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    Stochastic Model Predictive (STOMP) Glycaemic Control for the Intensive Care Unit: Development and virtual trial validation (2015)

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    Type of Content
    Journal Article
    UC Permalink
    http://hdl.handle.net/10092/10010
    
    Publisher's DOI/URI
    https://doi.org/10.1016/j.bspc.2014.09.011
    
    Publisher
    University of Canterbury. Mechanical Engineering
    Collections
    • Engineering: Journal Articles [1528]
    Authors
    Stewart, K.W.
    Pretty, C.G.
    Tomlinson, H.
    Fisk, L.
    Shaw, Geoff cc
    Chase, Geoff cc
    show all
    Abstract

    Critically ill patients often experience stress-induced hyperglycaemia, which results in increased morbidity and mortality. Glycaemic control (GC) can be implemented in the intensive care unit (ICU) to safely manage hyperglycaemia. Two protocols SPRINT and STAR, have been implemented in the Christchurch ICU, and have been successful in treating hyperglycaemia while decreasing the risk of hypoglycaemia. This paper presents a new GC protocol that implements the probabilistic, stochastic forecasting methods of STAR, while formalizing the control methodology using model predictive control (MPC) theory to improve the ability to tune the dynamic response of the controller. This Stochastic Model Predictive (STOMP) controller predicts the response to a given insulin/nutrition intervention, and attributes weighted penalty values to several key performance metrics. The controller thus chooses an intervention at each hour that minimizes the sum of these penalties over a prediction window of 6 hours, which is twice as long as the 3-hour window used in STAR. Clinically validated virtual trials were used to evaluate the relative performance of STOMP. Results showed STOMP was able to obtain results very similar to STAR with both protocols maintaining approximately 85% of time within 4.4-8.0 mmol/L glycaemic band, and only 4-5 patients of the 149 patient STAR cohort having blood glucose (BG) < 2.2 mmol/L. STOMP was able to attain similar results to STAR while further increasing ease of controller tuning for different clinical requirements and reducing the number of BG measurements required by 35%.

    Citation
    Stewart, K.W., Pretty, C.G., Tomlinson, H., Fisk, L., Shaw, G.M., Chase, J.G. (2015) Stochastic Model Predictive (STOMP) Glycaemic Control for the Intensive Care Unit: Development and virtual trial validation. Biomedical Signal Processing and Control, 16, pp. 61-67.
    This citation is automatically generated and may be unreliable. Use as a guide only.
    ANZSRC Fields of Research
    32 - Biomedical and clinical sciences::3202 - Clinical sciences::320208 - Endocrinology
    32 - Biomedical and clinical sciences::3202 - Clinical sciences::320212 - Intensive care
    40 - Engineering::4003 - Biomedical engineering::400303 - Biomechanical engineering
    Rights
    https://hdl.handle.net/10092/17651

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    • Stochastic Model Predictive (STOMP) Glycemic Control for the Intensive Care Unit 

      Stewart, K. W.; Chase, Geoff; Pretty, C. G.; Tomlinson, H.; Fisk, L.; Shaw, Geoff (University of Canterbury. Mechanical Engineering, 2014)
      Objective: Critically ill patients often experience stress-induced hyperglycemia, which has been shown to result in increased morbidity and mortality. Stochastic Model Predictive (STOMP) Control is a new model-based ...
    • Virtual Patients and Clinical Trial Simulations to Improve Safety of Glucose Control in Intensive Care 

      Fisk, L.; Le Compte, A.J.; Shaw, Geoff; Chase, Geoff (University of Canterbury. Mechanical Engineering, 2012)
      Despite the potential clinical benefits of normalizing blood glucose in critically ill patients, the risk of hypoglycemia is a major barrier to widespread clinical adoption of accurate glycemic control. To compare five ...
    • Clinical Glycaemic Performance of the STAR Protocol 

      Stewart, K.W.; Tomlinson, H.; Pretty, C.G.; Fisk, L.; Thomas, F.; Shaw, Geoff; Benyo, B.; Illyes, A.; Szabo-Nemedi, N.; Chase, Geoff (University of Canterbury. Mechanical Engineering, 2015)
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