Eran Atlas

Nocturnal Glucose Control with an Artificial Pancreas at a Diabetes Camp

BACKGROUND Recent studies have shown that an artificial-pancreas system can improve glucose control and reduce nocturnal hypoglycemia. However, it is not known whether such results can be replicated in settings outside the hospital. METHODS In this multicenter, multinational, randomized, crossover trial, we assessed the short-term safety and efficacy of an artificial pancreas system for control of nocturnal glucose levels in patients (10 to 18 years of age) with type 1 diabetes at a diabetes camp. In two consecutive overnight sessions, we randomly assigned 56 patients to receive treatment with an artificial pancreas on the first night and a sensor-augmented insulin pump (control) on the second night or to the reverse order of therapies on the first and second nights. Thus, all the patients received each treatment in a randomly assigned order. The primary end points were the number of hypoglycemic events (defined as a sensor glucose value of <63 mg per deciliter [3.5 mmol per liter] for at least 10 consecutive minutes), the time spent with glucose levels below 60 mg per deciliter (3.3 mmol per liter), and the mean overnight glucose level for individual patients. RESULTS On nights when the artificial pancreas was used, versus nights when the sensor-augmented insulin pump was used, there were significantly fewer episodes of nighttime glucose levels below 63 mg per deciliter (7 vs. 22) and significantly shorter periods when glucose levels were below 60 mg per deciliter (P=0.003 and P=0.02, respectively, after adjustment for multiplicity). Median values for the individual mean overnight glucose levels were 126.4 mg per deciliter (interquartile range, 115.7 to 139.1 [7.0 mmol per liter; interquartile range, 6.4 to 7.7]) with the artificial pancreas and 140.4 mg per deciliter (interquartile range, 105.7 to 167.4 [7.8 mmol per liter; interquartile range, 5.9 to 9.3]) with the sensor-augmented pump. No serious adverse events were reported. CONCLUSIONS Patients at a diabetes camp who were treated with an artificial-pancreas system had less nocturnal hypoglycemia and tighter glucose control than when they were treated with a sensor-augmented insulin pump. (Funded by Sanofi and others; ClinicalTrials.gov number, NCT01238406.).

MD-Logic Artificial Pancreas System: A pilot study in adults with type 1 diabetes

OBJECTIVE Current state-of-the-art artificial pancreas systems are either based on traditional linear control theory or rely on mathematical models of glucose-insulin dynamics. Blood glucose control using these methods is limited due to the complexity of the biological system. The aim of this study was to describe the principles and clinical performance of the novel MD-Logic Artificial Pancreas (MDLAP) System. RESEARCH DESIGN AND METHODS The MDLAP applies fuzzy logic theory to imitate lines of reasoning of diabetes caregivers. It uses a combination of control-to-range and control-to-target strategies to automatically regulate individual glucose levels. Feasibility clinical studies were conducted in seven adults with type 1 diabetes (aged 19–30 years, mean diabetes duration 10 ± 4 years, mean A1C 6.6 ± 0.7%). All underwent 14 full, closed-loop control sessions of 8 h (fasting and meal challenge conditions) and 24 h. RESULTS The mean peak postprandial (overall sessions) glucose level was 224 ± 22 mg/dl. Postprandial glucose levels returned to <180 mg/dl within 2.6 ± 0.6 h and remained stable in the normal range for at least 1 h. During 24-h closed-loop control, 73% of the sensor values ranged between 70 and 180 mg/dl, 27% were >180 mg/dl, and none were <70 mg/dl. There were no events of symptomatic hypoglycemia during any of the trials. CONCLUSIONS The MDLAP system is a promising tool for individualized glucose control in patients with type 1 diabetes. It is designed to minimize high glucose peaks while preventing hypoglycemia. Further studies are planned in the broad population under daily-life conditions.

MD-Logic Artificial Pancreas System: A Pilot Study in Adults with Type 1 Diabetes Mellitus

OBJECTIVE Current state-of-the-art artificial pancreas systems are either based on traditional linear control theory or rely on mathematical models of glucose-insulin dynamics. Blood glucose control using these methods is limited due to the complexity of the biological system. The aim of this study was to describe the principles and clinical performance of the novel MD-Logic Artificial Pancreas (MDLAP) System. RESEARCH DESIGN AND METHODS The MDLAP applies fuzzy logic theory to imitate lines of reasoning of diabetes caregivers. It uses a combination of control-to-range and control-to-target strategies to automatically regulate individual glucose levels. Feasibility clinical studies were conducted in seven adults with type 1 diabetes (aged 19–30 years, mean diabetes duration 10 ± 4 years, mean A1C 6.6 ± 0.7%). All underwent 14 full, closed-loop control sessions of 8 h (fasting and meal challenge conditions) and 24 h. RESULTS The mean peak postprandial (overall sessions) glucose level was 224 ± 22 mg/dl. Postprandial glucose levels returned to <180 mg/dl within 2.6 ± 0.6 h and remained stable in the normal range for at least 1 h. During 24-h closed-loop control, 73% of the sensor values ranged between 70 and 180 mg/dl, 27% were >180 mg/dl, and none were <70 mg/dl. There were no events of symptomatic hypoglycemia during any of the trials. CONCLUSIONS The MDLAP system is a promising tool for individualized glucose control in patients with type 1 diabetes. It is designed to minimize high glucose peaks while preventing hypoglycemia. Further studies are planned in the broad population under daily-life conditions.

Night glucose control with MD‐Logic artificial pancreas in home setting: a single blind, randomized crossover trial—interim analysis

Artificial pancreas (AP) systems have shown an improvement in glucose control and a reduced risk of nocturnal hypoglycemia under controlled conditions but remain to be evaluated under daily‐life conditions.

Clinical Evaluation of a Personalized Artificial Pancreas

OBJECTIVE An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration. RESEARCH DESIGN AND METHODS We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, nsubjects = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or “brain,” can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates. RESULTS Initial glucose values ranged from 84–251 mg/dL. Blood glucose was kept in the near-normal range (80–180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively. CONCLUSIONS These encouraging short-term results reveal the ability of a control algorithm tailored to an individual’s glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes.

Feasibility Study of Automated Overnight Closed-Loop Glucose Control Under MD-Logic Artificial Pancreas in Patients with Type 1 Diabetes: The DREAM Project

BACKGROUND Artificial pancreas systems may offer a potential major impact on the normalization of metabolic control and preventing hypoglycemic events. This study aims to establish near-normal overnight glucose control and reduce the risk of nocturnal hypoglycemia using the MD-Logic Artificial Pancreas (MDLAP), an algorithm that was developed by our research group. This inpatient feasibility study is the first step towards implementing an overnight closed-loop MDLAP system at the patients home. SUBJECTS AND METHODS Seven patients with type 1 diabetes (three adolescents and four adults; mean±SD age, 20.6±4.7 years; duration of diabetes, 9.6±2.6 years; body mass index, 24.3±3.9 kg/m(2); and glycated hemoglobin, 7.8±0.8%) participated in a total of 14 closed-loop overnight sessions. Each participant underwent two closed-loop inpatient sessions starting at dinner alone and at dinner following exercise. The closed-loop inpatient sessions were compared with data derived from nights spent at home with an open-loop system in a similar scenario to the study protocol. RESULTS The mean percentage of time spent in the near normal glucose range of 63-140 mg/dL was 83±16%, and the median (interquartile range) was 85% (78-92%) for the overnight closed-loop sessions compared with 34±31% and 27% (6-57%) in the homecare open-loop setting, respectively. During the overnight closed-loop sessions at dinner alone 92±9% of the sensor values ranged within target range, compared with 73±19% for the sessions following exercise (P=0.03). No hypoglycemic (<63 mg/dL) events occurred during the closed-loop sessions. CONCLUSION Closed-loop insulin delivery under MDLAP is a feasible and safe solution to control overnight glycemia.

The “Glucositter” overnight automated closed loop system for type 1 diabetes: A randomized crossover trial

Tight glucose control is needed to prevent long‐term diabetes complications; this is hindered by the risk of hypoglycemia, especially at night.

Automatic Learning Algorithm for the MD-Logic Artificial Pancreas System

BACKGROUND Applying real-time learning into an artificial pancreas system could effectively track the unpredictable behavior of glucose-insulin dynamics and adjust insulin treatment accordingly. We describe a novel learning algorithm and its performance when integrated into the MD-Logic Artificial Pancreas (MDLAP) system developed by the Diabetes Technology Center, Schneider Childrens Medical Center of Israel, Petah Tikva, Israel. METHODS The algorithm was designed to establish an initial patient profile using open-loop data (Initial Learning Algorithm component) and then make periodic adjustments during closed-loop operation (Runtime Learning Algorithm component). The MDLAP system, integrated with the learning algorithm, was tested in seven different experiments using the University of Virginia/Padova simulator, comprising adults, adolescents, and children. The experiments included simulations using the open-loop and closed-loop control strategy under nominal and varying insulin sensitivity conditions. The learning algorithm was automatically activated at the end of the open-loop segment and after every day of the closed-loop operation. Metabolic control parameters achieved at selected time points were compared. RESULTS The percentage of time glucose levels were maintained within 70-180 mg/dL for children and adolescents significantly improved when open-loop was compared with day 6 of closed-loop control (P<0.0001) and remained unaltered for the adult group (P=0.11) during nominal conditions. In varying insulin sensitivity conditions, the percentage of time glucose levels were below 70 mg/dL was significantly reduced by approximately sevenfold (P<0.001). These observations were correlated with significant reduction in the Low Blood Glucose Index (P<0.001). CONCLUSIONS The new algorithm was effective in characterizing the patient profiles from open-loop data and in adjusting treatment to provide better glycemic control during closed-loop control in both conditions. These findings warrant corroboratory clinical trials.

MD-Logic overnight type 1 diabetes control in home settings: A multicentre, multinational, single blind randomized trial

To evaluate the safety, efficacy and need for remote monitoring of the MD‐Logic closed‐loop system during short‐term overnight use at home.

Closing the loop: Closing the loop

Closed-loop algorithms can be found in every aspect of everyday modern life. Automation and control are used constantly to provide safety and to improve quality of life. Closed-loop systems and algorithms can be found in home appliances, automobiles, aviation and more. Can one imagine nowadays driving a car without ABS, cruise control or even anti-sliding control? Similar principles of automation and control can be used in the management of diabetes mellitus (DM). The idea of an algorithmic/technological way to control glycaemia is not new and has been researched for more than four decades. However, recent improvements in both glucose-sensing technology and insulin delivery together with advanced control and systems engineering made this dream of an artificial pancreas possible. The artificial pancreas may be the next big step in the treatment of DM since the use of insulin analogues. An artificial pancreas can be described as internal or external devices that use continuous glucose measurements to automatically manage exogenous insulin delivery with or without other hormones in an attempt to restore glucose regulation in individuals with DM using a control algorithm. This device as described can be internal or external; can use different types of control algorithms with bi-hormonal or uni-hormonal design; and can utilise different ways to administer them. The different designs and implementations have transitioned recently from in silico simulations to clinical evaluation stage with practical applications in mind. This may mark the beginning of a new era in diabetes management with the introduction of semi-closed-loop systems that can prevent or minimise nocturnal hypoglycaemia, to hybrid systems that will manage blood glucose (BG) levels with minimal user intervention to finally fully automated systems that will take the user out of the loop. More and more clinical trials will be needed for the artificial pancreas to become a reality but initial encouraging results are proof that we are on the right track. We attempted to select recent publications that will present these current achievements in the quest for the artificial pancreas and that will inspire others to continue to progress this field of research.