New closed-loop insulin systems Boughton, Charlotte K.; Hovorka, Roman
Diabetologia,
05/2021, Volume:
64, Issue:
5
Journal Article
Peer reviewed
Open access
Advances in diabetes technologies have enabled the development of automated closed-loop insulin delivery systems. Several hybrid closed-loop systems have been commercialised, reflecting rapid ...transition of this evolving technology from research into clinical practice, where it is gradually transforming the management of type 1 diabetes in children and adults. In this review we consider the supporting evidence in terms of glucose control and quality of life for presently available closed-loop systems and those in development, including dual-hormone closed-loop systems. We also comment on alternative ‘do-it-yourself’ closed-loop systems. We remark on issues associated with clinical adoption of these approaches, including training provision, and consider limitations of presently available closed-loop systems and areas for future enhancements to further improve outcomes and reduce the burden of diabetes management.
Graphical abstract
Background:
Patient-driven innovation in diabetes management has resulted in a group of people with type 1 diabetes who choose to build and share knowledge around a do-it-yourself (DIY) open source ...artificial pancreas systems (OpenAPS). The purpose of this study was to examine Twitter data to understand how patients, caregivers, and care partners perceive OpenAPS, the personal and emotional ramifications of using OpenAPS, and the influence of OpenAPS on daily life.
Methods:
Qualitative netnography was used to analyze #OpenAPS on Twitter over a two-year period.
Results:
There were 328 patients, caregivers, and care partners who generated 3347 tweets. One overarching theme, OpenAPS changes lives, and five subthemes emerged from the data: (1) OpenAPS use suggests self-reported A1C and glucose variability improvement, (2) OpenAPS improves sense of diabetes burden and quality of life, (3) OpenAPS is perceived as safe, (4) patient/caregiver–provider interaction related to OpenAPS, and (5) technology adaptation for user needs.
Conclusions:
As users of a patient-driven technology, OpenAPS users are self-reporting improved A1C, day-to-day glucose levels, and quality of life. Safety features important to individuals with diabetes are perceived to be embedded into OpenAPS technology. Twitter analysis provides insight on a patient population driving an innovative solution to improve their quality of diabetes care.
The development of artificial pancreas has received a new impulse from recent technological advancements in subcutaneous continuous glucose monitoring and subcutaneous insulin pump delivery systems. ...However, the availability of innovative sensors and actuators, although essential, does not guarantee optimal glycemic regulation. Closed-loop control of blood glucose levels still poses technological challenges to the automatic control expert, most notable of which are the inevitable time delays between glucose sensing and insulin actuation.
A new in silico model is exploited for both design and validation of a linear model predictive control (MPC) glucose control system. The starting point is a recently developed meal glucose-insulin model in health, which is modified to describe the metabolic dynamics of a person with type 1 diabetes mellitus. The population distribution of the model parameters originally obtained in healthy 204 patients is modified to describe diabetic patients. Individual models of virtual patients are extracted from this distribution. A discrete-time MPC is designed for all the virtual patients from a unique input-output-linearized approximation of the full model based on the average population values of the parameters. The in silico trial simulates 4 consecutive days, during which the patient receives breakfast, lunch, and dinner each day.
Provided that the regulator undergoes some individual tuning, satisfactory results are obtained even if the control design relies solely on the average patient model. Only the weight on the glucose concentration error needs to be tuned in a quite straightforward and intuitive way. The ability of the MPC to take advantage of meal announcement information is demonstrated. Imperfect knowledge of the amount of ingested glucose causes only marginal deterioration of performance. In general, MPC results in better regulation than proportional integral derivative, limiting significantly the oscillation of glucose levels.
The proposed in silico trial shows the potential of MPC for artificial pancreas design. The main features are a capability to consider meal announcement information, delay compensation, and simplicity of tuning and implementation.
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.
Background:
Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs ...remains challenging.
Method:
An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements. The additional information on meals and exercise is incorporated with personalized estimates of plasma insulin concentration and glucose measurement data in an adaptive model identification algorithm.
Results:
The efficacy of the proposed personalized and adaptive modeling algorithm is demonstrated using clinical data involving closed-loop experiments of the artificial pancreas system, and the results demonstrate accurate glycemic modeling with the average root-mean-square error (mean absolute error) of 25.50 mg/dL (18.18 mg/dL) for six-step (30 minutes ahead) predictions.
Conclusions:
The approach presented is able to identify reliable time-varying individualized glucose-insulin models.
Background:
Linear empirical dynamic models have been widely used for glucose prediction. The extension of the concept of seasonality, characteristic of other domains, is explored here for the ...improvement of prediction accuracy.
Methods:
Twenty time series of 8-hour postprandial periods (PP) for a same 60g-carbohydrate meal were collected from a closed-loop controller validation study. A single concatenated time series was produced representing a collection of data from similar scenarios, resulting in seasonality. Variability in the resulting time series was representative of worst-case intrasubject variability. Following a leave-one-out cross-validation, seasonal and nonseasonal autoregressive integrated moving average models (SARIMA and ARIMA) were built to analyze the effect of seasonality in the model prediction accuracy. Further improvement achieved from the inclusion of insulin infusion rate as exogenous variable was also analyzed. Prediction horizons (PHs) from 30 to 300 min were considered.
Results:
SARIMA outperformed ARIMA revealing a significant role of seasonality. For a 5-h PH, average MAPE was reduced in 26.62%. Considering individual runs, the improvement ranged from 6.3% to 54.52%. In the best-performing case this reduction amounted to 29.45%. The benefit of seasonality was consistent among different PHs, although lower PHs benefited more, with MAPE reduction over 50% for PHs of 60 and 120 minutes, and over 40% for 180 min. Consideration of insulin infusion rate into the seasonal model further improved performance, with a 61.89% reduction in MAPE for 30-min PH and reductions over 20% for PHs over 180 min.
Conclusions:
Seasonality improved model accuracy allowing for the extension of the PH significantly.
The long-term benefit provided by advanced hybrid closed-loop (AHCL) systems needs to be assessed in general populations and specific subpopulations.
A prospective evaluation of subjects initiating ...the AHCL system 780G was performed. Time in range (70–180 mg/dl) (TIR), <70 mg/dl, <54 mg/dl, >180 mg/dl and >250 mg/dl were compared, at baseline and after one year, in different subpopulations, according to previous treatment (pump vs MDI), age (> or ≤25 years old) and hypoglycaemia risk at baseline.
135 subjects were included (age: 35 ± 15 years, 64 % females, diabetes duration: 21 ± 12 years). An increase in TIR was found, from 67.26 ± 11.80 % at baseline to 77.41 ± 8.85 % after one year (p < 0.001). All the subgroups showed a significant improvement in TIR, time > 180 mg/dl and >250 mg/dl. At the 1-year evaluation, no significant differences were found, between previous pump users and MDI subjects. Children and young adults had a lower time < 70 mg/dl than adults. Subjects with a high risk of hypoglycaemia at baseline had a higher time spent at <70 mg/dl and <54 mg/dl than low-risk individuals.
The initial benefit provided by the AHCL system is sustained in the long term. MDI subjects obtain the same outcomes as subjects with pump experience.
OBJECTIVE: The development of an artificial pancreas is an open research problem that faces the challenge of creating a control algorithm capable of dosing insulin automatically and driving blood ...glucose to healthy levels. Many of these approaches, including artificial intelligence,
are based on techniques that could result in and undesirable outcome because most of them include neither detect meal intake or meal size information. To overcome that issue, some meal count-detection algorithms reported in scientific publications have shown not only a good performance on
blood glucose regulation but fewer hypoglicemia and hyperglycemia events too. METHODS: We reviewed the most relevant authors and publications and main databases (particularly SCOPUS and Google Scholar), focusing on algorithms of detection and estimation of meal intake from multiple
approaches. RESULTS: A wide range of approaches and proposals have been found. The majority of them include trials on in silico patients rather than in vivo ones. Most of procedures require as inputs glucose samples from continuous glucose monitoring devices as basal insulin and bolus
as well. Most of approaches could be grouped by 2 categories: mathematical model based and not model based. CONCLUSION: A combination of methods seems to reach better results.
Originally, the future of automated insulin delivery (AID) systems, or artificial pancreas systems (APS), was having them at all, in any form. We’ve learned in the last half dozen years that the ...future of all artificial pancreas systems holds higher time in range, less work required to manage automated insulin delivery systems to improve quality of life, and the ability to input critical information back into the system itself. The data and user experience stories make it clear: APS works. APS are an improvement over other diabetes therapy methods when they are made available, accessible, and affordable. Understanding the unmet expectations of current users of first generation APS technology may also aid in the development of improved technology and user experiences for the future of APS.
An emerging group of people with type 1 diabetes are not waiting for commercial solutions, choosing to manage their condition with open-source artificial pancreas systems (APS). Our aim was to ...explore their perspectives on the future of APS.
Semi-structured telephone interviews were conducted (in Australia, October 2018 to January 2019) with 23 adults with type 1 diabetes currently using open-source APS. Interviews were recorded, transcribed and analysed thematically.
Participants described five key features of open-source APS they value: compatibility, user-led design, customisability, ability to evolve faster and community-driven. They attributed the success of the open-source APS movement to benefits they derive from these features: choice, solutions that meet their needs, ownership, staying one step ahead and real-time support. They expressed hope that future commercial products and healthcare would benefit from their learnings and from collaboration with the open-source APS community.
Participants believed that there will always be a place for the open-source community. It will continue to build on and advance commercial products, respond to user needs, offering a higher degree of control and customisation than afforded by commercial products and generating optimism for the future. Participants desired that future commercial diabetes technologies would be inspired by the open-source community and developed collaboratively with people with diabetes.
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