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A Review of Closed-Loop Algorithms for Glycemic Control in the Treatment of Type 1 Diabetes

Schnitzer Diabetes Center, mailstop OP05-DC, Oregon Health and Science University. 3181 SW Sam Jackson Park Road, Portland OR 97239, USA
Author to whom correspondence should be addressed.
Algorithms 2009, 2(1), 518-532;
Received: 30 October 2008 / Revised: 23 January 2009 / Accepted: 25 February 2009 / Published: 12 March 2009
(This article belongs to the Special Issue Sensor Algorithms)
With the discovery of insulin came a deeper understanding of therapeutic options for one of the most devastating chronic diseases of the modern era, diabetes mellitus. The use of insulin in the treatment of diabetes, especially in those with severe insulin deficiency (type 1 diabetes), with multiple injections or continuous subcutaneous infusion, has been largely successful, but the risk for short term and long term complications remains substantial. Insulin treatment decisions are based on the patient’s knowledge of meal size, exercise plans and the intermittent knowledge of blood glucose values. As such, these are open loop methods that require human input. The idea of closed loop control of diabetes treatment is quite different: automated control of a device that delivers insulin (and possibly glucagon or other medications) and is based on continuous or very frequent glucose measurements. Closed loop insulin control for type 1 diabetes is not new but is far from optimized. The goal of such a system is to avoid short-term complications (hypoglycemia) and long-term complications (diseases of the eyes, kidneys, nerves and cardiovascular system) by mimicking the normal insulin secretion pattern of the pancreatic beta cell. A control system for automated diabetes treatment consists of three major components, (1) a glucose sensing device that serves as the afferent limb of the system; (2) an automated control unit that uses algorithms which acquires sensor input and generates treatment outputs; and (3) a drug delivery device (primarily for delivery of insulin), which serves as the system’s efferent limb. There are several major issues that highlight the difficulty of interacting with the complex unknowns of the biological world. For example, development of accurate continuous glucose monitors is crucial; the state of the art in 2009 is that such devices sometimes experience drift and are intended only to supplement information received from standard intermittent blood glucose data. In addition, it is important to acknowledge that an “automated” closed loop pancreas cannot approach the complexity of the normal human endocrine pancreas, which takes continuous data from substrates, hormones, paracrine compounds and autonomic neural inputs, and in response, secretes four hormones. Another major issue is the substantial absorption/action delay of insulin given by the subcutaneous route. Because of this delay, some researchers have recently given a portion of the meal-related insulin in an open loop manner before the meal and found this hybrid approach to be superior to closed loop control. Proportional-Integral-Derivative (PID) systems adapted from the industrial sector utilize control algorithms that alter output based on proportional (difference between actual and target levels), derivative (rate of change) and integral (time-related summative) errors in glucose. These algorithms have proven to be very promising in limited clinical trials. Related algorithms include a “fading memory” system that combines the proportional-derivative components of a classic PID system with time-relating decay of input signals that allow greater emphasis on more recent glucose values, a characteristic noted in mammalian beta-cells. Model Predictive Control (MPC) systems are highly adaptive methods that utilize mathematical models based on observations of biological behavior patterns using system identification and are now undergoing testing in humans. The application of further mathematical models, such as fuzzy control and artificial neural networks, are also promising, but are largely clinically untested. In summary, the prospects for closed loop control of glycemia in persons with diabetes have improved considerably. Major limitations include the delayed absorption/action of subcutaneous insulin and the imperfect stability of currently-available continuous glucose sensors. The potential for improved glycemic control in persons with diabetes brings with it the potential for reduction in the frequency of acute and chronic complications of diabetes. View Full-Text
Keywords: diabetes; sensor; algorithm; closed loop diabetes; sensor; algorithm; closed loop
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El Youssef, J.; Castle, J.; Ward, W.K. A Review of Closed-Loop Algorithms for Glycemic Control in the Treatment of Type 1 Diabetes. Algorithms 2009, 2, 518-532.

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