Evaluation of glucose controllers in virtual environment: methodology and sample application

A Review of Safety and Design Requirements of the Artificial Pancreas

As clinical studies with artificial pancreas systems for automated blood glucose control in patients with type 1 diabetes move to unsupervised real-life settings, product development will be a focus of companies over the coming years. Directions or requirements regarding safety in the design of an artificial pancreas are, however, lacking. This review aims to provide an overview and discussion of safety and design requirements of the artificial pancreas. We performed a structured literature search based on three search components—type 1 diabetes, artificial pancreas, and safety or design—and extended the discussion with our own experiences in developing artificial pancreas systems. The main hazards of the artificial pancreas are over- and under-dosing of insulin and, in case of a bi-hormonal system, of glucagon or other hormones. For each component of an artificial pancreas and for the complete system we identified safety issues related to these hazards and proposed control measures. Prerequisites that enable the control algorithms to provide safe closed-loop control are accurate and reliable input of glucose values, assured hormone delivery and an efficient user interface. In addition, the system configuration has important implications for safety, as close cooperation and data exchange between the different components is essential.

In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes

Although various basal-bolus insulin therapy (BBIT) protocols have been used in the clinical environment, safer and more effective BBIT protocols are required for glucose control in hospitalized patients with type 2 diabetes (T2D). Modeling approaches could provide an evaluation environment for developing the optimal BBIT protocol prior to clinical trials at low cost and without risk of danger. In this study, an in-silico model was proposed to evaluate subcutaneous BBIT protocols in hospitalized patients with T2D. The proposed model was validated by comparing the BBIT protocol and sliding-scale insulin therapy (SSIT) protocol. The model was utilized for in-silico trials to compare the protocols of adjusting basal-insulin dose (BBIT1) versus adjusting total-daily-insulin dose (BBIT2). The model was also used to evaluate two different initial total-daily-insulin doses for various levels of renal function. The BBIT outcomes were superior to those of SSIT, which is consistent with earlier studies. BBIT2 also outperformed BBIT1, producing a decreased daily mean glucose level and longer time-in-target-range. Moreover, with a standard dose, the overall daily mean glucose levels reached the target range faster than with a reduced-dose for all degrees of renal function. The in-silico studies demonstrated several significant findings, including that the adjustment of total-daily-insulin dose is more effective than changes to basal-insulin dose alone. This research represents a first step toward the eventual development of an advanced model for evaluating various BBIT protocols.

Model-based control of plasma glycemia: Tests on populations of virtual patients

Closed-loop devices delivering medical treatments in an automatic fashion clearly require a thorough preliminary phase according to which the proposed control law is tested and validated as realistically as possible, before arranging in vivo experiments in a clinical setting. The present note develops a virtual environment aiming to validate a recently proposed model-based glucose control law on a solid simulation framework. From a theoretical viewpoint, the artificial pancreas has been designed by suitably exploiting a minimal set of delay differential equations modeling the glucose-insulin regulatory system; on the other hand, the validation platform makes use of a different, multi-compartmental model to build up a population of virtual patients. Simulations are carried out by properly addressing the available technological limits and the unavoidable uncertainties in real-time continuous glucose sensors as well as possible malfunctioning on the insulin delivery devices. The results show the robustness of the proposed control law that turns out to be efficient and extremely safe on a heterogenous population of virtual patients.

Stochastic Virtual Population of Subjects With Type 1 Diabetes for the Assessment of Closed-Loop Glucose Controllers

Closed-loop glucose control is an emerging treatment approach to manage type 1 diabetes. Closed-loop systems consist of a continuous glucose monitor, an insulin infusion pump, and a dosing algorithm that directs insulin delivery based on sensor levels. Testing of dosing algorithms in computer simulations may replace animal testing, accelerates development, and saves resources. We propose here a novel approach to generate a virtual population, to be used in metabolic simulators, from routine experimental data through the process that we term "stochastic e-cloning." We build on a nonlinear physiologically motivated time-varying model of glucose regulation. We adopt the Bayesian approach to estimate model parameters and to obtain the joint posterior probability distribution of time-invariant and time-varying parameters with the use of the Markov chain Monte Carlo methodology. The estimation process combines prior knowledge and experimental data to generate a sample from the posterior distribution, which can be subsequently used to conduct in silico experiments reflecting population and individual variability, and associated uncertainty as closely as possible. The approach is exemplified using data collected in 12 young subjects with type 1 diabetes. We demonstrate unbiased fit to the data, physiological plausibility of parameter estimates, and results of in silico testing using a stochastic virtual subject.

A review of artificial pancreas technologies with an emphasis on bi-hormonal therapy

Since the discovery of insulin, great progress has been made to improve the accuracy and safety of automated insulin delivery systems to help patients with type 1 diabetes achieve their treatment goals without causing hypoglycaemia. In recent years, bioengineering technology has greatly advanced diabetes management, with the development of blood glucose meters, continuous glucose monitors, insulin pumps and control systems for automatic delivery of one or more hormones. New insulin analogues have improved subcutaneous absorption characteristics, but do not completely eliminate the risk of hypoglycaemia. Insulin effect is counteracted by glucagon in non-diabetic individuals, while glucagon secretion in those with type 1 diabetes is impaired. The use of glucagon in the artificial pancreas is therefore a logical and feasible option for preventing and treating hypoglycaemia. However, commercially available glucagon is not stable in aqueous solution for long periods, forming potentially cytotoxic fibrils that aggregate quickly. Therefore, a more stable formulation of glucagon is needed for long-term use and storage in a bi-hormonal pump. In addition, a model of glucagon action in type 1 diabetes is lacking, further limiting the inclusion of glucagon into systems employing model-assisted control. As a result, although several investigators have been working to help develop bi-hormonal systems for patients with type 1 diabetes, most continue to utilize single hormone systems employing only insulin. This article seeks to focus on the attributes of glucagon and its use in bi-hormonal systems.

Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models

The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the "same" patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin-glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose-insulin models may be sufficient to describe patient dynamics in most situations.

Closed-loop insulin delivery: from bench to clinical practice

Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.

An advisory protocol for rapid- and slow-acting insulin therapy based on a run-to-run methodology

BACKGROUND

Emerging technology, such as an artificial pancreatic beta-cell, is not likely to be affordable to people who live in developing nations in the next 20-30 years. However, multiple-daily injection (MDI) therapy can be improved using similar advanced control algorithms designed for continuous glucose monitoring and continuous insulin infusion pumps.

METHODS

A simulation study of run-to-run control was developed for MDI therapy. Rapid- and slow-acting insulins were used in the protocol, which uses pre- and postprandial glucose measurements. The key information for the synthesis of the control algorithm is the subject insulin sensitivity that is calculated for two cases: (a) when the subject's glycemia and insulin dosing information is known (sensitivity response) and (b) when there is no previous information about the subject's response to the insulin protocol. In the latter case, this information needs to be estimated recursively using online data. After the sensitivity is recalculated, the run-to-run correction scheme is updated, obtaining an adaptive MDI therapy. The robustness of the advisory algorithm was evaluated by constant random parameter variations and superimposing sinusoidal oscillations on glucose-insulin model parameters to simulate intra-individual variability of the glucoregulatory system.

RESULTS

Optimal glycemic control has been achieved for both cases (a and b) despite variable meals (15% variation in carbohydrate content and 15-min variation in timing) and parametric variations in the glucose-insulin model. In Case (b), no profound hypoglycemic (<60 mg/dL) or hyperglycemic (>180 mg/dL) events were observed on average during all evaluations.

CONCLUSIONS

This work shows that the run-to-run framework for insulin updating can be successfully extended to an adaptive MDI protocol. These results motivate the practical implementation of this scheme in portable units such as personal digital assistants or smartphones.

Randomized trial of model predictive control for improved anemia management

BACKGROUND AND OBJECTIVES

Variable hemoglobin (Hb) response to erythropoiesis stimulating agents may result in adverse outcomes. The utility of model predictive control for drug dosing was previously demonstrated.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

This was a double-blinded, randomized, controlled trial to test model predictive control for dosing erythropoietin in ESRD patients. The trial included 60 hemodialysis patients who were randomized into a treatment arm (30 subjects) that received erythropoietin doses on the basis of the computer recommendations or a control arm (30 subjects) that received erythropoietin doses on the basis of recommendations from a standard anemia management protocol (control). The subjects were followed for 8 months, and the proportions of measured Hb within the target of 11 to 12 g/dl and outside 9 to 13 g/dl were measured. Variability of the Hb level was measured by the absolute difference between the achieved Hb and the target Hb of 11.5 g/dl as well as the area under the Hb curve.

RESULTS

Model predictive control resulted in 15 observations >13 or <9 g/dl (outliers), a mean absolute difference between achieved Hb and 11.5 g/dl of 0.98 +/- 0.08 g/dl, and an area under the Hb curve of 2.86 +/- 1.46. The control group algorithm resulted in 30 Hb outliers (P = 0.051), produced a mean absolute difference between achieved Hb and 11.5 g/dl of 1.18 +/- 0.18 g/dl (P < 0.001 difference in variance), and an area under the Hb curve of 3.38 +/- 2.69 (P = 0.025 difference in variance).

CONCLUSIONS

Model predictive control of erythropoietin administration improves anemia management.

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.

A gain-scheduling model predictive controller for blood glucose control in type 1 diabetes

This paper presents a control strategy for blood glucose (BG) level regulation in type 1 diabetic patients. To design the controller, model-based predictive control scheme has been applied to a newly developed diabetic patient model. The controller is provided with a feedforward loop to improve meal compensation, a gain-scheduling scheme to account for different BG levels, and an asymmetric cost function to reduce hypoglycemic risk. A simulation environment that has been approved for testing of artificial pancreas control algorithms has been used to test the controller. The simulation results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against model-patient mismatch and errors in meal estimation.

Identification of intraday metabolic profiles during closed-loop glucose control in individuals with type 1 diabetes

BACKGROUND

Algorithms for closed-loop insulin delivery can be designed and tuned empirically; however, a metabolic model that is predictive of clinical study results can potentially accelerate the process.

METHODS

Using data from a previously conducted closed-loop insulin delivery study, existing models of meal carbohydrate appearance, insulin pharmacokinetics, and the effect on glucose metabolism were identified for each of the 10 subjects studied. Insulin's effects to increase glucose uptake and decrease endogenous glucose production were described by the Bergman minimal model, and compartmental models were used to describe the pharmacokinetics of subcutaneous insulin absorption and glucose appearance following meals. The composite model, comprised of only five equations and eight parameters, was identified with and without intraday variance in insulin sensitivity (S(I)), glucose effectiveness at zero insulin (GEZI), and endogenous glucose production (EGP) at zero insulin.

RESULTS

Substantial intraday variation in SI, GEZI and EGP was observed in 7 of 10 subjects (root mean square error in model fit greater than 25 mg/dl with fixed parameters and nadir and/or peak glucose levels differing more than 25 mg/dl from model predictions). With intraday variation in these three parameters, plasma glucose and insulin were well fit by the model (R(2) = 0.933 +/- 0.00971 [mean +/- standard error of the mean] ranging from 0.879-0.974 for glucose; R(2) = 0.879 +/- 0.0151, range 0.819-0.972 for insulin). Once subject parameters were identified, the original study could be reconstructed using only the initial glucose value and basal insulin rate at the time closed loop was initiated together with meal carbohydrate information (glucose, R(2) = 0.900 +/- 0.015; insulin delivery, R(2) = 0.640 +/- 0.034; and insulin concentration, R(2) = 0.717 +/- 0.041).

CONCLUSION

Metabolic models used in developing and comparing closed-loop insulin delivery algorithms will need to explicitly describe intraday variation in metabolic parameters, but the model itself need not be comprised by a large number of compartments or differential equations.

Overnight closed-loop insulin delivery with model predictive control: assessment of hypoglycemia and hyperglycemia risk using simulation studies

BACKGROUND

Hypoglycemia and hyperglycemia during closed-loop insulin delivery based on subcutaneous (SC) glucose sensing may arise due to (1) overdosing and underdosing of insulin by control algorithm and (2) difference between plasma glucose (PG) and sensor glucose, which may be transient (kinetics origin and sensor artifacts) or persistent (calibration error [CE]). Using in silico testing, we assessed hypoglycemia and hyperglycemia incidence during over-night closed loop. Additionally, a comparison was made against incidence observed experimentally during open-loop single-night in-clinic studies in young people with type 1 diabetes mellitus (T1DM) treated by continuous SC insulin infusion.

METHODS

Simulation environment comprising 18 virtual subjects with T1DM was used to simulate overnight closed-loop study with a model predictive control (MPC) algorithm. A 15 h experiment started at 17:00 and ended at 08:00 the next day. Closed loop commenced at 21:00 and continued for 11 h. At 18:00, protocol included meal (50 g carbohydrates) accompanied by prandial insulin. The MPC algorithm advised on insulin infusion every 15 min. Sensor glucose was obtained by combining model-calculated noise-free interstitial glucose with experimentally derived transient and persistent sensor artifacts associated with FreeStyle Navigator (FSN). Transient artifacts were obtained from FSN sensor pairs worn by 58 subjects with T1DM over 194 nighttime periods. Persistent difference due to FSN CE was quantified from 585 FSN sensor insertions, yielding 1421 calibration sessions from 248 subjects with diabetes.

RESULTS

Episodes of severe (PG < or = 36 mg/dl) and significant (PG < or = 45 mg/dl) hypoglycemia and significant hyperglycemia (PG > or = 300 mg/dl) were extracted from 18,000 simulated closed-loop nights. Severe hypoglycemia was not observed when FSN CE was less than 45%. Hypoglycemia and hyperglycemia incidence during open loop was assessed from 21 overnight studies in 17 young subjects with T1DM (8 males; 13.5 +/- 3.6 years of age; body mass index 21.0 +/- 4.0 kg/m2; duration diabetes 6.4 +/- 4.1 years; hemoglobin A1c 8.5% +/- 1.8%; mean +/- standard deviation) participating in the Artificial Pancreas Project at Cambridge. Severe and significant hypoglycemia during simulated closed loop occurred 0.75 and 17.11 times per 100 person years compared to 1739 and 3479 times per 100 person years during experimental open loop, respectively. Significant hyperglycemia during closed loop and open loop occurred 75 and 15,654 times per 100 person years, respectively.

CONCLUSIONS

The incidence of severe and significant hypoglycemia reduced 2300- and 200-fold, respectively, during stimulated overnight closed loop with MPC compared to that observed during open-loop overnight clinical studies in young subjects with T1DM. Hyperglycemia was 200 times less likely. Overnight closed loop with the FSN and the MPC algorithm is expected to reduce substantially the risk of hypoglycemia and hyperglycemia.

Artificial pancreas: an emerging approach to treat Type 1 diabetes

Intensive insulin therapy aimed at achieving normal glucose levels significantly reduces the complications that are associated with diabetes but is also associated with an increased risk of low glucose levels (hypoglycemia). The growing use of continuous glucose monitors has stimulated the development of the artificial pancreas, a closed-loop insulin-delivery system aimed at restoring near-normal glucose levels while reducing the risk of hypoglycemia. The artificial pancreas comprises three components: a continuous glucose monitor, an insulin infusion pump and a control algorithm delivering insulin according to real-time glucose readings. In this article, we review closed-loop glucose control, including its components, development, testing and clinical application.

Mathematical modeling research to support the development of automated insulin-delivery systems

The world leaders in glycemia modeling convened during the Eighth Annual Diabetes Technology Meeting in Bethesda, Maryland, on 14 November 2008, to discuss the current practices in mathematical modeling and make recommendations for its use in developing automated insulin-delivery systems. This report summarizes the collective views of the 25 participating experts in addressing the following four topics: current practices in modeling efforts for closed-loop control; framework for exchange of information and collaboration among research centers; major barriers for the development of accurate models; and key tasks for developing algorithms to build closed-loop control systems. Among the participants, the following main conclusions and recommendations were widely supported: 1. Physiologic variance represents the single largest technical challenge to creating accurate simulation models. 2. A Web site describing different models and the data supporting them should be made publically available, with funding agencies and journals requiring investigators to provide open access to both models and data. 3. Existing simulation models should be compared and contrasted, using the same evaluation and validation criteria, to better assess the state of the art, understand any inherent limitations in the models, and identify gaps in data and/or model capability.

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.

A benchmark data set for model-based glycemic control in critical care

BACKGROUND

Hyperglycemia is prevalent in critical care. That tight control saves lives is becoming more clear, but the "how" and "for whom" in repeating the initial results remain elusive. Model-based methods can provide tight, patient-specific control, as well as providing significant insight into the etiology and evolution of this condition. However, it is still often difficult to compare results due to lack of a common benchmark. This article puts forward a benchmark data set for critical care glycemic control in a medical intensive care unit (ICU). Based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies, it provides a benchmark for comparing and analyzing performance in model-based glycemic control.

METHODS

Data from 20 of the first 150 postpilot patients treated under SPRINT are presented. All patients had longer than a 5-day length of stay (LoS) in the Christchurch ICU. The benchmark data set matches overall patient data and glycemic control results for the entire cohort and this particular LoS >5-day group. The mortality outcome (n =3, 15%) also matches SPRINT results for this patient group.

RESULTS

Data cover 20 patients and 6372 total patient hours with an average of 339.4 hours per patient. It includes insulin and nutrition inputs along with 4182 blood glucose measurements at an average of 224.3 measurements per patient, averaging a measurement approximately every 1.5 hours (16 per day). Data are available via download in a Microsoft Excel format. A series of cumulative distribution functions and tables are used to summarize data in this article.

CONCLUSION

Model-based methods can provide tighter, more adaptable "one method fits all" solutions using methods that enable patient-specific modeling and control. A benchmark data set will enable easier model and protocol development for groups lacking clinical data, as well as providing a benchmark to compare results of different protocols on a single (virtual) cohort based on real clinical data.

How would patients behave if they were continually informed of their blood glucose levels? A simulation study using a "virtual" patient

BACKGROUND

The emergence of real-time glucose sensors for people with diabetes may replace discontinuous monitoring (self-monitored blood glucose [BG]) in the future. In this study, we use a computer-generated "virtual" patient to predict changes in behavior that may result from an increased awareness of BG levels.

METHODS

The employed strategy required educated patients with type 1 diabetes to simulate a virtual patient using the DIABLOG Scientific simulator, interactive computer program. Thirty patients with a mean age of 34 years and duration of diabetes of 18 years (15 with continuous subcutaneous insulin infusion, 15 with intensive conventional therapy) simulated several daily glucose profiles with conventional self-monitoring of BG, using the time-lapse function of the program. Thereafter they had access to the actual glucose value either in a watch-type display or in a graphical display. Behavioral changes were monitored and stored.

RESULTS

Mean BG value improved from 154 to 139 mg/dL (P < 0.05). The analysis of the process and behavioral changes revealed that patients recognized an impending hypoglycemia with the sensor in 94% of cases (59% without sensor) and reacted adequately in 98% of cases in order to avoid hypoglycemia. The frequency of hypoglycemia could be reduced from 1.7 per week to 0.5 per week. Unnecessary interventions (mostly by administration of carbohydrates) doubled to 3.2 per week using the continuous measurement system. Impending hyperglycemia could only be prevented in 25% of cases without a sensor, and with sensor this ratio could only be marginally improved to 29%. Supplementary insulin administration resulted in hypoglycemia only in a few cases.

CONCLUSIONS

With the continuous measurement of their actual BG, subjects could reduce the frequency of hypoglycemia by 50% but increased the number of unnecessary interventions (excess carbohydrate intake or prematurely without a real hypoglycemia threat). Hyperglycemia prevention is a more difficult task. Simulation with an in silico disease model is a realistic alternative to studies in patients. A simulation program such as DIABLOG could be valuable for education in order to more rapidly and reliably recognize impending hypo- and hyperglycemia episodes.

Automated glucose control in the ICU: effect of nutritional protocol and measurement error

Tight glycaemic control has been shown to reduce mortality and morbidity in critically ill subjects. Using in silico computational approach, the objective of this study was to evaluate the effect of nutrition and the measurement error on glucose control. In silico simulation environment describing 21 synthetic subjects was used to simulate a 48 h clinical trial with an adaptive model predictive controller in the intensive care unit. Two types of nutritional protocols, simple and complex, and various levels of the measurement error (ME) were evaluated. The simple nutritional protocol resulted in more efficacious glucose control compared to that obtained with the complex nutritional protocol. A considerable deterioration was noted with the increasing level of the ME. Severe hypoglycaemia episodes (<2.8 mM) were observed with the ME>10%. We conclude that nutritional protocol should be kept simple to facilitate efficacious glucose control with an adaptive model predictive controller. The measurement error of the glucose measuring device should be less or equal to 10%

Intense exercise in type 1 diabetes: exploring the role of continuous glucose monitoring

Development of the external artificial pancreas (AP) is anticipated to be incremental, starting with simple and progressing to more complex applications incorporating exercise periods of various duration and intensity. Most studies investigating the effect of exercise on glucose excursions in subjects with type 1 diabetes either explored moderate exercise, which exerts different effects compared to intense exercise, or did not adopt continuous glucose monitoring combined with frequent plasma glucose measurements. Such studies could provide vital information. Performance of continuous glucose monitors during intense exercise could be evaluated to a greater extent. Frequently sampled blood glucose would facilitate better understanding of the relationship between intense exercise and metabolic processes, providing helpful information to patients with type 1 diabetes, clinicians, and researchers involved in the development of the AP.

Recent progress in analytical instrumentation for glycemic control in diabetic and critically ill patients

Implementing strict glycemic control can reduce the risk of serious complications in both diabetic and critically ill patients. For this reason, many different analytical, mainly electrochemical and optical sensor approaches for glucose measurements have been developed. Self-monitoring of blood glucose (SMBG) has been recognised as being an indispensable tool for intensive diabetes therapy. Recent progress in analytical instrumentation, allowing submicroliter samples of blood, alternative site testing, reduced test time, autocalibration, and improved precision, is comprehensively described in this review. Continuous blood glucose monitoring techniques and insulin infusion strategies, developmental steps towards the realization of the dream of an artificial pancreas under closed loop control, are presented. Progress in glucose sensing and glycemic control for both patient groups is discussed by assessing recent published literature (up to 2006). The state-of-the-art and trends in analytical techniques (either episodic, intermittent or continuous, minimal-invasive, or noninvasive) detailed in this review will provide researchers, health professionals and the diabetic community with a comprehensive overview of the potential of next-generation instrumentation suited to either short- and long-term implantation or ex vivo measurement in combination with appropriate body interfaces such as microdialysis catheters.

The artificial pancreas: how sweet engineering will solve bitter problems

An artificial pancreas is a closed-loop system containing only synthetic materials which substitutes for an endocrine pancreas. No artificial pancreas system is currently approved; however, devices that could become components of such a system are now becoming commercially available. An artificial pancreas will consist of functionally integrated components that will continuously sense glucose levels, determine appropriate insulin dosages, and deliver the insulin. Any proposed closed loop system will be closely scrutinized for its safety, efficacy, and economic impact. Closed loop control utilizes models of glucose homeostasis which account for the influences of feeding, stress, insulin, exercise, and other factors on blood glucose levels. Models are necessary for understanding the relationship between blood glucose levels and insulin dosing; developing algorithms to control insulin dosing; and customizing each user's system based on individual responses to factors that influence glycemia. Components of an artificial pancreas are now being developed, including continuous glucose sensors; insulin pumps for parenteral delivery; and control software, all linked through wireless communication systems. Although a closed-loop system providing glucagon has not been reported in 40 years, the use of glucagon to prevent hypoglycemia is physiologically attractive and future devices might utilize this hormone. No demonstration of long-term closed loop control of glucose in a free-living human with diabetes has been reported to date, but many centers around the world are working on closed loop control systems. It is expected that many types of artificial pancreas systems will eventually be available, and they will greatly benefit patients with diabetes.

Grading system to assess clinical performance of closed-loop glucose control

Closed-loop control of the glucose concentration in type 1 diabetes has been the subject of extensive research over the last 3 decades. Building on the recent progress in continuous glucose sensing techniques, several prototypes of a closed-loop system have been developed. To complement existing measures of glucose control, we designed a grading system specifically designed to provide clinical assessment of closed-loop systems including that of glucose controllers. The system introduces six grades, A-F, describing the level of control and the therapeutic intervention during outside-meal and postprandial conditions. Grades A and B represent excellent and good glucose control, respectively, without the need for a corrective therapeutic action. Grade C represents suboptimal control with a recommendation for a corrective action. Grade D represents poor control requiring a corrective action. Grades E and F represent very poor and life-threatening control, respectively, with a need for an immediate corrective action or requiring external assistance. The outcome of grading is the quantification of time spent in each grade. The grading system is exemplified using data obtained with a model predictive controller within an in silico simulation environment. We conclude that the grading system provides suitable means to assess efficacy and safety of glucose controllers complementing existing measures of glucose control.