First use of model predictive control in outpatient wearable artificial pancreas

Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review

INTRODUCTION

Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components.

METHOD

A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy.

RESULTS

The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31-60 min in 34%, 61-90 min in 11%, 91-120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%).

CONCLUSION

The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.

The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP

Machine learning has become a popular tool for learning models of complex dynamics from biomedical data. In Type 1 Diabetes (T1D) management, these models are increasingly been integrated in decision support systems (DSS) to forecast glucose levels and provide preventive therapeutic suggestions, like corrective insulin boluses (CIB), accordingly. Typically, models are chosen based on their prediction accuracy. However, since patient safety is a concern in this application, the algorithm should also be physiologically sound and its outcome should be explainable. This paper aims to discuss the importance of using tools to interpret the output of black-box models in T1D management by presenting a case-of-study on the selection of the best prediction algorithm to integrate in a DSS for CIB suggestion. By retrospectively "replaying" real patient data, we show that two long-short term memory neural networks (LSTM) (named p-LSTM and np-LSTM) with similar prediction accuracy could lead to different therapeutic decisions. An analysis with SHAP-a tool for explaining black-box models' output-unambiguously shows that only p-LSTM learnt the physiological relationship between inputs and glucose prediction, and should therefore be preferred. This is verified by showing that, when embedded in the DSS, only p-LSTM can improve patients' glycemic control.

Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence

We present a robust insulin delivery system that includes automated meal detection and carbohydrate content estimation using machine learning for meal insulin dosing called robust artificial pancreas (RAP). We conducted a randomized, single-center crossover trial to compare postprandial glucose control in the four hours following unannounced meals using a hybrid model predictive control (MPC) algorithm and the RAP system. The RAP system includes a neural network model to automatically detect meals and deliver a recommended meal insulin dose. The meal detection algorithm has a sensitivity of 83.3%, false discovery rate of 16.6%, and mean detection time of 25.9 minutes. While there is no significant difference in incremental area under the curve of glucose, RAP significantly reduces time above range (glucose >180 mg/dL) by 10.8% (P = 0.04) and trends toward increasing time in range (70-180 mg/dL) by 9.1% compared with MPC. Time below range (glucose <70 mg/dL) is not significantly different between RAP and MPC.

Control of blood glucose induced by meals for type-1 diabetics using an adaptive backstepping algorithm

In this study, an adaptive backstepping method is proposed to regulate the blood glucose induced by meals for type-1 diabetic patients. The backstepping controller is used to control the blood glucose level and an adaptive algorithm is utilized to compensate for the blood glucose induced by meals. Moreover, the effectiveness of the proposed method is evaluated by comparing results in two different case studies: in the presence of actuator faults and the loss of control input for a short while during treatment. Effects of unannounced meals three times a day are investigated for a nominal patient in every case. It is argued that adaptive backstepping is the preferred control method in either case. The Lyapunov theory is used to prove the stability of the proposed method. Obtained results, indicated that the adaptive backstepping controller is stable, and the desired level of glucose concentration is being tracked efficiently.

Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence

Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

Intelligent automated drug administration and therapy: future of healthcare

In the twenty-first century, the collaboration of control engineering and the healthcare sector has matured to some extent; however, the future will have promising opportunities, vast applications, and some challenges. Due to advancements in processing speed, the closed-loop administration of drugs has gained popularity for critically ill patients in intensive care units and routine life such as personalized drug delivery or implantable therapeutic devices. For developing a closed-loop drug delivery system, the control system works with a group of technologies like sensors, micromachining, wireless technologies, and pharmaceuticals. Recently, the integration of artificial intelligence techniques such as fuzzy logic, neural network, and reinforcement learning with the closed-loop drug delivery systems has brought their applications closer to fully intelligent automatic healthcare systems. This review's main objectives are to discuss the current developments, possibilities, and future visions in closed-loop drug delivery systems, for providing treatment to patients suffering from chronic diseases. It summarizes the present insight of closed-loop drug delivery/therapy for diabetes, gastrointestinal tract disease, cancer, anesthesia administration, cardiac ailments, and neurological disorders, from a perspective to show the research in the area of control theory.

Blood glucose concentration control for type 1 diabetic patients: a multiple-model strategy

In this study, a multiple-model strategy is evaluated as an alternative closed-loop method for subcutaneous insulin delivery in type 1 diabetes. Non-linearities of the glucose-insulin regulatory system are considered by modelling the system around five different operating points. After conducting some identification experiments in the UVA/Padova metabolic simulator (accepted simulator by the US Food and Drug Administration (FDA)), five transfer functions are obtained for these operating points. Paying attention to some physiological facts, the control objectives such as the required settling time and permissible bounds of overshoots and undershoots are determined for any transfer functions. Then, five PID controllers are tuned to achieve these objectives and a bank of controllers is constructed. To cope with difficulties of the presence of delays in subcutaneous blood glucose (BG) measuring and in administration of insulin, a glucose-dependent setpoint is considered as the desired trajectory for the BG concentration. The performance of the obtained closed-loop glucose-insulin regulatory system is investigated on the in silico adult cohort of the UVA/Padova metabolic simulator. The obtained results show that the proposed multiple-model strategy leads to a closed-loop mechanism with limited hyperglycemia and no severe hypoglycemia.

Embedded Model Predictive Control for a Wearable Artificial Pancreas

While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this work, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC that has been evaluated in multiple clinical studies. The proposed embedded zone MPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programming problems inherent to MPC with linear models subject to convex constraints. Off-line closed-loop data generated by the FDA-accepted UVA/Padova simulator is used to select an optimization algorithm and corresponding tuning parameters. Through hardware-in-the-loop in silico results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded zone MPC manages to achieve comparable performance of that of the full-version zone MPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% vs. 83.1% for announced meals, with an equivalence test yielding p = 0.0013 and 66.2% vs. 66.0% for unannounced meals with p = 0.0028.

Efficacy of the Diabeloop closed-loop system to improve glycaemic control in patients with type 1 diabetes exposed to gastronomic dinners or to sustained physical exercise

AIMS

To compare closed-loop (CL) and open-loop (OL) systems for glycaemic control in patients with type 1 diabetes (T1D) exposed to real-life challenging situations (gastronomic dinners or sustained physical exercise).

METHODS

Thirty-eight adult patients with T1D were included in a three-armed randomized pilot trial (Diabeloop WP6.2 trial) comparing glucose control using a CL system with use of an OL device during two crossover 72-hour periods in one of the three following situations: large (gastronomic) dinners; sustained and repeated bouts of physical exercise (with uncontrolled food intake); or control (rest conditions). Outcomes included time in spent in the glucose ranges of 4.4-7.8 mmol/L and 3.9-10.0 mmol/L, and time in hypo- and hyperglycaemia.

RESULTS

Time spent overnight in the tight range of 4.4 to 7.8 mmol/L was longer with CL (mean values: 63.2% vs 40.9% with OL; P ≤ .0001). Time spent during the day in the range of 3.9 to 10.0 mmol/L was also longer with CL (79.4% vs 64.1% with OL; P ≤ .0001). Participants using the CL system spent less time during the day with hyperglycaemic excursions (glucose >10.0 mmol/L) compared to those using an OL system (17.9% vs 31.9%; P ≤ .0001), and the proportions of time spent during the day with hyperglycaemic excursions of those using the CL system in the gastronomic dinner and physical exercise subgroups were of similar magnitude to those in the control subgroup (18.1 ± 6.3%, 17.2 ± 8.1% and 18.4 ± 12.5%, respectively). Finally, times spent in hypoglycaemia were short and not significantly different among the groups.

CONCLUSIONS

The Diabeloop CL system is superior to OL devices in reducing hyperglycaemic excursions in patients with T1D exposed to gastronomic dinners, or exposed to physical exercise followed by uncontrolled food and carbohydrate intake.

Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes

Recent, rapid changes in the treatment of type 1 diabetes have allowed for commercialization of an "artificial pancreas" that is better described as a closed-loop controller of insulin delivery. This review presents the current state of closed-loop control systems and expected future developments with a discussion of the human factor issues in allowing automation of glucose control. The goal of these systems is to minimize or prevent both short-term and long-term complications from diabetes and to decrease the daily burden of managing diabetes. The closed-loop systems are generally very effective and safe at night, have allowed for improved sleep, and have decreased the burden of diabetes management overnight. However, there are still significant barriers to achieving excellent daytime glucose control while simultaneously decreasing the burden of daytime diabetes management. These systems use a subcutaneous continuous glucose sensor, an algorithm that accounts for the current glucose and rate of change of the glucose, and the amount of insulin that has already been delivered to safely deliver insulin to control hyperglycemia, while minimizing the risk of hypoglycemia. The future challenge will be to allow for full closed-loop control with minimal burden on the patient during the day, alleviating meal announcements, carbohydrate counting, alerts, and maintenance. The human factors involved with interfacing with a closed-loop system and allowing the system to take control of diabetes management are significant. It is important to find a balance between enthusiasm and realistic expectations and experiences with the closed-loop system.

Diabetes Technology: Monitoring, Analytics, and Optimal Control

Over the past 50 years, the diabetes technology field progressed remarkably through self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), risk and variability analysis, mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). This review follows these developments, beginning with an overview of the functioning of the human metabolic system in health and in diabetes and of its detailed quantitative network modeling. The review continues with a brief account of the first AP studies that used intravenous glucose monitoring and insulin infusion, and with notes about CSII and CGM-the technologies that made possible the development of contemporary AP systems. In conclusion, engineering lessons learned from AP research, and the clinical need for AP systems to prove their safety and efficacy in large-scale clinical trials, are outlined.

Mathematical Modelling of Endocrine Systems

Hormone rhythms are ubiquitous and essential to sustain normal physiological functions. Combined mathematical modelling and experimental approaches have shown that these rhythms result from regulatory processes occurring at multiple levels of organisation and require continuous dynamic equilibration, particularly in response to stimuli. We review how such an interdisciplinary approach has been successfully applied to unravel complex regulatory mechanisms in the metabolic, stress, and reproductive axes. We discuss how this strategy is likely to be instrumental for making progress in emerging areas such as chronobiology and network physiology. Ultimately, we envisage that the insight provided by mathematical models could lead to novel experimental tools able to continuously adapt parameters to gradual physiological changes and the design of clinical interventions to restore normal endocrine function.

Model individualization for artificial pancreas

BACKGROUND AND OBJECTIVE

The inter-subject variability characterizing the patients affected by type 1 diabetes mellitus makes automatic blood glucose control very challenging. Different patients have different insulin responses, and a control law based on a non-individualized model could be ineffective. The definition of an individualized control law in the context of artificial pancreas is currently an open research topic. In this work we consider two novel identification approaches that can be used for individualizing linear glucose-insulin models to a specific patient.

METHODS

The first approach belongs to the class of black-box identification and is based on a novel kernel-based nonparametric approach, whereas the second is a gray-box identification technique which relies on a constrained optimization and requires to postulate a model structure as prior knowledge. The latter is derived from the linearization of the average nonlinear adult virtual patient of the UVA/Padova simulator. Model identification and validation are based on in silico data collected during simulations of clinical protocols designed to produce a sufficient signal excitation without compromising patient safety. The identified models are evaluated in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean square error.

RESULTS

Both identification approaches were used to identify a linear individualized glucose-insulin model for each adult virtual patient of the UVA/Padova simulator. The resulting model simulation performance is significantly improved with respect to the performance achieved by a linear average model.

CONCLUSIONS

The approaches proposed in this work have shown a good potential to identify glucose-insulin models for designing individualized control laws for artificial pancreas.

The International Diabetes Closed-Loop Study: Testing Artificial Pancreas Component Interoperability

BACKGROUND

Use of artificial pancreas (AP) requires seamless interaction of device components, such as continuous glucose monitor (CGM), insulin pump, and control algorithm. Mobile AP configurations also include a smartphone as computational hub and gateway to cloud applications (e.g., remote monitoring and data review and analysis). This International Diabetes Closed-Loop study was designed to demonstrate and evaluate the operation of the inControl AP using different CGMs and pump modalities without changes to the user interface, user experience, and underlying controller.

METHODS

Forty-three patients with type 1 diabetes (T1D) were enrolled at 10 clinical centers (7 United States, 3 Europe) and 41 were included in the analyses (39% female, >95% non-Hispanic white, median T1D duration 16 years, median HbA1c 7.4%). Two CGMs and two insulin pumps were tested by different study participants/sites using the same system hub (a smartphone) during 2 weeks of in-home use.

RESULTS

The major difference between the system components was the stability of their wireless connections with the smartphone. The two sensors achieved similar rates of connectivity as measured by percentage time in closed loop (75% and 75%); however, the two pumps had markedly different closed-loop adherence (66% vs. 87%). When connected, all system configurations achieved similar glycemic outcomes on AP control (73% [mean] time in range: 70-180 mg/dL, and 1.7% [median] time <70 mg/dL).

CONCLUSIONS

CGMs and insulin pumps can be interchangeable in the same Mobile AP system, as long as these devices achieve certain levels of reliability and wireless connection stability.

Automated closed-loop control of diabetes: the artificial pancreas

The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.

Trusting telemedicine: A discussion on risks, safety, legal implications and liability of involved stakeholders

OBJECTIVES

The main purpose of the article is to raise awareness among all the involved stakeholders about the risks and legal implications connected to the development and use of modern telemedicine systems. Particular focus is given to the class of "active" telemedicine systems, that imply a real-world, non-mediated, interaction with the final user. A secondary objective is to give an overview of the European legal framework that applies to these systems, in the effort to avoid defensive medicine practices and fears, which might be a barrier to their broader adoption.

METHODS

We leverage on the experience gained during two international telemedicine projects, namely MobiGuide (pilot studies conducted in Spain and Italy) and AP@home (clinical trials enrolled patients in Italy, France, the Netherlands, United Kingdom, Austria and Germany), whose development our group has significantly contributed to in the last 4 years, to create a map of the potential criticalities of active telemedicine systems and comment upon the legal framework that applies to them. Two workshops have been organized in December 2015 and March 2016 where the topic has been discussed in round tables with system developers, researchers, physicians, nurses, legal experts, healthcare economists and administrators.

RESULTS

We identified 8 features that generate relevant risks from our example use cases. These features generalize to a broad set of telemedicine applications, and suggest insights on possible risk mitigation strategies. We also discuss the relevant European legal framework that regulate this class of systems, providing pointers to specific norms and highlighting possible liability profiles for involved stakeholders.

CONCLUSIONS

Patients are more and more willing to adopt telemedicine systems to improve home care and day-by-day self-management. An essential step towards a broader adoption of these systems consists in increasing their compliance with existing regulations and better defining responsibilities for all the involved stakeholders.

Fractional calculus in pharmacokinetics

We are witnessing the birth of a new variety of pharmacokinetics where non-integer-order differential equations are employed to study the time course of drugs in the body: this is dubbed "fractional pharmacokinetics". The presence of fractional kinetics has important clinical implications such as the lack of a half-life, observed, for example with the drug amiodarone and the associated irregular accumulation patterns following constant and multiple-dose administration. Building models that accurately reflect this behaviour is essential for the design of less toxic and more effective drug administration protocols and devices. This article introduces the readers to the theory of fractional pharmacokinetics and the research challenges that arise. After a short introduction to the concepts of fractional calculus, and the main applications that have appeared in literature up to date, we address two important aspects. First, numerical methods that allow us to simulate fractional order systems accurately and second, optimal control methodologies that can be used to design dosing regimens to individuals and populations.

Assessing the effectiveness of a 3-month day-and-night home closed-loop control combined with pump suspend feature compared with sensor-augmented pump therapy in youths and adults with suboptimally controlled type 1 diabetes: a randomised parallel study protocol

INTRODUCTION

Despite therapeutic advances, many individuals with type 1 diabetes are unable to achieve tight glycaemic target without increasing the risk of hypoglycaemia. The objective of this study is to determine the effectiveness of a 3-month day-and-night home closed-loop glucose control combined with a pump suspend feature, compared with sensor-augmented insulin pump therapy in youths and adults with suboptimally controlled type 1 diabetes.

METHODS AND ANALYSIS

The study adopts an open-label, multi-centre, multi-national (UK and USA), randomised, single-period, parallel design and aims for 84 randomised patients. Participants are youths (6-21 years) or adults (>21 years) with type 1 diabetes treated with insulin pump therapy and suboptimal glycaemic control (glycated haemoglobin (HbA1c) ≥7.5% (58 mmol/mol) and ≤10% (86 mmol/mol)). Following a 4-week run-in period, eligible participants will be randomised to a 3-month use of automated closed-loop insulin delivery combined with pump suspend feature or to sensor-augmented insulin pump therapy. Analyses will be conducted on an intention-to-treat basis. The primary outcome is the time spent in the target glucose range from 3.9 to 10.0 mmol/L based on continuous glucose monitoring levels during the 3-month free-living phase. Secondary outcomes include HbA1c at 3 months, mean glucose, time spent below and above target; time with glucose levels <3.5 and <2.8 mmol/L; area under the curve when sensor glucose is <3.5 mmol/L, time with glucose levels >16.7 mmol/L, glucose variability; total, basal and bolus insulin dose and change in body weight. Participants' and their families' perception in terms of lifestyle change, daily diabetes management and fear of hypoglycaemia will be evaluated.

ETHICS AND DISSEMINATION

Ethics/institutional review board approval has been obtained. Before screening, all participants/guardians will be provided with oral and written information about the trial. The study will be disseminated by peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

NCT02523131; Pre-results.

Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials

BACKGROUND

Closed-loop artificial pancreas systems have been in development for several years, including assessment in numerous varied outpatient clinical trials. We aimed to summarise the efficacy and safety of artificial pancreas systems in outpatient settings and explore the clinical and technical factors that can affect their performance.

METHODS

We did a systematic review and meta-analysis of randomised controlled trials comparing artificial pancreas systems (insulin only or insulin plus glucagon) with conventional pump therapy (continuous subcutaneous insulin infusion [CSII] with blinded continuous glucose monitoring [CGM] or unblinded sensor-augmented pump [SAP] therapy) in adults and children with type 1 diabetes. We searched Medline, Embase, and the Cochrane Central Register of Controlled Trials for studies published from 1946, to Jan 1, 2017. We excluded studies not published in English, those involving pregnant women or participants who were in hospital, and those testing adjunct medications other than glucagon. The primary outcome was the mean difference in percentage of time blood glucose concentration remained in target range (3·9-10 mmol/L or 3·9-8 mmol/L, depending on the study), assessed by random-effects meta-analysis. This study is registered with PROSPERO, number 2015:CRD42015026854.

FINDINGS

We identified 984 reports; after exclusions, 27 comparisons from 24 studies (23 crossover and one parallel design) including a total of 585 participants (219 in adult studies, 265 in paediatric studies, and 101 in combined studies) were eligible for analysis. Five comparisons assessed dual-hormone (insulin and glucagon), two comparisons assessed both dual-hormone and single-hormone (insulin only), and 20 comparisons assessed single-hormone artificial pancreas systems. Time in target was 12·59% higher with artificial pancreas systems (95% CI 9·02-16·16; p<0·0001), from a weighted mean of 58·21% for conventional pump therapy (I²=84%). Dual-hormone artificial pancreas systems were associated with a greater improvement in time in target range compared with single-hormone systems (19·52% [95% CI 15·12-23·91] vs 11·06% [6·94 to 15·18]; p=0·006), although six of seven comparisons compared dual-hormone systems to CSII with blinded CGM, whereas 21 of 22 single-hormone comparisons had SAP as the comparator. Single-hormone studies had higher heterogeneity than dual-hormone studies (I² 79% vs 66%). Bias assessment characteristics were incompletely reported in 12 of 24 studies, no studies masked participants to the intervention assignment, and masking of outcome assessment was not done in 12 studies and was unclear in 12 studies.

INTERPRETATION

Artificial pancreas systems uniformly improved glucose control in outpatient settings, despite heterogeneous clinical and technical factors.

FUNDING

None.

Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target

BACKGROUND

Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period.

OBJECTIVE

To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects.

METHODS

A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 ± 10.4 years, disease duration 22.6 ± 9.9 years, and A1c 7.8% ± 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithm-driven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (C_max), and time to C_max glucose concentrations and time in range (<70, 70-180, >180 mg/dL).

RESULTS

CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2<OL1<OL2 (PG_mean 123 ± 47 and 125 ± 44 vs. 152 ± 53 and 159 ± 54 mg/dL) and C_max OL 217.1 ± 67.0 mg/dL versus CL 183.3 ± 63.9 mg/dL, P < 0.0001. Time-in-range was higher with CL versus OL (80% vs. 64%; P < 0.001). Neither the time below 70 mg/dL (CL 6.1% vs. OL 3.2%; P > 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054).

CONCLUSIONS

This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov : study number NCT02100488.

A review of personalized blood glucose prediction strategies for T1DM patients

This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.

Safety of using real-time sensor glucose values for treatment decisions in adolescents with poorly controlled type 1 diabetes mellitus: a pilot study

BACKGROUND

This study explored the safety of using real-time sensor glucose (SG) data for treatment decisions in adolescents with poorly controlled type 1 diabetes.

METHODS

Ten adolescents with type 1 diabetes, HbA1c ≥9% on insulin pumps were admitted to the clinical research center and a continuous glucose sensor was inserted. Plasma glucose was measured at least hourly using Yellow Springs Instrument's (YSI) glucose analyzer. Starting at dinner, SG rather than YSI was used for treatment decisions unless YSI was <70 mg/dL (<3.9 mmol/L) or specific criteria indicating SG and YSI were very discordant were met. Participants were discharged after lunch the next day.

RESULTS

Ten participants (seven males; 15.2-17.8 year old) completed the study. The range of differences between high glucose correction doses using SG vs YSI for calculations was -2 (SG < YSI dose) to +1 (SG > YSI dose); this difference was two units in only 2 of 23 correction doses given (all SG < YSI dose). There were five episodes of mild hypoglycemia in two patients, two of which occurred after using SG for dose calculations. There was no severe hypoglycemia and no YSI glucose >350 mg/dL (19.4 mmol/L). Mean (±SE) pre- and postmeal YSI glucose were 163 ± 11 and 183 ± 12 mg/dL (9.1 ± 0.6 and 10.2 ± 0.7 mmol/L), respectively.

CONCLUSION

Use of real-time continuous glucose monitoring for treatment decisions was safe and did not result in significant over- or undertreatment. Use of SG for treatment decisions under supervised inpatient conditions is a suitable alternative to repeated fingerstick glucose monitoring. Outpatient studies using SG in real-time are needed.

Model Free iPID Control for Glycemia Regulation of Type-1 Diabetes

OBJECTIVE

The objective is to design a fully automated glycemia controller of Type-1 Diabetes (T1D) in both fasting and postprandial phases on a large number of virtual patients.

METHODS

A model-free intelligent proportional-integral-derivative (iPID) is used to infuse insulin. The feasibility of iPID is tested in silico on two simulators with and without measurement noise. The first simulator is derived from a long-term linear time-invariant model. The controller is also validated on the UVa/Padova metabolic simulator on 10 adults under 25 runs/subject for noise robustness test.

RESULTS

It was shown that without measurement noise, iPID mimicked the normal pancreatic secretion with a relatively fast reaction to meals as compared to a standard PID. With the UVa/Padova simulator, the robustness against CGM noise was tested. A higher percentage of time in target was obtained with iPID as compared to standard PID with reduced time spent in hyperglycemia.

CONCLUSION

Two different T1D simulators tests showed that iPID detects meals and reacts faster to meal perturbations as compared to a classic PID. The intelligent part turns the controller to be more aggressive immediately after meals without neglecting safety. Further research is suggested to improve the computation of the intelligent part of iPID for such systems under actuator constraints. Any improvement can impact the overall performance of the model-free controller.

SIGNIFICANCE

The simple structure iPID is a step for PID-like controllers since it combines the classic PID nice properties with new adaptive features.

"Learning" Can Improve the Blood Glucose Control Performance for Type 1 Diabetes Mellitus

BACKGROUND

A learning-type artificial pancreas has been proposed to exploit the repetitive nature in the blood glucose dynamics. We clinically evaluated the efficacy of the learning-type artificial pancreas.

METHODS

We conducted a pilot clinical study in 10 participants of mean age 36.1 years (standard deviation [SD] 12.7; range 16-58) with type 1 diabetes. Each trial was conducted for eight consecutive mornings. The first two mornings were open-loop to obtain the individualized parameters. Then, the following six mornings were closed-loop, during which a learning-type model predictive control algorithm was employed to calculate the insulin infusion rate. To evaluate the algorithm's robustness, each participant took exercise or consumed alcohol on the fourth or sixth closed-loop day and the order was determined randomly. The primary outcome was the percentage of time spent in the target glucose range of 3.9-8.0 mmol/L between 0900 and 1200 h.

RESULTS

The percentage of time with glucose spent in target range was significantly improved from 51.6% on day 1 to 71.6% on day 3 (mean difference between groups 17.9%, confidence interval [95% CI] 3.6-32.1; P = 0.020). There were no hypoglycemic episodes developed on day 3 compared with two episodes on day 1. There was no difference in the percentage of time with glucose spent in target range between exercise day versus day 5 and alcohol day versus day 5.

CONCLUSIONS

The learning-type artificial pancreas system achieved good glycemic regulation and provided increased effectiveness over time. It showed a satisfactory performance even when the blood glucose was challenged by exercise or alcohol.

Remote Blood Glucose Monitoring in mHealth Scenarios: A Review

Glucose concentration in the blood stream is a critical vital parameter and an effective monitoring of this quantity is crucial for diabetes treatment and intensive care management. Effective bio-sensing technology and advanced signal processing are therefore of unquestioned importance for blood glucose monitoring. Nevertheless, collecting measurements only represents part of the process as another critical task involves delivering the collected measures to the treating specialists and caregivers. These include the clinical staff, the patient's significant other, his/her family members, and many other actors helping with the patient treatment that may be located far away from him/her. In all of these cases, a remote monitoring system, in charge of delivering the relevant information to the right player, becomes an important part of the sensing architecture. In this paper, we review how the remote monitoring architectures have evolved over time, paralleling the progress in the Information and Communication Technologies, and describe our experiences with the design of telemedicine systems for blood glucose monitoring in three medical applications. The paper ends summarizing the lessons learned through the experiences of the authors and discussing the challenges arising from a large-scale integration of sensors and actuators.

Coming of age: the artificial pancreas for type 1 diabetes

The artificial pancreas (closed-loop system) addresses the unmet clinical need for improved glucose control whilst reducing the burden of diabetes self-care in type 1 diabetes. Glucose-responsive insulin delivery above and below a preset insulin amount informed by sensor glucose readings differentiates closed-loop systems from conventional, threshold-suspend and predictive-suspend insulin pump therapy. Insulin requirements in type 1 diabetes can vary between one-third-threefold on a daily basis. Closed-loop systems accommodate these variations and mitigate the risk of hypoglycaemia associated with tight glucose control. In this review we focus on the progress being made in the development and evaluation of closed-loop systems in outpatient settings. Randomised transitional studies have shown feasibility and efficacy of closed-loop systems under supervision or remote monitoring. Closed-loop application during free-living, unsupervised conditions by children, adolescents and adults compared with sensor-augmented pumps have shown improved glucose outcomes, reduced hypoglycaemia and positive user acceptance. Innovative approaches to enhance closed-loop performance are discussed and we also present the outlook and strategies used to ease clinical adoption of closed-loop systems.

Day-and-Night Closed-Loop Glucose Control in Patients With Type 1 Diabetes Under Free-Living Conditions: Results of a Single-Arm 1-Month Experience Compared With a Previously Reported Feasibility Study of Evening and Night at Home

OBJECTIVE

After testing of a wearable artificial pancreas (AP) during evening and night (E/N-AP) under free-living conditions in patients with type 1 diabetes (T1D), we investigated AP during day and night (D/N-AP) for 1 month.

RESEARCH DESIGN AND METHODS

Twenty adult patients with T1D who completed a previous randomized crossover study comparing 2-month E/N-AP versus 2-month sensor augmented pump (SAP) volunteered for 1-month D/N-AP nonrandomized extension. AP was executed by a model predictive control algorithm run by a modified smartphone wirelessly connected to a continuous glucose monitor (CGM) and insulin pump. CGM data were analyzed by intention-to-treat with percentage time-in-target (3.9-10 mmol/L) over 24 h as the primary end point.

RESULTS

Time-in-target (mean ± SD, %) was similar over 24 h with D/N-AP versus E/N-AP: 64.7 ± 7.6 vs. 63.6 ± 9.9 (P = 0.79), and both were higher than with SAP: 59.7 ± 9.6 (P = 0.01 and P = 0.06, respectively). Time below 3.9 mmol/L was similarly and significantly reduced by D/N-AP and E/N-AP versus SAP (both P < 0.001). SD of blood glucose concentration (mmol/L) was lower with D/N-AP versus E/N-AP during whole daytime: 3.2 ± 0.6 vs. 3.4 ± 0.7 (P = 0.003), morning: 2.7 ± 0.5 vs. 3.1 ± 0.5 (P = 0.02), and afternoon: 3.3 ± 0.6 vs. 3.5 ± 0.8 (P = 0.07), and was lower with D/N-AP versus SAP over 24 h: 3.1 ± 0.5 vs. 3.3 ± 0.6 (P = 0.049). Insulin delivery (IU) over 24 h was higher with D/N-AP and SAP than with E/N-AP: 40.6 ± 15.5 and 42.3 ± 15.5 vs. 36.6 ± 11.6 (P = 0.03 and P = 0.0004, respectively).

CONCLUSIONS

D/N-AP and E/N-AP both achieved better glucose control than SAP under free-living conditions. Although time in the different glycemic ranges was similar between D/N-AP and E/N-AP, D/N-AP further reduces glucose variability.

Hybrid Closed-Loop Insulin Delivery in Type 1 Diabetes During Supervised Outpatient Conditions

BACKGROUND

Efficacy and safety of the Medtronic Hybrid Closed-Loop (HCL) system were tested in subjects with type 1 diabetes in a supervised outpatient setting.

METHODS

The HCL system is a prototype research platform that includes a sensor-augmented insulin pump in communication with a control algorithm housed on an Android-based cellular device. Nine subjects with type 1 diabetes (5 female, mean age 53.3 years, mean A1C 7.2%) underwent 9 studies totaling 571 hours of closed-loop control using either default or personalized parameters. The system required meal announcements with estimates of carbohydrate (CHO) intake that were based on metabolic kitchen quantification (MK), dietician estimates (D), or subject estimates (Control). Postprandial glycemia was compared for MK, D, and Control meals.

RESULTS

The overall sensor glucose mean was 145 ± 43, the overall percentage time in the range 70-180 mg/dL was 80%, the overall percentage time <70 mg/dL was 0.79%. Compared to intervals of default parameter use (225 hours), intervals of personalized parameter use (346 hours), sensor glucose mean was 158 ± 49 and 137 ± 37 mg/dL (P < .001), respectively, and included more time in range (87% vs 68%) and less time below range (0.54% vs 1.18%). Most subjects underestimated the CHO content of meals, but postprandial glycemia was not significantly different between MK and matched Control meals (P = .16) or between D and matched Control meals (P = .76). There were no episodes of severe hypoglycemia.

CONCLUSIONS

The HCL system was efficacious and safe during this study. Personally adapted HCL parameters were associated with more time in range and less time below range than default parameters. Accurate estimates of meal CHO did not contribute to improved postprandial glycemia.

2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial

BACKGROUND

An artificial pancreas (AP) that can be worn at home from dinner to waking up in the morning might be safe and efficient for first routine use in patients with type 1 diabetes. We assessed the effect on glucose control with use of an AP during the evening and night plus patient-managed sensor-augmented pump therapy (SAP) during the day, versus 24 h use of patient-managed SAP only, in free-living conditions.

METHODS

In a crossover study done in medical centres in France, Italy, and the Netherlands, patients aged 18-69 years with type 1 diabetes who used insulin pumps for continuous subcutaneous insulin infusion were randomly assigned to 2 months of AP use from dinner to waking up plus SAP use during the day versus 2 months of SAP use only under free-living conditions. Randomisation was achieved with a computer-generated allocation sequence with random block sizes of two, four, or six, masked to the investigator. Patients and investigators were not masked to the type of intervention. The AP consisted of a continuous glucose monitor (CGM) and insulin pump connected to a modified smartphone with a model predictive control algorithm. The primary endpoint was the percentage of time spent in the target glucose concentration range (3·9-10·0 mmol/L) from 2000 to 0800 h. CGM data for weeks 3-8 of the interventions were analysed on a modified intention-to-treat basis including patients who completed at least 6 weeks of each intervention period. The 2 month study period also allowed us to asses HbA1c as one of the secondary outcomes. This trial is registered with ClinicalTrials.gov, number NCT02153190.

FINDINGS

During 2000-0800 h, the mean time spent in the target range was higher with AP than with SAP use: 66·7% versus 58·1% (paired difference 8·6% [95% CI 5·8 to 11·4], p<0·0001), through a reduction in both mean time spent in hyperglycaemia (glucose concentration >10·0 mmol/L; 31·6% vs 38·5%; -6·9% [-9·8% to -3·9], p<0·0001) and in hypoglycaemia (glucose concentration <3·9 mmol/L; 1·7% vs 3·0%; -1·6% [-2·3 to -1·0], p<0·0001). Decrease in mean HbA1c during the AP period was significantly greater than during the control period (-0·3% vs -0·2%; paired difference -0·2 [95% CI -0·4 to -0·0], p=0·047), taking a period effect into account (p=0·0034). No serious adverse events occurred during this study, and none of the mild-to-moderate adverse events was related to the study intervention.

INTERPRETATION

Our results support the use of AP at home as a safe and beneficial option for patients with type 1 diabetes. The HbA1c results are encouraging but preliminary.

FUNDING

European Commission.

Artificial Pancreas Project at Cambridge 2013

The development and clinical testing of closed-loop systems (the artificial pancreas) is underpinned by advances in continuous glucose monitoring and benefits from concerted academic and industry collaborative efforts. This review describes the progress of the Artificial Pancreas Project at the University of Cambridge from 2006 to 2014. Initial studies under controlled laboratory conditions, designed to collect representative safety and performance data, were followed by short to medium free-living unsupervised outpatient studies demonstrating the safety and efficacy of closed-loop insulin delivery using a model predictive control algorithm. Accompanying investigations included assessment of the psychosocial impact and key factors affecting glucose control such as insulin kinetics and glucose absorption. Translation to other disease conditions such as critical illness and Type 2 diabetes took place. It is concluded that innovation of iteratively enhanced closed-loop systems will provide tangible means to improve outcomes and quality of life in people with Type 1 diabetes and their families in the next decade.

Sensitivity of the Predictive Hypoglycemia Minimizer System to the Algorithm Aggressiveness Factor

BACKGROUND

The Predictive Hypoglycemia Minimizer System ("Hypo Minimizer"), consisting of a zone model predictive controller (the "controller") and a safety supervision module (the "safety module"), aims to mitigate hypoglycemia by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The "aggressiveness factor," a pivotal variable in the system, governs the speed and magnitude of the controller's insulin dosing characteristics in response to changes in CGM levels.

METHODS

Twelve adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 24 hours. This analysis focused primarily on the effect of the aggressiveness factor on the automated insulin-delivery characteristics of the controller, and secondarily on the glucose control results.

RESULTS

As aggressiveness increased from "conservative" to "medium" to "aggressive," the controller recommended less insulin (-3.3% vs -14.4% vs -19.5% relative to basal) with a higher frequency (5.3% vs 14.4% vs 20.3%) during the critical times when the CGM was reading 90-120 mg/dl and decreasing. Blood glucose analyses indicated that the most aggressive setting resulted in the most desirable combination of the least time spent <70 mg/dl and the most time spent 70-180 mg/dl, particularly in the overnight period. Hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia did not occur with any of the aggressiveness values.

CONCLUSION

The Hypo Minimizer's controller took preemptive action to prevent hypoglycemia based on predicted changes in CGM glucose levels. The most aggressive setting was quickest to take action to reduce insulin delivery below basal and achieved the best glucose metrics.

Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes

AIMS

To test in an outpatient setting the safety and efficacy of continuous subcutaneous insulin infusion (CSII) driven by a modular model predictive control (MMPC) algorithm informed by continuous glucose monitoring (CGM) measurement.

METHODS

13 patients affected by type 1 diabetes participated to a non-randomized outpatient 42-h experiment that included two evening meals and overnight periods (in short, dinner & night periods). CSII was patient-driven during dinner & night period 1 and MMPC-driven during dinner&night period 2. The study was conducted in hotels, where patients could move around freely. A CGM system (G4 Platinum; Dexcom Inc., San Diego, CA, USA) and insulin pump (AccuChek Combo; Roche Diagnostics, Mannheim, Germany) were connected wirelessly to a smartphone-based platform (DiAs, Diabetes Assistant; University of Virginia, Charlottesville, VA, USA) during both periods.

RESULTS

A significantly lower percentage of time spent with glucose levels <3.9 mmol/l was achieved in period 2 compared with period 1: 1.96 ± 4.56% vs 12.76 ± 15.84% (mean ± standard deviation, p < 0.01), together with a greater percentage of time spent in the 3.9-10 mmol/l target range: 83.56 ± 14.02% vs 62.43 ± 29.03% (p = 0.04). In addition, restricting the analysis to the overnight phases, a lower percentage of time spent with glucose levels <3.9 mmol/l (1.92 ± 4.89% vs 12.7 ± 19.75%; p = 0.03) was combined with a greater percentage of time spent in 3.9-10 mmol/l target range in period 2 compared with period 1 (92.16 ± 8.03% vs 63.97 ± 2.73%; p = 0.01). Average glucose levels were similar during both periods.

CONCLUSIONS

The results suggest that MMPC managed by a wearable system is safe and effective during evening meal and overnight. Its sustained use during this period is currently being tested in an ongoing randomized 2-month study.

Retrofitting of continuous glucose monitoring traces allows more accurate assessment of glucose control in outpatient studies

BACKGROUND

Glucose control in artificial pancreas (AP) studies is commonly assessed by metrics such as the percentage of time with blood glucose (BG) concentration below 70 mg/dL or in the nearly normal range 70-180 mg/dL (in brief, time in hypoglycemia and time in target, respectively). In outpatient studies these control metrics can be computed only from continuous glucose monitoring (CGM) sensor data, with the risk of an unfair assessment because of their inaccuracy. The aim of the present article is to show that the control metrics can be much more accurately determined if CGM data are preprocessed by a recently proposed retrofitting algorithm.

SUBJECTS AND METHODS

Data from 47 type 1 diabetes subjects are considered. Subjects were studied in a closed-loop control trial prescribing three 24-h admissions. Glucose concentration was monitored using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus CGM sensor. Frequent BG reference values were collected in parallel with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH). To simulate the few reference values available in outpatient conditions, we down-sampled the YSI data and provided to the retrofitting algorithm only the reference values that would have been collected in outpatient protocols. Time in hypoglycemia, time in target, mean, and SD of glucose profile were computed on the basis of both the original and the retrofitted CGM traces and compared with those computed using the frequently obtained YSI data.

RESULTS

Using the retrofitted traces, the average error affecting the estimation of time in hypoglycemia and time in target was approximately halved with respect to the original CGM traces (from 4.5% to 1.9% and from 8.7% to 4.4%, respectively). Error in mean and SD was reduced even further, from 10.0 mg/dL to 3.5 mg/dL and from 8.6 mg/dL to 2.9 mg/dL, respectively.

CONCLUSIONS

The improved accuracy of retrofitted CGM with respect to the original CGM traces allows a more reliable assessment of glucose control in outpatient AP studies.

In Silico Closed-Loop Control Validation Studies for Optimal Insulin Delivery in Type 1 Diabetes

This study presents a general closed-loop control strategy for optimal insulin delivery in type 1 Diabetes Mellitus (T1DM). The proposed control strategy aims toward an individualized optimal insulin delivery that consists of a patient-specific model predictive controller, a state estimator, a personalized scheduling level, and an open-loop optimization problem subjected to patient-specific process model and constraints. This control strategy can be also modified to address the case of limited patient data availability resulting in an "approximation" control strategy. Both strategies are validated in silico in the presence of predefined and unknown meal disturbances using both a novel mathematical model of glucose-insulin interactions and the UVa/Padova Simulator model as a virtual patient. The robustness of the control performance is evaluated under several conditions such as skipped meals, variability in the meal time, and metabolic uncertainty. The simulation results of the closed-loop validation studies indicate that the proposed control strategies can potentially achieve improved glycaemic control.

A novel, stable, aqueous glucagon formulation using ferulic acid as an excipient

Commercial glucagon is unstable due to aggregation and degradation. In closed-loop studies, it must be reconstituted frequently. For use in a portable pump for 3 days, a more stable preparation is required. At alkaline pH, curcumin inhibited glucagon aggregation. However, curcumin is not sufficiently stable for long-term use. Here, we evaluated ferulic acid, a stable breakdown product of curcumin, for its ability to stabilize glucagon. Ferulic acid-formulated glucagon (FAFG), composed of ferulic acid, glucagon, L-methionine, polysorbate-80, and human serum albumin in glycine buffer at pH 9, was aged for 7 days at 37°C. Glucagon aggregation was assessed by transmission electron microscopy (TEM) and degradation by high-performance liquid chromatography (HPLC). A cell-based protein kinase A (PKA) assay was used to assess in vitro bioactivity. Pharmacodynamics (PD) of unaged FAFG, 7-day aged FAFG, and unaged synthetic glucagon was determined in octreotide-treated swine. No fibrils were observed in TEM images of fresh or aged FAFG. Aged FAFG was 94% intact based on HPLC analysis and there was no loss of bioactivity. In the PD swine analysis, the rise over baseline of glucose with unaged FAFG, aged FAFG, and synthetic native glucagon (unmodified human sequence) was similar. After 7 days of aging at 37°C, an alkaline ferulic acid formulation of glucagon exhibited significantly less aggregation and degradation than that seen with native glucagon and was bioactive in vitro and in vivo. Thus, this formulation may be stable for 3-7 days in a portable pump for bihormonal closed-loop treatment of T1D.

Designing an artificial pancreas architecture: the AP@home experience

The latest achievements in sensor technologies for blood glucose level monitoring, pump miniaturization for insulin delivery, and the availability of portable computing devices are paving the way toward the artificial pancreas as a treatment for diabetes patients. This device encompasses a controller unit that oversees the administration of insulin micro-boluses and continuously drives the pump based on blood glucose readings acquired in real time. In order to foster the research on the artificial pancreas and prepare for its adoption as a therapy, the European Union in 2010 funded the AP@home project, following a series of efforts already ongoing in the USA. This paper, authored by members of the AP@home consortium, reports on the technical issues concerning the design and implementation of an architecture supporting the exploitation of an artificial pancreas platform. First a PC-based platform was developed by the authors to prove the effectiveness and reliability of the algorithms responsible for insulin administration. A mobile-based one was then adopted to improve the comfort for the patients. Both platforms were tested on real patients, and a description of the goals, the achievements, and the major shortcomings that emerged during those trials is also reported in the paper.

Multicenter closed-loop insulin delivery study points to challenges for keeping blood glucose in a safe range by a control algorithm in adults and adolescents with type 1 diabetes from various sites

BACKGROUND

The Control to Range Study was a multinational artificial pancreas study designed to assess the time spent in the hypo- and hyperglycemic ranges in adults and adolescents with type 1 diabetes while under closed-loop control. The controller attempted to keep the glucose ranges between 70 and 180 mg/dL. A set of prespecified metrics was used to measure safety.

RESEARCH DESIGN AND METHODS

We studied 53 individuals for approximately 22 h each during clinical research center admissions. Plasma glucose level was measured every 15-30 min (YSI clinical laboratory analyzer instrument [YSI, Inc., Yellow Springs, OH]). During the admission, subjects received three mixed meals (1 g of carbohydrate/kg of body weight; 100 g maximum) with meal announcement and automated insulin dosing by the controller.

RESULTS

For adults, the mean of subjects' mean glucose levels was 159 mg/dL, and mean percentage of values 71-180 mg/dL was 66% overall (59% daytime and 82% overnight). For adolescents, the mean of subjects' mean glucose levels was 166 mg/dL, and mean percentage of values in range was 62% overall (53% daytime and 82% overnight). Whereas prespecified criteria for safety were satisfied by both groups, they were met at the individual level in adults only for combined daytime/nighttime and for isolated nighttime. Two adults and six adolescents failed to meet the daytime criterion, largely because of postmeal hyperglycemia, and another adolescent failed to meet the nighttime criterion.

CONCLUSIONS

The control-to-range system performed as expected: faring better overnight than during the day and performing with variability between patients even after individualization based on patients' prior settings. The system had difficulty preventing postmeal excursions above target range.