Challenges and recent progress in the development of a closed-loop artificial pancreas

Bequette, B. Wayne

doi:10.1016/j.arcontrol.2012.09.007

Cited by 161 publications

(107 citation statements)

References 136 publications

(152 reference statements)

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“…Sensor dropouts and attenuation, insulin infusion failure or erroneously announced meals are common issues encountered during typical AP operation [42]. The need to fulfill strict safety requirements has been evident in most AP designs and alternative software architectures have been evaluated.…”

Section: Software Architecturementioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform.

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Section: Software Architecturementioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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“…12 The challenge stems from that insulin delivered subcutaneous has a delayed onset and offset of action. 13 Premeal and correction boluses are still required with the 670G system.…”

Section: Diabetes Technology and Therapeuticsmentioning

confidence: 99%

Will the First Approved Automated Insulin Delivery System Be a Game-Changer in Type 1 Diabetes Management?

Castle

2017

Diabetes Technology & Therapeutics

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I n a person without diabetes, glucose is maintained in a very narrow range. To accomplish this, beta cells have a clever mechanism by which they respond to rising glucose levels with increased insulin secretion. As glucose levels rise, more glucose is metabolized by the beta cell, which in turn stimulates insulin secretion. In contrast, people with type 1 diabetes (T1D) must rely on insulin delivery via injections or via an insulin pump. As stand-alone devices, insulin pumps lack an automated feedback mechanism by which to adjust insulin delivery rates. They have set basal rate profiles and rely on users to input grams of carbohydrate and glucose values to determine insulin bolus amounts.The commercial availability of continuous glucose monitoring (CGM) starting in 2000 dramatically changed the options available to manage diabetes. 1 These devices allow for viewing of sensed glucose values in real time. But what has been lacking up until recently is the availability of automated insulin delivery systems, which modulate insulin delivery in response to sensed glucose values. Often these systems are termed artificial pancreas systems, although this term is somewhat misleading as these systems typically only deliver insulin, do not mimic the other functions of the pancreas, and lack many inputs that control hormone secretion of the normal human pancreas. 2 The first system to provide some form of insulin automation was the Medtronic 530G with Enlite. This system includes a threshold suspend feature, such that when the sensed glucose value drops to a certain threshold (settable between 60 and 90 mg/dL), the system alarms. If the user does not respond to the alarm, such as while asleep, insulin delivery is suspended for 2 h, or until manually resumed. In a study of 247 patients with documented nocturnal hypoglycemia, the threshold suspend feature significantly reduced hypoglycemia; the percent time of the sensor reading <50 mg/dL was decreased by 57%. 3 However, the Medtronic 530G system was not designed to prevent hypoglycemia, only to reduce the severity and duration of hypoglycemia once it occurs. The Medtronic 640G system was designed to prevent hypoglycemia using a predictive low-glucose suspend (PLGS) feature. With this feature, when hypoglycemia is predicted using a linear prediction, insulin is automatically suspended and then resumed again once the sensed glucose is predicted to rise above a set threshold. 4 In a study of children with T1D, use of PLGS reduced the percentage of time with sensed glucose values <70 mg/dL by 54% in children 11-14 years of age (10.1% on control nights to 4.6% on intervention nights) and by 50% in children 4-10 years of age (6.2% on control nights to 3.1% on intervention nights). 5 Although the 530G and 640G systems significantly reduce hypoglycemia, they were not designed to address hyperglycemia. Uncontrolled diabetes remains the leading cause of kidney failure, lower-limb amputations, and adult-onset blindness. 6 In data collected by the T1D Exchange, the vast majority of yout...

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“…These challenges include overcoming large meal disturbances, the effects of exercise, and the delays associated with subcutaneous glucose sensing and insulin delivery. 2 One of the most difficult aspects of the diabetes therapy routine is dealing with meals, and it has been shown that inaccurate estimation of meal sizes occurs frequently, resulting in additional glucose fluctuations. 3 Recent behavioral studies have also shown that people with T1DM are interested in an automated system but are concerned with relinquishing full control.…”

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confidence: 99%

Design of the Glucose Rate Increase Detector

Harvey

Dassau

Zisser

et al. 2014

J Diabetes Sci Technol

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The primary goal of the artificial pancreas (AP) is to eliminate the occurrence of severe hypoglycemia and reduce the time spent in hyperglycemia (>180 mg/dL) in an effort to improve quality of life and reduce long-term complications. 1 Safe and effective control of type 1 diabetes mellitus (T1DM) using an AP has been researched widely for several decades, with many advances, but several challenges remain. These challenges include overcoming large meal disturbances, the effects of exercise, and the delays associated with subcutaneous glucose sensing and insulin delivery. 2 One of the most difficult aspects of the diabetes therapy routine is dealing with meals, and it has been shown that inaccurate estimation of meal sizes occurs frequently, resulting in additional glucose fluctuations. 3 Recent behavioral studies have also shown that people with T1DM are interested in an automated system but are concerned with relinquishing full control. 4,5 Therefore, an automatic AP that is safe and robust to daily living conditions and is trusted by the users is critical.The AP is a multilayered device that will contain several features, including a core glucose controller, devices for monitoring of glucose and possibly other biologically relevant compounds or signals, software to interface with the user, safety systems to monitor the status of the system, and telemedicine to convey information about the system to the user and family and/or medical personnel. The core of the AP is the controller, the design of which has been explored by several research teams, with promising results. [6][7][8][9][10][11] Continuous glucose monitoring (CGM) devices and insulin pumps are continually being improved, and are at a performance level that enables automatic control. 12,13 Currently, longer clinical trials with several meals and exercise are being performed with good results. 6,14 Generally, the trials with meals larger than 50 g of carbohydrate (CHO) use a feed-forward approach, announcing meals and giving a full AbstractThe Glucose Rate Increase Detector (GRID), a module of the Health Monitoring System (HMS), has been designed to operate in parallel to the glucose controller to detect meal events and safely trigger a meal bolus. The GRID algorithm was tuned on clinical data with 40-70 g CHO meals and tested on simulation data with 50-100 g CHO meals. Active closed-and open-loop protocols were executed in silico with various treatments, including automatic boluses based on a 75 g CHO meal and boluses based on simulated user input of meal size. An optional function was used to reduce the recommended bolus using recent insulin and glucose history. For closed-loop control of a 3-meal scenario (50, 75, and 100 g CHO), the GRID improved median time in the 80-180 mg/dL range by 17% and in the >180 range by 14% over unannounced meals, using an automatic bolus for a 75 g CHO meal at detection. Under open-loop control of a 75 g CHO meal, the GRID shifted the median glucose peak down by 73 mg/dL and earlier by 120 min and reduced the time >...

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Challenges and recent progress in the development of a closed-loop artificial pancreas

Cited by 161 publications

References 136 publications

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Will the First Approved Automated Insulin Delivery System Be a Game-Changer in Type 1 Diabetes Management?

Design of the Glucose Rate Increase Detector

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