5,825 research outputs found

    The development of a glucose prediction model in critically ill patients

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    Purpose: The aim of the current study is to develop a prediction model for glucose levels applicable for all patients admitted to the ICU with an expected ICU stay of at least 24 h. This model will be incorporated in a closed-loop glucose system to continuously and automatically control glucose values. Methods: Data from a previous single-center randomized controlled study was used. All patients received a FreeStyle Navigator II subcutaneous CGM system from Abbott during their ICU stay. The total dataset was randomly divided into a training set and a validation set. A glucose prediction model was developed based on historical glucose data. Accuracy of the prediction model was determined using the Mean Squared Difference (MSD), the Mean Absolute Difference (MAD) and a Clarke Error Grid (CEG). Results: The dataset included 94 ICU patients with a total of 134,673 glucose measurements points that were used for modelling. MSD was 0.410 +/- 0.495 for the model, the MAD was 5.19 +/- 2.63 and in the CEG 99.8% of the data points were in the clinically acceptable regions. Conclusion: In this study a glucose prediction model for ICU patients is developed. This study shows that it is possible to accurately predict a patient's glucose 30 min ahead based on historical glucose data. This is the first step in the development of a closed-loop glucose system. (C) 2021 Elsevier B.V. All rights reserved

    Challenges and Opportunities in Design of Control Algorithm for Artificial Pancreas

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    With discovery of the insulin, Type-1 diabetes converted from a fatal and acute to a chronic disease which includes micro-vascular complications which range from Kidney disease to stroke and micro-vascular complications such as retinopathy, nephropathy and neuropathy. Artificial pancreas is a solution to improve the quality of life for people with this very fast growing disease in the world and to reduce the costs. Despite technological advances e.g., in subcutaneous sensors and actuators for insulin injection, modeling of blood glucose dynamics and control algorithms still need significant improvement. In this paper, we investigate challenges and opportunities for development of efficient algorithm for designing robust artificial pancreas. We discuss the state of the art and summarize clinical and in silico assessment results. We contrast conventional integer order system approach with a newly proposed fractal control and summarize its benefits

    Contributions to modelling and control for improved hypoglycaemia and variability mitigation by dual-hormone artificial pancreas systems

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    [ES] Las personas con diabetes tipo 1 carecen de la capacidad de secretar insulina y, por lo tanto, necesitan regular su glucosa en sangre con la administración de insulina exógena. El páncreas artificial se presenta como la solución tecnológica ideal para alcanzar los objetivos terapéuticos de la normoglucemia, liberando al paciente de la carga actual de autocontrol y manejo. Sin embargo, el riesgo de hipoglucemia y la variabilidad glucémica siguen siendo factores limitantes en los algoritmos de control actuales integrados en el páncreas artificial. El propósito de la presente tesis es profundizar en el conocimiento de la hipoglucemia y avanzar los algoritmos de control del páncreas artificial para minimizar la incidencia de hipoglucemia y reducir la variabilidad glucémica. Después de proporcionar una visión general del estado del arte del control de la glucosa y el páncreas artificial, esta tesis aborda temas relacionados con el modelado y el control, con las siguientes contribuciones: Se presenta una extensión del modelo de Bergman Minimal que tiene en cuenta la respuesta contrarreguladora a la hipoglucemia. Este modelo explica la relación entre los diversos cambios fisiológicos producidos durante la hipoglucemia, con la adrenalina y los ácidos grasos libres como actores principales. Como resultado, se obtiene una mejor comprensión de la hipoglucemia, lo que permite explicar una auto-potenciación paradójica de la hipoglucemia como se modela a través de enfoques funcionales en el ampliamente utilizado simulador de diabetes tipo 1 UVA-Padova, que se utilizará en esta tesis para la validación in silico de los controladores desarrollados. Se realiza una evaluación de las métricas de variabilidad de la glucosa y los índices de calidad de control. La evaluación de la variabilidad glucémica en el desempeño de los controladores es necesaria; pero todavía no hay un conjunto de métricas de variabilidad glucémica que sea considerado como el "gold estándar". Por tanto, se lleva a cabo un análisis de las métricas de variabilidad disponibles en la literatura para definir un conjunto de indicadores recomendables. Debido a las limitaciones de los sistemas de páncreas artificiales unihormonales para mitigar la hipoglucemia en escenarios difíciles como el ejercicio, esta tesis se centra en el desarrollo de nuevos algoritmos de control bihormonales, con infusión simultanea de insulina y glucagón. Se propone un controlador coordinado bihormonal con estructuras de control paralelas como un algoritmo de control factible para la mitigación de la hipoglucemia y la reducción de la variabilidad glucémica, demostrando un rendimiento superior al de las estructuras de control utilizadas actualmente con lazos de control independientes de insulina y glucagón. Los controladores están diseñados y evaluados in silico en escenarios desafiantes y su rendimiento se evalúa principalmente con el conjunto de métricas definidas previamente como las recomendables.[CA] Les persones amb diabetis tipus 1 no tenen la capacitat de secretar insulina secreta i per tant, necessiten regular la seva glucosa en sang amb l'administració d'insulina exògena. El Pàncrees Artificial es presenta com la solució tecnològica ideal per assolir els objectius terapèutics de la normoglucèmia, alliberant al pacient de la càrrega actual d'autocontrol. No obstant, el risc d'hipoglucèmia i l'alta variabilitat glucèmica continuen sent un factor limitant en els algoritmes de control actuals integrats en el Pàncrees Artificials. El propòsit de la present tesi és aprofundir en el coneixement de la hipoglucèmia i millorar els algoritmes de control per corregir amb antelació la dosi excessiva d'insulina, minimitzant la incidència d'hipoglucèmia i reduint la variabilitat glucèmica. Després de donar una visió general de l'estat de l'art del control de la glucosa i el pàncrees artificial, aquesta tesi aborda aspectes de modelització i control, amb les següents contribucions: Es presenta una extensió del model Minimal de Bergman amb la contrarregulació. Aquest model explica la relació entre els diversos canvis siològics produïts durant la hipoglucèmia. Així, permet comprendre millor la hipoglucèmia i comparar els resultats amb els proporcionats per l'enfocament funcional del simulador de diabetis tipus 1 més utilitzat a la comunitat científica. Es realitza una avaluació de les mètriques de variabilitat glucèmica i dels índexs de qualitat de control. Es necessària l'avaluació de la variabilitat glucèmica en el rendiment dels controladors; però encara no hi ha un conjunt de mètriques considerades com les "gold standard". Per tant, es realitza una anàlisi de les mètriques de variabilitat disponibles a la literatura per definir un conjunt d'indicadors recomanables. Es proposa un controlador bi-hormonal coordinat amb estructures de control paral.leles com un algoritme de control viable per a la mitigació d'hipoglucèmia i la reducció de la variabilitat glucèmica. Els controladors estan dissenyats i avaluats in-silico en escenaris desafiadors i el seu rendiment es valora principalment amb el conjunt de mètriques definides prèviament com les mètriques recomanables.[EN] People with Type 1 Diabetes lack the ability to secrete insulin and therefore need to regulate their blood glucose with exogenous insulin delivery. The Artificial Pancreas is presented as the ideal technological solution to reach the therapeutic goals of normoglycaemia, freeing the patient from the current burden of self-control and management. Nevertheless, the risk of hypoglycaemia and the high glycaemic variability are still a limiting factors in the current control algorithms integrated in the Artificial Pancreas. The purpose of the present thesis is to delve into knowledge of hypoglycaemia and to advance in the artificial pancreas control algorithms in order to minimise hypoglycaemia incidence and reduce glycaemic variability. After providing an overview of the state of the art in the eld of glucose control and articial pancreas, this thesis addresses issues on modelling and control, with the following contributions: An extension of the Bergman Minimal model accounting for counterregulatory response to hypoglycaemia is presented. This model explains the relationship between the several physiological changes produced during hypoglycaemia, with adrenaline and free fatty acids as main players. As a result, a better understanding of hypoglycaemia is gained, allowing to explain a paradoxical auto-potentiation of hypoglycaemia as modeled through functional approaches in the widespread used UVA-Padova Type 1 Diabetes simulator, which will be used in this thesis for in silico validation of the developed controllers. An assessment of glucose variability metrics and control quality indices is carried out. The evaluation of the glycaemic variability on the controllers performance is necessary; but there is not a gold standard variability metrics yet. Therefore, an analysis of the variability metrics available in literature is conducted in order to define a recommendable set of indicators. Due to the limitations of single-hormone artificial pancreas systems in mitigating hypoglycaemia in challenging scenarios such as exercise, this thesis focuses on the developement of new dual-hormone control algorithms, with concomitant infusion of insulin and glucagon. A coordinated dual-hormone controller with parallel control structures is proposed as a feasible control algorithm for hypoglycaemia mitigation and glycaemic variability reduction, demonstrating superior performance as currently used control structures with independent insulin and glucagon control loops. The controllers are designed and evaluated in-silico under challenging scenarios and their performance are assessed mainly with the set of metrics defined previously as the recommendable ones.Moscardó García, V. (2019). Contributions to modelling and control for improved hypoglycaemia and variability mitigation by dual-hormone artificial pancreas systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/120456TESI

    Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

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    Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the Continuous Glucose Monitoring System (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute one-hour glucose prediction error of 6.20% (range: 4.97-8.06%) with an average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the methods and results are generalisable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies

    A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System

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    In this paper, we build a new, simple, and interpretable mathematical model to describe the human glucose-insulin system. Our ultimate goal is the robust control of the blood glucose (BG) level of individuals to a desired healthy range, by means of adjusting the amount of nutrition and/or external insulin appropriately. By constructing a simple yet flexible model class, with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) and intensive care unit (ICU); different choices of appropriate model functions describing uptake of nutrition and removal of glucose differentiate between the models. In both cases, the available data is sparse and collected in clinical settings, major factors that have constrained our model choice to the simple form adopted. The model has the form of a linear stochastic differential equation (SDE) to describe the evolution of the BG level. The model includes a term quantifying glucose removal from the bloodstream through the regulation system of the human body, and another two terms representing the effect of nutrition and externally delivered insulin. The parameters entering the equation must be learned in a patient-specific fashion, leading to personalized models. We present numerical results on patient-specific parameter estimation and future BG level forecasting in T2DM and ICU settings. The resulting model leads to the prediction of the BG level as an expected value accompanied by a band around this value which accounts for uncertainties in the prediction. Such predictions, then, have the potential for use as part of control systems which are robust to model imperfections and noisy data. Finally, a comparison of the predictive capability of the model with two different models specifically built for T2DM and ICU contexts is also performed.Comment: 47 pages, 9 figures, 7 table

    Model-Based Closed-Loop Glucose Control in Critical Illness

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    Stress hyperglycemia is a common complication in critically ill patients and is associated with increased mortality and morbidity. Tight glucose control (TGC) has shown promise in reducing mean glucose levels in critically ill patients and may mitigate the harmful repercussions of stress hyperglycemia. Despite the promise of TGC, care must be taken to avoid hypoglycemia, which has been implicated in the failure of some previous clinical attempts at TGC using intensive insulin therapies. In fact, a single hypoglycemic event has been shown to result in worsened patient outcomes. The nature of tight glucose regulation lends itself to automatic monitoring and control, thereby reducing the burden on clinical staff. A blood glucose target range of 110-130 mg/dL has been identified in the High-Density Intensive Care (HIDENIC) database at the University of Pittsburgh Medical Center (UPMC). A control framework comprised of a zone model predictive controller (zMPC) with moving horizon estimation (MHE) is proposed to maintain euglycemia in critically ill patients. Using continuous glucose monitoring (CGM) the proposed control scheme calculates optimized insulin and glucose infusion to maintain blood glucose concentrations within the target zone. Results from an observational study employing continuous glucose monitors at UPMC are used to reconstruct blood glucose from noisy CGM data, identify a model of CGM error in critically ill patients, and develop an in silico virtual patient cohort. The virtual patient cohort recapitulates expected physiologic trends with respect to insulin sensitivity and glycemic variability. Furthermore, a mechanism is introduced utilizing proportional-integral-derivative (PID) to modulate basal pancreatic insulin secretion rates in virtual patients. The result is virtual patients who behave realistically in simulated oral glucose tolerance tests and insulin tolerance tests and match clinically observed responses. Finally, in silico trials are used to simulate clinical conditions and test the developed control system under realistic conditions. Under normal conditions the control system is able to tightly control glucose concentrations within the target zone while avoiding hypoglycemia. To safely counteract the effect of faulty CGMs a system to detect sensor error and request CGM recalibration is introduced. Simulated in silico tests of this system results in accurate detection of excessive error leading to higher quality control and hypoglycemia reduction

    Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

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    Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal
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