Automated Control of an Adaptive Bihormonal, Dual-Sensor Artificial Pancreas and Evaluation During Inpatient Studies

Jacobs, Peter G.; Youssef, Joseph El; Castle, Jessica R.; Bakhtiani, Parkash A.; Branigan, Deborah; Breen, Matthew; Bauer, Dávid; Preiser, Nicholas; Leonard, Gerald; Stonex, Tara M.; Ward, W. Kenneth

doi:10.1109/tbme.2014.2323248

Cited by 72 publications

(43 citation statements)

References 45 publications

(61 reference statements)

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“…The AP system architecture has been clinically evaluated for three potential configurations, based on the hardware platform where the controller was implemented, specifically: computer/laptop [7,[16][17][18], tablet [8,19,20] and smartphone [21]. In this study, we examine two possible AP hardware configurations; we refer to them as Configuration A and Configuration B.…”

Section: Hardware Configurations Of the Ap Systemmentioning

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: Hardware Configurations Of the Ap Systemmentioning

confidence: 99%

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

et al. 2016

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“…In pre-approval studies the system has been shown to reduce hemoglobin A1C and glucose variability (34). Multiple other systems are being developed including a bihormonal system using insulin and glucagon (35).…”

Section: New Therapiesmentioning

confidence: 99%

Type 1 diabetes: where are we in 2017?

Copenhaver

Hoffman

2017

Transl. Pediatr

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Type 1 diabetes mellitus is a chronic state of insulin deficiency which results from destruction of beta cells by the immune system. The long term microvascular and macrovascular complications can be devastating. Since the discovery of insulin almost 100 years ago new medical therapies have improved the long-term survival for people with type 1 diabetes. Each year we come closer to discovering a cure but much work still needs to be done to eliminate this disease.

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“…The short answer to this question is therefore nodglucagon will not be essential for AP systems to reach the market. (29,43) Reduction in severe and moderate hypoglycemia (20,44) Reduction in time spent hyperglycemic and hypoglycemic and increased time in target range in outpatient settings (45) Reduction in time spent hyperglycemic and hypoglycemic and increased time in target range in outpatient settings (24,46) Reduction in hyperglycemia and hypoglycemia and increase in time in target in an inpatient settings (42,46) Reduction in hyperglycemia and hypoglycemia and increase time in target in outpatient setting (9) That said, results from insulin/glucagon studies have been outstanding and have generated enthusiasm in the field (9,30,31) and in the popular press (32). Conceptually, an insulin/glucagon approach is appealing.…”

Section: Key Question: Must Ap Systems Use Glucagon?mentioning

confidence: 99%

Pathway to Artificial Pancreas Systems Revisited: Moving Downstream

Kowalski

2015

Diabetes Care

104

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Artificial pancreas (AP) systems, a long-sought quest to replicate mechanically islet physiology that is lost in diabetes, are reaching the clinic, and the potential of automating insulin delivery is about to be realized. Significant progress has been made, and the safety and feasibility of AP systems have been demonstrated in the clinical research center and more recently in outpatient "real-world" environments. An iterative road map to AP system development has guided AP research since 2009, but progress in the field indicates that it needs updating. While it is now clear that AP systems are technically feasible, it remains much less certain that they will be widely adopted by clinicians and patients. Ultimately, the true success of AP systems will be defined by successful integration into the diabetes health care system and by the ultimate metric: improved diabetes outcomes.An electromechanical approach to improve glycemic control and quality of life for people with diabetesdan artificial pancreas (AP) (or an automated insulin-delivery system or bionic pancreas)dhas been a long-sought technological goal of diabetes researchers (1). However, a number of significant challenges needed to be overcome to deliver an AP system to people with diabetes. The past 10 years have seen many of these challenges addressed, and recent studies have demonstrated compelling safety and efficacy of the prototype systems (2). Technical feasibility is only a step toward declaring victory. Research and development efforts will continue to improve upon first-generation AP systems. But, it is clear that resources will need to be deployed to address clinical adoption challengesdincluding device usability and reimbursement.Research and development efforts over the past 10 years have addressed a number of the critical issues facing AP development, and recent studies again signal a move to a new phase and a call for a new road map to AP systems. A number of groups have demonstrated proof of concept with a variety of algorithmic approaches and closed-loop strategies and the data are compelling. In a Bench to Clinic narrative, Cefalu and Tamborlane (3) dive much deeper than this Perspective intends into the "how" of AP systems. They note quite astutely that "it is not the journey, but the destination that matters" (3).It may come as a surprise to many clinicians, but a small and growing group of lay, "do-it-yourself," technically savvy people with diabetes or loved ones of people with diabetes has been using semiautomated closed-loop systems at home for well over 2 years with impressive results (4-6). These systems, similar to systems being studied in academic studies, combine off-the-shelf insulin pumps and continuous glucose monitors with control algorithms (computer software that interprets glucose information and drives the dosing of insulin) powered by cell phone devices. The results from academia and these anecdotal reports are harbingers of a JDRF, New York, NY

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Automated Control of an Adaptive Bihormonal, Dual-Sensor Artificial Pancreas and Evaluation During Inpatient Studies

Cited by 72 publications

References 45 publications

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

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

Type 1 diabetes: where are we in 2017?

Pathway to Artificial Pancreas Systems Revisited: Moving Downstream

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