Fully Closed Loop Glucose Control With a Bihormonal Artificial Pancreas in Adults With Type 1 Diabetes: An Outpatient, Randomized, Crossover Trial

Blauw, Helga; Onvlee, A. Joannet; Klaassen, Michel; Bon, Arianne C. van; DeVries, J. Hans

doi:10.2337/dc20-2106

Cited by 60 publications

(46 citation statements)

References 10 publications

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“…The closed-loop insulin delivery system has been shown to improve glycaemic control in individuals with type 1 or type 2 diabetes [ 8 , 9 , 41 ]. Nevertheless, several aspects of the artificial pancreas require further enhancement [ 6 , 10 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

et al. 2021

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Background Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. Methods We used data from The Maastricht Study, an observational population‐based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman’s correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). Results Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). Conclusions Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

et al. 2021

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“…Regarding the dual-hormone closed-loop systems, Beta Bionics company (Irvine, CA, USA), Inreda Diabetic company (Goor, the Netherland), Institut de Recherches Cliniques de Montreal (IRCM, Montreal, QC, Canada), and Oregon Health and Science University (Portland, OR, USA) are the major groups [ 50 - 53 ]. We will refer to them as the Beta Bionics, Inreda, IRCM, and Oregon groups, respectively.…”

Section: Major Groupsmentioning

confidence: 99%

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

2021

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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.

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“…Furthermore, the transmitters, depicted by the devices with a circle and triangle, contain the blood glucose sensors and accelerometers. Finally, the cannulas (purple G and blue I) serve as an injection spot for the regulatory hormones [ 1 ].…”

Section: Figurementioning

confidence: 99%

“…Closed loop bi-hormonal artificial pancreas systems, such as the artificial pancreas (AP™) developed by Inreda Diabetic B.V. (Goor, The Netherlands), fully regulate blood glucose levels of type 1 diabetes mellitus (T1DM) patients by administration of insulin and glucagon using a closed loop regulator as shown in Figure 1 [ 1 ]. The hardware consists of a mainframe, generally worn on the hip, two transmitters that measure blood glucose levels, worn in the abdomen region, and two cannulas through which insulin and glucagon are administered [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Movements and Postures Using Wearable Sensors for Implementation in a Bi-Hormonal Artificial Pancreas System

Sawaryn

Klaassen²,

Beijnum

et al. 2021

Sensors

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Background: Closed loop bi-hormonal artificial pancreas systems, such as the artificial pancreas (AP™) developed by Inreda Diabetic B.V., control blood glucose levels of type 1 diabetes mellitus patients via closed loop regulation. As the AP™ currently does not classify postures and movements to estimate metabolic energy consumption to correct hormone administration levels, considerable improvements to the system can be made. Therefore, this research aimed to investigate the possibility to use the current system to identify several postures and movements. Methods: seven healthy participants took part in an experiment where sequences of postures and movements were performed to train and assess a computationally sparing algorithm. Results: Using accelerometers, one on the hip and two on the abdomen, user-specific models achieved classification accuracies of 86.5% using only the hip sensor and 87.3% when including the abdomen sensors. With additional accelerometers on the sternum and upper leg for identification, 90.0% of the classified postures and movements were correct. Conclusions: The current hardware configuration of the AP™ poses no limitation to the identification of postures and movements. If future research shows that identification can still be done accurately in a daily life setting, this algorithm may be an improvement for the AP™ to sense physical activity.

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Fully Closed Loop Glucose Control With a Bihormonal Artificial Pancreas in Adults With Type 1 Diabetes: An Outpatient, Randomized, Crossover Trial

Cited by 60 publications

References 10 publications

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

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

Identification of Movements and Postures Using Wearable Sensors for Implementation in a Bi-Hormonal Artificial Pancreas System

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