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.

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We have separated the effect of insulin on glucose distribution/transport, glucose disposal, and endogenous production (EGP) during an intravenous glucose tolerance test (IVGTT) by use of a dualtracer dilution methodology. Six healthy lean male subjects (age 33 Ϯ 3 yr, body mass index 22.7 Ϯ 0.6 kg/m A new model described the kinetics of the two glucose tracers and native glucose with the use of a two-compartment structure for glucose and a onecompartment structure for insulin effects. Insulin sensitivities of distribution/transport, disposal, and EGP were similar (11.5 Ϯ 3.8 vs. 10.4 Ϯ 3.9 vs. 11.1 Ϯ 2.7 ϫ 10 Ϫ2 ml⅐kg Ϫ1 ⅐min Ϫ1per mU/l; P ϭ nonsignificant, ANOVA). When expressed in terms of ability to lower glucose concentration, stimulation of disposal and stimulation of distribution/transport accounted each independently for 25 and 30%, respectively, of the overall effect. Suppression of EGP was more effective (P Ͻ 0.01, ANOVA) and accounted for 50% of the overall effect. EGP was suppressed by 70% (52-82%) (95% confidence interval relative to basal) within 60 min of the IVGTT; glucose distribution/ transport was least responsive to insulin and was maximally activated by 62% (34-96%) above basal at 80 min compared with maximum 279% (116-565%) activation of glucose disposal at 20 min. The deactivation of glucose distribution/transport was slower than that of glucose disposal and EGP (P Ͻ 0.02) with half-times of 207 (84-510), 12 (7-22), and 29 (16-54) min, respectively. The minimal-model insulin sensitivity was tightly correlated with and linearly related to sensitivity of EGP (r ϭ 0.96, P Ͻ 0.005) and correlated positively but nonsignificantly with distribution/transport sensitivity (r ϭ 0.73, P ϭ 0.10) and disposal sensitivity (r ϭ 0.55, P ϭ 0.26). We conclude that, in healthy subjects during an IVGTT, the two peripheral insulin effects account jointly for approximately one-half of the overall insulin-stimulated glucose lowering, each effect contributing equally. Suppression of EGP matches the effect in the periphery.glucose kinetics; compartment modeling; D-[U-13 C]glucose; 3-O-methyl-D-glucose; insulin action; glucose transport; glucose disposal; endogenous glucose production; intravenous glucose tolerance test Glossary New Model EGP 0Endogenous glucose production extrapolated to zero insulin concentration (mmol/min) EGP b EGP at basal insulin concentration (mmol/min) F 01Total non-insulin-dependent glucose flux (mmol/min) g 1 (t), g 3 (t)Concentrations of D-[U-13 C]glucose and 3-O-methyl-D-glucose in the accessible compartment (mmol/l) G(t)Total glucose concentration in the accessible compartment (mmol/l) I(t), I b Plasma insulin and basal (preexperimental) plasma insulin (mU/l) k 03 Transfer rate constant of 3-O-methyl-D-glucose excretion (minTransfer rate constant from nonaccessible to accessible compart-Deactivation rate constants (minActivation rate constants (min Ϫ2per mU/l) q 1 (t), q 2 (t)Masses of D-[U-13 C]glucose in the two compartments (mmol) q 3 (t), q 4 (t)Masses of 3-O-methyl-D-glucose in ...

We investigated insulin lispro kinetics with bolus and continuous subcutaneous insulin infusion (CSII) modes of insulin delivery. Seven subjects with type-1 diabetes treated by CSII with insulin lispro have been studied during prandial and postprandial conditions over 12 hours. Eleven alternative models of insulin kinetics have been proposed implementing a number of putative characteristics. We assessed 1) the effect of insulin delivery mode, i.e., bolus or basal, on the insulin absorption rate, the effects of 2) insulin association state and 3) insulin dose on the rate of insulin absorption, 4) the remote insulin effect on its volume of distribution, 5) the effect of insulin dose on insulin disappearance, 6) the presence of insulin degradation at the injection site, and finally 7) the existence of two pathways, fast and slow, of insulin absorption. An iterative two-stage parameter estimation technique was used. Models were validated through assessing physiological feasibility of parameter estimates, posterior identifiability, and distribution of residuals. Based on the principle of parsimony, best model to fit our data combined the slow and fast absorption channels and included local insulin degradation. The model estimated that 67(53-82)% [mean (interquartile range)] of delivered insulin passed through the slow absorption channel [absorption rate 0.011(0.004-0.029) min(-1)] with the remaining 33% passed through the fast channel [absorption rate 0.021(0.011-0.040) min(-1)]. Local degradation rate was described as a saturable process with Michaelis-Menten characteristics [VMAX = 1.93(0.62 - 6.03) mU min(-1), KM = 62.6(62.6 - 62.6) mU]. Models representing the dependence of insulin absorption rate on insulin disappearance and the remote insulin effect on its volume of distribution could not be validated suggesting that these effects are not present or cannot be detected during physiological conditions.

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OBJECTIVE -To evaluate a fully automated algorithm for the establishment of tight glycemic control in critically ill patients and to compare the results with different routine glucose management protocols of three intensive care units (ICUs) across Europe (Graz, Prague, and London).RESEARCH DESIGN AND METHODS -Sixty patients undergoing cardiac surgery (age 67 Ϯ 9 years, BMI 27.7 Ϯ 4.9 kg/m 2 , 17 women) with postsurgery blood glucose levels Ͼ120 mg/dl (6.7 mmol/l) were investigated in three different ICUs (20 per center). Patients were randomized to either blood glucose management (target range 80 -110 mg/dl [4.4 -6.1 mmol/l]) by the fully automated model predictive control (MPC) algorithm (n ϭ 30, 10 per center) or implemented routine glucose management protocols (n ϭ 30, 10 per center). In all patients, arterial glucose was measured hourly to describe the glucose profile until the end of the ICU stay but for a maximum period of 48 h. CONCLUSIONS -The data suggest that the MPC algorithm is safe and effective in controlling glycemia in critically ill postsurgery patients. RESULTS Diabetes Care 29:271-276, 2006E pidemiological studies have revealed a significant relationship between impaired glycemic control and poor outcome in patients with acute cardiovascular events (1-3), postoperative wound infections (4,5), and trauma (6). Patients with diabetes are affected, but patients with stress hyperglycemia with no previous diagnosis of diabetes also have a poor prognosis (1,2,7,8). Critical illness and trauma induce counterregulatory hormone release and alterations in carbohydrate metabolism such as enhanced hepatic gluconeogenesis, insulin resistance, and relative insulin deficiency (9,10).A growing body of evidence indicates that treatment of hyperglycemia improves clinical outcome (11). In a prospective randomized trial in Leuven, postoperative patients were treated with an intensive insulin protocol (12). Strict glycemic control (80 -110 mg/dl) resulted in a reduction of in-hospital mortality and a decrease in organ system dysfunction compared with moderate hyperglycemia (180 -200 mg/dl). In another study performed on a mixed medical-surgical population, the implementation of an intensive glucose management protocol led to decreased mortality, morbidity, and length of intensive care unit (ICU) stay of critically ill adult patients (13).Based on this clinical evidence, efforts have to be made to maintain strict glycemic control in critically ill patients. To achieve this goal, the implementation of complex intensive insulin infusion protocols based on frequent bedside glucose monitoring is required. Numerous guidelines have been developed and tested to implement tight glycemic control in ICUs (13-18). However, most of these guidelines still require user interventions or intuitive decisions of ICU staff.The development of a closed-loop control system that automatically regulates the dose of insulin based on glucose measurements could permit tight glycemic control without increasing the work-

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 ADICOL's 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.

Focused research is underway to improve the delivery of tight glycaemic control at the intensive care unit. A major component is the development of safe, efficacious and effective insulin titration algorithms, which are normally evaluated in time-consuming resource-demanding clinical studies. Simulation studies with virtual critically ill patients can substantially accelerate the development process. For this purpose, we created a model of glucoregulation in the critically ill. The model includes five submodels: a submodel of endogenous insulin secretion, a submodel of insulin kinetics, a submodel of enteral glucose absorption, a submodel of insulin action and a submodel of glucose kinetics. Model parameters are estimated utilizing prior knowledge and data collected routinely at the intensive care unit to represent the high intersubject and temporal variation in insulin needs in the critically ill. Bayesian estimation combined with the regularization method is used to estimate (i) time-invariant model parameters and (ii) a time-varying parameter, the basal insulin concentration, which represents the temporal variation in insulin sensitivity. We propose a validation process to validate virtual patients developed for the purpose of testing glucose controllers. The parameter estimation and the validation are exemplified using data collected in six critically ill patients treated at a medical intensive care unit. In conclusion, a novel glucoregulatory model has been developed to create a virtual population of critically ill facilitating in silico testing of glucose controllers at the intensive care unit.