164 results for Hann, C.E.

  • Rocket Roll Dynamics and Disturbance – Minimal modelling and system identification

    Hann, C.E.; Snowdon, M.; Rao, A.; Tang, R.; Korevaar, A.; Skinner, G.; Keall, A.; Chen, X.Q.; Chase, J.G. (2010)


    University of Canterbury Library

    The roll dynamics of a 5kg, 1.3 m high sounding rocket are analyzed in a vertical wind tunnel. Significant turbulence in the tunnel makes the system identification of the effective inertia, damping and asymmetry with respect to roll challenging. A novel method is developed which decouples the disturbance from the rocket frame’s intrinsic roll dynamics and allows accurate prediction of roll rate and angle. The parameter identification method is integral-based, and treats wind disturbances as equivalent to a movement in the actuator fins. The method is robust, requires minimal computation, and gave a realistic disturbance distribution reflecting the randomness of the turbulent wind flow. The mean absolute roll rate of the rocket frame observed in experiments was 16.4 degree/s and the model predicted the roll rate with a median error of 0.51 degrees/s with a 90th percentile of 1.25 degrees/s. The roll angle (measured by an encoder), was tracked by the model with a median absolute error of 0.25 degrees and a 90th percentile of 0.50 degrees. These results prove the concept of this minimal modeling approach which will be extended to pitch and yaw dynamics in the future.

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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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  • 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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  • 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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  • 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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  • Investigating the Applicability of qALPV Modeling to ICU Models for Glycaemic Control

    Kovacs, L.; Gyorgy, A.; Szalay, P.; Benyo, B.; Benyo, Z.; Hann, C.E.; Chase, J.G. (2010)

    Conference Contributions - Published
    University of Canterbury Library

    Maintenance of glucose levels in intensive care unit (ICU) patients via control of insulin inputs is currently an active research field. Different published models that address this problem are analysed from control theory point of view. This paper analyzes the three most used ICU metabolic system models in the literature, two of which have been validated in clinical trials or alternate clinical use. Global control theoretical characteristics are determined using nonlinear analysis. Quasi affine linear parameter varying (qALPV) modeling methodology is then investigated for further robust nonlinear model based control.

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  • Impact of system identification methods in metabolic modeling and control

    Wong, J.; Shaw, G.M.; Hann, C.E.; Lotz, T.; Lin, J.; Chase, J.G. (2006)

    Conference Contributions - Published
    University of Canterbury Library

    Metabolic modelling can significantly improve control of hyperglycaemia. Clinical control demands physiological accuracy in identifying patient specific parameters. However, typically used non-linear and non-convex identification methods and models can deliver sub-optimal results, affecting control prediction. This research compares a typical non-linear method and a novel linear, convex method for an accepted metabolic control model using retrospective clinical control data. Results show increased errors in fitting for the non-linear fitting method. A significant (140-660X) increase in computational efficiency is also reported. The methods and results presented can be readily applied and generalised to a wider set of pharmacokinetic and pharmacodynamic systems that use similar linear and non-linear models.

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  • Efficient computation of the infimum in H∞ control for seismic structures

    Wu, W-H.; Hann, C.E.; Chase, J.G. (2006)

    Conference Contributions - Published
    University of Canterbury Library

    An important consideration in the design of H∞ controllers is the optimal norm of the H∞ control problem. This value determines the lowest value of the H∞ norm that can be obtained with the problem and system defined. Hence, it represents a design limit, but one that is computationally intractable and difficult to obtain. A new method for determining the optimal H∞ norm of a state feedback system is presented. It is based on the application of discriminant to check a stability condition on the Hamiltonian matrix that is associated with the infimum value. In addition, a generalized eigenvalue problem is deduced from the discriminant stability condition to avoid any required iteration. The overall approach provides a highly accurate approximation of the optimal value with minimum computation compared to other approaches in the literature.

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  • Insulin + nutrition control for tight critical care glycaemic regulation

    Chase, J.G.; Wong, J.; Lin, J.; LeCompte, A.; Lotz, T.; Lonergan, T.; Willacy, M.B.; Hann, C.E.; Shaw, G.M. (2006)

    Conference Contributions - Published
    University of Canterbury Library

    A new insulin and nutrition control method for tight glycaemic control in critical care is presented from concept to clinical trials to clinical practice change. The primary results show that the method can provide very tight glycaemic control in critical care for a very critically ill cohort. More specifically, the final clinical practice change protocol provided 2100 hours of control with average blood glucose of 5.8 +/- 0.9 mmol/L for an initial 10 patient pilot study. It also used less insulin, while providing the same or greater nutritional input, as compared to retrospective hospital control for a relatively very critically ill cohort with high insulin resistance.

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  • Clinical cardiovascular identification with limited data and fast forward simulation

    Hann, C.E.; Chase, J.G.; Shaw, G.M.; Andreassen, S.; Smith, B.W. (2006)

    Conference Contributions - Published
    University of Canterbury Library

    A minimal cardiac model has been developed that captures the major dynamics of the cardio-vascular system (CVS). This model is extended to simulate time varying disease state including reflex actions and an integral based identification method is presented that enables linear and convex parameter identification. Two common time varying disease states are identified to within 10% without false identification. Also the valve law in this model is reformulated in terms of Heaviside functions, and a unique closed form analytical solution is obtained for the ventricular interaction equation. This enables rapid forward simulations of the model. Clinically, the method ensures medical staff can rapidly obtain a patient specific model and can simulate a large number of therapy combinations to find the best treatment

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  • Highly correlated model-based testing of insulin sensitivity – initial results for a proposed low-intensity test

    Lotz, T.; Chase, J.G.; McAuley, K.A.; Shaw, G.M.; Wong, X-W.; Lin, J.; LeCompte, A.; Hann, C.E.; Mann, J.I. (2006)

    Conference Contributions - Other
    University of Canterbury Library

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  • Real-time integral based structural health monitoring

    Singh-Levett, I.; Chase, J.G.; Hann, C.E.; Deam, B.L. (2006)

    Conference Contributions - Published
    University of Canterbury Library

    An algorithm has been developed to provide real-time structural health monitoring during earthquake events. For a given input ground acceleration the algorithm matches the Bouc-Wen hysteresis model to structural response data using piecewise least squares fitting. The methodology identifies pre-yield and post-yield stiff-ness, elastic and plastic components of displacement and final residual displacement. This approach is particularly useful for rapid assessment of structural safety by owners or civil defense authorities. The algorithm is tested with simulated response data using the El Centro and Kobe earthquake records. Using simulated data for a two degree of freedom shear building model, the algorithm captures stiffness to within 2% of the real value and permanent deflection to within 5% when significant non-linear response occurs. This is achieved with acceleration data sampled at 1KHz and displacement data sampled at 10Hz

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  • Integral-based identification of patient specific parameters for a minimal cardiac model

    Hann, C.E.; Chase, J.G.; Shaw, G.M. (2006)

    Journal Articles
    University of Canterbury Library

    A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and two disease states, Valvular Stenosis and Pulmonary Embolism, are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 minutes) to within a mean value of 4 – 8% in the presence of 5 – 15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 5% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.

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  • Fast normalized cross correlation for motion tracking using basis functions

    Hii, A.; Hann, C.E.; Chase, J.G.; Van Houten, E.E.W. (2006)

    Journal Articles
    University of Canterbury Library

    Digital Image-based Elasto-tomography (DIET) is an emerging method for noninvasive breast cancer screening. Effective clinical application of the DIET system requires highly accurate motion tracking of the surface of an actuated breast with minimal computation. Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate foundation for motion tracking. However, even using fast fourier transform (FFT) methods, it is too computationally intense for rapidly managing several large images. A significantly faster method of calculating the NCC is presented that uses rectangular approximations in place of randomly placed landmark points or the natural marks on the breast. These approximations serve as an optimal set of basis functions that are automatically detected, dramatically reducing computational requirements. To prove the concept, the method is shown to be 37-150 times faster than the FFT-based NCC with the same accuracy for simulated data, a visco-elastic breast phantom experiment and human skin. Clinically, this approach enables thousands of randomly placed points to be rapidly and accurately tracked providing high resolution for the DIET system

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  • Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study

    Wong, X-W.; Chase, J.G.; Shaw, G.M.; Hann, C.E.; Lotz, T.; Lin, J.; Singh-Levett, I.; Hollingsworth, L.J.; Wong, O.S.W.; Andreassen, S. (2006)

    Journal Articles
    University of Canterbury Library

    This paper develops and presents a pilot study of a long-term controller for safe regulation of glycaemia under elevated insulin resistance and glucose intolerance in critically ill patients by modulating enteral nutrition inputs in addition to conventional basal-bolus intravenous insulin therapy. Clinical proof-of-concept pilot trials of the algorithm are performed which show the algorithm adaptability to time-varying intraas well as inter-patient variability in condition while requiring relatively infrequent glucose measurement. This research is a step towards randomized, comparative cohort studies of clinical outcomes using the developed protocol. Previous blood glucose control research includes controlled experiments in insulin infusion by Hovorka et al. [26], Chee et al. [27], and Chase et al. [18, 28]. Adaptive bolus-based control using insulin-alone by Chase et al. [18], is the basis of this work. The primary difference in this research is the improvement in control under elevated insulin resistance by modulation of nutritional support in addition to insulin input

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  • Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

    Chase, J.G.; Hann, C.E.; Jackson, M.; Lin, J.; Lotz, T.; Wong, X-W.; Shaw, G.M. (2006)

    Journal Articles
    University of Canterbury Library

    Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the Continuous Glucose Monitoring System (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute one-hour glucose prediction error of 6.20% (range: 4.97-8.06%) with an average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the methods and results are generalisable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies.

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