Thomas Vering

Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.

Closing the Loop: The Adicol Experience

The objective of the project Advanced Insulin Infusion using a Control Loop (ADICOL) was to develop a treatment system that continuously measures and controls the glucose concentration in subjects with type 1 diabetes. The modular concept of the ADICOLs extracorporeal artificial pancreas consisted of a minimally invasive subcutaneous glucose system, a handheld PocketPC computer, and an insulin pump (D-Tron, Disetronic, Burgdorf, Switzerland) delivering subcutaneously insulin lispro. The present paper describes a subset of ADICOL activities focusing on the development of a glucose controller for semi-closed-loop control, an in silico testing environment, clinical testing, and system integration. An incremental approach was adopted to evaluate experimentally a model predictive glucose controller. A feasibility study was followed by efficacy studies of increasing complexity. The ADICOL project demonstrated feasibility of a semi-closed-loop glucose control during fasting and fed conditions with a wearable, modular extracorporeal artificial pancreas.

On‐line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with Type 1 diabetes

Aims  To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system.

Minimally invasive suction sampling unit for interstitial fluid enhanced by electroosmotic mass transport

Background: Minimally invasive glucose monitoring is an important tool for a better management of diabetes in order to avoid long-term complications. Methods: A vacuum operated ultrafiltration sampling unit for interstitial fluid (ISF) with integrated electroosmotic flow enhancement has been developed. The novel device was tested with three non-diabetic, healthy volunteers. Results: Flow measurements showed an increased ISF transport depending on the applied electrical current. Preliminary online glucose measurements showed reasonable results. Conclusion: Electroosmotically enhanced ISF harvesting seems to be a promising approach for generating minimally invasive glucose monitoring devices, although results have shown significant ISF flow for the applied microsensors.