Automated hybrid closed-loop control with a proportional-integral-derivative based system in adolescents and adults with type 1 diabetes: individualizing settings for optimal performance

The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment

In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented.

The Impact of a Recently Approved Automated Insulin Delivery System on Glycemic, Sleep, and Psychosocial Outcomes in Older Adults With Type 1 Diabetes: A Pilot Study

BACKGROUND

Older adults with type 1 diabetes (≥65 years) are often under-represented in clinical trials of automated insulin delivery (AID) systems. We sought to test the efficacy of a recently FDA-approved AID system in this population.

METHODS

Participants with type 1 diabetes used sensor-augmented pump (SAP) therapy for four weeks and then used an AID system (Control-IQ) for four weeks. In addition to glucose control variables, patient-reported outcomes (PRO) were assessed with questionnaires and sleep parameters were assessed by actigraphy.

RESULTS

Fifteen older adults (mean age 68.7 ± 3.3, HbA1c of 7.0 ± 0.8) completed the pilot trial. Glycemic outcomes improved during AID compared to SAP. During AID use, mean glucose was 146.0 mg/dL; mean percent time in range (TIR, 70-180 mg/dL) was 79.6%; median time below 70 mg/dL was 1.1%. The AID system was in use 92.6% ± 7.0% of the time. Compared to SAP, while participants were on AID the TIR increased significantly (+10%, P = .002) accompanied by a reduction in both time above 180 mg/dL (-6.9%, P = .005) and below 70 mg/dl (-0.4%, P = .053). Diabetes-related distress decreased significantly while using AID (P = .028), but sleep parameters remained unchanged.

CONCLUSIONS

Use of this AID system in older adults improved glycemic control with high scores in ease of use, trust, and usability. Participants reported an improvement in diabetes distress with AID use. There were no significant changes in sleep.

Two Decades of Research in Artificial Pancreas: Insights from a Bibliometric Analysis

BACKGROUND

Artificial pancreas is a well-known research topic devoted to achieving better glycemic outcomes that has been attracting increasing attention over the years. However, there is a lack of systematic, chronological, and synthesizing studies that show the background of the knowledge generation in this field. This study implements a bibliometric analysis to recognize the main documents, type of publications, research categories, countries, keywords, organizations, and authors related to this topic.

METHODS

Web of Science core collection database was accessed from 2000 to 2020 in order to select high-quality scientific documents based on a specific search query. Bibexcel, MS Excel, Power BI, R-Studio, VOSviewer, and CorText software were used for a descriptive and network analysis based on the local database obtained. Bibliometric parameters as the h-index, frequencies, co-authorship and co-ocurrences were computed.

RESULTS

A total of 756 documents were included that show a growing scientific production on this topic with an increasing contribution from engineering. Outstanding authors, organizations, and countries were identified. An analysis of trends in research was conducted according to the scientific categories of the Web of Science database to identify the main research interests of the last 2 decades and the emerging areas with greater prominence in the coming years. A keyword network analysis allowed to identify the main stages in the development of the AP research over time.

CONCLUSIONS

Results reveal a comprehensive background of the knowledge generation for the AP topic during the last 2 decades, which has been strengthened with international collaborations and a remarkable interdisciplinarity between endocrinology and engineering, giving rise to a growing number of research areas over time, where computer science and medical informatics stand out as the main emerging research areas.

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones.

Sleep and diabetes-specific psycho-behavioral outcomes of a new automated insulin delivery system in young children with type 1 diabetes and their parents

BACKGROUND

Data on the use of Control-IQ, the latest FDA-approved automated insulin delivery (AID) system for people with T1D 6 years of age or older is still scarce, particularly regarding nonglycemic outcomes. Children with T1D and their parents are at higher risk for sleep disturbances. This study assesses sleep, psycho-behavioral and glycemic outcomes of AID compared to sensor-augmented pump therapy (SAP) therapy in young children with T1D and their parents.

METHODS

Thirteen parents and their young children (ages 7-10) on insulin pump therapy were enrolled. Children completed an initial 4-week study with SAP using their own pump and a study CGM followed by a 4-week phase of AID. Sleep outcomes for parents and children were evaluated through actigraphy watches. Several questionnaires were administered at baseline and at the end of each study phase. CGM data were used to assess glycemic outcomes.

RESULTS

Actigraphy data did not show any significant change from SAP to AID, except a reduction of number of parental awakenings during the night (p = 0.036). Parents reported statistically significant improvements in Pittsburgh Sleep Quality Index total score (p = 0.009), Hypoglycemia Fear Survey total score (p = 0.011), diabetes-related distress (p = 0.032), and depression (p = 0.023). While on AID, time in range (70-180 mg/dL) significantly increased compared to SAP (p < 0.001), accompanied by a reduction in hyperglycemia (p = 0.001).

CONCLUSIONS

These results suggest that use of AID has a positive impact on glycemic outcomes in young children as well as sleep and diabetes-specific quality of life outcomes in their parents.

Impact of the Hybrid Closed-Loop System on Sleep and Quality of Life in Youth with Type 1 Diabetes and Their Parents

Background: Insufficient sleep is common in youth with type 1 diabetes (T1D) and parents, likely secondary to diabetes-related disturbances, including fear of hypoglycemia, nocturnal glucose monitoring, hypoglycemia, and device alarms. Hybrid closed-loop (HCL) systems improve glycemic variability and potentially reduce nocturnal awakenings. Methods: Adolescents with T1D (N = 37, mean age 13.9 years, 62% female, mean HbA1c 8.3%) and their parents were enrolled in this observational study when starting the Medtronic 670G HCL system. Participants completed study measures (sleep and psychosocial surveys and actigraphy with sleep diaries) before starting auto mode and ∼3 months later. Results: Based on actigraphy data, neither adolescents' nor parents' sleep characteristics changed significantly pre-post device initiation. Adolescents' mean total sleep time decreased from 7 h 16 min (IQR: [6:43-7:47]) to 7 h 9 min (IQR: [6:44-7:52]), while parents' total sleep time decreased from 6 h 47 min (IQR: [6:16-7:10]) to 6 h 38 min (IQR: [5:57-6:57]). Although there were no significant differences in most of the survey measures, there was a moderate effect for improved sleep quality in parents and fear of hypoglycemia in adolescents. In addition, adolescents reported a significant increase in self-reported glucose monitoring satisfaction. Adolescents averaged 44.7% use of auto mode at 3 months. Conclusions: Our data support previous research showing youth with T1D and their parents are not achieving the recommended duration of sleep. Lack of improvement in sleep may be due to steep learning curves involved with new technology. We observed moderate improvements in parental subjective report of sleep quality despite no change in objective measures of sleep duration. Further evaluation of sleep with long-term HCL use and larger sample size is needed.

Safety and Efficacy of Initializing the Control-IQ Artificial Pancreas System Based on Total Daily Insulin in Adolescents with Type 1 Diabetes

Objective: To assess the safety and efficacy of a simplified initialization for the Tandem t:slim X2 Control-IQ hybrid closed-loop system, using parameters based on total daily insulin ("MyTDI") in adolescents with type 1 diabetes under usual activity and during periods of increased exercise. Research Design and Methods: Adolescents with type 1 diabetes 12-18 years of age used Control-IQ for 5 days at home using their usual parameters. Upon arrival at a 60-h ski camp, participants were randomized to either continue Control-IQ using their home settings or to reinitialize Control-IQ with MyTDI parameters. Control-IQ use continued for 5 days following camp. The effect of MyTDI on continuous glucose monitoring outcomes were analyzed using repeated measures analysis of variance (ANOVA): baseline, camp, and at home. Results: Twenty participants were enrolled and completed the study; two participants were excluded from the analysis due to absence from ski camp (1) and illness (1). Time in range was similar between both groups at home and camp. A tendency to higher time <70 mg/dL in the MyTDI group was present but only during camp (median 3.8% vs. 1.4%, P = 0.057). MyTDI users with bolus/TDI ratios >40% tended to show greater time in the euglycemic range improvements between baseline and home than users with ratios <40% (+16.3% vs. -9.0%, P = 0.012). All participants maintained an average of 95% time in closed loop (84.1%-100%). Conclusions: MyTDI is a safe, effective, and easy way to determine insulin parameters for use in the Control-IQ artificial pancreas. Future modifications to account for the influence of carbohydrate intake on MyTDI calculations might further improve time in range.

Trust in hybrid closed loop among people with diabetes: Perspectives of experienced system users

Automated closed loop systems will greatly change type 1 diabetes management; user trust will be essential for acceptance of this new technology. This qualitative study explored trust in 32 individuals following a hybrid closed loop trial. Participants described how context-, system-, and person-level factors influenced their trust in the system. Participants attempted to override the system when they lacked trust, while trusting the system decreased self-management burdens and decreased stress. Findings highlight considerations for fostering trust in closed loop systems. Systems may be able to engage users by offering varying levels of controls to match trust preferences.

One Year Clinical Experience of the First Commercial Hybrid Closed-Loop System

OBJECTIVE

In September 2016, the U.S. Food and Drug Administration approved the Medtronic 670G "hybrid" closed-loop system. In Auto Mode, this system automatically controls basal insulin delivery based on continuous glucose monitoring data but requires users to enter carbohydrates and blood glucose for boluses. To track real-world experience with this first commercial closed-loop device, we prospectively followed pediatric and adult patients starting the 670G system.

RESEARCH DESIGN AND METHODS

This was a 1-year prospective observational study of patients with type 1 diabetes starting the 670G system between May 2017 and May 2018 in clinic.

RESULTS

Of the total of 84 patients who received 670G and consented, 5 never returned for follow-up, with 79 (aged 9-61 years) providing data at 1 week and 3, 6, 9, and/or 12 months after Auto Mode initiation. For the 86% (68 out of 79) with 1-week data, 99% (67 out of 68) successfully started. By 3 months, at least 28% (22 out of 79) had stopped using Auto Mode; at 6 months, 34% (27 out of 79); at 9 months, 35% (28 out of 79); and by 12 months, 33% (26 out of 79). The primary reason for continuing Auto Mode was desire for increased time in range. Reasons for discontinuation included sensor issues in 62% (16 out of 26), problems obtaining supplies in 12% (3 out of 26), hypoglycemia fear in 12% (3 out of 26), multiple daily injection preference in 8% (2 out of 26), and sports in 8% (2 out of 26). At all visits, there was a significant correlation between hemoglobin A1c (HbA1c) and Auto Mode utilization.

CONCLUSIONS

While Auto Mode utilization correlates with improved glycemic control, a focus on usability and human factors is necessary to ensure use of Auto Mode. Alarms and sensor calibration are a major patient concern, which future technology should alleviate.

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.

Controlling the AP Controller: Controller Performance Assessment and Modification

BACKGROUND

Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time.

METHODS

A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time. Controller performance assessment and modification system also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects.

RESULTS

Using a multivariable simulation platform for T1D, the CPAMS is used to enhance the BGC regulation in people with T1D by means of automated insulin delivery with an adaptive learning predictive controller. Controller performance assessment and modification system increases the percentage of time in the target range (70-180) mg/dL by 52.3% without causing any hypoglycemia and hyperglycemia events.

CONCLUSIONS

The results demonstrate a significant improvement in the multivariable AP controller performance by using CPAMS.

Feasibility Studies of an Insulin-Only Bionic Pancreas in a Home-Use Setting

BACKGROUND

We tested the safety and performance of the "insulin-only" configuration of the bionic pancreas (BP) closed-loop blood-glucose control system in a home-use setting to assess glycemic outcomes using different static and dynamic glucose set-points.

METHOD

This is an open-label non-randomized study with three consecutive intervention periods. Participants had consecutive weeks of usual care followed by the insulin-only BP with (1) an individualized static set-point of 115 or 130 mg/dL and (2) a dynamic set-point that automatically varied within 110 to 130 mg/dL, depending on hypoglycemic risk. Human factors (HF) testing was conducted using validated surveys. The last five days of each study arm were used for data analysis.

RESULTS

Thirteen participants were enrolled with a mean age of 28 years, mean A1c of 7.2%, and mean daily insulin dose of 0.6 U/kg (0.4-1.0 U/kg). The usual care arm had an average glucose of 145 ± 20 mg/dL, which increased in the static set-point arm (159 ± 8 mg/dL, P = .004) but not in the dynamic set-point arm (154 ± 10 mg/dL, P = ns). There was no significant difference in time spent in range (70-180 mg/dL) among the three study arms. There was less time <70 mg/dL with both the static (1.8% ± 1.4%, P = .009) and dynamic set-point (2.7±1.5, P = .051) arms compared to the usual-care arm (5.5% ± 4.2%). HF testing demonstrated preliminary user satisfaction and no increased risk of diabetes burden or distress.

CONCLUSIONS

The insulin-only configuration of the BP using either static or dynamic set-points and initialized only with body weight performed similarly to other published insulin-only systems.

MiniMed 670G hybrid closed loop artificial pancreas system for the treatment of type 1 diabetes mellitus: overview of its safety and efficacy

Introduction: Automated insulin delivery for people with type 1 diabetes has been a major goal in the diabetes technology field for many years. While a fully automated system has not yet been accomplished, the MiniMed™ 670G artificial pancreas (AP) system is the first commercially available insulin pump that automates basal insulin delivery, while still requiring user input for insulin boluses. Determining the safety and efficacy of this system is essential to the development of future devices striving for more automation. Areas Covered: This review will provide an overview of how the MiniMed 670G system works including its safety and efficacy, how it compares to similar devices, and anticipated future advances in diabetes technology currently under development. Expert Opinion: The ultimate goal of advanced diabetes technologies is to reduce the burden and amount of management required of patients with diabetes. In addition to reducing patient workload, achieving better glucose control and improving hemoglobin A1c (HbA1c) values are essential for reducing the threat of diabetes-related complications further down the road. Current devices come close to reaching these goals, but understanding the unmet needs of patients with diabetes will allow future technologies to achieve these goals more quickly.

Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems

An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

Effectiveness of artificial pancreas in the non-adult population: A systematic review and network meta-analysis

OBJECTIVE

Artificial pancreas is a technology that minimizes user input by bridging continuous glucose monitoring and insulin pump treatment, and has proven safety in the adult population. The purpose of this systematic review and meta-analysis is to evaluate the efficacy of closed-loop (CL) systems in the glycemic control of non-adult type 1 diabetes patients in both a pairwise and network meta-analysis (NMA) context and investigate various parameters potentially affecting the outcome.

METHODS

Literature was systematically searched using the MEDLINE (1966-2018), Scopus (2004-2018), Cochrane Central Register of Controlled Trials (CENTRAL) (1999-2018), Clinicaltrials.gov (2008-2018) and Google Scholar (2004-2018) databases. Studies comparing the glycemic control in CL (either single- or dual-hormone) with continuous subcutaneous insulin infusion (CSII) in people with diabetes (PWD) aged <18 years old were deemed eligible. The primary outcome analysis was conducted with regard to time spent in the target glycemic range. All outcomes were evaluated in NMA in order to investigate potential between-algorithm differences. Pairwise meta-analysis and meta-regression were performed using the RevMan 5.3 and Open Meta-Analyst software. For NMA, the package pcnetmetain R 3.5.1 was used.

RESULTS

The meta-analysis was based on 25 studies with a total of 504 PWD. The CL group was associated with significantly higher percentage of time spent in the target glycemic range (Mean (SD): 67.59% (SD: 8.07%) in the target range and OL PWD spending 55.77% (SD: 11.73%), MD: -11.97%, 95% CI [-18.40, -5.54%]) and with lower percentages of time in hyperglycemia (MD: 3.01%, 95% CI [1.68, 4.34%]) and hypoglycemia (MD: 0.67%, 95% CI [0.21, 1.13%]. Mean glucose was also decreased in the CL group (MD: 0.75 mmol/L, 95% CI [0.18-1.33]). The NMA arm of the study showed that the bihormonal modality was superior to other algorithms and standard treatment in lowering mean glucose and increasing time spent in the target range. The DiAs platform was superior to PID in controlling hypoglycemia and mean glucose. Time in target range and mean glucose were unaffected by the confounding factors tested.

CONCLUSIONS

The findings of this meta-analysis suggest that artificial pancreas systems are superior to the standard sensor-augmented pump treatment of type 1 diabetes mellitus in non-adult PWD. Between-algorithm differences are also addressed, implying a superiority of the bihormonal treatment modality. Future large-scale studies are needed in the field to verify these outcomes and to determine the optimal algorithm to be used in the clinical setting.

Safety Evaluation of the MiniMed 670G System in Children 7-13 Years of Age with Type 1 Diabetes

OBJECTIVE

To evaluate the safety of in-home use of the MiniMed™ 670G system with SmartGuard™ technology in children with type 1 diabetes (T1D).

METHODS

Participants (N = 105, ages 7-13 years, mean age 10.8 ± 1.8 years) were enrolled at nine centers (eight in the United States and one in Israel) and completed a 2-week baseline run-in phase in Manual Mode followed by a 3-month study phase with Auto Mode enabled. Sensor glucose (SG), glycated hemoglobin (HbA1c), percentage of SG values across glucose ranges, and SG variability, during the run-in and study phases were compared. Participants underwent frequent sample testing with i-STAT® venous reference measurement during a hotel period (6 days/5 nights) to evaluate the system's continuous glucose monitoring performance.

RESULTS

Auto Mode was used a median of 81% of the time. From baseline to end of study, overall SG dropped by 6.9 ± 17.2 mg/dL (P < 0.001), HbA1c decreased from 7.9% ± 0.8% to 7.5% ± 0.6% (P < 0.001), percentage of time in target glucose range (70-180 mg/dL) increased from 56.2% ± 11.4% to 65.0% ± 7.7% (P < 0.001), and the SG coefficient of variation decreased from 39.6% ± 5.4% to 38.5% ± 3.8% (P = 0.009). The percentage of SG values within target glucose range was 68.2% ± 9.1% and that of i-STAT reference values was 65.6% ± 17.7%. The percentage of values within 20%/20 of the i-STAT reference was 85.2%. There were no episodes of severe hypoglycemia or diabetic ketoacidosis during the study phase.

CONCLUSION

In-home use of MiniMed 670G system Auto Mode for 3 months by children with T1D, similar to MiniMed 670G system use by adolescents and adults with T1D, was safe and associated with reduced HbA1c levels and increased time in target glucose range, compared with baseline.

Considering Plasma Insulin Concentrations in Adaptive Model Predictive Control for Artificial Pancreas Systems

Adaptive and personalized model predictive control (MPC) algorithms that explicitly consider insulin dosing constraints are necessary to prevent overdose-induced hypoglycemia in type 1 diabetes. A personalized plasma insulin concentration (PIC) estimator is integrated with adaptive models to characterize the temporal dynamics of PIC and blood glucose concentration (BGC). The dynamic profile trajectories of the PIC and BGC are used to adapt the control problems and constraints on-line to accurately reflect the concurrent metabolic state of the individuals and improve glucose regulation. The adaptive MPC algorithm, explicitly considering the PIC in the insulin dose computations, is demonstrated to effectively control BGC without any manual user input on meal information while avoiding excessive insulin administration that can cause hypoglycemia. Simulation results demonstrate the ability of the proposed approach with an average 71.14 % of time spent in the target range (BGC ϵ [70, 180]mg/dL).

Advances in Glucose Monitoring and Automated Insulin Delivery: Supplement to Endocrine Society Clinical Practice Guidelines

Endocrine Society guideline recommendations on diabetes technology in adults originate from the 2016 guideline titled “Diabetes Technology—Continuous Subcutaneous Insulin Infusion Therapy and Continuous Glucose Monitoring in Adults: An Endocrine Society Clinical Practice Guideline.” Society recommendations on diabetes technology in children are contained in the 2011 guideline titled “Continuous Glucose Monitoring: An Endocrine Society Clinical Practice Guideline.” The field of diabetes technology is a rapidly advancing one, with new devices released annually and data from clinical trials published frequently. This report describes the most recent findings since the 2011 and 2016 guidelines were written, combining summaries of new literature with the authors’ clinical experience of new devices. Although we describe what we believe to be important scientific and technological updates since these guidelines were published, we are not advancing formal additions or amendments to previously offered recommendations.

Psychosocial and Human Factors During a Trial of a Hybrid Closed Loop System for Type 1 Diabetes Management

BACKGROUND

Hybrid closed loop (HCL) systems are designed to automate insulin delivery to improve type 1 diabetes (T1D) outcomes and reduce user burden and distress. Because the systems only automate some aspects of diabetes care, psychosocial and human factors remain an important consideration in their use. Thus, we examined whether psychosocial and human factors (i.e., distress related to diabetes management, fear of hypoglycemia, and technology attitudes) would (1) change after using the system and (2) predict glycemic outcomes during the trial.

SUBJECTS AND METHODS

Fourteen adults and 15 adolescents with T1D participated in a multisite clinical trial of an investigational version of the MiniMed™ 670G system (Medtronic, Northridge, CA) over 4 to 5 days in a semisupervised outpatient setting. Users completed surveys assessing psychosocial and human factors before beginning the HCL system and at the conclusion of the study. t-Tests and regression analyses were conducted to examine whether these factors changed following trial exposure to the HCL system and predicted glycemic outcomes during the trial.

RESULTS

Diabetes management distress decreased and diabetes technology attitudes became more positive over the trial period. Fear of hypoglycemia did not change over the trial period. There was a trend toward greater pretrial management distress predicting less time in range during the trial, controlling for time in range before the trial.

CONCLUSIONS

Results suggest that this system is promising for enhancing technology attitudes and reducing management distress. Psychosocial factors, such as management distress, may negatively impact glycemic outcomes and should be a priority area for further investigation.

Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas 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.

Efficacy and safety of the artificial pancreas in the paediatric population with type 1 diabetes

BACKGROUND

Type 1 diabetes (DM1) is one of the most common chronic diseases in childhood and requires life-long insulin therapy and continuous health care support. An artificial pancreas (AP) or closed-loop system (CLS) have been developed with the aim of improving metabolic control without increasing the risk of hypoglycaemia in patients with DM1. As the impact of APs have been studied mainly in adults, the aim of this review is to evaluate the efficacy and safety of the AP in the paediatric population with DM1.

MAIN BODY

The real advantage of a CLS compared to last-generation sensor-augmented pumps is the gradual modulation of basal insulin infusion in response to glycaemic variations (towards both hyperglycaemia and hypoglycaemia), which has the aim of improving the proportion of time spent in the target glucose range and reducing the mean glucose level without increasing the risk of hypoglycaemia. Some recent studies demonstrated that also in children and adolescents an AP is able to reduce the frequency of hypoglycaemic events, an important limiting factor in reaching good metabolic control, particularly overnight. However, the advantages of the AP in reducing hyperglycaemia, increasing the time spent in the target glycaemic range and thus reducing glycated haemoglobin are less clear and require more clinical trials in the paediatric population, in particular in younger children.

CONCLUSIONS

Although the first results from bi-hormonal CLS are promising, long-term, head-to-head studies will have to prove their superiority over insulin-only approaches. More technological progress, the availability of more fast-acting insulin, further developments of algorithms that could improve glycaemic control after meals and physical activity are the most important challenges in reaching an optimal metabolic control with the use of the AP in children and adolescents.

Optimizing Hybrid Closed-Loop Therapy in Adolescents and Emerging Adults Using the MiniMed 670G System

OBJECTIVE

The MiniMed 670G System is the first commercial hybrid closed-loop (HCL) system for management of type 1 diabetes. Using data from adolescent and young adult participants, we compared insulin delivery patterns and time-in-range metrics in HCL (Auto Mode) and open loop (OL). System alerts, usage profiles, and operational parameters were examined to provide suggestions for optimal clinical use of the system.

RESEARCH DESIGN AND METHODS

Data from 31 adolescent and young adult participants (14-26 years old) at three clinical sites in the 670G pivotal trial were analyzed. Participants had a 2-week run-in period in OL, followed by a 3-month in-home study phase with HCL functionality enabled. Data were compared between baseline OL and HCL use after 1 week, 1 month, 2 months, and 3 months.

RESULTS

Carbohydrate-to-insulin (C-to-I) ratios were more aggressive for all meals with HCL compared with baseline OL. Total daily insulin dose and basal-to-bolus ratio did not change during the trial. Time in range increased 14% with use of Auto Mode after 3 months (P < 0.001), and HbA_1c decreased 0.75%. Auto Mode exits were primarily due to sensor/insulin delivery alerts and hyperglycemia. The percentage of time in Auto Mode gradually declined from 87%, with a final use rate of 72% (-15%).

CONCLUSIONS

In transitioning young patients to the 670G system, providers should anticipate immediate C-to-I ratio adjustments while also assessing active insulin time. Users should anticipate occasional Auto Mode exits, which can be reduced by following system instructions and reliably bolusing for meals. Unique 670G system functionality requires ongoing clinical guidance and education from providers.

Sleep in Type 1 Diabetes: Implications for Glycemic Control and Diabetes Management

PURPOSE OF REVIEW

To highlight recent findings from studies of sleep in type 1 diabetes (T1D), with a focus on the role of sleep in self-management, the cognitive and psychosocial outcomes related to sleep disturbances, and factors associated with sleep disturbances specific to T1D.

RECENT FINDINGS

People with T1D experience higher rates of sleep disturbances than people without diabetes, and these disturbances have negative implications for glycemic control and diabetes management, as well as psychosocial and cognitive outcomes. Inconsistent sleep timing (bedtime and wake time) has emerged as a potential target for interventions, as variability in sleep timing has been linked with poorer glycemic control and adherence to treatment. Sleep-promoting interventions and new diabetes technology have the potential to improve sleep in people with T1D. Sleep is increasingly considered a critical factor in diabetes management, but more multi-method and longitudinal research is needed. We emphasize the importance of sufficient and consistent sleep for people with T1D, and the need for providers to routinely assess sleep among patients with T1D.

Expectations and Attitudes of Individuals With Type 1 Diabetes After Using a Hybrid Closed Loop System

Purpose The first hybrid closed loop (HCL) system, which automates insulin delivery but requires user inputs, was approved for treatment of type 1 diabetes (T1D) by the US Food and Drug Administration in September 2016. The purpose of this study was to explore the benefits, expectations, and attitudes of individuals with T1D following a clinical trial of an HCL system. Methods Thirty-two individuals with T1D (17 adults, 15 adolescents) participated in focus groups after 4 to 5 days of system use. Content analysis generated themes regarding perceived benefits, hassles, and limitations. Results Some participants felt misled by terms such as "closed loop" and "artificial pancreas," which seemed to imply a more "hands-off" experience. Perceived benefits were improved glycemic control, anticipated reduction of long-term complications, better quality of life, and reduced mental burden of diabetes. Hassles and limitations included unexpected tasks for the user, difficulties wearing the system, concerns about controlling highs, and being reminded of diabetes. Conclusion Users are willing to accept some hassles and limitations if they also perceive health and quality-of-life benefits beyond current self-management. It is important for clinicians to provide a balanced view of positives and negatives to help manage expectations.

Automated blood glucose control in type 1 diabetes: A review of progress and challenges

Since the 2000s, research teams worldwide have been working to develop closed-loop (CL) systems able to automatically control blood glucose (BG) levels in patients with type 1 diabetes. This emerging technology is known as artificial pancreas (AP), and its first commercial version just arrived in the market. The main objective of this paper is to present an extensive review of the clinical trials conducted since 2011, which tested various implementations of the AP for different durations under varying conditions. A comprehensive table that contains key information from the selected publications is provided, and the main challenges in AP development and the mitigation strategies used are discussed. The development timelines for different AP systems are also included, highlighting the main evolutions over the clinical trials for each system.

Twelve-Week 24/7 Ambulatory Artificial Pancreas With Weekly Adaptation of Insulin Delivery Settings: Effect on Hemoglobin A1c and Hypoglycemia

OBJECTIVE

Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks.

RESEARCH DESIGN AND METHODS

Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A_1c (HbA_1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials.

RESULTS

Twenty-nine patients completed the trial. HbA_1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (-0.3, 95% CI -0.5 to -0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (-3.1, 95% CI -4.1 to -2.1, P < 0.001) and overnight from 4.1 to 1.1% (-3.1, 95% CI -4.2 to -1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events.

CONCLUSIONS

Use of our novel adaptive AP yielded significant reductions in HbA_1c and hypoglycemia.

In-home nighttime predictive low glucose suspend experience in children and adults with type 1 diabetes

Overnight predictive low glucose suspend (PLGS) reduces hypoglycemia across all ages; however, there are no reports on behavior or experience differences across age groups, especially in pediatrics. As run-in for a subsequent randomized clinical trial (RCT), 127 subjects (50% male) ages 4-45 yr utilized the experimental PLGS system nightly for 5-10 nights (PLGS active phase). We analyzed the number of blood glucose (BG) checks and boluses given per age group. During the subsequent 42 night RCT phase, we analyzed sensor use, skin reactions, errors, and reasons why the experimental system was not used. In 821 nights of active PLGS, subjects ages 4-6 yr (and their parents) tested BG levels 75% of nights compared with 65% of nights (7-10 yr), 53% of nights (11-14 yr), 33% of nights (15-25 yr), and 28% of nights (26-45 yr), respectively (p < 0.001). Likewise, youngest subjects (and parents) administered insulin boluses 56% of nights during active PLGS use compared with 48%, 33%, 20%, and 25%, respectively (p < 0.001). This was unrelated to study requirements. During the RCT phase, subjects 4-6 yr experienced more frequent and severe skin reactions (p = 0.02), while adult subjects (26-45 yr) wore individual sensors a median of 26 h longer than the youngest subjects (p < 0.001). Technical problems with the sensor (errors, miscalibrations, etc.), traveling, and BG levels >270 at bedtime (study requirement) were primary contributors to non-system use. Understanding the different use patterns and challenges in pediatrics and adolescence is needed to direct patient education to optimize use of PLGS and future artificial pancreas systems.

Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes

Continuous Glucose Monitoring (CGM) has been demonstrated to be clinically valuable, reducing risks of hypoglycemia and hyperglycemia, glycemic variability (GV), and improving patient quality of life for a wide range of patient populations and clinical indications. Use of CGM can help reduce HbA1c and mean glucose. One CGM device, with accuracy (%MARD) of approximately 10%, has recently been approved for self-adjustment of insulin dosages (nonadjuvant use) and approved for reimbursement for therapeutic use in the United States. CGM had previously been used off-label for that purpose. CGM has been demonstrated to be clinically useful in both type 1 and type 2 diabetes for patients receiving a wide variety of treatment regimens. CGM is beneficial for people using either multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). CGM is used both in retrospective (professional, masked) and real-time (personal, unmasked) modes: both approaches can be beneficial. When CGM is used to suspend insulin infusion when hypoglycemia is detected until glucose returns to a safe level (low-glucose suspend), there are benefits beyond sensor-augmented pump (SAP), with greater reduction in the risk of hypoglycemia. Predictive low-glucose suspend provides greater benefits in this regard. Closed-loop control with insulin provides further improvement in quality of glycemic control. A hybrid closed-loop system has recently been approved by the U.S. FDA. Closed-loop control using both insulin and glucagon can reduce risk of hypoglycemia even more. CGM facilitates rigorous evaluation of new forms of therapy, characterizing pharmacodynamics, assessing frequency and severity of hypo- and hyperglycemia, and characterizing several aspects of GV.

Adaptive Deep Brain Stimulation for Movement Disorders: The Long Road to Clinical Therapy

Continuous high-frequency DBS is an established treatment for essential tremor and Parkinson's disease. Current developments focus on trying to widen the therapeutic window of DBS. Adaptive DBS (aDBS), where stimulation is dynamically controlled by feedback from biomarkers of pathological brain circuit activity, is one such development. Relevant biomarkers may be central, such as local field potential activity, or peripheral, such as inertial tremor data. Moreover, stimulation may be directed by the amplitude or the phase (timing) of the biomarker signal. In this review, we evaluate existing aDBS studies as proof-of-principle, discuss their limitations, most of which stem from their acute nature, and propose what is needed to take aDBS into a chronic setting. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems

OBJECTIVE

The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus. In this paper, we aim to lower the energy consumption of the AP by reducing controller updates, that is, the number of times the decision-making algorithm is invoked to compute an appropriate insulin dose.

METHODS

Physiological insights into glucose management are leveraged to design an event-triggered model predictive controller (MPC) that operates efficiently, without compromising patient safety. The proposed event-triggered MPC is deployed on a wearable platform. Its robustness to latent hypoglycemia, model mismatch, and meal misinformation is tested, with and without meal announcement, on the full version of the US-FDA accepted UVA/Padova metabolic simulator.

RESULTS

The event-based controller remains on for 18 h of 41 h in closed loop with unannounced meals, while maintaining glucose in 70-180 mg/dL for 25 h, compared to 27 h for a standard MPC controller. With meal announcement, the time in 70-180 mg/dL is almost identical, with the controller operating a mere 25.88% of the time in comparison with a standard MPC.

CONCLUSION

A novel control architecture for AP systems enables safe glycemic regulation with reduced processor computations.

SIGNIFICANCE

Our proposed framework integrated seamlessly with a wide variety of popular MPC variants reported in AP research, customizes tradeoff between glycemic regulation and efficacy according to prior design specifications, and eliminates judicious prior selection of controller sampling times.

Glucose Outcomes with the In-Home Use of a Hybrid Closed-Loop Insulin Delivery System in Adolescents and Adults with Type 1 Diabetes

BACKGROUND

The safety and effectiveness of the in-home use of a hybrid closed-loop (HCL) system that automatically increases, decreases, and suspends insulin delivery in response to continuous glucose monitoring were investigated.

METHODS

Adolescents (n = 30, ages 14-21 years) and adults (n = 94, ages 22-75 years) with type 1 diabetes participated in a multicenter (nine sites in the United States, one site in Israel) pivotal trial. The Medtronic MiniMed^® 670G system was used during a 2-week run-in phase without HCL control, or Auto Mode, enabled (Manual Mode) and, thereafter, with Auto Mode enabled during a 3-month study phase. A supervised hotel stay (6 days/5 nights) that included a 24-h frequent blood sample testing with a reference measurement (i-STAT) occurred during the study phase.

RESULTS

Adolescents (mean ± standard deviation [SD] 16.5 ± 2.29 years of age and 7.7 ± 4.15 years of diabetes) used the system for a median 75.8% (interquartile range [IQR] 68.0%-88.4%) of the time (2977 patient-days). Adults (mean ± SD 44.6 ± 12.79 years of age and 26.4 ± 12.43 years of diabetes) used the system for a median 88.0% (IQR 77.6%-92.7%) of the time (9412 patient-days). From baseline run-in to the end of study phase, adolescent and adult HbA_1c levels decreased from 7.7% ± 0.8% to 7.1% ± 0.6% (P < 0.001) and from 7.3% ± 0.9% to 6.8% ± 0.6% (P < 0.001, Wilcoxon signed-rank test), respectively. The proportion of overall in-target (71-180 mg/dL) sensor glucose (SG) values increased from 60.4% ± 10.9% to 67.2% ± 8.2% (P < 0.001) in adolescents and from 68.8% ± 11.9% to 73.8% ± 8.4% (P < 0.001) in adults. During the hotel stay, the proportion of in-target i-STAT^® blood glucose values was 67.4% ± 27.7% compared to SG values of 72.0% ± 11.6% for adolescents and 74.2% ± 17.5% compared to 76.9% ± 8.3% for adults. There were no severe hypoglycemic or diabetic ketoacidosis events in either cohort.

CONCLUSIONS

HCL therapy was safe during in-home use by adolescents and adults and the study phase demonstrated increased time in target, and reductions in HbA_1c, hyperglycemia and hypoglycemia, compared to baseline.

TRIAL REGISTRATION

Clinicaltrials.gov identifier: NCT02463097.