DiAs Web Monitoring: A Real-Time Remote Monitoring System Designed for Artificial Pancreas Outpatient Trials

Place, Jérôme; Robert, Anthony; Brahim, Najib Ben; Keith-Hynes, Patrick; Farret, Anne; Pelletier, Marie-Josée; Buckingham, Bruce A.; Breton, Marc D.; Kovatchev, Boris; Renard, Éric

doi:10.1177/193229681300700603

Cited by 51 publications

(40 citation statements)

References 23 publications

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“…In Configuration A, the data acquisition unit wirelessly sends information to the handheld controller [22]. The measured glucose values and insulin treatment can be saved on a secure virtual cloud [36,37] for analysis and evaluation by the patient and their healthcare provider. Schematic artificial pancreas (AP) system architecture.…”

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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“…10,11 DiAs operates on a commercially available AndroidÔ (Google, Mountain View, CA)-based phone, enabling wireless communication with the CGM and the insulin pump, as well as data transfer through the wireless telephone network or WiFi to a secure central server for remote monitoring and automated alerts about patient and system state. 15 Its modular architecture allows different modes of operation (e.g.. pumponly, CLC) to be swapped in during use for clinical trials. The DiAs graphical user interface (GUI) is designed to be operated by the patient.…”

Section: Clc Technologymentioning

confidence: 99%

Multinight “Bedside” Closed-Loop Control for Patients with Type 1 Diabetes

BrownSue

KovatchevBoris²,

BretonMarc³

et al. 2015

Diabetes Technology & Therapeutics

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Background: Studies of closed-loop control (CLC) systems have improved glucose levels in patients with type 1 diabetes. In this study we test a new CLC concept aiming to ''reset'' the patient overnight to nearnormoglycemia each morning, for several consecutive nights. Subjects and Methods: Ten insulin pump users with type 1 diabetes (mean age, 46.4 -8.5 years) were enrolled in a two-center (in the United States and Italy) randomized crossover trial comparing 5 consecutive nights of CLC (23:00-07:00 h) in an outpatient setting versus sensor-augmented insulin pump therapy of the same duration at home. Primary end points included time spent in 80-140 mg/dL as measured by continuous glucose monitoring overnight and fasting blood glucose distribution at 7:00 h. Results: Compared with sensor-augmented pump therapy, CLC improved significantly time spent between 80 and 140 mg/dL (54.5% vs. 32.2%; P < 0.001) and between 70 and 180 mg/dL (85.4% vs. 59.1%; P < 0.001); CLC reduced the mean glucose level at 07:00 h (119.3 vs. 152.9 mg/dL; P < 0.001) and overnight mean glucose level (139.0 vs. 170.3 mg/dL; P < 0.001) using a marginally lower amount of insulin (6.1 vs. 6.8 units; P = 0.1). Tighter overnight control led to improved daytime control on the next day: the overnight/next-day control correlation was r = 0.52, P < 0.01. Conclusions: Multinight CLC of insulin delivery (artificial pancreas) results in significant improvement in morning and overnight glucose levels and time in target range, with the potential to improve daytime control when glucose levels were ''reset'' to near-normoglycemia each morning.

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“…Very recently, Place et al have presented the DiAs Web Monitoring system, 20 which was successfully used in the JDRF trials of the DiAs platform. Even if the design of the interface is rather similar to our system, the technological platform used has several differences, in particular since we used an agentbased design to allow an easy software components interchange.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Artificial Pancreas Trials Through Agent-based Technologies

Lanzola

Scarpellini

Palma

et al. 2014

J Diabetes Sci Technol

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The increase in the availability and reliability of network connections lets envision systems supporting a continuous remote monitoring of clinical parameters useful either for overseeing chronic diseases or for following clinical trials involving outpatients. We report here the results achieved by a telemedicine infrastructure that has been linked to an artificial pancreas platform and used during a trial of the AP@home project, funded by the European Union. The telemedicine infrastructure is based on a multiagent paradigm and is able to deliver to the clinic any information concerning the patient status and the operation of the artificial pancreas. A web application has also been developed, so that the clinic staff and the researchers involved in the design of the blood glucose control algorithms are able to follow the ongoing experiments. Albeit the duration of the experiments in the trial discussed in the article was limited to only 2 days, the system proved to be successful for monitoring patients, in particular overnight when the patients are sleeping. Based on that outcome we can conclude that the infrastructure is suitable for the purpose of accomplishing an intelligent monitoring of an artificial pancreas either during longer trials or whenever that system will be used as a routine treatment.

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DiAs Web Monitoring: A Real-Time Remote Monitoring System Designed for Artificial Pancreas Outpatient Trials

Cited by 51 publications

References 23 publications

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

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

Multinight “Bedside” Closed-Loop Control for Patients with Type 1 Diabetes

Monitoring Artificial Pancreas Trials Through Agent-based Technologies

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