Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes

Favero, Simone Del; Place, Jérôme; Kropff, Jort; Messori, Mirko; Keith-Hynes, Patrick; Visentin, Roberto; Monaro, Marco; Galasso, Silvia; Boscari, Federico; Toffanin, Chiara; Palma, Federico Di; Lanzola, Giordano; Scarpellini, Stefania; Farret, Anne; Kovatchev, Boris; Avogaro, Angelo; Bruttomesso, Daniela; Magni, Lalo; DeVries, J. Hans; Cobelli, Claudio; Renard, Éric

doi:10.1111/dom.12440

Cited by 88 publications

(50 citation statements)

References 32 publications

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“…Another reason for worse mean glucose and time-in-target during the AP is the prudent-by-design tuning of the algorithm, because this trial was the first with the children-specific version of our MMPC algorithm, previously tested only in adults (10,14,15).The data collected in this camp trial will allow a more effective tuning of the algorithm, thus likely improving its efficacy.…”

Section: Discussionmentioning

confidence: 99%

Randomized Summer Camp Crossover Trial in 5- to 9-Year-Old Children: Outpatient Wearable Artificial Pancreas Is Feasible and Safe

et al. 2016

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OBJECTIVEThe Pediatric Artificial Pancreas (PedArPan) project tested a children-specific version of the modular model predictive control (MMPC) algorithm in 5-to 9-yearold children during a camp. RESEARCH DESIGN AND METHODSA total of 30 children, 5-to 9-years old, with type 1 diabetes completed an outpatient, open-label, randomized, crossover trial. Three days with an artificial pancreas (AP) were compared with three days of parent-managed sensoraugmented pump (SAP). RESULTSOvernight time-in-hypoglycemia was reduced with the AP versus SAP, median (25 th -75 th percentiles): 0.0% (0.0-2.2) vs. 2.2% (0.0-12.3) (P 5 0.002), without a significant change of time-in-target, mean: 56.0% (SD 22.5) vs. 59.7% (21.2) (P 5 0.430), but with increased mean glucose 173 mg/dL (36) vs. 150 mg/dL (39) (P 5 0.002). Overall, the AP granted a threefold reduction of time-in-hypoglycemia (P < 0.001) at the cost of decreased time-in-target, 56.8% (13.5) vs. 63.1% (11.0) (P 5 0.022) and increased mean glucose 169 mg/dL (23) vs. 147 mg/dL (23) (P < 0.001). CONCLUSIONSThis trial, the first outpatient single-hormone AP trial in a population of this age, shows feasibility and safety of MMPC in young children. Algorithm retuning will be performed to improve efficacy.Only three artificial pancreas (AP) trials have focused on the prepubertal population so far: two single-hormone AP studies, performed inpatient for less than 1 day (1,2) and a recent dual-hormone AP study, performed in a camp for 5 days (3). Here we report the first outpatient single-hormone AP trial focusing on 5-to 9-year-old children.Data were collected in the Pediatric Artificial Pancreas (PedArPan) camp, where sensor-augmented pump (SAP) therapy was compared with the modular model predictive control algorithm (MMPC) (4,5), running on the wearable platform Diabetes Assistant (DiAs) (6).

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Section: Discussionmentioning

confidence: 99%

Randomized Summer Camp Crossover Trial in 5- to 9-Year-Old Children: Outpatient Wearable Artificial Pancreas Is Feasible and Safe

et al. 2016

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“…A future design aim is that the application can be employed concurrently with other applications run by the user. To this end, a smartphone-based AP platform (currently, for the development phase of the Diabetes Assistant (DiAS) platform, the smartphone is employed as a dedicated controller, and no other phone-related functions are permitted) (DiAS; University of Virginia, Charlottesville, VA, USA) has been developed [22] and evaluated in outpatient studies [10,[23][24][25][26]. Extended studies involving a two-month evening and night closed-loop control study have also been performed [27].…”

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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“…1 Numerous advances on the AP have been done in recent years, especially for treatment of type 1 diabetes mellitus, with several research teams completing closed-loop experiments in controlled environments, [2][3][4] and more recently in an ambulatory setting and at home. [5][6][7][8] The core of any AP is a control algorithm that calculates the appropriate insulin dosing to maintain glucose concentration at a safe healthy level at all times. The most common controllers used in the AP are algorithms based on model predictive control (MPC), 9,10 proportional-integral-derivative approach, 11 and fuzzy-logic design.…”

Section: Original Articlementioning

confidence: 99%

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

Sanz

Doyle

Dassau

2016

J Diabetes Sci Technol

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Background: Model predictive control (MPC) performance depends on the accuracy of the prediction model implemented by the controller. Complex physiology and modeling limitations often prevent the ability to provide long and accurate glucose predictions, which results in the need to account for prediction errors. Method:Optimal insulin dosage by Zone-MPC is calculated by solving an optimization problem in which a scalar index is minimized by penalizing relative input deviations and glucose predictions out of the reference zone. The controller's tuning parameters are the penalties on the input variable (insulin). Positive and negative relative inputs are penalized differently. A dynamic adaptation of the tuning parameters based on the accuracy of the model in recent history is implemented in this article and compared in silico to aggressive and conservative tunings of the same controller structure.Results: Similar average glucose and time in the safe glucose range (70-180 mg/dL) are achieved for the adaptive design and traditional controller configurations. However, percentage time under 70 mg/dL is significantly reduced, both for announced meals using bolus compensation and unannounced meals with a meal detection algorithm triggered bolus. No differences in the average insulin delivered were observed between the adaptive design and the conservative or aggressive tuning for the bolus strategy, and the adaptive controller delivered less insulin in the other scenario considered. Conclusions:The adaptive strategy provides safe and effective glucose management as well as significant reduction of hypoglycemia events. No abnormal insulin delivery profiles were observed upon the application of the adaptive strategy.

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Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes

Abstract: The results suggest that MMPC managed by a wearable system is safe and effective during evening meal and overnight. Its sustained use during this period is currently being tested in an ongoing randomized 2-month study.

Cited by 88 publications

References 32 publications

Randomized Summer Camp Crossover Trial in 5- to 9-Year-Old Children: Outpatient Wearable Artificial Pancreas Is Feasible and Safe

Randomized Summer Camp Crossover Trial in 5- to 9-Year-Old Children: Outpatient Wearable Artificial Pancreas Is Feasible and Safe

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

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

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