Feasibility of Outpatient Fully Integrated Closed-Loop Control

Abstract

OBJECTIVETo evaluate the feasibility of a wearable artificial pancreas system, the Diabetes Assistant (DiAs), which uses a smart phone as a closed-loop control platform.RESEARCH DESIGN AND METHODSTwenty patients with type 1 diabetes were enrolled at the Universities of Padova, Montpellier, and Virginia and at Sansum Diabetes Research Institute. Each trial continued for 42 h. The United States studies were conducted entirely in outpatient setting (e.g., hotel or guest house); studies in Italy and France were hybrid hospital–hotel admissions. A continuous glucose monitoring/pump system (Dexcom Seven Plus/Omnipod) was placed on the subject and was connected to DiAs. The patient operated the system via the DiAs user interface in open-loop mode (first 14 h of study), switching to closed-loop for the remaining 28 h. Study personnel monitored remotely via 3G or WiFi connection to DiAs and were available on site for assistance.RESULTSThe total duration of proper system communication functioning was 807.5 h (274 h in open-loop and 533.5 h in closed-loop), which represented 97.7% of the total possible time from admission to discharge. This exceeded the predetermined primary end point of 80% system functionality.CONCLUSIONSThis study demonstrated that a contemporary smart phone is capable of running outpatient closed-loop control and introduced a prototype system (DiAs) for further investigation. Following this proof of concept, future steps should include equipping insulin pumps and sensors with wireless capabilities, as well as studies focusing on control efficacy and patient-oriented clinical outcomes.

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

“…[40][41][42][43][44][45] The results from these investigations were summarized in a 2011 review of the artificial pancreas field, 46 pointing out the superiority of closed-loop control over insulin pump therapy in terms of (1) increased time within target glucose range, (2) reduced incidence of hypoglycemia, and (3) better overnight control. Subsequent studies confirmed these findings in outpatient setting, 47 at diabetes camps for children, 48 and at patients' homes. 49 The superior glucose control achieved by these trials suggests that when CGM information is processed appropriately by advanced algorithms, the clinical outcomes are better, even than those achieved by state-of-the art sensoraugmented insulin pump therapy.…”

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

“…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].…”

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

“…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.…”

“…9 The transition of CLC to a wearable outpatient system began in 2012 with the introduction of the Diabetes Assistant (DiAs)-the first wearable CLC system using a smartphone as a computational platform for its control algorithm. 10,11 Other recent trials confirmed the effectiveness of overnight CLC at diabetes camps for children 12 and at patients' homes, 13,14 placing laptop computers equipped with control algorithms at their patients' bedside.…”