21 results for Andreassen, S.

  • A simulation model of insulin saturation and glucose balance for glycaemic control in ICU patients

    Pielmeier, U.; Andreassen, S.; Nielsen, B.S.; Chase, J.G.; Haure, P. (2010)

    Journal Articles
    University of Canterbury Library

    Hyperglycaemia due to reduced insulin sensitivity is prevalent in critically ill patients and increases mortality and complications. However, consistent tight control has proven elusive. In particular, properly accounting for the saturation of insulin action is important in intensive insulin therapy. This paper introduces a composite metabolic model of insulin kinetics and blood glucose balance. Saturation of insulin action at high insulin concentrations is modelled as a non-linearity and reduced insulin sensitivity is modelled as either a scaling of peripheral insulin (before the non-linearity) or as a scaling of insulin effect (after the non-linearity). Retrospective clinical data from 10 intensive care patients are used to evaluate these approaches based on the resulting accuracy in predicting glycaemic response to intervention. For predictions of blood glucose longer than 1/2 hour ahead scaling of insulin effect gave a 1.6 fold smaller RMS error. Results for short-term (1-hour) and long-term (8-hour) predictions were 16% and 34% RMS error for scaling of insulin effect compared to 22% and 59% for scaling of peripheral insulin, respectively (P< 0.01). It can be concluded that scaling the insulin effect is a more suitable approach in this model structure.

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  • Prediction of hemodynamic changes towards PEEP titrations at different volemic levels using a minimal cardiovascular model.

    Starfinger, C.; Chase, J.G.; Hann, C.E.; Shaw, G.M.; Lambert, P.; Smith, B.W.; Sloth, E.; Larsson, A.; Andreassen, S.; Rees, S. (2008)

    Journal Articles
    University of Canterbury Library

    A cardiovascular system model and parameter identification method have previously been validated for porcine experiments of induced pulmonary embolism and positive end-expiratory pressure (PEEP) titrations, accurately tracking all the main hemodynamic trends. In this research, the model and parameter identification process are further validated by predicting the effect of intervention. An overall population-specific rule linking specific model parameters to increases in PEEP is formulated to predict the hemodynamic effects on arterial pressure, pulmonary artery pressure and stroke volume. Hemodynamic changes are predicted for an increase from 0 to 10cmH₂O with median absolute percentage errors less than 7% (systolic pressures) and 13% (stroke volume). For an increase from 10 to 20cmH₂O median absolute percentage errors are less than 11% (systolic pressures) and 17% (stroke volume). These results validate the general applicability of such a rule, which is not pig-specific, but holds over for all analyzed pigs. This rule enables physiological simulation and prediction of patient response. Overall, the prediction accuracy achieved represents a further clinical validation of these models, methods and overall approach to cardiovascular diagnosis and therapy guidance.

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  • Glucose-Insulin Pharmacodynamic Surface Modeling Comparison

    Chase, J.G.; Andreassen, S.; Pielmeier, U.; Hann, C.E. (2008)

    Conference Contributions - Published
    University of Canterbury Library

    invited

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  • Prediction Validation of Two Glycaemic Control Models in Critical Care

    Pielmeier, U.; Chase, J.G.; Andreassen, S.; Haure, P.; Nielsen, B.S.; Shaw, G.M. (2008)

    Conference Contributions - Published
    University of Canterbury Library

    Invited paper

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  • When to measure blood glucose - Cohort-Specific Glycaemic Control

    Pielmeier, U.; Andreassen, S.; Chase, J.G. (2008)

    Conference Contributions - Published
    University of Canterbury Library

    Model-based control systems are better fitted to glycaemic control in intensive care than ad-hoc protocols, but depend on predictive accuracy and facilitation of clinical routines. A general method to customize and visualize modelbased blood glucose predictions is presented. Customization is based on admission type and diabetic status of patients. Blood glucose concentrations of 14 critically ill patients from two intensive care units were retrospectively predicted. Relative prediction errors were found to be highest for diabetic I and II patients, and lowest for non-diabetic trauma and head-injured patients. Standard deviations of mean relative prediction errors are proposed to be used for display of accuracy of model-based blood glucose predictions in prospectively controlled patients. The method provides for an optimized timing of blood sampling to facilitate tight glucose management in the ICU.

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  • Receptor-based Models of Insulin Saturation Dynamics

    Andreassen, S.; Pielmeier, U.; Chase, J.G. (2008)

    Conference Contributions - Published
    University of Canterbury Library

    Normalisation of blood glucose by intensive insulin therapy has beneficial effects on the mortality and morbidity of intensive care patients, but also increases the risk of life threatening hypoglycaemia. Attempts to improve the control of blood glucose with model based systems have shown promising results, but require that the saturation of the effect of insulin on glucose balance at high plasma insulin concentrations is modeled appropriately. This saturation is often ignored in commonly used models of glucose metabolism, such as the minimal model, but may be important in patients with reduced insulin sensitivity. In this paper three simple models of insulin saturation are explored, all of them ascribing saturation to properties of the binding between insulin and its receptor. The models can be fitted to data from patients with normal or near normal insulin sensitivity, and they all predict that the plasma concentration at which half-insulin effect is reached is about 50 mU/l, also in patients with reduced insulin sensitivity. This prediction can be tested against clinical data, and if true will lead to advice on insulin therapy that avoids infusions that exceed 8 U/hour, in order to avoid saturation and the associated risk of hypoglycaemia.

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  • Model-Based Insulin Sensitivity and Pharmacodynamic Surfaces

    Chase, J.G.; Andreassen, S.; Pielmeier, U. (2008)

    Conference Contributions - Other
    University of Canterbury Library

    Objective: The minimal model (MM) is widely used for model-based insulin sensitivity testing. A pharmacodynamic (PD) surface analysis shows how the MM can under-predict insulin sensitivity and its changes over time, particularly in high(er) insulin dose tests. Methods: PD surfaces at steady state are fitted to N = 77 clinical results for: 1) the MM; 2) a receptor model for type 1 diabetes (RM); and 3) an MM-derived nonlinear metabolic control model (CM). The MM has no insulin effect saturation. The CM has insulin effect saturation and glucose removal saturation can be added. The RM model saturates the combined insulin and glucose removal effect. Errors are reported as: 1) RMS; 2) Mode of the Absolute Error (AME) distribution; and 3) Frequency of Errors Near Zero (FNZ) - over all 77 reported results. Results: Results for the MM are: RMS = 4.77; AME = -0.05, FNZ = 3 (of 77). For the RM: RMS = 0.04; AME = -0.01, FNZ = 32. For the CM: RMS = 0.07; AME = -0.01, FNZ = 36. Adding glucose saturation effects to the CM yields: RMS = 0.06; AME = -0.01, FNZ = 39. CM and RM have small and tight error distributions. Conclusions: The MM consistently under-predicts insulin saturation resulting in large errors due to the shape of its PD surface. The ability to fit a single or small group of data sets can yield large error for others, illustrating the value of using a large set of clinical results to test these models. The results show that insulin and/or glucose saturation dynamics are necessary to yield consistent model-based insulin sensitivity values.

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  • Impact of Human Factors on Clinical Protocol Performance: A Proposed Assessment Framework and Case Examples

    Chase, J.G.; Andreassen, S.; Jensen, K.; Shaw, G.M. (2008)

    Journal Articles
    University of Canterbury Library

    Invited journal symposium paper

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  • Model-based identification of PEEP titrations during different volemic levels

    Starfinger, C.; Chase, J.G.; Hann, C.E.; Shaw, G.M.; Lambert, P.; Smith, B.W.; Sloth, E.; Larsson, A.; Andreassen, S.; Rees, S. (2008)

    Journal Articles
    University of Canterbury Library

    A cardiovascular system (CVS) model has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture all main hemodynamic trends in a porcine model of pulmonary embolism. In this research, a slightly extended CVS model and parameter identification process are presented and validated in a porcine experiment of positive end-expiratory pressure (PEEP) titrations at different volemic levels. The model is extended to more physiologically represent the separation of venous and arterial circulation. Errors for the identified model are within 5% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents another clinical validation of the underlying fundamental CVS model, and the methods and approach of using them for cardiovascular diagnosis in critical care.

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  • Diagnosis using a minimal cardiac model including reflex actions

    Hann, C.E.; Chase, J.G.; Andreassen, S.; Smith, B.W.; Shaw, G.M. (2005)

    Conference Contributions - Other
    University of Canterbury Library

    Heart disease is difficult to diagnose due to often confusing clinical a data. A minimal cardiac model has been developed that captures the major dynamics of the cardiovascular system (CVS). To assist medical staff in diagnosis and treatment, a fast accurate patient-specific parameter identification method, which can account for time varying disease state and the body’s natural reflex response, is required.

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  • Simulating cardiac disease from onset with a minimal cardiac model including reflex actions

    Hann, C.E.; Chase, J.G.; Andreassen, S.; Smith, B.W.; Shaw, G.M. (2005)

    Conference Contributions - Published
    University of Canterbury Library

    Cardiac disease state difficult to diagnose - Limited data - Reflex actions

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  • A fully identifiable physiological model of insulin kinetics for clinical applications

    Lotz, T.; Chase, J.G.; Andreassen, S.; Hann, C.E.; Lin, J.; Wong, J.; McAuley, K.A. (2005)

    Conference Contributions - Published
    University of Canterbury Library

    Why model insulin kinetics? - Glycaemic control for critically ill and Diabetes - Diagnosis of insulin resistance - Current models not physiological or difficult to identify!

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  • A Glucose-Insulin Pharmacodynamic Surface Modeling Validation and Comparison of Metabolic System Models

    Chase, J.G.; Andreassen, S.; Pielmeier, U.; Hann, C.E.; McAuley, K.A.; Mann, J.I. (2009)

    Journal Articles
    University of Canterbury Library

    invited special edition

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  • Comparison of Identification Methods of a Time-varying Insulin Sensitivity Parameter in a Simulation Model of Glucose Metabolism in the Critically Ill

    Pielmeier, U.; Andreassen, S.; Nielsen, B.S.; Hann, C.E.; Chase, J.G.; Haure, P. (2009)

    Conference Contributions - Published
    University of Canterbury Library

    6-pages (invited)

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  • Regulation of Blood Sugar in Intensive Care Patients

    Pielmeier, U.; Andreassen, S.; Chase, J.G.; Haure, P.; Shaw, G.M. (2007)

    Conference Contributions - Other
    University of Canterbury Library

    High blood sugar levels are frequent in intensive care patients, resulting in higher mortality and morbidity, and longer stay. GlucoSafe, a computer decision support system, is developed to assist clinicians in regulating blood sugar. The system uses a physiological model of sugar metabolism, including insulin production and action, and intestinal uptake of nutrients. However, efficacy will depend on how accurately it can predict future blood glucose levels (BG) after a glycemic control intervention, based on previously measured BG values. 1-10 hour forward predictions were made using GlucoSafe (GS) and a clinically tested model (CC) from New Zealand for 11 hyperglycemic patients, 6 from New Zealand and 5 from Denmark. As expected, relative RMS prediction error increases with prediction interval for both models and cohorts. Fig. 1 shows similar predictive power for GS and CC up to 3-5 hours. GS outperforms CC for predictions beyond 5 hours. A CC-based protocol has been successfully applied for glycemic control in Christchurch. Therefore, GlucoSafe is expected to be a safe, effective tool for blood sugar regulation in intensive care.

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  • Simulation of Cardiovascular System Diseases by Including the Autonomic Nervous System in a Minimal Model

    Smith, B.W.; Andreassen, S.; Shaw, G.M.; Jensen, P.L.; Rees, S.E.; Chase, J.G. (2007)

    Journal Articles
    University of Canterbury Library

    Diagnosing cardiovascular system (CVS) diseases from clinically measured data is difficult, due to the complexity of the hemodynamic and autonomic nervous system (ANS) interactions. Physiological models could describe these interactions to enable simulation of a variety of diseases, and could be combined with parameter estimation algorithms to help clinicians diagnose CVS dysfunctions. This paper presents modifications to an existing CVS model to include a minimal physiological model of ANS activation. A minimal model is used so as to minimise the number of parameters required to specify ANS activation, enabling the effects of each parameter on hemodynamics to be easily understood. The combined CVS and ANS model is verified by simulating a variety of CVS diseases, and comparing simulation results with common physiological understanding of ANS function and the characteristic hemodynamics seen in these diseases. The model of ANS activation is required to simulate hemodynamic effects such as increased cardiac output in septic shock, elevated pulmonary artery pressure in left ventricular infarction, and elevated filling pressures in pericardial tamponade. This is the first known example of a minimal CVS model that includes a generic model of ANS activation and is shown to simulate diseases from throughout the CVS.

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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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  • 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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  • Identification of time-varying cardiac disease state using a minimal cardiac model with reflex actions

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

    Conference Contributions - Published
    University of Canterbury Library

    A minimal cardiac model that accurately captures the essential cardio- vascular system dynamics has been developed. Standard parameter identification methods for this model are highly non-linear and non-convex, hindering clinical application, given the limited measurements available in an intensive care unit. This paper presents an integral based identification method that transforms the problem into a linear, convex problem. Five common disease states including four fundamental types of shock, are identified to within 10% without false identification. Clinically, it enables medical staff to rapidly obtain a patient specific model to assist in diagnosis and therapy selection.

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  • Cardiovascular system modelling of heart-lung interaction during mechanical ventilation

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

    Conference Contributions - Published
    University of Canterbury Library

    Choosing suitable ventilation strategies for critically ill patients with lung disorders involves considering the difficult and poorly understood trade-off between achieving adequate ventilation, while maintaining suitable perfusion. This study presents a minimal cardiovascular system model that includes variations in pulmonary vascular resistance during mechanical ventilation. The model is shown to capture transient haemodynamics from experimentally measured data. It also enables investigation into the effects of time varying resistance on pulmonary haemodynamics. The study shows the potential usefulness of this model in a tool to assist clinical staff in optimising ventilation pressures while maintaining adequate pulmonary perfusion

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