DiAs User Interface: A Patient-Centric Interface for Mobile Artificial Pancreas Systems

Keith-Hynes, Patrick; Guerlain, Stephanie; Mize, Benton; Hughes-Karvetski, Colleen; Khan, Momin; McElwee-Malloy, Molly; Kovatchev, Boris

doi:10.1177/193229681300700602

Cited by 78 publications

(50 citation statements)

References 38 publications

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“…The DiAs graphical user interface (GUI) is designed to be operated by the patient. 16 DiAs runs the USS Virginia CLC algorithm, which is based on a mathematical model of the human glucose-insulin dynamics and uses Kalman filtering to predict hypoglycemia and hyperglycemic risks 30 min ahead. Basal rate is then modulated up or down based on the predicted glycemic risk.…”

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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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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“…In silico results are very promising in terms of mean glucose, time in target, and number of patients in the critical zone of the CVGA. This algorithm, complemented with the SSM, 22 has been implemented on the Diabetes Assistant 37 and tested in an outpatient trial, 38 following the first outpatient studies, 39,40 which implemented a simpler hypo-hyper mitigation system always complemented with the SSM. …”

Section: Discussionmentioning

confidence: 99%

Artificial Pancreas: Model Predictive Control Design from Clinical Experience

Toffanin

Messori

Palma

et al. 2013

J Diabetes Sci Technol

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Background: The objective of this research is to develop a new artificial pancreas that takes into account the experience accumulated during more than 5000 h of closed-loop control in several clinical research centers. The main objective is to reduce the mean glucose value without exacerbating hypo phenomena. Controller design and in silico testing were performed on a new virtual population of the University of Virginia/Padova simulator. Methods: A new sensor model was developed based on the Comparison of Two Artificial Pancreas Systems for Closed-Loop Blood Glucose Control versus Open-Loop Control in Patients with Type 1 Diabetes trial AP@home data. The Kalman filter incorporated in the controller has been tuned using plasma and pump insulin as well as plasma and continuous glucose monitoring measures collected in clinical research centers. New constraints describing clinical knowledge not incorporated in the simulator but very critical in real patients (e.g., pump shutoff) have been introduced. The proposed model predictive control (MPC) is characterized by a low computational burden and memory requirements, and it is ready for an embedded implementation. Results: The new MPC was tested with an intensive simulation study on the University of Virginia/Padova simulator equipped with a new virtual population. It was also used in some preliminary outpatient pilot trials. The obtained results are very promising in terms of mean glucose and number of patients in the critical zone of the control variability grid analysis. Conclusions: The proposed MPC improves on the performance of a previous controller already tested in several experiments in the AP@home and JDRF projects. This algorithm complemented with a safety supervision module is a significant step toward deploying artificial pancreases into outpatient environments for extended periods of time.

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“…Nonetheless, nonportable laptop-based platforms have been used bedside in outpatient studies for overnight closed-loop control only [76]. Recently, an important step forward for the implementation of an ambulatory AP has been proposed at the University of Virginia [77] (fig. 6) running a control algorithm.…”

Section: Technologymentioning

confidence: 99%

Artificial Pancreas: A Review of Fundamentals and Inpatient and Outpatient Studies

Favero¹,

Bruttomesso²,

Cobelli³

2014

Frontiers in Diabetes

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The artificial pancreas (AP) is a device for automated modulation of insulin infusion that aims to maintain blood glucose in a nearly normal range. The core of the AP is the control algorithm, which is in charge of computing an effective insulin dose on the basis of continuous glucose monitoring readings. In the last 6 years, AP prototypes based on subcutaneous glucose sensing and subcutaneous insulin delivery have been extensively studied in clinical trials first on hospitalized patients and, more recently, in an outpatient setting. In this chapter we review the state of the art of the field, starting with a description of the various AP system components. In particular, we focus on the control techniques employed in the AP and on the principles on which they are based. We then move to AP testing: the preclinical stage (mostly done in silico), the inpatient clinical studies, and finally the outpatient studies. We also discuss the technological requirements for an ambulatory AP. © 2015 S. Karger AG, BaselAlthough type 1 diabetes is associated with increased morbidity and decreased life expectancy, strong evidence indicates that good metabolic control decreases diabetes complications [1-3]. Tight glucose control, however, increases the risk of hypoglycemia. To make diabetes control easier, new insulin analogues have been developed and infusion devices have been improved. Glucose monitoring has improved, too, with the introduction of devices for continuous glucose monitoring (CGM) which detect in real time the rate and direction of glucose changes. More recently, pumps and CGM devices have been connected to form an integrated system, the so-called 'artificial pancreas' (AP), whose scope is to ensure better glycemic control by frequently changing the insulin infusion rate on the basis of past, present, and forecasted glucose readings, as computed by a suitable control algorithm ( fig. 1).The pathway that led to the present models of AP started with the development of the first portable insulin pump by Kadish [4] in 1964. Prototypes of the AP with intra-

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DiAs User Interface: A Patient-Centric Interface for Mobile Artificial Pancreas Systems

Cited by 78 publications

References 38 publications

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

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

Artificial Pancreas: Model Predictive Control Design from Clinical Experience

Artificial Pancreas: A Review of Fundamentals and Inpatient and Outpatient Studies

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