Postprandial response improvement via safety layer in closed-loop blood glucose controllers

León-Vargas, Fabián; Garelli, Fabricio; Battista, Hernán De; Vehı́, Josep

doi:10.1016/j.bspc.2014.10.003

Cited by 34 publications

(19 citation statements)

References 37 publications

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“…SMRC showed an improvement of postprandial control with a significant decrement of postprandial peak and increment of timein-range (70-180mg/dL) without a clinically meaningful increased risk of hypoglycemia [62]. Simulation validation of SMRC in combination with other controllers can be found in [63] demonstrating the benefit of the SAFE loop.…”

Section: Dtmentioning

confidence: 99%

Insulin Estimation and Prediction: A Review of the Estimation and Prediction of Subcutaneous Insulin Pharmacokinetics in Closed-Loop Glucose Control

et al. 2018

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is currently the safest infusion route for a commercial artificial pancreas, as opposed to the endogenous pancreas that secretes insulin directly into portal circulation (the vessels that connect the pancreas and other organs with the liver, which acts as a first blood filter before entering the main circulatory system). Even in approaches with concomitant infusion of glucagon (the socalled dual-hormone artificial pancreas), mechanisms are necessary to avoid an excess of insulin delivery which may lead to late hypoglycemia putting at stake the patient's safety. Independently of how these mechanisms are incorporated into the control schemes, all of them must rely on pharmacokinetic models predicting either circulating plasma insulin or a measure of "insulin-onboard", such as the insulin depot remaining at the subcutaneous tissue before entering circulation. In this article, methods to constrain insulin delivery are reviewed, as well as the subcutaneous insulin pharmacokinetic models and estimators on which they rely upon. A big challenge in the prediction of physiological signals, such as insulin concentration, is the large intra-subject variability that patients suffer. Indeed, in terms of control engineering, a patient is a highly time-varying uncertain plant. The intra-day and day-today patient's behavior change due to circadian rhythms (24-hour rhythmic physiological oscillations driven by the body clock, for instance, daily patterns in insulin sensitivity), and other multiple sources of uncertainty arise in key physiological processes such as meal absorption and subcutaneous insulin absorption. Despite this fact, the use of population models for the prediction of insulin pharmacokinetics, that is, how the infused insulin appears in blood, is still common practice. The impact of variability on the model prediction and its implication in closed-loop performance is analyzed. Nevertheless, large intra-subject variability suggests that real-time state and pharmacokinetic parameters estimation is convenient, even when individualized models are considered. The availability of continuous glucose measurements allows to address this problem should an observable glucose-insulin model be available. Different observer techniques proposed to this purpose are reviewed and discussed. The subcutaneous insulin route The pancreas secretes insulin into the portal vein towards the liver, which acts as a first filter before insulin reaches systemic circulation. In the liver, insulin promotes glucose storage in hepatic cells decreasing glucose production. In the fat and muscle cells, insulin acts as a key that triggers the mobilization of glucose transporters to the cell membrane promoting glucose uptake by the cell. As a result of both actions, plasma glucose concentration decreases. The dynamic lag of insulin action is estimated to be about 30 minutes [3]. An artificial pancreas is a classic closed-loop glucose control system (see Figure 1) that

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Section: Dtmentioning

confidence: 99%

Insulin Estimation and Prediction: A Review of the Estimation and Prediction of Subcutaneous Insulin Pharmacokinetics in Closed-Loop Glucose Control

et al. 2018

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“…is layer is based on the sliding mode reference conditioning technique [21] and acts on the glucose reference signal when a specific constraint, related with the maximum IOB allowed (IOB), is violated. Such technique has already been applied in different control schemes [22][23][24][25], but so far it is not clear the best methodology to tune IOB. e selection of the constraint IOB is critical in the design of the closed-loop system.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Bertachi

Biagi

Beneyto

et al. 2020

Journal of Healthcare Engineering

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The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

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“…Este enfoque es el que se tiene en cuenta a la hora de representar variaciones en los modelos de DMT1 [88]. Varios propuestas fueron realizadas para incluir la variación intra-paciente en la literatura [69,77,[91][92][93][94].…”

Section: Variación Intra-pacienteunclassified

“…Esta técnica, desarrollada en general para el control de sistemas dinámicos con restricciones, fue aplicada por primera vez al problema de la DMT1 en [141]. Este algoritmo funciona como una capa de seguridad añadiendo una restricción en la IOB alrededor de cualquier controlador principal y ha demostrado reducir el número y la gravedad de los eventos hipoglucémicos [94]. Esta estrategia ha sido validada conéxito en ensayos clínicos como parte de un controlador principal en España y Argentina [20,163].…”

Section: Algoritmo Safeunclassified

“…El algoritmo Time Enable se define para ser utilizado en configuraciones híbridas. El lazo SAFE propuesto anteriormente[94,141] donde se utiliza una restricción IOB constante se reinterpreta para ser usado en ensayos clínicos. Se busca proporcionar un criterio para establecer la restricción IOB y, a partir de ese punto, disminuir de forma segura la acción de lazo-abierto para dar paso al controlador de lazo-cerrado.…”

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Modelado y simulación de tecnologías para el tratamiento de la diabetes

Rosales¹,

Garelli²,

Battista³

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En las últimas dos décadas hubo un gran crecimiento en la investigación y el desarrollo de sistemas de Páncreas Artificial (PA). Estos sistemas, compuestos por un lazo de control realimentado, una bomba de insulina y un sensor continuo de glucosa, permiten la regulación automática de glucosa para personas con Diabetes Mellitus Tipo I (DMT1). Aún así, las limitaciones que presenta este desafío, como los son la gran variabilidad inter- e intrapaciente, la incertidumbre y retardos del sistema, dificultan la concepción de un control de lazo cerrado puro. Además de estas limitaciones, el riesgo de exponer a la persona a hiper- e hipoglucemias es un gran impedimento a la hora de desarrollar las estrategias de control glucémico. En esta tesis se plantean objetivos que cubren distintos aspectos del desarrollo de terapias para el control de la glucosa en la DMT1. En primer lugar, se desarrolló una plataforma de simulación que cuenta con un amplio espectro de configuraciones. Luego, a partir de la estimación de la insulina activa en el cuerpo (IOB), se proponen dos algoritmos para el tratamiento a lazo abierto: uno para periódos posprandiales y otro para la actividad física. A su vez, a partir de la limitación de IOB, se proponen técnicas de lazo cerrado. Por un lado, se propone un algoritmo que permite la transición en confi guraciones híbridas, entre el bolo posprandial de lazo abierto y el controlador de lazo cerrado. Por otro lado, a partir de una participación interdisciplinaria, se presenta el desarrollo y ensayo clínico de un algoritmo de control de lazo cerrado sin bolo para la compensación de comidas. Por último, se presenta el algoritmo GSAFE para lazos cerrados de control puro donde se propone adaptar la salida de un controlador principal con el fin de imponer una limitación en la pendiente de caída de glucosa con el objetivo de prevenir futuras hipoglucemias debido a la sobreactuación.

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Postprandial response improvement via safety layer in closed-loop blood glucose controllers

Cited by 34 publications

References 37 publications

Insulin Estimation and Prediction: A Review of the Estimation and Prediction of Subcutaneous Insulin Pharmacokinetics in Closed-Loop Glucose Control

Insulin Estimation and Prediction: A Review of the Estimation and Prediction of Subcutaneous Insulin Pharmacokinetics in Closed-Loop Glucose Control

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Modelado y simulación de tecnologías para el tratamiento de la diabetes

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