Management of Type 1 Diabetes Mellitus Using Open-Source Automated Insulin Delivery During Pregnancy: A Case Series

Impact of AID on Glycemic Profile and Maternal/Neonatal Outcomes in Pregnancy: A Review of the Evidence From Observational Studies

The mainstay of type 1 diabetes (T1D) management in pregnancy is optimization of glucose levels in a tight range. Achieving euglycemia has been revolutionized by advances in diabetes technology, including the development of automated insulin delivery (AID) systems. A small but growing population of gravidas with T1D elects to pursue off-label use of AID systems in pregnancy, and their outcomes have been described in numerous observational cohorts. This review aims to aggregate data from all available observational studies examining glycemic, maternal, and neonatal outcomes associated with antenatal AID use. A total of 243 pregnancies managed antenatally with AID were described in 24 publications, with largely reassuring outcomes data. Time in range (TIR) with commercial AID systems was generally acceptable, with many patients reaching pregnancy target TIR > 70% by the third trimester. Time in range with open-source AID systems appeared even higher, although with the potential tradeoff of worse time below range (TBR). Clinically, there do not appear to be major differences in pregnancy outcomes between AID systems and other methods of insulin delivery, although this assumption is based largely on indirect comparisons with other population-level reports rather than direct comparisons within analytic observational cohorts. Clinical outcomes appear superior with open-source AID compared with commercial AID, although this should be interpreted with caution based on the small sample size of this subpopulation (n = 16) and potential confounding. The real-world evidence generated by these observational studies provides invaluable information for patients and providers seeking to improve outcomes for gravidas with T1D.

Automated Insulin Delivery for Pregnant Women With Type 1 Diabetes: Where Do We Stand?

Automated insulin delivery (AID) systems mimic an artificial pancreas via a predictive algorithm integrated with continuous glucose monitoring (CGM) and an insulin pump, thereby providing AID. Outside of pregnancy, AID has led to a paradigm shift in the management of people with type 1 diabetes (T1D), leading to improvements in glycemic control with lower risk for hypoglycemia and improved quality of life. As the use of AID in clinical practice is increasing, the number of women of reproductive age becoming pregnant while using AID is also expected to increase. The requirement for lower glucose targets than outside of pregnancy and for frequent adjustments of insulin doses during pregnancy may impact the effectiveness and safety of AID when using algorithms for non-pregnant populations with T1D. Currently, the CamAPS® FX is the only AID approved for use in pregnancy. A recent randomized controlled trial (RCT) with CamAPS® FX demonstrated a 10% increase in time in range in a pregnant population with T1D and a baseline glycated hemoglobin (HbA1c) ≥ 48 mmol/mol (6.5%). Off-label use of AID not approved for pregnancy are currently also being evaluated in ongoing RCTs. More evidence is needed on the impact of AID on maternal and neonatal outcomes. We review the current evidence on the use of AID in pregnancy and provide an overview of the completed and ongoing RCTs evaluating AID in pregnancy. In addition, we discuss the advantages and challenges of the use of current AID in pregnancy and future directions for research.

Use of Automated Insulin Delivery in Pregnancies Complicated by Type 1 Diabetes

Background: Diabetes during pregnancy is associated with significant maternal and foetal health risks. Insulin requirements also change during pregnancy. This necessitates careful and effective management of diabetes. Although commonly used in clinical practice, the US Food and Drug Administration (FDA)-approved algorithms for automated insulin delivery (AID) systems do not have pregnancy-specific glycaemic targets. This review aims to evaluate the safety and efficacy of AID systems in reaching glycaemic targets in pregnant women with type 1 diabetes (T1D). Methods: In this retrospective case review, six pregnant women with T1D used three types of AID systems. Two patients used Omnipod 5, two patients used Control-I Q and two patients used Do-I t-Yourself (DIY) Loop. Results: Across trimesters, the two patients using Omnipod 5 had an average time in range (TIR) of 68 and 82%. Patients using Control-I Q had an average TIR of 77 and 69%. Both the patients using DIY Loop had an average TIR of 85%. Hypoglycaemia occurrence was minimal. Additionally, four of the six patients had uncomplicated vaginal deliveries in their third trimester, and four of the six patients achieved guideline-r ecommended TIR targets. Birth complications for the other two patients were resolved shortly after birth. Throughout the pregnancies, insulin needs approximately doubled. Conclusions: AID systems can achieve near-desired glycaemic targets with minimal hypoglycaemia in pregnant women with T1D. Randomized controlled trials are needed to confirm these findings and to win FDA indications in pregnancy.

Retrospective Comparison of Commercially Available Automated Insulin Delivery With Open-Source Automated Insulin Delivery Systems in Type 1 Diabetes

BACKGROUND

Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems.

MATERIALS AND METHODS

A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed.

RESULTS

The PWT1D using OS-AID systems spent more time in range (TIR)70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR)181-250 mg/dL (11.1% vs 19.6%), less TAR>250 mg/dL (2.5% vs 4.3%), and more time below range (TBR)54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR<54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%).

CONCLUSION

In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.

Effect of artificial pancreas system use on glycaemic control among pregnant women with type 1 diabetes mellitus: A meta-analysis of randomized controlled trials

AIM

To assess the efficacy of artificial pancreas systems (APS) use among pregnant women with type 1 diabetes mellitus (T1DM) by conducting a meta-analysis.

METHODS

We searched five databases, including EMBASE, Web of Science, PubMed, Cochrane Library and SCOPUS, for literature on APS use among pregnant women with T1DM before October 9, 2023. The primary endpoint was 24-hour time in range (TIR; 3.5-7.8 mmol/L). Secondary endpoints included glycaemic metrics for 24-hour (mean blood glucose [MBG], time above range [TAR], time below range [TBR]), and overnight TIR and TBR.

RESULTS

We identified four randomized controlled trials involving 164 participants; one study with 16 participants focused on overnight APS use, and the other three focused on 24-hour APS use. Compared with standard care, APS exhibited a favourable effect on 24-hour TIR (standard mean difference [SMD] = 0.53, 95% confidence interval [CI] 0.25, 0.80, P < 0.001), overnight TIR (SMD = 0.67, 95% CI 0.39, 0.95, P < 0.001), and overnight TBR (<3.5 mmol/L; SMD = -0.49, 95% CI -0.77, -0.21 P < 0.001), while there was no significant difference in 24-hour TAR, 24-hour TBR, or MBG between the two groups. We further conducted subgroup analyses after removing the trial focused on overnight APS use and showed that 24-hour APS use reduced not only the 24-hour TIR (SMD = 0.41, 95% CI 0.12, 0.71; P = 0.007) but also the 24-hour TBR (<2.8 mmol/L; SMD = -0.77, 95% CI -1.32, -0.23, P = 0.006).

CONCLUSION

Our findings suggest that APS might improve 24-hour TIR and overnight glycaemic control, and 24-hour APS use also significantly reduced 24-hour TBR (2.8 mmol/L) among pregnant women with T1DM.

Efficacy and safety of Android artificial pancreas system use at home among adults with type 1 diabetes mellitus in China: protocol of a 26-week, free-living, randomised, open-label, two-arm, two-phase, crossover trial

INTRODUCTION

Do-it-yourself artificial pancreas system (DIY APS) is built using commercially available insulin pump, continuous glucose monitoring (CGM) and an open-source algorithm. Compared with commercial products, DIY systems are affordable, allow personalised settings and provide updated algorithms, making them a more promising therapy for most patients with type 1 diabetes mellitus (T1DM). Many small and self-reported observational studies have found that their real-world use was associated with potential metabolic and psychological benefits. However, rigorous-designed studies are urgently needed to confirm its efficacy and safety.

METHODS AND ANALYSIS

In this 26-week randomised, open-label, two-arm, two-phase, crossover trial, participants aged 18-75 years, with T1DM and glycated haemoglobin (HbA1c) 7-11%, will use AndroidAPS during one 12-week period and sensor-augmented pump during another 12-week period. This study will recruit at least 24 randomised participants. AndroidAPS consists of three components: (1) real-time CGM; (2) insulin pump; (3) AndroidAPS algorithm implemented in Android smartphone. The primary endpoint is time in range (3.9-10.0 mmol/L) derived from CGM. The main secondary endpoints include percentage of sensor glucose values below, within and above target range; mean sensor glucose value; measures of glycaemic variability and centralised HbA1c. Safety endpoints mainly include the frequency of hypoglycaemia events, diabetic ketoacidosis and other serious adverse events.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University. There will be verbal and written information regarding the trial given to each participant. The study will be disseminated through peer-reviewed publications and conference presentations.

OVERALL STATUS

Recruiting.

STUDY START

11 February 2023.

PRIMARY COMPLETION

31 July 2024.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT05726461).

Practical Guidance on Open Source and Commercial Automated Insulin Delivery Systems: A Guide for Healthcare Professionals Supporting People with Insulin-Requiring Diabetes

As increasing numbers of people with insulin-managed diabetes use automated insulin delivery (AID) systems or seek such technologies, healthcare providers are faced with a steep learning curve. Healthcare providers need to understand how to support these technologies to help inform shared decision making, discussing available options, implementing them in the clinical setting, and guiding users in special situations. At the same time, there is a growing diversity of commercial and open source automated insulin delivery systems that are evolving at a rapid pace. This practical guide seeks to provide a conversational framework for healthcare providers to first understand and then jointly assess AID system options with users and caregivers. Using this framework will help HCPs in learning how to evaluate potential new commercial or open source AID systems, while also providing a guide for conversations to help HCPs to assess the readiness and understanding of users for AID systems. The choice of an AID system is not as simple as whether the system is open source or commercially developed, and indeed there are multiple criteria to assess when choosing an AID system. Most importantly, the choices and preferences of the person living with diabetes should be at the center of any decision around the ideal automated insulin delivery system or any other diabetes technology. This framework highlights issues with AID use that may lead to burnout or perceived failures or may otherwise cause users to abandon the use of AID. It discusses the troubleshooting of basic AID system operation and discusses more advanced topics regarding how to maximize the time spent on AID systems, including how to optimize settings and behaviors for the best possible outcomes with AID technology for people with insulin-requiring diabetes. This practical approach article demonstrates how healthcare providers will benefit from assessing and better understanding all available AID system options to enable them to best support each individual.