Overnight glucose control in people with type 1 diabetes

Boiroux, Dimitri; Duun‐Henriksen, Anne Katrine; Schmidt, Signe; Nørgaard, Kirsten; Madsbad, Sten; Poulsen, Niels Kjølstad; Madsen, Henrik; Jørgensen, John Bagterp

doi:10.1016/j.bspc.2017.08.005

Cited by 43 publications

(20 citation statements)

References 60 publications

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“…However, unlike the UVa/Padova simulator, results from this software are not FDA approved substitutes for pre-clinical trials. While this may lead to several additional steps prior to clinical studies, the Cambridge model has significantly fewer mathematical states and parameters than the UVa/Padova model [58], while maintaining physiological significance and modeling diurnal variations, which has allowed some researchers to modify and adapt it more readily for their specific needs [76].…”

Section: ) Minimal Glucoregulatory Modelsmentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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Section: ) Minimal Glucoregulatory Modelsmentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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“…The fact that parameters are physiological may (and in fact does) help in the identification process, given that it is easier to detect mistaken estimations. Although the model considered in this article is essentially different to the others in literature, many other MPC‐based approaches for AP consider this kind of individualization 5,23,25‐27 …”

Section: Glucose‐insulin Modelmentioning

confidence: 99%

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

González

Rivadeneira

Ferramosca

et al. 2020

Optim Control Appl Methods

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Summary In this work, the problem of regulating blood glucose (glycemia) in type I diabetic patients is studied by means of an impulsive zone model predictive control (iZMPC), which bases its predictions on a novel long‐term glucose‐insulin model. Taking advantage of the impulsive version of the model—which features real‐life properties of diabetes patients that some other popular models do not—the given control guarantees the stability under moderate‐to‐severe plant‐model mismatch and disturbances. Long‐term scenarios—including meals and physiological parameter variations—are simulated and the results are satisfactory as every hyperglycemic and hypoglycemic episodes are suitably controlled.

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“…In this design, we use a modified version of the asymmetric, time-varying, exponential reference signal. 37 The equation describing the time-varying reference is given by Each model prediction uses x k k  | , the estimate of x k , as the initial condition computed by means of a hybrid implementation of the Kalman filter. 38,39 Our implementation includes an observable and nonobservable (open-loop) submodels.…”

Section: Multistage Model Predictive Controlmentioning

confidence: 99%

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

García-Tirado

Colmegna

Corbett

et al. 2019

J Diabetes Sci Technol

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Background: Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. Methods: A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. Results: In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). Conclusion: An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

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Overnight glucose control in people with type 1 diabetes

Cited by 43 publications

References 60 publications

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

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