Automated Insulin Delivery in Real Life (AID-IRL): Real-World User Perspectives on Commercial AID

Commercial automated insulin delivery (AID) systems are usually assessed based on clinical outcomes, ignoring uptake. A qualitative study evaluated user experiences when switching to currently available commercial AID. Interview feedback was coded on key themes including the adoption experience with regards to quality of life, clinical outcomes, and users’ expectations. Most felt their learning curve was easy. Most saw reduced hypoglycemia and increased time in range, although there were outliers. Many mentioned post-meal hyperglycemia as an improvement area for commercial AID. Users with one particular continuous glucose monitor (CGM) type reported sleep disruption. Companies should consider real-world user feedback with regards to improving training materials for new users with less CGM experience and by improving target flexibility and postprandial algorithm performance, plus reducing manual interventions required by users.     

Twelve-month results of the ADAPT randomized controlled trial: Reproducibility and sustainability of advanced hybrid closed-loop therapy outcomes versus conventional therapy in adults with type 1 diabetes

Aims

To reassess the 6-month efficacy and to assess the 12-month sustained efficacy of the MiniMed™ 780G advanced hybrid closed-loop automated insulin delivery (AID) system compared to multiple daily injections plus intermittently scanned glucose monitoring (MDI+isCGM) in people with type 1 diabetes not meeting glucose targets.

Methods

The ADAPT study was a prospective, multicentre, open-label, randomized control trial in people with type 1 diabetes, with a glycated haemoglobin (HbA1c) concentration of at least 8.0% (64 mmol/mol), on MDI+isCGM therapy. After a 6-month study phase, participants randomized at baseline to MDI+isCGM switched to AID (SWITCH) while the others continued AID therapy (SUSTAIN) for an additional 6 months. The primary endpoint of this continuation phase was the within-group change in mean HbA1c between 6 and 12 months, with superiority in the SWITCH group and noninferiority in the SUSTAIN group ( ClinicalTrials.gov : NCT04235504).

Results

A total of 39 SWITCH and 36 SUSTAIN participants entered the continuation phase. In the SWITCH group, HbA1c was significantly decreased by −1.4% (95% confidence interval [CI] −1.7% to −1.1%; P < 0.001) from a mean ± SD of 8.9% ± 0.8% (73.9 ± 8.6 mmol/mol) at 6 months to 7.5% ± 0.6% (58.5 ± 6.9 mmol/mol) at 12 months. Mean HbA1c increased by 0.1% (95% CI −0.05% to +0.25%), from 7.3% ± 0.6% (56.5 ± 6.7 mmol/mol) to 7.4% ± 0.8% (57.7 ± 9.1 mmol/mol) in the SUSTAIN group, meeting noninferiority criteria. Three severe hypoglycaemia events occurred in two SWITCH participants during the continuation phase.

Conclusion

ADAPT study phase glycaemic improvements were reproduced and sustained in the continuation phase, supporting the early adoption of AID therapy in people with type 1 diabetes not meeting glucose targets on MDI therapy.     

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

Background:Older adults with type 1 diabetes (≥65 years) are often under-represented in clinical trials of automated insulin delivery (AID) systems. We sought to test the efficacy of a recently FDA-approved AID system in this population.Methods:Participants with type 1 diabetes used sensor-augmented pump (SAP) therapy for four weeks and then used an AID system (Control-IQ) for four weeks. In addition to glucose control variables, patient-reported outcomes (PRO) were assessed with questionnaires and sleep parameters were assessed by actigraphy.Results:Fifteen older adults (mean age 68.7 ± 3.3, HbA1c of 7.0 ± 0.8) completed the pilot trial. Glycemic outcomes improved during AID compared to SAP. During AID use, mean glucose was 146.0 mg/dL; mean percent time in range (TIR, 70-180 mg/dL) was 79.6%; median time below 70 mg/dL was 1.1%. The AID system was in use 92.6% ± 7.0% of the time. Compared to SAP, while participants were on AID the TIR increased significantly (+10%, P = .002) accompanied by a reduction in both time above 180 mg/dL (−6.9%, P = .005) and below 70 mg/dl (−0.4%, P = .053). Diabetes-related distress decreased significantly while using AID (P = .028), but sleep parameters remained unchanged.Conclusions:Use of this AID system in older adults improved glycemic control with high scores in ease of use, trust, and usability. Participants reported an improvement in diabetes distress with AID use. There were no significant changes in sleep.     

How It Started,How It Is Going: The Future of Artificial Pancreas Systems (Automated Insulin Delivery Systems)

Originally, the future of automated insulin delivery (AID) systems, or artificial pancreas systems (APS), was having them at all, in any form. We’ve learned in the last half dozen years that the future of all artificial pancreas systems holds higher time in range, less work required to manage automated insulin delivery systems to improve quality of life, and the ability to input critical information back into the system itself. The data and user experience stories make it clear: APS works. APS are an improvement over other diabetes therapy methods when they are made available, accessible, and affordable. Understanding the unmet expectations of current users of first generation APS technology may also aid in the development of improved technology and user experiences for the future of APS.     

Short-term fully closed-loop insulin delivery using faster insulin aspart compared with standard insulin aspart in type 2 diabetes

We evaluated the efficacy and safety of short-term fully closed-loop insulin delivery using faster versus standard insulin aspart in type 2 diabetes. Fifteen adults with insulin-treated type 2 diabetes underwent 22 hours of closed-loop insulin delivery with either faster or standard insulin aspart in a double-blind randomized crossover design. Basal-bolus regimen was replaced by model predictive control algorithm-directed insulin delivery based on sensor glucose levels. The primary outcome was time with plasma glucose in target range (5.6–10.0 mmol/L) and did not differ between treatments (mean difference [95% CI] 3.3% [−8.2; 1.7], P = 0.17). Mean glucose and glucose variability were comparable, as was time spent below and above target range. Hypoglycaemia (<3.5 mmol/L) occurred once with faster insulin aspart and twice with standard insulin aspart. Mean total insulin dose was higher with faster insulin aspart (mean difference [95% CI] 3.7 U [0.7; 6.8], P = 0.021). No episodes of severe hypoglycaemia or other serious adverse events occurred. In conclusion, short-term fully closed-loop in type 2 diabetes may require higher dose of faster insulin aspart compared with standard insulin aspart to achieve comparable glucose control.     

One-year real-world performance of the DBLG1 closed-loop system: Data from 3706 adult users with type 1 diabetes in Germany

Aim

The Diabeloop Generation 1 (DBLG1) system is an interoperable hybrid closed-loop solution that was commercialized in Germany in March 2021. We report the longitudinal glycaemic outcomes among the first 3706 users in a real-world setting.

Methods

We performed a retrospective data collection of all consenting adult patients with type 1 diabetes who were equipped in Germany with the DBLG1 system before 30 April 2022, and with a minimum 14 days of closed-loop usage.

Results

In total, 3706 users (41% women, age 45.1 ± 14.5 years) met the inclusion criteria, reaching a mean follow-up of 131.0 ± 85.1 days, an overall 485 600 days of continuous glucose monitoring data, and a median time spent in closed-loop of 95.0% (IQR 89.1-97.4). The median percentage time in range (70-180 mg/dl) was 72.1% (IQR 65.0-78.9); the time below 70 mg/dl was 0.9% (0.5-1.7), the time below 54 mg/dl was 0.1% (0.1-0.3), and the median Glucose Management Index was 7.0% (6.8-7.3). Exploratory analysis of a subset of 2460 patients in whom baseline glycated haemoglobin (HbA1c) was available [7.4% (IQR 6.9-8.0)] showed that the achieved mean time in range was influenced by baseline HbA1c, ranging from 65.8 ± 9.9% (A1c ≥8.5%) to 81.3 ± 6.8% (A1c <6.5%).

Conclusion

This large real-world analysis confirms the relevance of the DBLG1 automated insulin delivery solution for the achievement of standards of care in adult patients with type 1 diabetes.     

Glycaemic control in individuals with type 1 diabetes using an open source artificial pancreas system (OpenAPS)

Open source artificial pancreas systems (OpenAPS) have gained considerable interest in the diabetes community. We analyzed continuous glucose monitoring (CGM) records of 80 OpenAPS users with type 1 diabetes (T1D). A total of 19 495 days (53.4 years) of CGM records were available. Mean glucose was 7.6 ± 1.1 mmol/L, time in range 3.9–10 mmol/L was 77.5 ± 10.5%, <3.9 mmol/L was 4.3 ± 3.6%, <3.0 mmol/L was 1.3 ± 1.9%, >10 mmol/L was 18.2 ± 11.0% and > 13.9 mmol/L was 4.1 ± 4.0%, respectively. In 34 OpenAPS users, additional CGM records were obtained while using sensor-augmented pump therapy (SAP). After changing from SAP to OpenAPS, lower mean glucose (−0.6 ± 0.7; P < 0.0001), lower estimated HbA1c (−0.4 ± 0.5%; P < 0.0001), higher time in range 3.9–10 mmol/L (+9.3 ± 9.5%; P < 0.0001), less time < 3.0 mmol/L (−0.7 ± 2.2%; P = 0.0171), lower coefficient of variation (−2.4 ± 5.8; P = 0.0198) and lower mean of daily differences (−0.6 ± 0.9 mmol/L; P = 0.0005) was observed. Glycaemic control using OpenAPS was comparable with results of more rigorously developed and tested AP systems. However, OpenAPS was used by a highly selective, motivated and technology-adept cohort, despite not being approved for the treatment of individuals with T1D.     

Hybrid closed-loop therapy: Where are we in 2021?

Hybrid closed-loop systems are characterized by the coexistence of algorithm-driven automated insulin delivery combined with manual mealtime boluses. Used correctly, these insulin delivery systems offer better glucose control and reduced risk of hypoglycaemia and represent the most advanced form of insulin delivery available for people with type 1 diabetes. The aim of this paper was to compare the currently available commercial hybrid closed-loop systems in the UK: the Medtronic 670G/780G, Tandem t:slim X2 Control IQ and CamAPS FX systems. The Medtronic 670G/780G systems use Guardian 3 sensor (7-day use, two to four calibrations per day), while Tandem and CamAPS systems use the calibration-free Dexcom G6 sensor (10 days). The CamAPS system is available as an android app, whereas the other two systems have the algorithm embedded in the insulin pump. During pivotal studies, depending on the study population and baseline glycated haemoglobin level, these systems achieve a time spent in the target range 3.9 to 10 mmol/L (70 to 180 mg/dL) of 65% to 76% with low burden of hypoglycaemia. All three systems allow a higher glucose target for announced exercise, while the Tandem system offers an additional night-time tighter target. The CamAPS system offers fully customizable glucose targets and is the only system licensed for use during pregnancy. Additional education is required for both users and healthcare professionals to harness the best performance from these systems as well as to troubleshoot when “automode exits” occur. We provide consensus recommendations to develop pragmatic pathways to guide patients, clinicians and commissioners in making informed decisions on the appropriate use of the diabetes technology.     

Do-It-Yourself Automated Insulin Delivery: A Leading Example of the Democratization of Medicine

Digital innovations have led to an explosion of data in healthcare, driving processes of democratization and foreshadowing the end of the paternalistic era of medicine and the inception of a new epoch characterized by patient-centered care. We illustrate that the “do it yourself” (DIY) automated insulin delivery (AID) innovation of diabetes is a leading example of democratization of medicine as evidenced by its application to the three pillars of democratization in healthcare (intelligent computing; sharing of information; and privacy, security, and safety) outlined by Stanford but also within a broader context of democratization. The heuristic algorithms integral to DIY AID have been developed and refined by human intelligence and demonstrate intelligent computing. We deliver examples of research in artificial pancreas technology which actively pursues the use of machine learning representative of artificial intelligence (AI) and also explore alternate approaches to AI within the DIY AID example. Sharing of information symbolizes the core philosophy behind the success of the DIY AID evolution. We examine data sharing for algorithm development and refinement, for sharing of the open-source algorithm codes online, for peer to peer support, and sharing with medical and scientific communities. Do it yourself AID systems have no regulatory approval raising safety concerns as well as medico-legal and ethical implications for healthcare professionals. Other privacy and security factors are also discussed. Democratization of healthcare promises better health access for all and we recognize the limitations of DIY AID as it exists presently, however, we believe it has great potential.     

Automated Insulin Delivery Systems: Today,Tomorrow and User Requirements

Automated insulin delivery (AID) is the most recent advance in type 1 diabetes (T1D) management. It has the potential to achieve glycemic targets without disabling hypoglycemia, to improve quality of life and reduce diabetes distress and burden associated with self-management. Several AID systems are currently licensed for use by people with T1D in Europe, United States, and the rest of the world. Despite AID becoming a reality in routine clinical practice over the last few years, the commercially hybrid AID and other systems, are still far from a fully optimized automated diabetes management tool. Implementation of AID systems requires education and support of healthcare professionals taking care of people with T1D, as well as users and their families. There is much to do to increase usability, portability, convenience and to reduce the burden associated with the use of the systems. Co-design, involvement of people with lived experience of T1D and robust qualitative assessment is critical to improving the real-world use of AID systems, especially for those who may have greater need. In addition to this, information regarding the psychosocial impact of the use of AID systems in real life is needed. The first commercially available AID systems are not the end of the development journey but are the first step in learning how to optimally automate insulin delivery in a way that is equitably accessible and effective for people living with T1D.     

Insulin Delivery Route for the Artificial Pancreas: Subcutaneous,Intraperitoneal, or Intravenous? Pros and Cons

Insulin delivery is a crucial component of a closed-loop system aiming at the development of an artificial pancreas. The intravenous route, which has been used in the bedside artificial pancreas model for 30 years, has clear advantages in terms of pharmacokinetics and pharmacodynamics, but cannot be used in any ambulatory system so far. Subcutaneous (SC) insulin infusion benefits from the broad expansion of insulin pump therapy that promoted the availability of constantly improving technology and fast-acting insulin analog use. However, persistent delays of insulin absorption and action, variability and shortterm stability of insulin infusion from SC-inserted catheters generate effectiveness and safety issues in view of an ambulatory, automated, glucose-controlled, artificial beta cell. Intraperitoneal insulin delivery, although still marginally used in diabetes care, may offer an interesting alternative because of its more-physiological plasma insulin profiles and sustained stability and reliability of insulin delivery.     

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.     

Metabolic and physiological responses to graded exercise testing in individuals with type 1 diabetes using insulin pump therapy

Aims

To profile acute glycaemic dynamics during graded exercise testing (GXT) and explore the influence of glycaemic indicators on the physiological responses to GXT in adults with type 1 diabetes using insulin pump therapy.

Methods

This was a retrospective analysis of pooled data from four clinical trials with identical GXT protocols. Data were obtained from 45 adults with type 1 diabetes using insulin pumps [(30 females); haemoglobin A1c 59.5 ± 0.5 mmol/mol (7.6 ± 1.0%); age 49.7 ± 13.0 years; diabetes duration 31.2 ± 13.5 years; V̇O_2peak 29.5 ± 8.0 ml/min/kg]. Integrated cardiopulmonary variables were collected continuously via spiroergometry. Plasma glucose was obtained every 3 min during GXT as well as the point of volitional exhaustion. Data were assessed via general linear modelling techniques with age and gender adjustment. Significance was accepted at p ≤ .05.

Results

Despite increasing duration and intensity, plasma glucose concentrations remained similar to rest values (8.8 ± 2.3 mmol/L) throughout exercise (p = .419) with an overall change of +0.3 ± 1.1 mmol/L. Starting glycaemia bore no influence on subsequent GXT responses. Per 1% increment in haemoglobin A1c there was an associated decrease in V̇O_2peak of 3.8 ml/min/kg (p < .001) and power_peak of 0.33 W/kg (p < .001) concomitant with attenuations in indices of peripheral oxygen extraction [(O₂ pulse) −1.2 ml/beat, p = .023].

Conclusion

In adults with long-standing type 1 diabetes using insulin pump therapy, circulating glucose remains stable during a graded incremental cycle test to volitional exhaustion. Glycaemic indicators are inversely associated with aerobic rate, oxygen economy and mechanical output across the exercise intensity spectrum. An appreciation of these nexuses may help guide appropriate decision making for optimal exercise management strategies.     

The challenges of achieving postprandial glucose control using closed‐loop systems in patients with type 1 diabetes

For patients with type 1 diabetes, closed‐loop delivery systems (CLS) combining an insulin pump, a glucose sensor and a dosing algorithm allowing a dynamic hormonal infusion have been shown to improve glucose control when compared with conventional therapy. Yet, reducing glucose excursion and simplification of prandial insulin doses remain a challenge. The objective of this literature review is to examine current meal‐time strategies in the context of automated delivery systems in adults and children with type 1 diabetes. Current challenges and considerations for post‐meal glucose control will also be discussed. Despite promising results with meal detection, the fully automated CLS has yet failed to provide comparable glucose control to CLS with carbohydrate‐matched bolus in the post‐meal period. The latter strategy has been efficient in controlling post‐meal glucose using different algorithms and in various settings, but at the cost of a meal carbohydrate counting burden for patients. Further improvements in meal detection algorithms or simplified meal‐priming boluses may represent interesting avenues. The greatest challenges remain in regards to the pharmacokinetic and dynamic profiles of available rapid insulins as well as sensor accuracy and lag‐time. New and upcoming faster acting insulins could provide important benefits. Multi‐hormone CLS (eg, dual‐hormone combining insulin with glucagon or pramlintide) and adjunctive therapy (eg, GLP‐1 and SGLT2 inhibitors) also represent promising options. Meal glucose control with the artificial pancreas remains an important challenge for which the optimal strategy is still to be determined.     

Artificial Pancreas Systems: Progress in Development of an Artificial Pancreas

This issue of Journal of Diabetes Science and Technology contains a collection of 12 original articles describing the latest advances in the development of algorithms for controlling insulin delivery in an artificial pancreas. Algorithms presented in this issue are affected by numerous quantifiable factors, including insulin pharmaco-kinetics, timing of meal carbohydrate appearance, meal size, amount of exercise, presence of stress, day-to-day variations in insulin sensitivity, insulin time-activity profiles, accuracy of glucose monitor calibration, metabolic profiles of both adults and neonates, and risks of hypoglycemia/hyperglycemia. These articles present theoretical advances in insulin delivery algorithms from modeled in silico patients, as well as clinical data from actual patients who have used closed loop systems. The novel approaches described in these articles are expected to bring us much closer to realization of a commercially available closed loop system for controlling glucose levels in patients with diabetes.     

Use of Microdialysis-Based Continuous Glucose Monitoring to Drive Real-Time Semi-Closed-Loop Insulin Infusion

Continuous glucose monitoring (CGM) and automated insulin delivery may make diabetes management substantially easier, if the quality of the resulting therapy remains adequate. In this study, a semi-closed-loop control algorithm was used to drive insulin therapy and its quality was compared to that of subject-directed therapy. Twelve subjects stayed at the study site for approximately 70 hours and were provided with the investigational Automated Pancreas System Test Stand (APS-TS), which was used to calculate insulin dosage recommendations automatically. These recommendations were based on microdialysis CGM values and common diabetes therapy parameters. For the first half of their stay, the subjects directed their diabetes therapy themselves, whereas for the second half, the insulin recommendations were delivered by the APS-TS (so-called algorithm-driven therapy). During subject-directed therapy, the mean glucose was 114 mg/dl compared to 125 mg/dl during algorithm-driven therapy. Time in target (90 to 150 mg/dl) was approximately 46% during subject-directed therapy and approximately 58% during algorithm-driven therapy. When subjects directed their therapy, approximately 2 times more hypoglycemia interventions (oral administration of carbohydrates) were required than during algorithm-driven therapy. No hyperglycemia interventions (delivery of addition insulin) were necessary during subject-directed therapy, while during algorithm-driven therapy, 2 hyperglycemia interventions were necessary. The APS-TS was able to adequately control glucose concentrations in the subjects. Time in target was at least comparable or moderately higher during closed-loop control and markedly fewer hypoglycemia interventions were required, thus increasing patient safety.     

Effect of fully automated closed-loop insulin delivery using faster aspart versus standard aspart on gluco-regulatory hormones in type 2 diabetes

We retrospectively assessed gluco-regulatory hormones over 10 h (including two meals) of fully automated closed-loop insulin delivery using faster (FA) versus standard insulin aspart (IAsp) in adults with type 2 diabetes [n = 15, age 59 ± 10 years, body mass index 34.5 ± 9.1 kg/m², glycated haemoglobin 7.7 ± 1.2% (60 ± 13 mmol/mol)]. Plasma concentration of human insulin, IAsp, C-peptide, glucagon, glucagon-like peptide 1, glucose-dependent insulinotropic peptide and peptide tyrosine tyrosine were measured every 15-30 min. Endogenous insulin secretion was calculated using C-peptide deconvolution and exposures to hormones were compared using their mean plasma concentrations. Ten-hour exposure of IAsp was higher with FA versus IAsp (P = .037) in line with the 10% higher insulin requirements to achieve similar glucose control. No significant difference was found for total insulin exposure and endogenous insulin secretion. Similarly, other gluco-regulatory hormones did not significantly differ. In conclusion, the faster pharmacokinetic profile and slightly higher aspart exposure of FA versus IAsp remained without significant effects on endogenous insulin secretion or other gluco-regulatory hormones. Further studies are warranted to explore the metabolic and endocrine effects of novel insulins with accelerated pharmacokinetic properties.     

Better glycaemic control with BioChaperone glargine lispro co-formulation than with insulin lispro Mix25 or separate glargine and lispro administrations after a test meal in people with type 2 diabetes

Because of its physico-chemical properties, insulin glargine is usually not mixable with rapid insulins. BioChaperone BC147 is a polyanionic amphiphilic polymer, solubilizing insulin glargine at neutral pH, and thus enabling stable glargine formulation with fast-acting insulin lispro (BioChaperone glargine lispro co-formulation [BC Combo]). We investigated pharmacokinetic (PK) endpoints and postprandial glucose (PPG) control after administration of BC Combo (75% insulin glargine, 25% insulin lispro), insulin lispro Mix25 (LMix) and separate injections of insulins glargine (75% total dose) and lispro (25% total dose [G + L]) immediately before ingestion of a mixed meal in people with type 2 diabetes mellitus (T2DM), using a randomized, double-blind, double-dummy crossover study design. Participants received individualized bolus doses (mean 0.62 U/kg) of BC Combo, LMix or G + L, together with a solid mixed meal (610 kcal, 50% carbohydrate, 30% fat, 20% protein). Insulin dosages were kept constant for each study day. Thirty-nine participants with T2DM (mean ± SD age and glycated haemoglobin 60.8 ± 7.5 years and 64 ± 6 mmol/mol, respectively) were randomized. BC Combo improved the predefined primary endpoint, early PPG control, compared to LMix (incremental area under the blood glucose concentration–time curve from 0 to 2 hours after the meal [ΔAUC_BG,0–2h] reduction of 18%; P = 0.0009) and G + L (ΔAUC_BG,0–2h reduction of 10%; P = 0.0450). The number of mealtime hypoglycaemic episodes per participant was lower with BC Combo (22 episodes in 14 participants) compared to LMix (43 episodes in 20 participants; P = 0.0028), but not significantly different from G + L (28 episodes in 19 participants; P = 0.2523). BC Combo demonstrated superior early PPG control with fewer hypoglycaemic episodes compared to LMix and superior early PPG control compared to separate G + L administrations.     

Efficacy of two telemonitoring systems to improve glycaemic control during basal insulin initiation in patients with type 2 diabetes: The TeleDiab-2 randomized controlled trial

TeleDiab-2 was a 13-month randomized controlled trial evaluating the efficacy and safety of two telemonitoring systems to optimize basal insulin (BI) initiation in subjects with inadequately controlled type 2 diabetes (HbA1c, 7.5%-10%). A total of 191 participants (mean age 58.7 years, mean HbA1c 8.9%) were randomized into three groups: group 1(G1, standard care, n = 63), group 2 (G2, interactive voice response system, n = 64) and group 3 (G3, Diabeo-BI app software, n = 64). The two telemonitoring systems proposed daily adjustments of BI doses, in order to facilitate the achievement of fasting blood glucose (FBG) values targeted at ~100 mg/dL. At 4 months follow-up, HbA1c reduction was significantly higher in the telemonitoring groups (G2: −1.44% and G3: −1.48% vs. G1: −0.92%; P < 0.002). Moreover, target FBG was reached by twice as many patients in the telemonitoring groups as in the control group, and insulin doses were also titrated to higher levels. No severe hypoglycaemia was observed in the telemonitoring groups and mild hypoglycaemia frequency was similar in all groups. In conclusion, both telemonitoring systems improved glycaemic control to a similar extent, without increasing hypoglycaemic episodes.     

Real-world performance of the MiniMed 780G advanced hybrid closed loop system in Latin America: Substantial improvement in glycaemic control with each technology iteration of the MiniMed automated insulin delivery system

Aim

We studied real-world performance of MiniMed (MM) 780G system users from Argentina, Brazil, Colombia and Chile (geographical analysis), and the effect of each technology iteration of the MM system on glycaemic control (technology iteration analysis).

Materials and Methods

CareLink data from August 2020 to September 2022 were extracted. Endpoints included continuous glucose monitoring metrics. For the geographical analysis, aggregated endpoints for MM780G system users were calculated. For the technology iteration analysis, MM780G system user outcomes were compared with outcomes when the same individuals were still using the MM640G or MM670G system.

Results

On average, 1025 MM780G system users from the geographical analysis were followed for 136 (SD 135) days, spent 91.5 (14.3)% in advanced hybrid closed loop, showed a glucose management indicator (GMI) of 6.7 (0.3)%, a time in range between 70 and 180 mg/dl (TIR) of 76.5 (9.0)%, and a time below range 70 mg/dl (TBR) of 2.7 (2.1)%. The percentage of users reaching targets of GMI <7%, TIR >70% and TBR <4% was 80.8%, 78.1% and 80.1%, respectively. The technology iteration analysis on users transitioning from MM640G to MM780G system (N = 381) showed 0.4% decrease in GMI (7.1% to 6.7%, p < .0001), 10.7% increase in TIR (65.9% to 76.6%, p < .0001), while TBR remained. The percentage of insulin delivered automatically increased as well (47.5%-57.7%, p < .0001). Users transitioning from MM670G system (N = 78) showed a similar but less pronounced pattern.

Conclusions

Real-world Latin American MM780G users on average showed good glucose control, achieving international targets. Glycaemic control increased with every technology iteration of the MM system, providing more automation each time.