from 173 patients (N = 42,941 hrs in total) who received insulin while in the Intensive Care Unit (ICU) and stayed for more than 72 hrs. Most importantly, the median per patient one-hour ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is now within the lower bound of typical glucometer measurement errors of 7-12%. This result further confirms that the model is suitable for developing model-based insulin therapies, and capable of delivering tight blood glucose control, in a real-time model based control framework with a tight prediction error range.

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  • Validation of a Model-based Virtual Trials Method for Tight Glycemic Control in Intensive Care

    Suhaimi, F.M.; Chase, J.G.; LeCompte, A.J.; Preiser, J-C,; Lin, J.; Shaw, G.M. (2010)


    University of Canterbury Library

    In-silico virtual trials offer significant advantages in cost, time and safety. However, no such method has been truly validated with clinical data. This study tests 2 matched cohorts from an independent ICU treated with 2 different glycaemic control protocols. The goal is to validate the in-silico virtual trials model and methods, including the underlying assumptions. A retrospective analysis used records from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted a BG range of 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, but the Glucontrol A cohort was slightly older (p = 0.0352). Results showed high correlation between self- and cross-validation virtual trials and clinical results. The virtual trials models and methods are thus validated on independent data.

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  • A 2 parameter model of lung mechanics to predict volume response and optimise ventilator therapy in ARDS

    Sundaresan, A.; Hann, C.E.; Chase, J.G.; Yuta, T.; Shaw, G.M. (2009)

    Conference Contributions - Other
    University of Canterbury Library

    A majority of patients admitted to the Intensive Care Unit (ICU) require some form of respiratory support. In the case of Acute Respiratory Distress Syndrome (ARDS), the patient often requires full intervention from a mechanical ventilator. ARDS is also associated with mortality rates as high as 70%. Despite many recent studies on ventilator treatment of the disease, there are no well established methods to determine the optimal Positive End expiratory Pressure (PEEP) ventilator setting for individual patients [1]. A model of fundamental lung mechanics is developed based on capturing the recruitment status of lung units. The model produces good correlation with clinical data, and is clinically applicable due to the minimal number of patient specific parameters to identify. The ability to use this identified patient specific model to optimize ventilator management and lung volume recruitment is demonstrated. It thus provides a platform for continuous monitoring of lung unit recruitment and capability for a patient.

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  • Hypoglycemia Detection in Critical Care Using Continuous Glucose Monitors: An in Silico Proof of Concept Analysis

    Pretty, C.G.; Chase, J.G.; Le Compte, A.J.; Shaw, G.M.; Signal, M. (2010)

    Journal Articles
    University of Canterbury Library

    Tight glycemic control (TGC) in critical care has shown distinct benefits but also been proven to be difficult to obtain. The risk of severe hypoglycemia (< 40 mg/dL) has been significantly increased in several, but not all, studies, raising significant concerns for safety. Continuous glucose monitors (CGMs) offer frequent measurement and thus the possibility of using them for early detection alarms to prevent hypoglycemia.

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  • What Makes Tight Glycemic Control Tight? The Impact of Variability and Nutrition in Two Clinical Studies

    Suhaimi, F.; LeCompte, A.J.; Preiser, J-C.; Shaw, G.M.; Massion, P.; Radermecker, R.P.; Pretty, C.G.; Lin, J.; Desaive, T.; Chase, J.G. (2010)

    Journal Articles
    University of Canterbury Library

    Tight glycemic control (TGC) remains controversial, and successful, consistent and effective protocols elusive. This research analyses data from 2 TGC trials for root causes of the differences achieved in control and thus potentially in glycemic and other outcomes. The goal is to uncover aspects of successful TGC and delineate the impact of differences in cohorts. Protocols that dose insulin blind to carbohydrate administration can suffer greater outcome glycemic variability, even if average cohort glycemic targets are met. While the cohorts varied significantly in model-assessed insulin resistance, their variability was similar. Such significant intra- and inter- patient variability is a further significant cause and marker of glycemic variability in TGC. The results strongly recommended that TGC protocols be explicitly designed to account for significant intra- and inter- patient variability in insulin resistance, as well as specifying or having knowledge of carbohydrate administration to minimise variability in glycemic outcomes across diverse cohorts and/or centres.

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  • Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

    Hann, C.E.; Chase, J.G.; Desaive, T.; Froissart, C.B.; Revie, J.; Stevenson, D.; Lambermont, B.; Ghuysen, A.; Kolh, P.; Shaw, G.M. (2010)

    Journal Articles
    University of Canterbury Library

    Lumped parameter approaches for modelling the cardiovascular system typically havemany parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found. By utilizing continuous information from the arterial/pulmonary pressurewaveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care.

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  • A fast generalizable solution method for glucose control algorithms

    Hann, C.E.; Docherty, P.; Chase, J.G.; Shaw, G.M. (2010)

    Journal Articles
    University of Canterbury Library

    In critical care tight control of blood glucose levels has been shown to lead to better clinical outcomes. The need to develop new protocols for tight glucose control, as well as the opportunity to optimize a variety of other drug therapies, has led to resurgence in model-based medical decision support in this area. One still valid hindrance to developing new model-based protocols using so-called virtual patients, retrospective clinical data, and Monte Carlo methods is the large amount of computational time and resources needed. This paper develops fast analytical-based methods for insulin–glucose system model that are generalizable to other similar systems. Exploiting the structure and partial solutions in a subset of the model is the key in finding accurate fast solutions to the full model. This approach successfully reduced computing time by factors of 5600–144000 depending on the numerical error management method, for large (50– 164 patients) virtual trials and Monte Carlo analysis. It thus allows new model-based or model-derived protocols to be rapidly developed via extensive simulation. The new method is rigorously compared to existing standard numerical solutions and is found to be highly accurate to within 0.2%.

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  • Subject-specific cardiovascular system model-based identification and diagnosis of septic shock with a minimally invasive data set: Animal experiments and proof of concept

    Chase, J.G.; Lambermont, B.; Starfinger, C.; Hann, C.E.; Shaw, G.M.; Ghuysen, A.; Kolh, P.; Dauby, P.C.; Desaive, T. (2011)

    Journal Articles
    University of Canterbury Library

    A cardiovascular system (CVS) model and parameter identification method have previously been validated for identifying different cardiac and circulatory dysfunctions in simulation and using porcine models of pulmonary embolism, hypovolemia with PEEP titrations, and induced endotoxic shock. However, these studies required both left and right heart catheters to collect the data required for subject-specific monitoring and diagnosis – a maximally invasive data set in a critical care setting although it does occur in practice. Hence, use of this model-based diagnostic would require significant additional invasive sensors for some subjects, which is unacceptable in some, if not all, cases. The main goal of this study is to prove the concept of using only measurements from one side of the heart (right) in a “minimal” data set to identify an effective patient-specific model that can capture key clinical trends in endotoxic shock. This research extends existing methods to a reduced and minimal data set requiring only a single catheter and reducing the risk of infection and other complications – a very common, typical situation in critical care patients, particularly after cardiac surgery. The extended methods and assumptions that found it are developed and presented in a case study for the patient-specific parameter identification of pig-specific parameters in an animal model of induced endotoxic shock. This case study is used to define the impact of this minimal data set on the quality and accuracy of the model-application for monitoring, detecting and diagnosing septic shock. Six anesthetized healthy pigs weighing 20-30 kg received a 0.5- mg/kg endotoxin infusion over a period of 30 mins from T0 to T30. For this research, only right heart measurements were obtained. Errors for the identified model are within 8% when the model is identified from data, re-simulated and then compared to the experimentally measured data, including measurements not used in the identification process for validation. Importantly, all identified parameter trends match physiologically and clinically and experimentally expected changes, indicating that no diagnostic power is lost. This work represents a further with human subjects validation for this model-based approach to cardiovascular diagnosis and therapy guidance in monitoring endotoxic disease states. The results and methods obtained can be readily extended from this case study to the other animal model results presented previously. Overall, these results provide further support for prospective, proof of concept clinical testing with humans.

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  • Cardiac output estimation using pulmonary mechanics in mechanically ventilated patients

    Sundaresan, A.; Chase, J.G.; Hann, C.E.; Shaw, G.M. (2010)

    Journal Articles
    University of Canterbury Library

    The application of positive end expiratory pressure (PEEP) in mechanically ventilated (MV) patients with acute respiratory distress syndrome (ARDS) decreases cardiac output (CO). Accurate measurement of CO is highly invasive and is not ideal for all MV critically ill patients. However, the link between the PEEP used in MV, and CO provides an opportunity to assess CO via MV therapy and other existing measurements, creating a CO measure without further invasiveness. This paper examines combining models of diffusion resistance and lung mechanics, to help predict CO changes due to PEEP. The CO estimator uses an initial measurement of pulmonary shunt, and estimations of shunt changes due to PEEP to predict CO at different levels of PEEP. Inputs to the cardiac model are the PV loops from the ventilator, as well as the oxygen saturation values using known respiratory inspired oxygen content. The outputs are estimates of pulmonary shunt and CO changes due to changes in applied PEEP. Data from two published studies are used to assess and initially validate this model. The model shows the effect on oxygenation due to decreased CO and decreased shunt, resulting from increased PEEP. It concludes that there is a trade off on oxygenation parameters. More clinically importantly, the model also examines how the rate of CO drop with increased PEEP can be used as a method to determine optimal PEEP, which may be used to optimise MV therapy with respect to the gas exchange achieved, as well as accounting for the impact on the cardiovascular system and its management.

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  • The identification of insulin saturation effects during the Dynamic Insulin Sensitivity Test

    Docherty, P.D.; Chase, J.G.; Hann, C.E.; Lotz, T.F.; Lin, J.; McAuley, K.A.; Shaw, G.M. (2010)

    Journal Articles
    University of Canterbury Library

    Background: Many insulin sensitivity (SI) tests identify a sensitivity metric that is proportional to the total available insulin and measured glucose disposal despite general acceptance that insulin action is saturable. Accounting for insulin action saturation may aid inter-participant and/or inter-test comparisons of insulin efficiency, and model-based glycaemic regulation. Method: Eighteen subjects participated in 46 dynamic insulin sensitivity tests (DIST, low-dose 40-50 minute insulinmodified IVGTT). The data was used to identify and compare SI metrics from three models: a proportional model (SIL), a saturable model (SIS and Q50) and a model similar to the Minimal Model (SG and SIG). The three models are compared using inter-trial parameter repeatability, and fit to data. Results: The single variable proportional model produced the metric with least intra-subject variation: 13.8% vs 40.1%/55.6%, (SIS/I50) for the saturable model and 15.8%/88.2% (SIG/SG) for the third model. The average plasma insulin concentration at half maximum action (I50) was 139.3 mU·L-1, which is comparable to studies which use more robust stepped EIC protocols. Conclusions: The saturation model and method presented enables a reasonable estimation of an overall patient-specific saturation threshold, which is a unique result for a test of such low dose and duration. The detection of previously published population trends and significant bias above noise suggests that the model and method successfully detects actual saturation signals. Furthermore, the saturation model allowed closer fits to the clinical data than the other models, and the saturation parameter showed a moderate distinction between NGT and IFG-T2DM subgroups. However, the proposed model did not provide metrics of sufficient resolution to enable confidence in the method for either SI metric comparisons across dynamic tests or for glycamic control.

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  • Organ Failure and Tight Glycemic Control in the SPRINT Study

    Chase, J.G.; Pretty, C.G.; Pfeifer, L.; Shaw, G.M.; Preiser, J-C.; Lin, J.; Hewett, D.; Moorhead, K.T.; Desaive, T.; LeCompte, A.J. (2010)

    Journal Articles
    University of Canterbury Library

    open access

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  • Pilot Trials of the STAR TGC Protocol in a Cardiac Surgery ICU

    LeCompte, A.J.; Penning, S.; Moorhead, K.T.; Massion, P.; Preiser, J-C.; Shaw, G.M.; Desaive, T.; Chase, J.G. (2010)

    Conference Contributions - Other
    University of Canterbury Library

    MD, 1-page.

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  • Monte Carlo Analysis Of A Glycaemic Control Protocol For Less Acute Wards

    Razak, N.N.; Lin, J.; Chase, J.G.; Shaw, G.M. (2010)

    Conference Contributions - Other
    University of Canterbury Library

    Tight glycaemic control (TGC) benefits medical and surgical intensive care unit (ICU) patients by reducing complications associated with hyperglycemia. However, when patients transfer to less acute wards, continuing the same level of TGC is difficult and they get “rebound hyperglycemia” and may return to ICU. Primarily due to a lack of nursing resources. The SPRINT+Glargine protocol was developed to support the transition of patients from ICU to less acute wards. Glargine is injected 1-2x/day, so it can potentially reduce the workload to match clinical resources.

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  • Model-based Cardiovascular Therapeutics: Capturing the patient-specific impact of inotrope therapy

    Desaive, T.; Starfinger, C.; Chase, J.G.; Hann, C.E.; Shaw, G.M. (2009)

    Conference Contributions - Other
    University of Canterbury Library

    Introduction: A model for the cardiovascular and circulatory systems (CVS) has previously been validated in silico, as well as in porcine models of pulmonary embolism (PE), septic shock, and positive end-expiratory pressure (PEEP) titrations at different volemic levels. An accurate CVS system model can be used to monitor and diagnose dysfunction and support clinical decisions. This research validates this model with respect to inotrope therapy commonly used in circulatory support, prior to first human clinical trials. Method: The model and parameter identification process is used to study the effect of different adrenaline doses in healthy and critically ill patients. The hemodynamic effects on arterial blood pressures and stroke volume (cardiac index) are simulated in the model and adrenaline-specific parameters identified. These parameters are then used to capture and predict the future responses to a change in dose and-or over time. Results are compared to clinical data from 3 adrenaline published dosing studies, comprising a total of N=37 data sets. Results: All identified parameter trends match clinically expected changes. The adrenaline-specific parameters are physiologically relevant. Absolute percentage errors for the patient-specific, predicted hemodynamic responses (N=15) are within 10% compared to clinical data. The adrenaline-specific parameters accurately and uniquely capture the impact of inotrope therapy on the CVS, independent of other model parameters. Conclusions: Clinically accurate prediction of the impact of circulatory support drugs, such as adrenaline, offers significant clinical potential for this type of model-based application. Overall, this work represents a further clinical validation of the underlying fundamental CVS model and methods, and their use for cardiovascular diagnosis and therapy selection in critical care. These results are presented as (further) justification for (beginning) human trials of this model-based diagnostic and therapeutic approach.

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