Modular closed-loop control of diabetes.

Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called "artificial pancreas," modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patient's basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability.

Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters.

A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.

Closing the loop.

Closed-loop algorithms can be found in every aspect of everyday modern life. Automation and control are used constantly to provide safety and to improve quality of life. Closed-loop systems and algorithms can be found in home appliances, automobiles, aviation and more. Can one imagine nowadays driving a car without ABS, cruise control or even anti-sliding control? Similar principles of automation and control can be used in the management of diabetes mellitus (DM). The idea of an algorithmic/technological way to control glycaemia is not new and has been researched for more than four decades. However, recent improvements in both glucose-sensing technology and insulin delivery together with advanced control and systems engineering made this dream of an artificial pancreas possible. The artificial pancreas may be the next big step in the treatment of DM since the use of insulin analogues. An artificial pancreas can be described as internal or external devices that use continuous glucose measurements to automatically manage exogenous insulin delivery with or without other hormones in an attempt to restore glucose regulation in individuals with DM using a control algorithm. This device as described can be internal or external; can use different types of control algorithms with bi-hormonal or uni-hormonal design; and can utilise different ways to administer them. The different designs and implementations have transitioned recently from in silico simulations to clinical evaluation stage with practical applications in mind. This may mark the beginning of a new era in diabetes management with the introduction of semi-closed-loop systems that can prevent or minimise nocturnal hypoglycaemia, to hybrid systems that will manage blood glucose (BG) levels with minimal user intervention to finally fully automated systems that will take the user out of the loop. More and more clinical trials will be needed for the artificial pancreas to become a reality but initial encouraging results are proof that we are on the right track. We attempted to select recent publications that will present these current achievements in the quest for the artificial pancreas and that will inspire others to continue to progress this field of research.

Closing the loop.

The dream of closing the loop is actually the dream of creating an artificial pancreas and freeing the patients from being involved with the care of their own diabetes. Insulin-dependent diabetes (type 1) is a chronic incurable disease which requires constant therapy without the possibility of any 'holidays' or insulin-free days. It means that patients have to inject insulin every day of their life, several times per day, and in order to do it safely they also have to measure their blood glucose levels several times per day. Patients need to plan their meals, their physical activities and their insulin regime - there is only very small room for spontaneous activities. This is why the desire for an artificial pancreas is so strong despite the fact that it will not cure the diabetic patients. Attempts to develop a closed-loop system started in the 1960s but never got to a clinical practical stage of development. In recent years the availability of continuous glucose sensors revived those efforts and stimulated the clinician and researchers to believe that closing the loop might be possible nowadays. Many papers have been published over the years describing several different ideas on how to close the loop. Most of the suggested systems have a sensing arm that measures the blood glucose repeatedly or continuously, an insulin delivery arm that injects insulin upon command and a computer that makes the decisions of when and how much insulin to deliver. The differences between the various published systems in the literature are mainly in their control algorithms. However, there are also differences related to the method and site of glucose measurement and insulin delivery. SC glucose measurements and insulin delivery are the most studied option but other combinations of insulin measurements and glucose delivery including intravascular and intraperitoneal (IP) are explored. We tried to select recent publications that we believe had influenced and inspired people interested in the field.