Zone Model Predictive Control and Moving Horizon Estimation for the Regulation of Blood Glucose in Critical Care Patients

Knab, Timothy D.; Clermont, Gilles

doi:10.1016/j.ifacol.2015.09.099

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…In [48], in addition to the models used in [67], the authors add another model representing the suppression of endogeneous glucose production by insulin, [71], again for purposes of controlling BG levels of critical care patients. Similarly, in [47], the ICING model is used with another model respresenting subcutaneous insulin dynamics, [84], in a linear zone model-predictive control framework. Nonlinear models representing glucose-insulin dynamics are also of interest and using these models for control purposes requires some modification in the computational methodology.…”

Section: Literature Reviewmentioning

confidence: 99%

A simple modeling framework for prediction in the human glucose–insulin system

Sirlanci

Levine

Wang

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic management. BG dynamics are nonlinear, complex, and nonstationary, which can be represented by nonlinear models. However, the sparsity of routinely collected data creates parameter identifiability issues when high-fidelity complex models are used, thereby resulting in inaccurate forecasts. One can use models with reduced physiological fidelity for robust and accurate parameter estimation and forecasting with sparse data. For this purpose, we approximate the nonlinear dynamics of BG regulation by a linear stochastic differential equation: we develop a linear stochastic model, which can be specialized to different settings: type 2 diabetes mellitus (T2DM) and intensive care unit (ICU), with different choices of appropriate model functions. The model includes deterministic terms quantifying glucose removal from the bloodstream through the glycemic regulation system and representing the effect of nutrition and externally delivered insulin. The stochastic term encapsulates the BG oscillations. The model output is in the form of an expected value accompanied by a band around this value. The model parameters are estimated patient-specifically, leading to personalized models. The forecasts consist of values for BG mean and variation, quantifying possible high and low BG levels. Such predictions have potential use for glycemic management as part of control systems. We present experimental results on parameter estimation and forecasting in T2DM and ICU settings. We compare the model’s predictive capability with two different nonlinear models built for T2DM and ICU contexts to have a sense of the level of prediction achieved by this model.

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Section: Literature Reviewmentioning

confidence: 99%

A simple modeling framework for prediction in the human glucose–insulin system

Sirlanci

Levine

Wang

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…In the ICU, the MPC application has been illustrated primarily to optimize the insulin injection [20], [22].…”

Section: Model-predictive Control Algorithm For Feeding Rate Controlmentioning

confidence: 99%

“…Control-based algorithms with a closed-loop action, including Model Predictive Control (MPC), have been developed to regulate blood glucose dynamics in ICU settings [20], [21], [22]. All these closed-loop algorithms have been developed to deliver insulin without nurse input.…”

Section: Introductionmentioning

confidence: 99%

Personalized Feeding Infusion Rate of Nutrition to Improve Glycemic Variability Control in Critical Care Patients

Abohtyra

2021

Preprint

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Control-based algorithms in the intensive care unit (ICU) patients have been developed to deliver a sufficient amount of insulin, but optimizing the rate of feeding of nutrition in ICU patients to improve glycemic variability control has not been done yet. Continuous feeding is commonly used for nutrition in critically ill patients who cannot be fed orally to maintain a normal blood sugar concentration, but optimizing its rate, for these individuals, is needed to avoid the adverse outcomes caused by medications such as insulin. This paper develops a control-based algorithm combines a predictive control algorithm with a revised nonlinear compartmental model used in the ICU to design personalized feeding function rates to improve patient glycemic variability. Our control algorithm is robust and acts very quickly to avoid medical intervention effects.

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“…Since the creation of the minimal model [6], glucose-insulin modeling has branched in many directions. There exist models for the ICU setting [22,31,32,35], such as the ICU minimal model (ICUMM) [23,65], the IC-ING model [36], and models that can potentially resolve ICU-related issues such as stress hypoglycemia [20,21,39,51,59,63] or the source of ultradian glycemic oscillations [39,59].…”

Section: Brief Survey Of the Glucose-insulin Modeling Landscapementioning

confidence: 99%

Delay-Induced Uncertainty in Physiological Systems

Karamched

Albers

Hripcsak

et al. 2020

Preprint

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Medical practice in the intensive care unit is based on the supposition that physiological systems such as the human glucose-insulin system are reliable. Reliability of dynamical systems refers to response to perturbation: A dynamical system is reliable if it behaves predictably following a perturbation. Here, we demonstrate that reliability fails for an archetypal physiological model, the Ultradian glucose-insulin model. Reliability failure arises because of the presence of delay. Using the theory of rank one maps from smooth dynamical systems, we precisely explain the nature of the resulting delay-induced uncertainty (DIU). We develop a recipe one may use to diagnose DIU in a general dynamical system. Guided by this recipe, we analyze DIU emergence first in a classical linear shear flow model and then in the Ultradian model. Our results potentially apply to a broad class of physiological systems that involve delay.

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Zone Model Predictive Control and Moving Horizon Estimation for the Regulation of Blood Glucose in Critical Care Patients

Cited by 8 publications

References 19 publications

A simple modeling framework for prediction in the human glucose–insulin system

A simple modeling framework for prediction in the human glucose–insulin system

Personalized Feeding Infusion Rate of Nutrition to Improve Glycemic Variability Control in Critical Care Patients

Delay-Induced Uncertainty in Physiological Systems

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