Active insulin infusion using optimal and derivative-weighted control

Lam, Z.-H.; Hwang, K.-S.; Lee, J.-Y.; Chase, J. Geoffrey; Wake, G. C.

doi:10.1016/s1350-4533(02)00147-9

Cited by 41 publications

(22 citation statements)

References 22 publications

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“…A more complex, higher performance example uses model predictive control on a 19th order, non-physiological model of the glucose-regulatory system, resulting in a 40% peak reduction and 23% reduction in settling time to basal level [174]. Optimal control using grid search theory, robust H-infinity control, simple PD controllers, and variable structure controllers have also been studied, to name a few, each using different models and/or approaches [173][174][175][176][177][178][179][180][181][182]. In each case, the focus has been on controlling absolute blood glucose excursion, focusing on the post-prandial regulation of glucose levels in Type 1…”

Section: Type 1 Diabetes Applications and Analyses Of Model-based Conmentioning

confidence: 99%

Model-based glycaemic control in critical care—A review of the state of the possible

Chase

Wong

Lotz

et al. 2006

Biomedical Signal Processing and Control

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109

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Hyperglycaemia is prevalent in critical care, as patients experience stress-induced hyperglycaemia, even with no history of diabetes. Hyperglycaemia in critical care is not largely benign, as once thought, and it has a deleterious effect on outcome. Recent studies have shown that tight glucose regulation to average levels from 6.1-7.75 mmol/L can reduce mortality 25-43%, while also significantly reducing other negative clinical outcomes.However, clinical results are highly variable and there is little agreement on what levels of performance can be achieved and how to achieve them. A typical clinical solution is to use ad-hoc protocols based primarily on experience, where large amounts of insulin, up to 50U/hr, are titrated against glucose measurements variably taken every 1-4 hours. When combined with the unpredictable and sudden metabolic changes that characterise this aspect of critical illness and/or clinical changes in nutritional support, this approach results in highly variable blood glucose levels. The overall result is sustained periods of hyper-or hypo-glycaemia, characterised by oscillations between these states, which can adversely affect clinical outcomes and mortality. The situation is exacerbated by exogenous nutritional support regimes with high dextrose content.Hence, there is an emerging, strong need for the more rigorous analysis and methods that model-based control methods bring to this type of problem. This paper reviews the state of the clinical and model-based control systems approach to the problem of managing hyperglycaemia in critical care, emphasising emerging methods and results. The overall goal is to present the fundamental problem and associated science and technologies involved. Thus, it is less a discussion of specific advantageous approaches than a presentation of the different factors that impact the problem and the different approaches taken to address them in the limited clinical engineering research done to date. These discussions are presented in the context of current and emerging clinical studies, both model-based and empirical protocol driven. Analogies to the Type 1 diabetes mellitus control problem are also noted where relevant and significant. Hence, it is more overview than specific analysis, where the overall conclusion is that there are many opportunities and unanswered questions remaining on which model-based control research can have significant clinical impact. Declaration of originality and author(s) agreement

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Section: Type 1 Diabetes Applications and Analyses Of Model-based Conmentioning

confidence: 99%

Model-based glycaemic control in critical care—A review of the state of the possible

Chase

Wong

Lotz

et al. 2006

Biomedical Signal Processing and Control

Self Cite

109

View full text Add to dashboard Cite

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“…The proof-of-concept clinical trials conducted effectively simulate a true feedback control system with a 15-minute sampling period, which works well and represents a realistic level of system performance Lam et al, 2002). They are designed specifically to test the effectiveness of the heavy derivative control methods and to verify the simulations and design that led to them.…”

Section: Clinical Trial Methodsmentioning

confidence: 99%

Derivative weighted active insulin control algorithms and trials

Chase¹,

Doran²,

Hudson³

et al. 2003

IFAC Proceedings Volumes

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Close control of blood glucose levels significantly reduces vascular complications in diabetes. Heavy derivative controllers utilising the data density available from emerging biosensors are developed to provide tight, optimal control of elevated blood glucose levels. A two-compartment human model is developed for intravenous infusion from physiologically verified subcutaneous infusion models to enable a first of its kind, proof-of-concept clinical trial. Results show tight control with very similar performance to modelled behaviour and strong correlation between modelled insulin used versus the amounts used in clinical trials to validate the models and methods developed.

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“…Additional aspect, important for modelling structure, can represent social and economical burden of the discussed disease and/or treatment efficacy, which is often interpreted through different pharmacoeconomical studies (Boutayeb et al, 2004;Tarride et al, 2010;Arnold, 2010;Atanasijević-Kunc et al, 2011), where cost-effectiveness analysis and cost -utility analysis provide a basic comparison of different treatment policies or drug efficacy. All mentioned situations can be interpreted as open or closed -loop problems (Atanasijević- Kunc et al, 2008c;Bellazzi, 2001;Lam, 2002;Makroglou, 2006), where especially in the second case the analysis should take into account stability and sensitivity of system behaviour. The modelling and simulation approach aims to indicate, use and/or develop mathematical problem presentations which can be used for the evaluation of the burden of diabetes type 2 (D2) mellitus and correlated processes which can contribute to disease development or can represent the source of expenses (direct and/or indirect).…”

Section: Topics In the Prevention Treatment And Complications Of Typmentioning

confidence: 99%