Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting

Feasibility and Preliminary Safety of Smartphone-Based Automated Insulin Delivery in Adolescents and Children With Type 1 Diabetes

BACKGROUND

A smartphone-based automated insulin delivery (AID) controller device can facilitate use of interoperable components and acceptance in adolescents and children.

METHODS

Pediatric participants (N = 20, 8F) with type 1 diabetes were enrolled in three sequential age-based cohorts: adolescents (12-<18 years, n = 8, 5F), school-age (8-<12 years, n = 7, 2F), and young children (2-<8 years, n = 5, 1F). Participants used the interoperable artificial pancreas system (iAPS) and zone model predictive control (MPC) on an unlocked smartphone for 48 hours, consumed unrestricted meals of their choice, and engaged in various unannounced exercises. Primary outcomes and stopping criteria were defined using fingerstick blood glucose (BG) data; secondary outcomes compared continuous glucose monitoring (CGM) data with preceding sensor augmented pump (SAP) therapy.

RESULTS

During AID, there was no more than one BG <50 mg/dL except in one young child participant; no instance of more than two episodes of BG ≥300 mg/dL lasting longer than 2 hours; and no adverse events. Despite large meals (total of 404.9 grams of carbs) and unannounced exercise (total of 182 minutes), overall CGM percent time in range (TIR) of 70 to 180 mg/dL during AID was statistically similar to SAP (63.5% vs 57.3%, respectively, P = .145). Overnight glucose standard deviation was 43 mg/dL (vs SAP 57.9 mg/dL, P = .009) and coefficient of variation was 25.7% (vs SAP 34.9%, P < .001). The percent time in closed-loop mode and connected to the CGM was 92.7% and 99.6%, respectively. Surveys indicated that participants and parents/guardians were satisfied with the system.

CONCLUSIONS

The smartphone-based AID was feasible and safe in sequentially younger cohorts of adolescents and children.

CLINICALTRIALS.GOV

NCT04255381 (https://clinicaltrials.gov/ct2/show/NCT04255381).

Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities

BACKGROUND

Hybrid closed-loop control of glucose levels in people with type 1 diabetes mellitus (T1D) is limited by the requirements on users to manually announce physical activity (PA) and meals to the artificial pancreas system. Multivariable automated insulin delivery (mvAID) systems that can handle unannounced PAs and meals without any manual announcements by the user can improve glycemic control by modulating insulin dosing in response to the occurrence and intensity of spontaneous physical activities.

METHODS

An mvAID system is developed to supplement the glucose measurements with additional physiological signals from a wristband device, with the signals analyzed using artificial intelligence algorithms to automatically detect the occurrence of PA and estimate its intensity. This additional information gained from the physiological signals enables more proactive insulin dosing adjustments in response to both planned exercise and spontaneous unanticipated physical activities.

RESULTS

In silico studies of the mvAID illustrate the safety and efficacy of the system. The mvAID is translated to pilot clinical studies to assess its performance, and the clinical experiments demonstrate an increased time in range and reduced risk of hypoglycemia following unannounced PA and meals.

CONCLUSIONS

The mvAID systems can increase the safety and efficacy of insulin delivery in the presence of unannounced physical activities and meals, leading to improved lives and less burden on people with T1D.

MiniMed 780G™ advanced hybrid closed-loop system performance in Egyptian patients with type 1 diabetes across different age groups: evidence from real-world users

BACKGROUND

Advanced hybrid closed loop (AHCL) system provides both automated basal rate and correction boluses to keep glycemic values in a target range.

OBJECTIVES

To evaluate the real-world performance of the MiniMed™ 780G system among different age groups of Egyptian patients with type 1diabetes.

METHODS

One-hundred seven AHCL system users aged from 3 to 71 years were enrolled. Data uploaded by patients were aggregated and analyzed. The mean glucose management indicator (GMI), percentage of time spent within glycemic ranges (TIR), time below range (TBR) and time above range (TAR) were determined.

RESULTS

Six months after initiating Auto Mode, patients spent a mean of 85.31 ± 22.04% of the time in Auto Mode (SmartGuard) and achieved a mean GMI of 6.95 ± 0.58% compared with 7.9 ± 2.1% before AHCL initiation (p < 0.001). TIR 70-180 mg/dL was increased post-AHCL initiation from 63.48 ± 10.14% to 81.54 ± 8.43% (p < 0.001) while TAR 180-250 mg/dL, TAR > 250 mg/dL, TBR < 70 mg/dL and TBR < 54 mg/dL were significantly decreased (p < 0.001). After initiating AHCL, TIR was greater in children and adults compared with adolescents (82.29 ± 7.22% and 83.86 ± 9.24% versus 78.4 ± 7.34%, respectively; p < 0.05). The total daily dose of insulin was increased in all age groups primarily due to increased system-initiated insulin delivery including auto correction boluses and basal insulin.

CONCLUSIONS

MiniMed™ 780G system users across different age groups achieved international consensus-recommended glycemic control with no serious adverse effects even in challenging age group as children and adolescents.

Assessment of Meal Anticipation for Improving Fully Automated Insulin Delivery in Adults With Type 1 Diabetes

OBJECTIVE

Meals are a consistent challenge to glycemic control in type 1 diabetes (T1D). Our objective was to assess the glycemic impact of meal anticipation within a fully automated insulin delivery (AID) system among adults with T1D.

RESEARCH DESIGN AND METHODS

We report the results of a randomized crossover clinical trial comparing three modalities of AID systems: hybrid closed loop (HCL), full closed loop (FCL), and full closed loop with meal anticipation (FCL+). Modalities were tested during three supervised 24-h admissions, where breakfast, lunch, and dinner were consumed per participant's home schedule, at a fixed time, and with a 1.5-h delay, respectively. Primary outcome was the percent time in range 70-180 mg/dL (TIR) during the breakfast postprandial period for FCL+ versus FCL.

RESULTS

Thirty-five adults with T1D (age 44.5 ± 15.4 years; HbA1c 6.7 ± 0.9%; n = 23 women and n = 12 men) were randomly assigned. TIR for the 5-h period after breakfast was 75 ± 23%, 58 ± 21%, and 63 ± 19% for HCL, FCL, and FCL+, respectively, with no significant difference between FCL+ and FCL. For the 2 h before dinner, time below range (TBR) was similar for FCL and FCL+. For the 5-h period after dinner, TIR was similar for FCL+ and FCL (71 ± 34% vs. 72 ± 29%; P = 1.0), whereas TBR was reduced in FCL+ (median 0% [0-0%] vs. 0% [0-0.8%]; P = 0.03). Overall, 24-h control for HCL, FCL, and FCL+ was 86 ± 10%, 77 ± 11%, and 77 ± 12%, respectively.

CONCLUSIONS

Although postprandial control remained optimal with hybrid AID, both fully AID solutions offered overall TIR >70% with similar or lower exposure to hypoglycemia. Anticipation did not significantly improve postprandial control in AID systems but also did not increase hypoglycemic risk when meals were delayed.

Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?

Research in the treatment of type 1 diabetes has been addressed into two main areas: the development of "intelligent insulins" capable of auto-regulating their own levels according to glucose concentrations, or the exploitation of artificial intelligence (AI) and its learning capacity, to provide decision support systems to improve automated insulin therapy. This review aims to provide a synthetic overview of the current state of these two research areas, providing an outline of the latest development in the search for "intelligent insulins," and the results of new and promising advances in the use of artificial intelligence to regulate automated insulin infusion and glucose control. The future of insulin treatment in type 1 diabetes appears promising with AI, with research nearly reaching the possibility of finally having a "closed-loop" artificial pancreas.

Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Fully Closed-Loop Insulin Delivery in Patients Undergoing Pancreatic Surgery

The central role of pancreas in glucose regulation imposes high demands on perioperative glucose management in patients undergoing pancreatic surgery. In a post hoc subgroup analysis of a randomized controlled trial, we evaluated the perioperative use of subcutaneous (SC) fully closed-loop (FCL; CamAPS HX) versus usual care (UC) insulin therapy in patients undergoing partial or total pancreatic resection. Glucose control was compared using continuous glucose monitoring (CGM) metrics (% time with CGM values between 5.6 and 10.0 mmol/L and more). Over the time of hospitalization, FCL resulted in better glucose control than UC with more time spent in the target range 5.6-10.0 mmol/L (mean [standard deviation] % time in target 77.7% ± 4.6% and 41.1% ± 19.5% in FCL vs. UC subjects, respectively; mean difference 36.6% [95% confidence interval 18.5-54.8]), without increasing the risk of hypoglycemia. Findings suggest that an adaptive SC FCL approach effectively accommodated the highly variable insulin needs in patients undergoing pancreatic surgery. Clinical trials registration: ClinicalTrials.gov, NCT04361799.

Diabetes Technology Meeting 2021

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

First Outpatient Clinical Trial of a Full Closed-Loop Artificial Pancreas System in South America

BACKGROUND

The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented.

METHOD

Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165.

RESULTS

The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% [16.3%] OL vs 48.0% [15.4%] CL), time in range (46.9% [15.6%] OL vs 50.9% [14.4%] CL), and mean glucose (188.9 [25.5] mg/dl OL vs 186.2 [24.7] mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL.

CONCLUSION

The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.

Outcomes of hybrid closed-loop insulin delivery activated 24/7 versus evening and night in free-living prepubertal children with type 1 diabetes: A multicentre, randomized clinical trial

AIM

To assess the safety and efficacy of hybrid closed-loop (HCL) insulin delivery 24/7 versus only evening and night (E/N), and on extended 24/7 use, in free-living children with type 1 diabetes.

MATERIALS AND METHODS

Prepubertal children (n = 122; 49 females/73 males; age, 8.6 ± 1.6 years; diabetes duration, 5.2 ± 2.3 years; insulin pump use, 4.6 ± 2.5 years; HbA1c 7.7% ± 0.7%/61 ± 5 mmol/mol) from four centres were randomized for 24/7 versus E/N activation of the Tandem Control-IQ system for 18 weeks. Afterwards, all children used the activated system 24/7 for 18 more weeks. The primary outcome was the percentage of time spent in the 70-180 mg/dL glucose range (TIR).

RESULTS

HCL was active 94.1% and 51.1% of the time in the 24/7 and E/N modes, respectively. TIR from baseline increased more in the 24/7 versus the E/N mode (52.9% ± 9.5% to 67.3% ± 5.6% [+14.4%, 95% CI 12.4%-16.7%] vs. 55.1% ± 10.8% to 64.7% ± 7.0% [+9.6%, 95% CI 7.4%-11.6%]; P = .001). Mean percentage time below range was similarly reduced, from 4.2% and 4.6% to 2.7%, and the mean percentage time above range decreased more in the 24/7 mode (41.9% to 30.0% [-11.9%, 95% CI 9.7%-14.6%] vs. 39.8% to 32.6% [-7.2%, 95% CI 5.0%-9.9%]; P = .007). TIR increased through the whole range of baseline levels and always more with 24/7 use. The results were maintained during the extension phase in those initially on 24/7 use and improved in those with initial E/N use up to those with 24/7 use. Neither ketoacidosis nor severe hypoglycaemia occurred.

CONCLUSIONS

The current study shows the safety and efficacy of the Tandem Control-IQ system in free-living children with type 1 diabetes for both E/N and 24/7 use; 24/7 use shows better outcomes, sustained for up to 36 weeks with no safety issues.

Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System

INTRODUCTION

Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control.

METHODS

A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module.

RESULTS

Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%).

CONCLUSIONS

The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.

Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions

Closed-loop (artificial pancreas) systems for automated insulin delivery have been likened to the holy grail of diabetes management. The first iterations of glucose-responsive insulin delivery were pioneered in the 1960s and 1970s, with the development of systems that used venous glucose measurements to dictate intravenous infusions of insulin and dextrose in order to maintain normoglycemia. Only recently have these bulky, bedside technologies progressed to miniaturized, wearable devices. These modern closed-loop systems use interstitial glucose sensing, subcutaneous insulin pumps, and increasingly sophisticated algorithms. As the number of commercially available hybrid closed-loop systems has grown, so too has the evidence supporting their efficacy. Future challenges in closed-loop technology include the development of fully closed-loop systems that do not require user input for meal announcements or carbohydrate counting. Another evolving avenue in research is the addition of glucagon to mitigate the risk of hypoglycemia and allow more aggressive insulin dosing.

The automated pancreas: A review of technologies and clinical practice

Insulin pumps and glucose sensors are effective in improving diabetes therapy and reducing acute complications. The combination of both devices using an algorithm-driven interoperable controller makes automated insulin delivery (AID) systems possible. Many AID systems have been tested in clinical trials and have proven safety and effectiveness. However, currently, none of these systems are available for routine use in children younger than 6 years in Europe. For continued use, both users and prescribers must have sound knowledge of the features of the individual AID systems. Presently, all systems require various user interactions (e.g. meal announcements) because fully automated systems are not yet developed. Open-source systems are non-regulated variants to circumvent existing regulatory conditions. There are risks here for both users and prescribers. To evaluate AID therapy, the metric data of the glucose sensors, 'time in target range' and 'glucose management index', are novel recognized and suitable parameters allowing a consultation based on real glucose and insulin pump download data from the daily life of people with diabetes. Read out via cloud-based software or automatic download of such individual treatment data provides the ideal technical basis for shared decision-making through telemedicine, which must be further evaluated for general use.

Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system

BACKGROUND AND OBJECTIVE

Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals.

METHODS

The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller.

RESULTS

Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight).

CONCLUSION

The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

Automated Insulin Delivery (AID) are systems developed for daily use by people with type 1 diabetes (T1D). To ensure the safety of users, it is essential to consider how the human factor affects the performance and safety of these devices. While there are numerous publications on hardware-related failures of AID systems, there are few studies on the human component of the system. From a control point of view, people with T1D using AID systems are at the same time the plant to be controlled and the plant operator. Therefore, users may induce faults in the controller, sensors, actuators, and the plant itself. Strategies to cope with the human interaction in AID systems are needed for further development of the technology. In this paper, we present an analysis of potential faults introduced by AID users when the system is under normal operation. This is followed by a review of current fault tolerant control (FTC) approaches to identify missing areas of research. The paper concludes with a discussion on future directions for the new generation of FTC AID systems.

[Individualization of diabetes treatment by automated insulin delivery]

Insulinpumpen und Glucosesensoren haben sich in Registerdaten als effektiv in der Verbesserung der Diabetestherapie und Reduktion akuter Komplikationen gezeigt. In der pädiatrischen Diabetologie ist die Nutzung mindestens eines technischen Geräts Standard. Durch die Kombination beider Systeme ergibt sich Möglichkeit der automatischen Insulinabgabe („automated insulin delivery“, AID).

Viele AID-Systeme sind in klinischen Studien getestet und haben sich als sicher und effektiv erwiesen. Die Versorgungsituation in Deutschland erlaubt es derzeit nur, Mitgliedern der gesetzlichen Krankenversicherungen ein bestimmtes System zu verordnen; dieses ist für Kinder, die jünger als 7 Jahre sind, nicht geeignet. Gründe liegen in gesetzlichen Hürden und mangelnder Zertifizierung durch die Hersteller. Die CE-Zertifikate können Probleme bei der Insulinverordnung mit sich bringen. „Open-source“-Systeme sind Varianten, mit denen bestehende Regularien umgangen werden können. Daraus ergeben sich sowohl für Nutzer wie auch für Verordner Risiken.

Die dauerhafte Nutzung setzt sowohl auf Anwender- als auch auf Behandlerseite die fundierte Kenntnis der Eigenschaften der einzelnen AID-Systeme voraus. Eine vollständige Automatisierung funktioniert noch nicht. Zur Evaluation der AID-Therapie sind die metrischen Daten der Glucosesensoren, die „Zeit im Zielbereich“ und der „Glucose Management Indicator“ anerkannte und geeignete Parameter, da sie eine Beratung auf Basis der reellen Daten aus dem Alltag der Menschen mit Diabetes zulassen.

Da alle Glucosesensoren über Cloud-basierte Software ausgelesen werden oder die Daten automatisch aus einem telefonverbundenen Empfangsgerät beziehen, ist die ideale technische Grundlage für eine telemedizinische Betreuung geschaffen, die noch der Ausgestaltung bedarf.

AID(„automated insulin delivery“)-Systeme in der Diabetologie

Insulinpumpen und Glukosesensoren können laut Registerdaten die Diabetestherapie verbessern sowie die Rate akuter Komplikationen reduzieren. In der pädiatrischen Diabetologie ist daher die Nutzung mindestens eines dieser technischen Geräte Standard. Deren Kombination macht Systeme zur automatischen Insulinabgabe („automated insulin delivery“ [AID]) möglich. Viele AID-Systeme wurden in klinischen Studien getestet und erwiesen sich als sicher und effektiv. Die Versorgungsituation in Deutschland jedoch lässt derzeit nur ein System als Verordnung bei Versicherten der gesetzlichen Krankenversicherungen zu, und Kinder unter 7 Jahren können damit derzeit nicht versorgt werden. Gründe hierfür sind gesetzliche Hürden und die mangelnde Zertifizierung durch die Hersteller. Die CE-Zertifikate können zudem zu Problemen bei der Insulinverordnung führen. Open-Source-Systeme sind nicht geprüfte Varianten, um bestehende regulatorische Verhältnisse zu umgehen. Deren Anwendung geht mit Risiken sowohl für Nutzer als auch Verordner einher. Für ihren dauerhaften Einsatz müssen sowohl Anwender als auch Behandler über fundierte Kenntnisse der Eigenschaften der einzelnen AID-Systeme verfügen. Zur Evaluation der AID-Therapie sind die metrischen Daten der Glukosesensoren, die „time in range“ und der Glukosemanagementindex die anerkannten und geeigneten Parameter, da sie eine Beratung auf Basis der reellen Werte aus dem Alltag der Menschen mit Diabetes zulassen. Da alle Glukosesensoren über Cloud-basierte Software ausgelesen werden oder die Daten direkt automatisch übermitteln, ist hiermit die ideale technische Grundlage für eine telemedizinische Betreuung geschaffen, die noch der Ausgestaltung bedarf.

Initiating hybrid closed loop: A program evaluation of an educator-led Control-IQ follow-up at a large pediatric clinic

BACKGROUND

Control-IQ (Tandem Diabetes) is a hybrid closed-loop (HCL) system that users self-initiate after completing online training. Best practices for clinical follow-up are not known. Our quality improvement objective was to evaluate the usefulness of an educator-led follow-up program for new HCL users in a type 1 diabetes pediatric clinic.

METHODS

We implemented an ''HCLCheck-in'' program, first determining when users started HCL, then having diabetes educators contact them for a follow-up call 2-weeks after start. Educators used a Clinical Tool to inform insulin dose and behavior recommendations, and used four benchmarks to determine need for further follow-up: ≥71% HCL use, ≥71% CGM use, ≥60% Time-in-Range (TIR, 70-180 mg/dL), <5% below 70 mg/dL. Family and educator satisfaction were surveyed.

RESULTS

One-hundred-twenty-three youth [mean age 13.6 ± 3.7 y, 53.7% female, mean HbA1c 7.6 ± 1.4% (60 mmol/mol)] completed an HCLCheck-in call a median (IQR) of 18(15, 21) days post-HCL start. 74 users (60%) surpassed benchmarks with 94% HCL use and 71% TIR. Of the 49 who did not, 16 completed a second call, and improved median TIR 12.5% (p = 0.03). HCL users reported high satisfaction with the program overall [median 10 (9, 10) out of 10]. Educators spent a median of 45 (32,70) minutes per user and rated satisfaction with the program as 8 (7,9.5) and the Tool as 9 (9, 10).

CONCLUSION

Our HCLCheck-in program received high satisfaction ratings and resulted in improved TIR for those initially not meeting benchmarks, suggesting users may benefit from early follow-up. Similar programs may be beneficial for other new technologies.

Current treatment options and challenges in patients with Type 1 diabetes: Pharmacological, technical advances and future perspectives

Type 1 diabetes mellitus imposes a significant burden of complications and mortality, despite important advances in treatment: subjects affected by this disease have also a worse quality of life-related to disease management. To overcome these challenges, different new approaches have been proposed, such as new insulin formulations or innovative devices. The introduction of insulin pumps allows a more physiological insulin administration with a reduction of HbA1c level and hypoglycemic risk. New continuous glucose monitoring systems with better accuracy have allowed, not only better glucose control, but also the improvement of the quality of life. Integration of these devices with control algorithms brought to the creation of the first artificial pancreas, able to independently gain metabolic control without the risk of hypo- and hyperglycemic crisis. This approach has revolutionized the management of diabetes both in terms of quality of life and glucose control. However, complete independence from exogenous insulin will be obtained only by biological approaches that foresee the replacement of functional beta cells obtained from stem cells: this will be a major challenge but the biggest hope for the subjects with type 1 diabetes. In this review, we will outline the current scenario of innovative diabetes management both from a technological and biological point of view, and we will also forecast some cutting-edge approaches to reduce the challenges that hamper the definitive cure of diabetes.

Time in Range for Closed-Loop Systems versus Standard of Care during Physical Exercise in People with Type 1 Diabetes: A Systematic Review and Meta-Analysis

The aim of this systematic review and meta-analysis was to compare time in range (TIR) (70–180 mg/dL (3.9–10.0 mmol/L)) between fully closed-loop systems (CLS) and standard of care (including hybrid systems) during physical exercise in people with type 1 diabetes (T1D). A systematic literature search was conducted in EMBASE, PubMed, Cochrane Central Register of Controlled Trials, and ISI Web of Science from January 1950 until January 2020. Randomized controlled trials including studies with different CLS were compared against standard of care in people with T1D. The meta-analysis was performed using the random effects model and restricted maximum likelihood estimation method. Six randomized controlled trials involving 153 participants with T1D of all age groups were included. Due to crossover test designs, studies were included repeatedly (a–d) if CLS or physical exercise interventions were different. Applying this methodology increased the comparisons to a total number of 266 participants. TIR was higher with an absolute mean difference (AMD) of 6.18%, 95% CI: 1.99 to 10.38% in favor of CLS. In a subgroup analysis, the AMD was 9.46%, 95% CI: 2.48% to 16.45% in children and adolescents while the AMD for adults was 1.07% 95% CI: −0.81% to 2.96% in favor of CLS. In this systematic review and meta-analysis CLS moderately improved TIR in comparison to standard of care during physical exercise in people with T1D. This effect was particularly pronounced for children and adolescents showing that the use of CLS improved TIR significantly compared to standard of care.

Full closed loop open-source algorithm performance comparison in pigs with diabetes

Understanding how automated insulin delivery (AID) algorithm features impact glucose control under full closed loop delivery represents a critical step toward reducing patient burden by eliminating the need for carbohydrate entries at mealtimes. Here, we use a pig model of diabetes to compare AndroidAPS and Loop open‐source AID systems without meal announcements. Overall time‐in‐range (70–180 mg/dl) for AndroidAPS was 58% ± 5%, while time‐in‐range for Loop was 35% ± 5%. The effect of the algorithms on time‐in‐range differed between meals and overnight. During the overnight monitoring period, pigs had an average time‐in‐range of 90% ± 7% when on AndroidAPS compared to 22% ± 8% on Loop. Time‐in‐hypoglycemia also differed significantly during the lunch meal, whereby pigs running AndroidAPS spent an average of 1.4% (+0.4/−0.8)% in hypoglycemia compared to 10% (+3/−6)% for those using Loop. As algorithm design for closed loop systems continues to develop, the strategies employed in the OpenAPS algorithm (known as oref1) as implemented in AndroidAPS for unannounced meals may result in a better overall control for full closed loop systems.

Add-on therapy with dapagliflozin under full closed loop control improves time in range in adolescents and young adults with type 1 diabetes: The DAPADream study

AIM

To investigate the effect of the sodium-glucose co-transporter-2 inhibitor dapagliflozin on glucose levels overnight and during the following day after two unannounced meals under full closed loop (FCL) conditions.

MATERIALS AND METHODS

For this single-centre, double-blind, randomized, placebo-controlled, cross-over trial, non-obese persons with type 1 diabetes (T1D) were studied twice (10 mg dapagliflozin bid vs. placebo) for 24 hours with two unannounced mixed meal tests 6 hours apart under FCL conditions. Primary outcome was sensor glucose time in range (TIR; 3.9-10 mmol/L). For safety evaluation, ß-hydroxybutyrate (BHB), glucagon, insulin and gastric inhibitory polypeptide were measured.

RESULTS

Fifteen adolescents (aged 15.4 ± 1.6 years, diabetes duration 10.0 ± 3.4 years, HbA1c 8.4% ± 0.9% [67.7 ± 10.1 mmol/mol]) and 15 young adults (aged 18.7 ± 0.8 years; diabetes duration 12.5 ± 3.6 years; HbA1c 8.3% ± 0.9% [68.5 ± 11.2 mmol/mol]) completed the trial. TIR was significantly higher in the intervention group compared with placebo (68% ± 6% vs. 50% ± 13%; P < .001); nocturnal glucose was significantly lower with dapagliflozin (6.2 ± 0.7 vs. 7.3 ± 1.7 mmol/L; P = .003) without an increase in time at less than 3.9 mmol/L (3.3% ± 6.0% vs 3.1% ± 5.2%; P = .75). Urinary glucose excretion was increased 3-fold using dapagliflozin (149 ± 42 vs. 49 ± 23 g/24 hours) with a total insulin reduction of 22% (39.7 ± 12.7 vs. 30.6 ± 10.4 U; P = .004). No abnormal elevated BHB values were observed.

CONCLUSIONS

In adolescents and adults with T1D, dapagliflozin significantly increased TIR on average by 259 minutes/day while reducing glycaemic variability during FCL control without any signs of hypoglycaemia or ketosis.

Closed-loop insulin delivery systems in children and adolescents with type 1 diabetes

Introduction: Optimal glycemic control remains challenging in children and adolescents with type 1 diabetes due to highly variable day-to-day and night-to-night insulin requirements. This hurdle could be addressed by glucose-responsive insulin delivery based on real-time continuous glucose measurements.Areas covered: This review summaries recent advances of closed-loop systems in children and adolescents with type 1 diabetes, using both single- and dual-hormone closed-loop systems. The main outcomes, proportions of time spent in target range 70-180 mg/dl, and time spent in hypoglycemia below 70 mg/dl, are assessed particularly during unsupervised free-living randomized controlled trials.Expert opinion: Noteworthy and clinically meaningful translation of experimental investigations from controlled in-hospital settings to unrestricted home studies have been achieved over the past years, resulting in the regulatory approval of the first hybrid closed-loop system also in the pediatric population and with several other advanced devices in the pipeline. Large multinational and pivotal clinical trials including broad age populations are underway to facilitate the use of closed-loop systems in routine clinical practice.

Cost, Hassle, and On-Body Experience: Barriers to Diabetes Device Use in Adolescents and Potential Intervention Targets

Background: Adolescents with diabetes have the highest A1cs of all age groups. Diabetes devices (insulin pumps and continuous glucose monitors [CGM]) can improve glycemic outcomes, and although the uptake of devices has increased, they remain underutilized in this population. This study characterizes adolescent-reported barriers to diabetes device use to determine targets for clinician intervention. Methods: We surveyed 411 adolescents with type 1 diabetes (mean age 16.30 ± 2.25 years) on barriers to diabetes device use, technology use attitudes (general and diabetes specific), benefits and burdens of CGM, self-efficacy for diabetes care, diabetes distress, family conflict, and depression. We characterize barriers to device uptake; assess demographic and psychosocial differences in device users, discontinuers, and nonusers; and determine differences in device use by gender and age. Results: The majority of adolescents used an insulin pump (n = 307, 75%) and more than half used CGM (n = 225, 55%). Cost/insurance-related concerns were the most commonly endorsed barrier category (61%) followed by wear-related issues (58.6%), which include the hassle of wearing the device (38%) and dislike of device on the body (33%). Adolescents who endorsed more barriers also reported more diabetes distress (P = 0.003), family conflict (P = 0.003), and depressive symptoms (P = 0.014). Pump and CGM discontinuers both endorsed more barriers and more negative perceptions of technology than current users, but reported no difference from device users in diabetes distress, family conflict, or depression. Gender was not related to the perceptions of devices. Conclusions: Clinicians can proactively assess attitudes toward diabetes technology and perceptions of benefits/burdens to encourage device uptake and potentially prevent device discontinuation among adolescents.

A clinical review of the t:slim X2 insulin pump

Insulin pumps are commonly used for intensive insulin therapy to treat type 1 diabetes in adults and youth. Insulin pump technologies have advanced dramatically in the last several years to integrate with continuous glucose monitors (CGM) and incorporate control algorithms. These control algorithms automate some insulin delivery in response to the glucose information received from the CGM to reduce the occurrence of hypoglycemia and hyperglycemia and improve overall glycemic control. The t:slim X2 insulin pump system became commercially available in 2016. It is an innovative insulin pump technology that can be updated remotely by the user to install new software onto the pump device as new technologies become available. Currently, the t:slim X2 pairs with the Dexcom G6 CGM and there are two advanced software options available: Basal-IQ, which is a predictive low glucose suspend (PLGS) technology, and Control-IQ, which is a Hybrid Closed Loop (HCL) technology. This paper will describe the different types of advanced insulin pump technologies, review how the t:slim X2 insulin pump works, and summarize the clinical studies leading to FDA approval and commercialization of the Basal-IQ and Control-IQ technologies.

Review of the Omnipod® 5 Automated Glucose Control System Powered by Horizon™ for the treatment of Type 1 diabetes

Type 1 diabetes (T1D) is a medical condition that requires constant management, including monitoring of blood glucose levels and administration of insulin. Advancements in diabetes technology have offered methods to reduce the burden on people with T1D. Several hybrid closed-loop systems are commercially available or in clinical trials, each with unique features to improve care for patients with T1D. This article reviews the Omnipod® 5 Automated Glucose Control System Powered by Horizon™ and the safety and efficacy data to support its use in the management of T1D.

Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy

INTRODUCTION

Type 1 diabetes is a lifelong disease with high management burden. The majority of people with type 1 diabetes fail to achieve glycemic targets. Algorithm-driven automated insulin delivery (closed-loop) systems aim to address these challenges. This review provides an overview of commercial and emerging closed-loop systems.

AREAS COVERED

We review safety and efficacy of commercial and emerging hybrid closed-loop systems. A literature search was conducted and clinical trials using day-and-night closed-loop systems during free-living conditions were used to report on safety data. We comment on efficacy where robust randomized controlled trial data for a particular system are available. We highlight similarities and differences between commercial systems.

EXPERT OPINION

Study data shows that hybrid closed-loop systems are safe and effective, consistently improving glycemic control when compared to standard therapy. While a fully closed-loop system with minimal burden remains the end-goal, these hybrid closed-loop systems have transformative potential in diabetes care.

Where Do We Stand with Closed-Loop Systems and Their Challenges?

Treatments for type 1 diabetes have advanced significantly over recent years. There are now multiple hybrid closed-loop systems commercially available and additional systems are in development. Challenges remain, however. This review outlines the recent advances in closed-loop systems and outlines the remaining challenges, including post-prandial hyperglycemia and exercise-related dysglycemia.

Glucose outcomes of a learning-type artificial pancreas with an unannounced meal in type 1 diabetes

BACKGROUND AND OBJECTIVES

Glycemic control with unannounced meals is the major challenge for artificial pancreas. In this study, we described the performance and safety of learning-type model predictive control (L-MPC) for artificial pancreas challenged by an unannounced meal in type 1 diabetes (T1D).

METHODS

This closed-loop (CL) system was tested in 29 T1D patients at one site in a 4 h inpatient open-label study. Participants used an L-MPC CL system for 6 days after 2-day system identification using open-loop (OL) insulin system. During the CL period, the L-MPC system was started from 8:00 am to noon each day. At 9:00 am, each participant consumed 50 g of carbohydrates with no prandial insulin bolus. At 9:30 am on CL-Day 4 or CL-Day 6, participants rode bicycles for 20 minutes or drank 50 ml of beer, in a random order.

RESULTS

As the primary outcome, TIR on CL-Day 3 was 65.2±23.3%, which was 9.8 points higher (95% CI 1.8 to 17.8; P = 0.019) than that on CL-Day 1. The time of glucose >10 mmol/L was decreased by 11.0% (95% CI -18.7 to 3.3; P = 0.007), and mean glucose level was decreased by 1.1 mmol/L (95% CI -1.1 to 0.5; P = 0.000). The total daily insulin dosage showed no significant difference (-0.1U, 95% CI -1.34 to 1.32; P = 0.982). Compared with OL-Day1 with a postprandial bolus, the TIR was increased by 13.7 points (95% CI 1.4 to 26.0; P = 0.030), the time of glucose >10 mmol/L and the mean glucose level were also decreased. Compared with the exercise day (CL-Day E, 62.0 ± 23.3%; P = 0.347) or alcohol day (CL-Day A, 64.0 ± 23.6%; P = 0.756), there was no statistically significant difference in terms of TIR, time of glucose >10 mmol/L and mean glucose level. No severe hypoglycemic events occurred and hypoglycemic episodes were not increased by using closed-loop insulin system.

CONCLUSION

The L-MPC CL insulin system achieved good glycemic control challenged by an unannounced meal.

New technical approach to the diabetes therapy

Optimal glycemic control remains challenging in individuals with type 1 diabetes. With the comprehensive clinical evidence on safety and efficiency, the adoption of continuous glucose monitoring (CGM), insulin pumps, and control algorithms merging the two into closed-loop systems is rapidly increasing. Particularly the CGM and intermittently scanned CGM improved diabetes management outcomes in large populations. A meaningful translation from clinical trials in highly controlled settings to numerous evaluations of closed-loop technology in the unrestricted home environment ended with its commercialization and use in routine clinical practice. Although it is still not a cure, the closed-loop currently seems to be the most promising advancement in the treatment of diabetes, with promising results also reported from routine clinical practice in children and adults with type 1 diabetes. We summarize different aspects of a technological approach to diabetes care, list currently available devices and systems in the pipeline, and the key supporting clinical evidence for their use. We consider human factors associated with technology use and the importance of health economics to support implementation and reimbursement.

Evolution of Diabetes Technology

Technological innovations have fundamentally changed diabetes care. Insulin pump use and continuous glucose monitoring are associated with improved glycemic control along with a better quality of life; automated insulin-dosing advisors facilitate and improve decision making. Glucose-responsive automated insulin delivery enables the highest targets for time in range, lowest rate and duration of hypoglycemia, and favorable quality of life. Clear targets for time in ranges and a standard visualization of the data will help the diabetes technology to be used more efficiently. Decision support systems within and integrated cloud environment will further simplify, unify, and improve modern routine diabetes care.

Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions

Background: The objective of this study was to assess the safety and performance of the Omnipod^® personalized model predictive control (MPC) algorithm in adults, adolescents, and children aged ≥6 years with type 1 diabetes (T1D) under free-living conditions using an investigational device. Materials and Methods: A 96-h hybrid closed-loop (HCL) study was conducted in a supervised hotel/rental home setting following a 7-day outpatient standard therapy (ST) phase. Eligible participants were aged 6-65 years with A1C <10.0% using insulin pump therapy or multiple daily injections. Meals during HCL were unrestricted, with boluses administered per usual routine. There was daily physical activity. The primary endpoints were percentage of time with sensor glucose <70 and ≥250 mg/dL. Results: Participants were 11 adults, 10 adolescents, and 15 children aged (mean ± standard deviation) 28.8 ± 7.9, 14.3 ± 1.3, and 9.9 ± 1.0 years, respectively. Percentage time ≥250 mg/dL during HCL was 4.5% ± 4.2%, 3.5% ± 5.0%, and 8.6% ± 8.8% per respective age group, a 1.6-, 3.4-, and 2.0-fold reduction compared to ST (P = 0.1, P = 0.02, and P = 0.03). Percentage time <70 mg/dL during HCL was 1.9% ± 1.3%, 2.5% ± 2.0%, and 2.2% ± 1.9%, a statistically significant decrease in adults when compared to ST (P = 0.005, P = 0.3, and P = 0.3). Percentage time 70-180 mg/dL increased during HCL compared to ST, reaching significance for adolescents and children: HCL 73.7% ± 7.5% vs. ST 68.0% ± 15.6% for adults (P = 0.08), HCL 79.0% ± 12.6% vs. ST 60.6% ± 13.4% for adolescents (P = 0.01), and HCL 69.2% ± 13.5% vs. ST 54.9% ± 12.9% for children (P = 0.003). Conclusions: The Omnipod personalized MPC algorithm was safe and performed well over 5 days and 4 nights of use by a cohort of participants ranging from youth aged ≥6 years to adults with T1D under supervised free-living conditions with challenges, including daily physical activity and unrestricted meals.

Faster Compared With Standard Insulin Aspart During Day-and-Night Fully Closed-Loop Insulin Therapy in Type 1 Diabetes: A Double-Blind Randomized Crossover Trial

OBJECTIVE

We evaluated the safety and efficacy of day-and-night fully closed-loop insulin therapy using faster (Faster-CL) compared with standard insulin aspart (Standard-CL) in young adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS

In a double-blind, randomized, crossover trial, 20 participants with type 1 diabetes on insulin pump therapy (11 females, aged 21.3 ± 2.3 years, HbA_1c 7.5 ± 0.5% [58.5 ± 5.5 mmol/mol]) underwent two 27-h inpatient periods with unannounced afternoon moderate-vigorous exercise and unannounced/uncovered meals. We compared Faster-CL and Standard-CL in random order. During both interventions, the fuzzy-logic control algorithm DreaMed GlucoSitter was used. Glucose sensor data were analyzed by intention-to-treat principle with the difference (between Faster-CL and Standard-CL) in proportion of time in range 70-180 mg/dL (TIR) over 27 h as the primary end point.

RESULTS

The proportion of TIR was similar for both arms: 53.3% (83% overnight) in Faster-CL and 57.9% (88% overnight) in Standard-CL (P = 0.170). The proportion of time in hypoglycemia <70 mg/dL was 0.0% for both groups. Baseline-adjusted interstitial prandial glucose increments 1 h after meals were greater in Faster-CL compared with Standard-CL (P = 0.017). The gaps between measured plasma insulin and estimated insulin-on-board levels at the beginning, at the end, and 2 h after the exercise were smaller in the Standard-CL group (P = 0.029, P = 0.003, and P = 0.004, respectively). No severe adverse events occurred.

CONCLUSIONS

Fully closed-loop insulin delivery using either faster or standard insulin aspart was safe and efficient in achieving near-normal glucose concentrations outside postprandial periods. The closed-loop algorithm was better adjusted to the standard insulin aspart.

A Clinical Overview of Insulin Pump Therapy for the Management of Diabetes: Past, Present, and Future of Intensive Therapy

IN BRIEF Insulin pump therapy is advancing rapidly. This article summarizes the variety of insulin pump technologies available to date and discusses important clinical considerations for each type of technology.

Performance of Omnipod Personalized Model Predictive Control Algorithm with Moderate Intensity Exercise in Adults with Type 1 Diabetes

Background: The objective of this study was to assess the safety and performance of the Omnipod^® personalized model predictive control (MPC) algorithm with variable glucose setpoints and moderate intensity exercise using an investigational device in adults with type 1 diabetes (T1D). Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient standard treatment phase. Adults aged 18-65 years with T1D and HbA1c between 6.0% and 10.0% were eligible. Subjects completed two moderate intensity exercise sessions of >30 min duration on consecutive days: the first with the glucose set point increased from 130 to 150 mg/dL and the second with a temporary basal rate of 50%, both started 90 min pre-exercise. Primary endpoints were percentage time in hypoglycemia <70 mg/dL and hyperglycemia ≥250 mg/dL. Results: Twelve subjects participated in the study, with (mean ± standard deviation) age 36.5 ± 14.4 years, diabetes duration 21.7 ± 15.7 years, HbA1c 7.6% ± 1.1%, and total daily dose 0.60 ± 0.22 U/kg. Outcomes for the 54-h HCL period were mean glucose: 136 ± 14 mg/dL, percentage time <70 mg/dL: 1.4% ± 1.3%, 70-180 mg/dL: 85.1% ± 9.3%, and ≥250 mg/dL: 1.8% ± 2.4%. In the 12-h period after exercise start, percentage time <70 mg/dL was 1.4% ± 2.7% with the raised glucose set point and 1.6% ± 3.0% with reduced basal rate. The percentage time <70 mg/dL overnight was 0% ± 0% on both study nights. Conclusions: The Omnipod personalized MPC algorithm performed well and was safe during day and night use in response to variable glucose set points and with temporarily raised glucose set point or reduced basal rate 90 min in advance of moderate intensity exercise in adults with T1D.

Current Diabetes Technology: Striving for the Artificial Pancreas. Diagnostics (Basel). 2019;9. [PMID: 30875898 PMCID: PMC6468523 DOI: 10.3390/diagnostics9010031]

Diabetes technology has continually evolved over the years to improve quality of life and ease of care for affected patients. Frequent blood glucose (BG) checks and multiple daily insulin injections have become standard of care in Type 1 diabetes (T1DM) management. Continuous glucose monitors (CGM) allow patients to observe and discern trends in their glycemic control. These devices improve quality of life for parents and caregivers with preset alerts for hypoglycemia. Insulin pumps have continued to improve and innovate since their emergence into the market. Hybrid closed-loop systems have harnessed the data gathered with CGM use to aid in basal insulin dosing and hypoglycemia prevention. As technology continues to progress, patients will likely have to enter less and less information into their pump system manually. In the future, we will likely see a system that requires no manual patient input and allows users to eat throughout the day without counting carbohydrates or entering in any blood sugars. As technology continues to advance, endocrinologists and diabetes providers need to stay current to better guide their patients in optimal use of emerging management tools.

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.

Biopsychosocial Factors Associated With Satisfaction and Sustained Use of Artificial Pancreas Technology and Its Components: a Call to the Technology Field

PURPOSE OF REVIEW

Summarize biopsychosocial factors associated with using continuous glucose monitors (CGMs), insulin pumps, and artificial pancreas (AP) systems and provide a "call to the field" about their importance to technology uptake and maintained use.

RECENT FINDINGS

Insulin pumps and CGMs are becoming standard of care for individuals with type 1 diabetes (T1D). AP systems combining a CGM, insulin pump, and automated dosing algorithm are available for commercial use. Despite improved glycemic control with AP system use, numerous barriers exist which may limit their benefit. Studies on components of AP systems (pumps, CGMs) are limited and demonstrate mixed results of their impact on fear of hypoglycemia, adherence, quality of life, depression and anxiety, and diabetes distress. Studies examining biopsychological factors associated specifically with sustained use of AP systems are also sparse. Biological, psychological and social impacts of AP systems have been understudied and the information they provide has not been capitalized upon.

Performance of the Omnipod Personalized Model Predictive Control Algorithm with Meal Bolus Challenges in Adults with Type 1 Diabetes

BACKGROUND

This study assessed the safety and performance of the Omnipod^® personalized model predictive control (MPC) algorithm using an investigational device in adults with type 1 diabetes in response to overestimated and missed meal boluses and extended boluses for high-fat meals.

MATERIALS AND METHODS

A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient open-loop run-in phase. Adults aged 18-65 years with type 1 diabetes and HbA1c 6.0%-10.0% were eligible. Primary endpoints were percentage time in hypoglycemia <70 mg/dL and hyperglycemia ≥250 mg/dL. Glycemic responses for 4 h to a 130% overestimated bolus and a missed meal bolus were compared with a 100% bolus for identical meals, respectively. The 12-h postprandial responses to a high-fat meal were compared using either a standard or extended bolus.

RESULTS

Twelve subjects participated in the study, with (mean ± standard deviation): age 35.4 ± 14.1 years, diabetes duration 16.5 ± 9.3 years, HbA1c 7.7 ± 0.9%, and total daily dose 0.58 ± 0.19 U/kg. Outcomes for the 54-h HCL period were mean glucose 153 ± 15 mg/dL, percentage time <70 mg/dL [median (interquartile range)]: 0.0% (0.0-1.2%), 70-180 mg/dL: 76.1% ± 8.0%, and ≥250 mg/dL: 4.5% ± 3.6%. After both the 100% and 130% boluses, postprandial percentage time <70 mg/dL was 0.0% (0.0-0.0%) (P = 0.50). After the 100% and missed boluses, postprandial percentage time ≥250 mg/dL was 0.2% ± 0.6% and 10.3% ± 16.5%, respectively (P = 0.06). Postprandial percentages time ≥250 mg/dL and <70 mg/dL were similar with standard or extended boluses for a high-fat meal.

CONCLUSIONS

The Omnipod personalized MPC algorithm performed well and was safe during day and night use in response to overestimated, missed, and extended meal boluses in adults with type 1 diabetes.