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Background: Multiple clinician adjustable parameters impact upon glycemia in people with type 1 diabetes (T1D) using Medtronic Mini Med 780G (MM780G) AHCL. These include glucose targets, carbohydrate ratios (CR), and active insulin time (AIT). Algorithm-based decision support advising upon potential settings adjustments may enhance clinical decision-making. Methods: Single-arm, two-phase exploratory study developing decision support to commence and sustain AHCL. Participants commenced investigational MM780G, then 8 weeks Phase 1-initial optimization tool evaluation, involving algorithm-based decision support with weekly AIT and CR recommendations. Clinicians approved or rejected CR and AIT recommendations based on perceived safety per protocol. Co-design resulted in a refined algorithm evaluated in a further identically configured Phase 2. Phase 2 participants also transitioned to commercial MM780G following "Quick Start" (algorithm-derived tool determining initial AHCL settings using daily insulin dose and weight). We assessed efficacy, safety, and acceptability of decision support using glycemic metrics, and the proportion of accepted CR and AIT settings per phase. Results: Fifty three participants commenced Phase 1 (mean age 24.4; Hba1c 61.5mmol/7.7%). The proportion of CR and AIT accepted by clinicians increased between Phases 1 and 2 respectively: CR 89.2% vs. 98.6%, p < 0.01; AIT 95.2% vs. 99.3%, p < 0.01. Between Phases, mean glucose percentage time < 3.9mmol (< 70mg/dl) reduced (2.1% vs. 1.4%, p = 0.04); change in mean TIR 3.9-10mmol/L (70-180mg/dl) was not statistically significant: 72.9% ± 7.8 and 73.5% ± 8.6. Quick start resulted in stable TIR, and glycemic metrics compared to international guidelines. Conclusion: The co-designed decision support tools were able to deliver safe and effective therapy. They can potentially reduce the burden of diabetes management related decision making for both health care practitioners and patients. Trial registration: Prospectively registered with Australia/New Zealand Clinical Trials Registry(ANZCTR) on 30th March 2021 as study ACTRN12621000360819. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To evaluate glycemic outcomes in youth (aged 13–25 years) with type 1 diabetes and high-risk glycemic control (HbA1c ≥8.5% [69 mmol/mol]) on multiple daily injection (MDI) therapy after transitioning to advanced hybrid closed loop (AHCL) therapy. RESEARCH DESIGN AND METHODS: This prospective, 3-month, single-arm, dual-center study enrolled 20 participants, and all completed the study. RESULTS: HbA1c decreased from 10.5 ± 2.1% (91.2 ± 22.8 mmol/mol) at baseline to 7.6 ± 1.1% (59.7 ± 11.9 mmol/mol), and time spent in target range 70–180 mg/dL (3.9–10.0 mmol/L) increased from 27.6 ± 13.2% at baseline to 66.5 ± 9.8% after 3 months of AHCL. Two episodes of diabetic ketoacidosis attributed to infusion set failure occurred. CONCLUSIONS: AHCL has the potential to improve suboptimal glycemia in youth with type 1 diabetes previously on MDI therapy. [ABSTRACT FROM AUTHOR]

Background: Open‐source automated insulin delivery (AID) is a user‐driven treatment modality used by thousands globally. Healthcare professionals' (HCPs) ability to support users of this technology is limited by a lack of knowledge of these systems. Aims: To describe the challenges experienced by HCPs supporting participants' use of open‐source automated insulin delivery in the Community deRivEd AuTomatEd insulin delivery (CREATE) study. Methods: Data were collected prospectively from the study team's fortnightly meetings and Slack Workspace (Slack Technologies, Ltd. 2018) during the first 4 months of the trial. Key topics were identified from minutes of meetings. Slack conversations were categorised by topic, with the number of posts per conversation, number of sites per conversation and involvement of experts in open‐source AID being recorded. Results: In the first 4 months of the trial, there were 254 conversations in Slack with a mean of 5.2 (±4.25) posts per conversation. The most frequent learning challenge was insulin pump and cannula problems relating to the DANA‐iTM insulin pump, which totalled 24.0% of all conversations. Experts on open‐source AID use were involved in 83.3% of conversations. Conclusions: A significant proportion of challenges related to specific devices, rather than AID. Challenges relating to the functioning of open‐source AID were more likely to involve input from experts in open‐source AID. This is the first report of challenges experienced by a multidisciplinary team in a supported open‐source environment that may inform expectations in routine clinical care. [ABSTRACT FROM AUTHOR]