9 results for Pielmeier, U.

  • 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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  • 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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  • 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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  • 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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  • 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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