Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes

Abstract

Recent, rapid changes in the treatment of type 1 diabetes have allowed for commercialization of an "artificial pancreas" that is better described as a closed-loop controller of insulin delivery. This review presents the current state of closed-loop control systems and expected future developments with a discussion of the human factor issues in allowing automation of glucose control. The goal of these systems is to minimize or prevent both short-term and long-term complications from diabetes and to decrease the daily burden of managing diabetes. The closed-loop systems are generally very effective and safe at night, have allowed for improved sleep, and have decreased the burden of diabetes management overnight. However, there are still significant barriers to achieving excellent daytime glucose control while simultaneously decreasing the burden of daytime diabetes management. These systems use a subcutaneous continuous glucose sensor, an algorithm that accounts for the current glucose and rate of change of the glucose, and the amount of insulin that has already been delivered to safely deliver insulin to control hyperglycemia, while minimizing the risk of hypoglycemia. The future challenge will be to allow for full closed-loop control with minimal burden on the patient during the day, alleviating meal announcements, carbohydrate counting, alerts, and maintenance. The human factors involved with interfacing with a closed-loop system and allowing the system to take control of diabetes management are significant. It is important to find a balance between enthusiasm and realistic expectations and experiences with the closed-loop system.

Figure 1.

Diagram of a simple PID controller applied to closed-loop glucose control. P indicates proportional term (scalar factor multiplied by the difference between current CGM and target glucose); I indicates integral term (scalar factor multiplied by the area under the error function); and D indicates derivative term (scalar factor multiplied by the current rate of change in the error function).

Figure 2.

Status screen for Loop, an open-source DIY MPC that runs on Apple iOS. Glucose values received from the CGM are denoted with dots, and the dashed lines reflect the predicted glucose dependent on active insulin and active carbohydrates modeled in the graphs below. The system alters insulin delivery in an effort to keep the eventual glucose within target (reflected by the shaded blue area in the glucose graph). BG, blood glucose; COB, carbohydrates on board; DIA, duration of insulin action; IOB, insulin on board.

Figure 3.

A simple fuzzy logic controller using current blood glucose (BG) level and rate of change in blood glucose.

Figure 4.

Automated insulin delivery configurations, with representative systems. (a) University of Virginia Diabetes Assistant (DiAS) (left): The user interacts only with the controller (Android phone). In this system all communication occurs through native Bluetooth without the need for any intermediary devices. Loop (right): The user interacts exclusively with the controller (iPhone) where he or she enters meal information. In this case, the iPhone commands the insulin pump through a Bluetooth-to-radio bridge known as the RileyLink. (b) Open Artificial Pancreas System (OpenAPS): The user interacts with the pump where he or she enters meal information. The “black box” modulates delivery based on data received from CGM and pump. (c) Medtronic 670G: The user interacts exclusively with the pump where he or she enters meal information. The pump is in direct communication with the proprietary sensor and holds the control algorithms. (d) OmniPod Horizon (planned future configuration): The user interacts with the smartphone where he or she enters meal information. The smartphone, insulet “patch pump,” and CGM communicate with each other directly via Bluetooth. The pump holds the control algorithms and data are sent from the smartphone to the cloud for additional services.

Figure 5.

Timeline. Selected references of automated insulin delivery are noted across the timeline, with selected commercial milestones highlighted across the top. CL, closed loop; JDRF, Juvenile Diabetes Research Foundation; SQ, subcutaneous.

Figure 6.

Closed-loop themes.