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    In Silico Monte Carlo Virtual Trials of a Model-Based Adaptive Type 1 Diabetes Mellitus Control Protocol (2008)

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    12612097_T1DM Control poster ver 1.0.pdf (506.7Kb)
    Type of Content
    Conference Contributions - Other
    UC Permalink
    http://hdl.handle.net/10092/2447
    
    Publisher
    University of Canterbury. Mechanical Engineering
    Collections
    • Engineering: Conference Contributions [2338]
    Authors
    Wong, X.W.
    Chase, Geoff cc
    Shaw, Geoff cc
    Lin, J.
    LeCompte, A.J.
    Hann, C.E.
    show all
    Abstract

    Objective: To test an in silico Type 1 diabetes control protocol while accounting for realistic physiological variability, and measurement and delivery error

    Methods: A Monte Carlo (MC) analysis uses clinically reported variations in physiological parameters, subcutaneous insulin absorption and delivery, nutritional carbohydrate counting intake, and SMBG error to test robustness. The model-based protocol is repeatedly tested on a 40 patient virtual cohort over 1.4M patient hours. The analysis is repeated for SMBG frequency of 2-10/day. Long term HbA1c is estimated from clinically reported formulas to assess performance.

    Results: The protocol controlled 100% of the cohort to ADA recommended HbA1c with SMBG frequency of 6/day. Peak control is achieved at SMBG frequency of 8/day. A small but significant decrease in time in the 72-144mg/dL band and consequent increase in mild and severe hypoglycaemia occurs at SMBG frequency of 10/day. Time spent in the 72-108mg/dL band is not significantly different to a no error and no variability simulation. Cohort HbA1c is reduced for all SMBG frequencies. Hypoglycaemia increases over the no error simulation, as expected. The difference in 95% confidence band for time in severe (=54mg/dL) and mild (=71mg/dL) hypoglycaemia spans an acceptable [1-6%] or 0.24-1.44hours/day versus the no error simulation for 6/day SMBG frequency.

    Conclusions: A MC simulation tool predicts long-term glycaemic control outcomes to test an adaptive control protocol in conditions of realistic variability and error. The protocol is shown to be robust, remaining effective and safe from hypoglycaemia compared to perfect no error or variability simulations, and clinical cohort control data. The protocol utilises the most commonly used forms of intervention (SMBG and MDI) and is thus applicable for most T1DM individuals.

    Citation
    Wong, X.W., Chase, J.G., Shaw, G.M., Lin, J., LeCompte, A.J., Hann, C.E. (2008) In Silico Monte Carlo Virtual Trials of a Model-Based Adaptive Type 1 Diabetes Mellitus Control Protocol. Bethesda, MD, USA: 8th Annual Diabetes Technology Meeting, 13-15 Nov 2008.
    This citation is automatically generated and may be unreliable. Use as a guide only.
    Rights
    https://hdl.handle.net/10092/17651

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    • Clinical validation of a model-based glycaemic control design approach and comparison to other clinical protocols 

      Chase, Geoff; Shaw, Geoff; Hann, C.E.; LeCompte, A.; Lonergan, T.; Willacy, M.B.; Wong, X-W.; Lin, J.; Lotz, T. (University of Canterbury. Mechanical Engineering.University of Canterbury. Mathematics and Statistics., 2006)
      Hyperglycaemia is prevalent in critical care and tight control can reduce mortality from 9-43% depending on the level of control and the cohort. This research presents a table-based method that varies both insulin dose ...
    • Validation of a Model-based Virtual Trials Method for Tight Glycemic Control in Intensive Care 

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      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 ...
    • Model-Based Insulin and Nutrition Administration for Tight Glycaemic Control in Critical Care 

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      Objective: Present a new model-based tight glycaemic control approach using variable insulin and nutrition administration. Background: Hyperglycaemia is prevalent in critical care. Current published protocols use insulin ...
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