Malgorzata E. Wilinska

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.

Overnight closed loop insulin delivery (artificial pancreas) in adults with type 1 diabetes: crossover randomised controlled studies

Objective To compare the safety and efficacy of overnight closed loop delivery of insulin (artificial pancreas) with conventional insulin pump therapy in adults with type 1 diabetes. Design Two sequential, open label, randomised controlled crossover, single centre studies. Setting Clinical research facility. Participants 24 adults (10 men, 14 women) with type 1 diabetes, aged 18-65, who had used insulin pump therapy for at least three months: 12 were tested after consuming a medium sized meal and the other 12 after consuming a larger meal accompanied by alcohol. Intervention During overnight closed loop delivery, sensor measurements of glucose were fed into a computer algorithm, which advised on insulin pump infusion rates at 15 minute intervals. During control nights, conventional insulin pump settings were applied. One study compared closed loop delivery of insulin with conventional pump therapy after a medium sized evening meal (60 g of carbohydrates) at 1900, depicting the scenario of “eating in.” The other study was carried out after a later large evening meal (100 g of carbohydrates) at 2030, accompanied by white wine (0.75 g/kg ethanol) and depicted the scenario of “eating out.” Main outcome measures The primary outcome was the time plasma glucose levels were in target (3.91-8.0 mmol/L) during closed loop delivery and a comparable control period. Secondary outcomes included pooled data analysis and time plasma glucose levels were below target (≤3.9 mmol/L). Results For the eating in scenario, overnight closed loop delivery of insulin increased the time plasma glucose levels were in target by a median 15% (interquartile range 3-35%), P=0.002. For the eating out scenario, closed loop delivery increased the time plasma glucose levels were in target by a median 28% (2-39%), P=0.01. Analysis of pooled data showed that the overall time plasma glucose was in target increased by a median 22% (3-37%) with closed loop delivery (P<0.001). Closed loop delivery reduced overnight time spent hypoglycaemic (plasma glucose ≤3.9 mmol/L) by a median 3% (0-20%), P=0.04, and eliminated plasma glucose concentrations below 3.0 mmol/L after midnight. Conclusion These two small crossover trials suggest that closed loop delivery of insulin may improve overnight control of glucose levels and reduce the risk of nocturnal hypoglycaemia in adults with type 1 diabetes. Trial registration ClinicalTrials.gov NCT00910767 and NCT00944619.

Insulin kinetics in type-1 diabetes: continuous and bolus delivery of rapid acting insulin

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/sup -1/] with the remaining 33% passed through the fast channel [absorption rate 0.021(0.011-0.040) min/sup -1/]. Local degradation rate was described as a saturable process with Michaelis-Menten characteristics [V/sub MAX/=1.93(0.62-6.03) mU min/sup -1/, K/sub M/=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.

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.

Simulation Environment to Evaluate Closed-Loop Insulin Delivery Systems in Type 1 Diabetes

Background: Closed-loop insulin delivery systems linking subcutaneous insulin infusion to real-time continuous glucose monitoring need to be evaluated in humans, but progress can be accelerated with the use of in silico testing. We present a simulation environment designed to support the development and testing of closed-loop insulin delivery systems in type 1 diabetes mellitus (T1DM). Methods: The principal components of the simulation environment include a mathematical model of glucose regulation representing a virtual population with T1DM, the glucose measurement model, and the insulin delivery model. The simulation environment is highly flexible. The user can specify an experimental protocol, define a population of virtual subjects, choose glucose measurement and insulin delivery models, and specify outcome measures. The environment provides graphical as well as numerical outputs to enable a comprehensive analysis of in silico study results. The simulation environment is validated by comparing its predictions against a clinical study evaluating overnight closed-loop insulin delivery in young people with T1DM using a model predictive controller. Results: The simulation model of glucose regulation is described, and population values of 18 synthetic subjects are provided. The validation study demonstrated that the simulation environment was able to reproduce the population results of the clinical study conducted in young people with T1DM. Conclusions: Closed-loop trials in humans should be preceded and concurrently guided by highly efficient and resource-saving computer-based simulations. We demonstrate validity of population-based predictions obtained with our simulation environment.

Closed-Loop Basal Insulin Delivery Over 36 Hours in Adolescents With Type 1 Diabetes: Randomized clinical trial

OBJECTIVE We evaluated the safety and efficacy of closed-loop basal insulin delivery during sleep and after regular meals and unannounced periods of exercise. RESEARCH DESIGN AND METHODS Twelve adolescents with type 1 diabetes (five males; mean age 15.0 [SD 1.4] years; HbA1c 7.9 [0.7]%; BMI 21.4 [2.6] kg/m2) were studied at a clinical research facility on two occasions and received, in random order, either closed-loop basal insulin delivery or conventional pump therapy for 36 h. During closed-loop insulin delivery, pump basal rates were adjusted every 15 min according to a model predictive control algorithm informed by subcutaneous sensor glucose levels. During control visits, subjects’ standard infusion rates were applied. Prandial insulin boluses were given before main meals (50–80 g carbohydrates) but not before snacks (15–30 g carbohydrates). Subjects undertook moderate-intensity exercise, not announced to the algorithm, on a stationary bicycle at a 140 bpm heart rate in the morning (40 min) and afternoon (20 min). Primary outcome was time when plasma glucose was in the target range (71–180 mg/dL). RESULTS Closed-loop basal insulin delivery increased percentage time when glucose was in the target range (median 84% [interquartile range 78–88%] vs. 49% [26–79%], P = 0.02) and reduced mean plasma glucose levels (128 [19] vs. 165 [55] mg/dL, P = 0.02). Plasma glucose levels were in the target range 100% of the time on 17 of 24 nights during closed-loop insulin delivery. Hypoglycemia occurred on 10 occasions during control visits and 9 occasions during closed-loop delivery (5 episodes were exercise related, and 4 occurred within 2.5 h of prandial bolus). CONCLUSIONS Day-and-night closed-loop basal insulin delivery can improve glucose control in adolescents. However, unannounced moderate-intensity exercise and excessive prandial boluses pose challenges to hypoglycemia-free closed-loop basal insulin delivery.

Closed-Loop Insulin Delivery During Pregnancy Complicated by Type 1 Diabetes

OBJECTIVE This study evaluated closed-loop insulin delivery with a model predictive control (MPC) algorithm during early (12–16 weeks) and late gestation (28–32 weeks) in pregnant women with type 1 diabetes. RESEARCH DESIGN AND METHODS Ten women with type 1 diabetes (age 31 years, diabetes duration 19 years, BMI 24.1 kg/m2, booking A1C 6.9%) were studied over 24 h during early (14.8 weeks) and late pregnancy (28.0 weeks). A nurse adjusted the basal insulin infusion rate from continuous glucose measurements (CGM), fed into the MPC algorithm every 15 min. Mean glucose and time spent in target (63–140 mg/dL), hyperglycemic (>140 to ≥180 mg/dL), and hypoglycemic (<63 to ≤50 mg/dL) were calculated using plasma and sensor glucose measurements. Linear mixed-effects models were used to compare glucose control during early and late gestation. RESULTS During closed-loop insulin delivery, median (interquartile range) plasma glucose levels were 117 (100.8–154.8) mg/dL in early and 126 (109.8–140.4) mg/dL in late gestation (P = 0.72). The overnight mean (interquartile range) plasma glucose time in target was 84% (50–100%) in early and 100% (94–100%) in late pregnancy (P = 0.09). Overnight mean (interquartile range) time spent hyperglycemic (>140 mg/dL) was 7% (0–40%) in early and 0% (0–6%) in late pregnancy (P = 0.25) and hypoglycemic (<63 mg/dL) was 0% (0–3%) and 0% (0–0%), respectively (P = 0.18). Postprandial glucose control, glucose variability, insulin infusion rates, and CGM sensor accuracy were no different in early or late pregnancy. CONCLUSIONS MPC algorithm performance was maintained throughout pregnancy, suggesting that overnight closed-loop insulin delivery could be used safely during pregnancy. More work is needed to achieve optimal postprandial glucose control.

Safety and Efficacy of 24-H Closed-Loop Insulin Delivery in Well-Controlled Pregnant Women With Type 1 Diabetes A randomized crossover case series

OBJECTIVE To evaluate the safety and efficacy of closed-loop insulin delivery in well-controlled pregnant women with type 1 diabetes treated with continuous subcutaneous insulin infusion (CSII). RESEARCH DESIGN AND METHODS A total of 12 women with type 1 diabetes (aged 32.9 years, diabetes duration 17.6 years, BMI 27.1 kg/m2, and HbA1c 6.4%) were randomly allocated to closed-loop or conventional CSII. They performed normal daily activities (standardized meals, snacks, and exercise) for 24 h on two occasions at 19 and 23 weeks’ gestation. Plasma glucose time in target (63–140 mg/dL) and time spent hypoglycemic were calculated. RESULTS Plasma glucose time in target was comparable for closed-loop and conventional CSII (median [interquartile range]: 81 [59–87] vs. 81% [54–90]; P = 0.75). Less time was spent hypoglycemic (<45 mg/dL [0.0 vs. 0.3%]; P = 0.04), with a lower low blood glucose index (2.4 [0.9–3.5] vs. 3.3 [1.9–5.1]; P = 0.03), during closed-loop insulin delivery. CONCLUSIONS Closed-loop insulin delivery was as effective as conventional CSII, with less time spent in extreme hypoglycemia.

Day and Night Closed-Loop Control in Adults With Type 1 Diabetes: A comparison of two closed-loop algorithms driving continuous subcutaneous insulin infusion versus patient self-management

OBJECTIVE To compare two validated closed-loop (CL) algorithms versus patient self-control with CSII in terms of glycemic control. RESEARCH DESIGN AND METHODS This study was a multicenter, randomized, three-way crossover, open-label trial in 48 patients with type 1 diabetes mellitus for at least 6 months, treated with continuous subcutaneous insulin infusion. Blood glucose was controlled for 23 h by the algorithm of the Universities of Pavia and Padova with a Safety Supervision Module developed at the Universities of Virginia and California at Santa Barbara (international artificial pancreas [iAP]), by the algorithm of University of Cambridge (CAM), or by patients themselves in open loop (OL) during three hospital admissions including meals and exercise. The main analysis was on an intention-to-treat basis. Main outcome measures included time spent in target (glucose levels between 3.9 and 8.0 mmol/L or between 3.9 and 10.0 mmol/L after meals). RESULTS Time spent in the target range was similar in CL and OL: 62.6% for OL, 59.2% for iAP, and 58.3% for CAM. While mean glucose level was significantly lower in OL (7.19, 8.15, and 8.26 mmol/L, respectively) (overall P = 0.001), percentage of time spent in hypoglycemia (<3.9 mmol/L) was almost threefold reduced during CL (6.4%, 2.1%, and 2.0%) (overall P = 0.001) with less time ≤2.8 mmol/L (overall P = 0.038). There were no significant differences in outcomes between algorithms. CONCLUSIONS Both CAM and iAP algorithms provide safe glycemic control.

A simulation model of glucose regulation in the critically ill

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.