First Look at Control-IQ: A New-Generation Automated Insulin Delivery System

A Glucose-Responsive Cannula for Automated and Electronics-Free Insulin Delivery

Automated delivery of insulin based on continuous glucose monitoring is revolutionizing the way insulin-dependent diabetes is treated. However, challenges remain for the widespread adoption of these systems, including the requirement of a separate glucose sensor, sophisticated electronics and algorithms, and the need for significant user input to operate these costly therapies. Herein, a user-centric glucose-responsive cannula is reported for electronics-free insulin delivery. The cannula-made from a tough, elastomer-hydrogel hybrid membrane formed through a one-pot solvent exchange method-changes permeability to release insulin rapidly upon physiologically relevant varying glucose levels, providing simple and automated insulin delivery with no additional hardware or software. Two prototypes of the cannula are evaluated in insulin-deficient diabetic mice. The first cannula-an ends-sealed, subcutaneously inserted prototype-normalizes blood glucose levels for 3 d and controls postprandial glucose levels. The second, more translational version-a cannula with the distal end sealed and the proximal end connected to a transcutaneous injection port-likewise demonstrates tight, 3-d regulation of blood glucose levels when refilled twice daily. This proof-of-concept study may aid in the development of "smart" cannulas and next-generation insulin therapies at a reduced burden-of-care toll and cost to end-users.

Safety and Feasibility Evaluation of Automated User Profile Settings Initialization and Adaptation With Control-IQ Technology

BACKGROUND

Optimization of automated insulin delivery (AID) settings is required to achieve desirable glycemic outcomes. We evaluated safety and efficacy of a computerized system to initialize and adjust insulin delivery settings for the t:slim X2 insulin pump with Control-IQ technology in adults with type 1 diabetes (T1D).

METHODS

After a 2-week continuous glucose monitoring (CGM) run-in period, adults with T1D using multiple daily injections (MDI) (N = 33, mean age 36.1 years, 57.6% female, diabetes duration 19.7 years) were transitioned to 13 weeks of Control-IQ technology usage. A computerized algorithm generated recommendations for initial pump settings (basal rate, insulin-to-carbohydrate ratio, and correction factor) and weekly follow-up settings to optimize glycemic outcomes. Physicians could override the automated settings changes for safety concerns.

RESULTS

Time in range 70 to 180 mg/dL improved from 45.7% during run-in to 69.1% during the last 30 days of Control-IQ use, a median improvement of 18.8% (95% confidence interval [CI]: 13.6-23.9, P < .001). This improvement was evident early in the study and was sustained over 13 weeks. Time <70 mg/dL showed a gradual decreasing trend over time. Percentage of participants achieving HbA1c <7% went from zero at baseline to 55% at study end (P < .001). Only six of the 318 automated settings adaptations (1.9%) were overridden by study investigators.

CONCLUSIONS

Computerized initiation and adaptation of Control-IQ technology settings from baseline MDI therapy was safe in adults with T1D. The use of this simplified system for onboarding and optimizing Control-IQ technology may be useful to increase uptake of AID and reduce staff and patient burden in clinical care.

Smartphone Bolus Feature Increases Number of Insulin Boluses in People With Low Bolus Frequency

BACKGROUND

The t:connect mobile app from Tandem Diabetes Care recently added a feature to allow t:slim X2 insulin pump users to initiate an insulin bolus from their personal smartphone. User experience and user interface considerations prioritized safety and ease of use, and we examined whether the smartphone bolus feature changed bolus behavior in individuals who bolused less than three times/day.

METHODS

We performed a retrospective analysis of t:slim X2 insulin pump users in the United States who had remotely updated their insulin pump software to be compatible with the smartphone bolus version of the app and who gave less than three boluses per day prior to the smartphone bolus update.

RESULTS

Of the 4470 early adopters who met these criteria, the median number of boluses was 2.2 per day (prior to smartphone bolus update) versus 2.7 per day (after smartphone bolus update), equating to approximately half a bolus more delivered per day (P < .001). Overall, a median of one bolus per day was administered by smartphone app as opposed to being initiated from the screen on the insulin pump.

CONCLUSION

This analysis found a significant increase in bolusing behavior among early adopters of the smartphone bolus feature of the t:connect mobile app.

Therapy Settings Associated with Optimal Outcomes for t:slim X2 with Control-IQ Technology in Real-World Clinical Care

Objective: To determine insulin dosing parameters that are associated with and predict optimal outcomes for people using t:slim X2 with Control-IQ technology (CIQ). Methods: Retrospective deidentified data from CIQ users were analyzed to determine the effect of Correction Factor, Carbohydrate-to-Insulin (C:I) Ratio, and basal rate settings (standardized by total daily insulin [TDI]) on glycemic control. We performed an associative analysis followed by linear regressions to determine the relative importance of the settings and confounding variables (e.g., age or number of user-initiated boluses) in predicting consensus glycemic outcomes. Results: Data from 20,764 individuals were analyzed (median age 39 years [interquartile range 19, 59], 55% female, TDI 46.4 U [33-65.2]). More aggressive Correction Factor settings, C:I ratio settings, and basal programs were all strongly associated with higher time in range (TIR, 70-180 mg/dL) and to a lesser degree to higher time <70 mg/dL. In linear regression, more aggressive Correction Factor predicted higher TIR, lower coefficient of variation, and importantly had only negligible impact on time below range. Higher basal rate settings and lower C:I ratio predicted increased TIR as well as increased hypoglycemia. The most important predictor in all glycemic outcomes was the average number of user-given boluses per day. Conclusion: Basal rates, C:I ratios, and Correction Factor settings all impact glycemic outcomes in CIQ users in usual clinical care. The correction Factor setting may be the most impactful "lever to pull" for clinicians and CIQ users to optimize TIR while not increasing hypoglycemia.

Multivariable identification based MPC for closed-loop glucose regulation subject to individual variability

The controller is important for the artificial pancreas to guide insulin infusion in diabetic therapy. However, the inter- and intra-individual variability and time delay of glucose metabolism bring challenges to control glucose within a normal range. In this study, a multivariable identification based model predictive control (mi-MPC) is developed to overcome the above challenges. Firstly, an integrated glucose-insulin model is established to describe insulin absorption, glucose-insulin interaction under meal disturbance, and glucose transport. On this basis, an observable glucose-insulin dynamic model is formed, in which the individual parameters and disturbances can be identified by designing a particle filtering estimator. Next, embedded with the identified glucose-insulin dynamic model, a mi-MPC method is proposed. In this controller, plasma glucose concentration (PGC), an important variable and indicator of glucose regulation, is estimated and controlled directly. Finally, the method was tested on 30 in-silico subjects produced by the UVa/Padova simulator. The results show that the mi-MPC method including the model, individual identification, and the controller can regulate glucose with the mean value of 7.45 mmol/L without meal announcement.

The Tandem Control-IQ advanced hybrid system improves glycemic control in children under 18 years of age with type 1 diabetes and night rest in caregivers

OBJECTIVE

To determine the impact of switching from the predictive low glucose suspend (PLGS) system to the advanced hybrid Tandem Control-IQ system on glucometrics and glycosylated haemoglobin (HbA1c) at one year. To assess the impact on the quality of life perceived by parents.

METHOD

Prospective study in 71 patients aged 6-18 years with type 1 diabetes (DM1), in treatment with PLGS, who switched to an advanced hybrid system. Glucometric data were collected before the change, at 4 and 8 weeks, and at one year of use; HbA1c before the change and after one year. The Diabetes Impact and Devices Satisfaction (DIDS) questionnaire was used at weeks 4 and 8.

RESULTS

An increase in time in range (TIR) was observed with a median of 76% (P<.001) at 4 weeks, which was maintained after one year (+8% in the total group). Overall, 73.24% of patients achieved a TIR above 70%. The subgroup with an initial TIR of less than 56% increased it by 14.4%. After one year there was a 0.3% reduction in HbA1c. Level 1 hypoglycaemia, level 1 and level 2 hyperglycaemia, mean glucose (GM) and coefficient of variation (CV) decreased. Auto mode stayed on 97% of the time and no dropouts occurred. Caregivers had a perception of better glycaemic control and less need to monitor blood glucose variations during the night. None of them would switch back to the previous system and they feel safe with the new system.

CONCLUSIONS

The Tandem Control-IQ advanced hybrid system was shown to be effective one year after its implementation with improvement in all glucometric parameters and HbA1c, as well as night-time rest in caregivers.

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.

The Evolution of Diabetes Technology - Options Towards Personalized Care

Advances in diabetes technology, especially in the last few decades, have transformed our ability to deliver care to persons with diabetes (PWD). Developments in glucose monitoring, especially continuous glucose monitoring systems (CGM), have revolutionized diabetes care and empowered our patients to manage their disease. CGM has also played an integral role in advancing automated insulin delivery systems. Currently available and upcoming advanced hybrid-closed loop systems aim to decrease patient involvement and are approaching the functionality of a fully automated artificial pancreas. Other advances, such as smart insulin pens and daily patch pumps, offer more options for patients and require less complicated and costly technology. Evidence to support the role of diabetes technology is growing, and PWD and clinicians must choose the right type of technology with a personalized strategy to manage diabetes effectively. Here, we review currently available diabetes technologies, summarize their individual features and highlight key patient factors to consider when creating a personalized treatment plan. We also address current challenges and barriers to the adoption of diabetes technologies.

Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases

Over the past decade there have been many advances in diabetes technologies, such as continuous glucose monitors (CGM s), insulin-delivery devices, and hybrid closed loop systems . Now most CGMs (Medtronic-Guardian, Dexcom-G6, and Abbott-Libre-2) have MARD values of < 10%, in contrast to two decades ago when the MARD used to be > 20%. In addition, the majority of the new CGMs do not require calibrations, and the latest CGMs last for 10-14 days. An implantable 6-months CGM by Eversense-3 is now approved in the USA and Europe. Recently, the FDA approved Libre 3 which provides real-time glucose values every minute. Even though it is approved as an iCGM it is not interoperable with automatic-insulin-delivery (AID) systems. The newer CGMs that are likely to be launched in the next few months in the USA include the 10-11 days Dexcom G7 (60% smaller than the existing G6), and the 7-days Medtronic Guardian 4. Most of the newer CGM have several features like automatic initialization, easy insertion, predictive alarms, and alerts. It has also been noticed that an arm insertion site might have better accuracy than abdomen or other sites, like the buttock for kids. Lag time between YSI and different sensors have been reported differently, sometimes it is down to 2-3 min; however, in many instances, it is still 15-20 min, especially when the rate of change of glucose is > 2 mg/min. We believe that in the next decade there will be a significant increase in the number of people who use CGM for their day-to-day diabetes care.

Diabetes Device Downloading: Benefits and Barriers Among Youth With Type 1 Diabetes

BACKGROUND

The majority of youth with type 1 diabetes (T1D) fail to meet glycemic targets despite increasing continuous glucose monitoring (CGM) use. We therefore aimed to determine the proportion of caregivers who review recent glycemic trends ("retrospective review") and make ensuant insulin adjustments based on this data ("retroactive insulin adjustments"). We additionally considered that fear of hypoglycemia and frequency of severe hypoglycemia would be associated with performing retrospective review.

METHODS

We conducted a cross-sectional survey of caregivers of youth with T1D, collecting demographics, diabetes technology usage, patterns of glucose data review/insulin dose self-adjustment, and Hypoglycemia Fear Survey (HFS).

RESULTS

Nineteen percent of eligible caregivers (191/1003) responded. Performing retrospective review was associated with younger child age (12.2 versus 15.4, P = .0001) and CGM use (92% versus 73%, P = .004), but was not associated with a significant improvement in child's HbA1c (7.89 versus 8.04, P = .65). Retrospective reviewers had significantly higher HFS-behavior scores (31.9 versus 27.7, P = .0002), which remained significantly higher when adjusted for child's age and CGM use (P = .005). Linear regression identified a significant negative association between HbA1c (%) and number of retroactive insulin adjustments (0.24 percent lower mean HbA1c per additional adjustment made, P = .02).

CONCLUSIONS

Retrospective glucose data review is associated with improved HbA1c when coupled with data-driven retroactive insulin adjustments. Barriers to data downloading existed even in this cohort of predominantly CGM-using T1D families.

Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4

Background: Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings. Methods: Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome. Results: Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%], P = 0.22). Median time <70 mg/dL improved from 3.0% with SAP to 1.6% with AID (-1.5% [-2.4% to -0.5%], P = 0.002). The adaptation system decreased initial basal rates by a median of 4% (-8%, 16%) and increased initial carbohydrate ratios by a median of 45% (32%, 59%) after 13 weeks. Conclusions: Automated adaptation of insulin delivery settings with AID use did not significantly improve time-in-range in this very well-controlled population. Additional study and further refinement of the adaptation system are needed, especially in populations with differing degrees of baseline glycemic control, who may show larger benefits from adaptation.

Evaluation of real-life clinical outcomes in Australian youth with type 1 diabetes on hybrid closed-loop therapy: A retrospective study

AIM

To determine the clinical outcomes and evaluate the perspectives of children with Type 1 diabetes (T1D) and their parents managing their child on hybrid closed-loop (HCL) therapy.

METHODS

Children with T1D on HCL attending a tertiary diabetes centre between April 2019 and July 2021 were included. A retrospective analysis of glycaemic data was conducted to determine the clinical outcomes. Time spent in closed loop, time in target glucose range (TIR 3.9-10 mmol/L), hypoglycaemia and hyperglycaemia were collected at baseline, 4 weeks, 3 and 6 months post-HCL. User experience was assessed by questionnaires administered to parents of children with T1D.

RESULTS

Seventy-one children, mean (SD) age of 12.2 (3.2) years were commenced on HCL. Ten (14%) discontinued HCL use, with 60% discontinuing within the first 6 months. Glycaemic outcomes were analysed in 52 children. Time spent in closed loop was 78 (21) % at 4 weeks, declined to 69 (28) % at 3 months (P = 0.037) and 63 (34) % at 6 months (P = 0.001). The mean %TIR increased from 59.8 at baseline to 67.6 at 3 months and 65.6 at 6 months with a mean adjusted difference of 7.8% points [95% CI 3.6, 11.9] and 5.5% points [95% CI 1.4, 9.5], respectively. There was a reduction in time > 10 mmol/L and time < 3.9 mmol/L from baseline to 6 months. Although families faced challenges with technology, better glucose control with reduced glycaemic fluctuations were reported.

CONCLUSIONS

HCL therapy is associated with improved glycaemia; however, adequate support and education are required for best outcomes.

Closed-Loop Diabetes Minipatch Based on a Biosensor and an Electroosmotic Pump on Hollow Biodegradable Microneedles

Developing a miniaturized, low-cost, and smart closed-loop system for diabetes could significantly improve life quality and benefit millions of people. Conventional closed-loop devices are large in size and exorbitant. Here, we unprecedentedly demonstrate an electrically controlled flexible closed-loop patch for continuous diabetes management by integrating hollow biodegradable microneedles with a biosensing device and an electroosmotic pump. The hollow microneedles were fabricated using a combination of soft lithography and micromachining. The outer layer of the microneedles was functionalized to serve as a biosensing device for the in situ sensitive and accurate monitoring of interstitial glucose. The inner layer of the microneedles was integrated with a flexible electroosmotic pump to deliver insulin, and the delivery rate was electrically controlled by the glucose level from the biosensing device. The closed-loop system successfully stabilized the blood glucose levels of diabetic rats in a normal and safe range. The system is painless, miniaturized, cost-effective, and flexible. It is anticipated that it could open up exciting new avenues for fundamental studies of new closed-loop devices as well as practical applications for diabetes management.

Multi-task disentangled autoencoder for time-series data in glucose dynamics

The objective of this study is to propose MD-VAE: a multi-task disentangled variational autoencoders (VAE) for exploring characteristics of latent representations (LR) and exploiting LR for diverse tasks including glucose forecasting, event detection, and temporal clustering.

Recent advances in closed-loop insulin delivery

Since the discovery of insulin 100 years ago, we have seen considerable advances across diabetes therapies. The more recent advent of glucose-responsive automated insulin delivery has started to revolutionise the management of type 1 diabetes in children and adults. Evolution of closed-loop insulin delivery from research to clinical practice has been rapid, and multiple systems are now commercially available. In this review, we summarise key evidence on currently available closed-loop systems and those in development. We comment on dual-hormone and do-it-yourself systems, as well as reviewing clinical evidence in special populations such as very young children, older adults and in pregnancy. We identify future directions for research and barriers to closed-loop adoption, including how these might be addressed to ensure equitable access to this novel therapy.

To sleep or not to sleep: An Italian Control-IQ-uestion

OBJECTIVE

Tandem Control-IQ is an advanced hybrid closed loop (AHCL) system with a Sleep Activity Mode to intensify glycemic control overnight. The aim of the study is to evaluate the effectiveness of using Sleep Mode or not among Tandem Control-IQ users.

RESEARCH DESIGN AND METHODS

We performed a retrospective Tandem Control-IQ data download for patients followed at IRCCS G. Gaslini Pediatric Diabetes Centre. We divided the patients into group 1 (Sleep Mode users) and group 2 (non-users) and compared their overall glycemic data, particularly during nighttime.

RESULTS

Group 1 (n = 49) does not show better nocturnal glycemic control as expected when compared with group 2 (n = 34). Group 2 shows a nighttime TIR% of 69.50 versus 66.25 (p = 0.20). Only the patients who do not use Sleep Mode and with sensor and automatic mode use ≥90% reached TIR >70% during nighttime, as well as lower nocturnal TAR% (18.80 versus 21.78, p = 0.05).

CONCLUSIONS

This is the first study that evaluates the real-life effectiveness of the use of Sleep Mode in young patients with T1D. Control-IQ Sleep Activity Mode may not be as effective in Italian patients as in American patients due to the different habits.

A Correction Insulin Bolus Delivery Strategy for Decision Support Systems in Type 1 Diabetes

Management of type 1 diabetes (T1D) requires affected individuals to perform multiple daily actions to keep their blood glucose levels within the safe rage and avoid adverse hypo-/hyperglycemic episodes. Decision support systems (DSS) for T1D are composite tools that implement multiple software modules aiming to ease such a burden and to improve glucose control. At the University of Padova, we are developing a new DSS that currently integrate a smart insulin bolus calculator for optimal insulin dosing and a rescue carbohydrate intake advisor to tackle hypoglycemia. However, a module specifically targeting hyperglycemia, that suggests the administration of corrective insulin boluses (CIB), is still missing. For such a scope, this work aims to assess a recent literature methodology, proposed by Aleppo et al., which provides a simple strategy for dealing with hyperglycemia. The methodology is tested retrospectively on clinical data of individuals with T1D. In particular, here we leveraged a novel in silico tool that first identifies a non-linear model of glucose-insulin dynamics on data, then uses such model to simulate and compare the glucose trace obtained by "replaying" the recorded scenario and the glucose trace obtained using the CIB delivery strategy under evaluation. Results show that the CIB delivery strategy significantly reduce the percentage of time spent in hyperglycemia (-15.63%) without inducing any hypoglycemic episode, demonstrating both safety and efficacy of its use. These preliminary results suggest that the CIB delivery strategy proposed by Aleppo et al. is a promising candidate to be included in our system to counteract hyperglycemia. Future work will extensively evaluate the methodology and will compare it against other competing approaches.

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

Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

An OGI model for personalized estimation of glucose and insulin concentration in plasma

Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC 9.49±3.81 mU/L, and PGC 0.89±0.19 mmol/L. For human, the OGI with PFM has the promise to identify disturbances (95.46%±0.65% accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.

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.

Efficacy, safety, and user experience of DIY or open-source artificial pancreas systems: a systematic review

The do-it-yourself artificial pancreas system (DIYAPS) is a patient-driven initiative with the potential to revolutionise diabetes management, automating insulin delivery with existing pumps and CGM combined with open-source algorithms. Given the considerable interest in this topic within the diabetes community, we have conducted a systematic review of DIYAPS efficacy, safety, and user experience. Following recognised procedures and reporting standards, we identified 10 eligible publications of 730 participants within the peer-reviewed literature. Overall, studies reported improvements in time in range, HbA1c (glycated haemoglobin), reduced hypoglycaemia, and improved quality of life with DIYAPS use. While results were positive, the identified studies were small, and the majority were observational and at high risk of bias. Further research including well-designed randomised trials comparing DIYAPS with appropriate comparators is recommended.

Feasibility Study of a Hybrid Closed-Loop System with Automated Insulin Correction Boluses

Background: The Medtronic MiniMed™ 670G system adjusts basal insulin delivery in response to continuous glucose monitoring levels and is already in use in clinical practice. We tested the home-based feasibility of the new MiniMed advanced hybrid closed-loop (AHCL) system, which includes several algorithm enhancements and an optional autocorrection bolus mode. Methods: Twelve adolescents and young adults (eight females) with type 1 diabetes [median (interquartile range)] aged 16.6 (15.9, 18.2) years and diabetes duration of 7.1 (4.7, 8.8) years] participated in this single-arm study. The first stage was a 6-day open-loop run-in period, with the predictive low-glucose suspend feature on. This was followed by 6 days/5 nights in a supervised hotel setting, using the AHCL system, including closed-loop challenges (missed meal bolus, late meal bolus, and physical activity); and finally, 3 weeks with unrestricted home use. Glycemic parameters were compared between the open-loop and closed-loop periods. Results: Participants spent 93.3% (4.7) of the time in SmartGuard™ Auto Mode. Hemoglobin A1C levels decreased from median (interquartile range) 7.1% (6.7, 7.9) at baseline to 6.8% (6.6, 7.4) at study end, after 4 weeks (P = 0.0027). Time in range (TIR) (70-180 mg/dL) was 68.4% (10.6) and time below 70 mg/dL was 4% (3.5) during open-loop; and 74% (6.1) and 2.6% (1.9), respectively, during the closed-loop at home phase (P = 0.06, P = 0.27). TIR increased during the nighttime, from 64.6% (17.4) to 80.7% (7.8), P = 0.007, without change in time below 70 mg/dL (P = 0.15). No serious adverse events occurred. Conclusions: The new AHCL system demonstrated safety and effectiveness in controlling day and night glucose levels.

Effects of real-time continuous glucose monitoring in type 1 diabetes: a meta-analysis of randomized controlled trials

AIMS

Self-monitoring of blood glucose (SMBG) represented a major breakthrough in the treatment of type 1 diabetes. The aim of the present meta-analysis is to assess the effect of continues glucose monitoring (CGM) and flash glucose monitoring (FGM), on glycemic control in type 1 diabetes.

MATERIALS AND METHODS

The present analysis includes randomized clinical trials comparing CGM or FGM with SMBG, with a duration of at least 12 weeks, identified in Medline or clinicaltrials.gov. The principal endpoint was HbA1c at the end of the trial. A secondary endpoint was severe hypoglycemia. Mean and 95% confidence intervals for HbA1c and Mantel-Haenzel odds ratio [MH-OR] for severe hypoglycemia were calculated, using random effect models. A sensitivity analysis was performed using fixed effect models. In addition, the following secondary endpoints were explored, using the same methods: time in range, health-related quality of life, and treatment satisfaction. Separate analyses were performed for trials comparing CGM with SMBG, and those comparing CGM + CSII and SMBG + MDI and CGM-regulated insulin infusion system (CRIS) and CSII + SMBG.

RESULTS

CGM was associated with a significantly lower HbA1c at endpoint in comparison with SMBG (- 0.24 [- 0.34, - 0.13]%); CGM was associated with a significantly lower risk of severe hypoglycemia than SMBG. Treatment satisfaction and quality of life were not measured, or not reported, in the majority of studies. FGM showed a significant reduction in the incidence of mild hypoglycemia and an increased treatment satisfaction, but no significant results are shown in HbA1c. CGM + CSII in comparison with SMBG + MDI was associated with a significant reduction in HbA1c. Only two trials with a duration of at least 12 weeks compared a CRIS with SMBG + CSII; HbA1c between the two treatment arms was not statistically significant (difference in means: - 0.23 [- 0.91; 0.46]%; p = 0.52).

CONCLUSION

GCM compared to SMBG has showed a reduction in HbA1c and severe hypoglycemia in patient with type 1 diabetes. The comparison between CGM + CSII and SMBG + MDI showed a large reduction in HbA1c; it is conceivable that the effects of CSII + CGM on glycemic control additives. The only comparison available between FGM and SMBG was conducted in patients in good control.

Diabetology 4.0: Scoping Review of Novel Insights and Possibilities Offered by Digitalization

Background

The increasing prevalence of diabetes mellitus and associated morbidity worldwide justifies the need to create new approaches and strategies for diabetes therapy. Therefore, the ongoing digitalization offers novel opportunities in this field.

Objective

The aim of this study is to provide an updated overview of available technologies, possibilities, and novel insights into diabetes therapy 4.0.

Methods

A scoping review was carried out, and a literature search was performed using electronic databases (MEDLINE [PubMed], Cochrane Library, Embase, CINAHL, and Web of Science). The results were categorized according to the type of technology presented.

Results

Different types of technology (eg, glucose monitoring systems, insulin pens, insulin pumps, closed-loop systems, mobile health apps, telemedicine, and electronic medical records) may help to improve diabetes treatment. These improvements primarily affect glycemic control. However, they may also help in increasing the autonomy and quality of life of people who are diagnosed with diabetes mellitus.

Conclusions

Diabetes technologies have developed rapidly over the last few years and offer novel insights into diabetes therapy and a chance to improve and individualize diabetes treatment. Challenges that need to be addressed in the following years relate to data security, interoperability, and the development of standards.

Diabetes education in pediatrics: How to survive diabetes

Diabetes mellitus is the most common abnormal carbohydrate metabolism disorder affecting millions of people worldwide. It is characterized by hyperglycemia as a result of ß-cell destruction or dysfunction by both genetic and environmental factors. Over time chronic hyperglycemia leads to microvascular (i.e., retinopathy, nephropathy and neuropathy) and macrovascular (i.e., ischemic heart disease, peripheral vascular disease, and cerebrovascular disease) complications of diabetes. Diabetes complication trials showed the importance of achieving near-normal glycemic control to prevent and/or reduce diabetes-related morbidity and mortality. There is a staggering rate of increased incidence of diabetes in youth, raising concerns for future generations' health, quality of life and its enormous economic burden. Despite advancements in the technology, diabetes management remains cumbersome. Training individuals with diabetes to gain life-long survival skills requires a comprehensive and ongoing diabetes education by a multidisciplinary team. Diabetes education and training start at the time of diagnosis of diabetes and should be continuous throughout the course of disease. The goal is to empower the individuals and families to gain diabetes self-management skills. Diabetes education must be individualized depending on the individual's age, education, family dynamics, and support. In this article, we review the history of diabetes, etiopathogenesis and clinical presentation of both type 1 and type 2 diabetes in children as well as adolescents. We then focus on diabetes management with education methods and materials.

7. Diabetes Technology: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

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.

Glycemic Outcomes of Use of CLC Versus PLGS in Type 1 Diabetes: A Randomized Controlled Trial

OBJECTIVE

Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system.

RESEARCH DESIGN AND METHODS

After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14-72 years; mean HbA_1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70-180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study.

RESULTS

All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = -6.0%; 95% CI -8.4%, -3.7%; P < 0.001) while time <54 mg/dL was similar (0.04%; 95% CI -0.05%, 0.13%; P = 0.41). HbA_1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference -0.34% [-3.7 mmol/mol]; 95% CI -0.57% [-6.2 mmol/mol], -0.11% [1.2 mmol/mol]; P = 0.0035).

CONCLUSIONS

Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA_1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.

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.

Case series of a hybrid closed-loop system used in pregnancies in clinical practice

BACKGROUND

Hybrid closed-loop (HCL) therapy is rarely studied in pregnancy. We present three cases of women with type 1 diabetes who used the Medtronic 670G HCL system for most or all of gestation.

METHODS

The Medtronic 670G system has a manual mode (no automated insulin delivery) and an auto mode (AM, HCL therapy). Women in this case series used AM off-label in gestation.

RESULTS

Case 1 started HCL therapy in the second trimester, her sensor glucose time spent <3.9 and >10 mmol/L improved thereafter. Case 1 had average sensor glucose (ASG) levels of 6.4 ± 2.4 mmol/L in the first trimester, 7.0 ± 2.7 mmol/L in the second trimester before HCL use, 7.1 ± 2.1 mmol/L in the second trimester after HCL use, and 6.8 ± 1.9 mmol/L in the third trimester. Case 1 continued AM during operative delivery and post-partum. Cases 2 and 3 used HCL therapy throughout gestation but with inconsistent time in AM. When they increased time in AM their glycaemic indices improved. Case 2 had ASG of 9.5 ± 3.4, 8.6 ± 2.9, and 7.9 ± 2.5 mmol/L in the first through third trimesters, respectively. Case 3 had ASG of 11.1 ± 4.8 and 3.9 to 10 mmol/L in the first and second trimesters, respectively. Case 2 continued HCL therapy post-partum, Case 3 did not.

CONCLUSIONS

CareLink® Clinical Software only reports the non-pregnant time in range. Nonetheless, this represents the first report of HCL therapy in pregnancy with a system approved by the Food and Drug Administration in non-pregnant populations.

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.

Automated Insulin Delivery in Children with Type 1 Diabetes

The advent of insulin pump therapy marked an important milestone in diabetes treatment in the past few decades and has become the tipping point for the development of automated insulin delivery systems (AID). Standalone insulin pump systems have evolved over the course of years and have been replaced by modern high-technology insulin pumps with continuous glucose monitor interface allowing real-time insulin dose adjustment to optimize treatment. This review summarizes evidence from AID studies conducted in children with type 1 diabetes and discusses the outlook for future generation AID systems from a pediatric treatment perspective.

Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: a randomised crossover trial

Background

Automated closed-loop control (CLC), known as the "artificial pancreas" is emerging as a treatment option for Type 1 Diabetes (T1D), generally superior to sensor-augmented insulin pump (SAP) treatment. It is postulated that evening-night (E-N) CLC may account for most of the benefits of 24-7 CLC; however, a direct comparison has not been done.

Methods

In this trial (NCT02679287), adults with T1D were randomised 1:1 to two groups, which followed different sequences of four 8-week sessions, resulting in two crossover designs comparing SAP vs E-N CLC and E-N CLC vs 24-7 CLC, respectively. Eligibility: T1D for at least 1 year, using an insulin pump for at least six months, ages 18 years or older. Primary hypothesis: E-N CLC compared to SAP will decrease percent time <70mg/dL (3.9mmol/L) measured by continuous glucose monitoring (CGM) without deterioration in HbA1c. Secondary Hypotheses: 24-7 CLC compared to SAP will increase CGM-measured time in target range (TIR, 70-180mg/dL; 3.9-10mmol/L) and will reduce glucose variability during the day.

Findings

Ninety-three participants were randomised and 80 were included in the analysis, ages 18-69 years; HbA1c levels 5.4-10.6%; 66% female. Compared to SAP, E-N CLC reduced overall time <70mg/dL from 4.0% to 2.2% () resulting in an absolute difference of 1.8% (95%CI: 1.2-2.4%), p<0.0001. This was accompanied by overall reduction in HbA1c from 7.4% at baseline to 7.1% at the end of study, resulting in an absolute difference of 0.3% (95% CI: 0.1-0.4%), p<0.0001. There were 5 severe hypoglycaemia adverse events attributed to user-directed boluses without malfunction of the investigational device, and no diabetic ketoacidosis events.

Interpretation

In type 1 diabetes, evening-night closed-loop control was superior to sensor-augmented pump therapy, achieving most of the glycaemic benefits of 24-7 closed-loop.

7. Diabetes Technology: Standards of Medical Care in Diabetes-2020

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc20-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes

BACKGROUND

Closed-loop systems that automate insulin delivery may improve glycemic outcomes in patients with type 1 diabetes.

METHODS

In this 6-month randomized, multicenter trial, patients with type 1 diabetes were assigned in a 2:1 ratio to receive treatment with a closed-loop system (closed-loop group) or a sensor-augmented pump (control group). The primary outcome was the percentage of time that the blood glucose level was within the target range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter), as measured by continuous glucose monitoring.

RESULTS

A total of 168 patients underwent randomization; 112 were assigned to the closed-loop group, and 56 were assigned to the control group. The age range of the patients was 14 to 71 years, and the glycated hemoglobin level ranged from 5.4 to 10.6%. All 168 patients completed the trial. The mean (±SD) percentage of time that the glucose level was within the target range increased in the closed-loop group from 61±17% at baseline to 71±12% during the 6 months and remained unchanged at 59±14% in the control group (mean adjusted difference, 11 percentage points; 95% confidence interval [CI], 9 to 14; P<0.001). The results with regard to the main secondary outcomes (percentage of time that the glucose level was >180 mg per deciliter, mean glucose level, glycated hemoglobin level, and percentage of time that the glucose level was <70 mg per deciliter or <54 mg per deciliter [3.0 mmol per liter]) all met the prespecified hierarchical criterion for significance, favoring the closed-loop system. The mean difference (closed loop minus control) in the percentage of time that the blood glucose level was lower than 70 mg per deciliter was -0.88 percentage points (95% CI, -1.19 to -0.57; P<0.001). The mean adjusted difference in glycated hemoglobin level after 6 months was -0.33 percentage points (95% CI, -0.53 to -0.13; P = 0.001). In the closed-loop group, the median percentage of time that the system was in closed-loop mode was 90% over 6 months. No serious hypoglycemic events occurred in either group; one episode of diabetic ketoacidosis occurred in the closed-loop group.

CONCLUSIONS

In this 6-month trial involving patients with type 1 diabetes, the use of a closed-loop system was associated with a greater percentage of time spent in a target glycemic range than the use of a sensor-augmented insulin pump. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; iDCL ClinicalTrials.gov number, NCT03563313.).

Advances in technology for management of type 1 diabetes

Technological advances have had a major effect on the management of type 1 diabetes. In addition to blood glucose meters, devices used by people with type 1 diabetes include insulin pumps, continuous glucose monitors, and, most recently, systems that combine both a pump and a monitor for algorithm-driven automation of insulin delivery. In the next 5 years, as many advances are expected in technology for the management of diabetes as there have been in the past 5 years, with improvements in continuous glucose monitoring and more available choices of systems that automate insulin delivery. Expansion of the use of technology will be needed beyond endocrinology practices to primary-care settings and broader populations of patients. Tools to support decision making will also need to be developed to help patients and health-care providers to use the output of these devices to optimise diabetes management.

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.

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.

A Century of Diabetes Technology: Signals, Models, and Artificial Pancreas Control

Arguably, diabetes mellitus is one of the best-quantified human conditions: elaborate in silico models describe the action of the human metabolic system; real-time signals such as continuous glucose monitoring are readily available; insulin delivery is being automated; and control algorithms are capable of optimizing blood glucose fluctuation in patients' natural environments. The transition of the artificial pancreas (AP) to everyday clinical use is happening now, and is contingent upon seamless concerted work of devices encompassing the patient in a digital treatment ecosystem. This review recounts briefly the story of diabetes technology, which began a century ago with the discovery of insulin, progressed through glucose monitoring and subcutaneous insulin delivery, and is now rapidly advancing towards fully automated clinically viable AP systems.

Successful At-Home Use of the Tandem Control-IQ Artificial Pancreas System in Young Children During a Randomized Controlled Trial

OBJECTIVE

Hybrid closed-loop (HCL) artificial pancreas (AP) systems are now moving from research settings to widespread clinical use. In this study, the inControl algorithm developed by TypeZero Technologies was embedded to a commercial Tandem t:slim X2 insulin pump, now called Control-IQ, paired with a Dexcom G6 continuous glucose monitor and tested for superiority against sensor augmented pump (SAP) therapy. Both groups were physician-monitored throughout the clinical trial.

RESEARCH DESIGN AND METHODS

In a randomized controlled trial, 24 school-aged children (6-12 years) with type 1 diabetes (T1D) participated in a 3-day home-use trial at two sites: Stanford University and the Barbara Davis Center (50% girls, 9.6 ± 1.9 years of age, 4.5 ± 1.9 years of T1D, baseline hemoglobin A1c 7.35% ± 0.68%). Study subjects were randomized 1:1 at each site to either HCL AP therapy with the Control-IQ system or SAP therapy with remote monitoring.

RESULTS

The primary outcome, time in target range 70-180 mg/dL, using Control-IQ significantly improved (71.0% ± 6.6% vs. 52.8% ± 13.5%; P = 0.001) and mean sensor glucose (153.6 ± 13.5 vs. 180.2 ± 23.1 mg/dL; P = 0.003) without increasing hypoglycemia time <70 mg/dL (1.7% [1.3%-2.1%] vs. 0.9% [0.3%-2.7%]; not significant). The HCL system was active for 94.4% of the study period. Subjects reported that use of the system was associated with less time thinking about diabetes, decreased worry about blood sugars, and decreased burden in managing diabetes.

CONCLUSIONS

The use of the Tandem t:slim X2 with Control-IQ HCL AP system significantly improved time in range and mean glycemic control without increasing hypoglycemia in school-aged children with T1D during remote monitored home use.

Current Diabetes Technology: Striving for the Artificial Pancreas

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.