Advanced automated insulin delivery in inpatients receiving nutritional support: a single-center, open-label, randomised controlled trial
Year: 2026
Type: article
Study designs: Randomized Controlled Trial, Clinical Trial
Abstract: AIMS: This study addressed the challenge of glucose management in inpatients receiving nutritional support, even with automated insulin delivery (AID). We evaluated whether advanced AID would improve glycaemic control compared with standard subcutaneous insulin therapy in inpatients receiving nutrition support. METHODS: We did a single-centre, open-label, randomised controlled trial in adult inpatients receiving nutrition support who required subcutaneous insulin therapy. Patients recruited from non-critical care surgical and medical wards were randomly assigned (1:1) to receive advanced AID or conventional subcutaneous insulin therapy (control group) given in accordance with local clinical practice. Patients were followed up for up to 20 days or until hospital discharge. The primary endpoint was the TIR, the proportion of time that sensor glucose concentrations were within the target range, 3.9-10.0 mmol/l. This trial is registered with ClinicalTrials.gov, number ChiCTR2300078746. RESULTS: Between February 2023 and September 2024, 32 patients were assessed for eligibility; of these, 26 were enrolled and randomly assigned to the closed-loop group (n = 13) or the control group (n = 13). TIR was 63.1 % [SD 12.8] in the closed-loop group and 31.5 % [20.1]in the control group (difference 31.7 percentage points [95 % CI 18.0-45.2; P < 0.001]). The time spent below the target range (< 3.9 mmol/l) did not differ between groups. No serious adverse event occurred in each group. No episodes of severe hypoglycaemia or hyperglycaemia with ketonaemia occurred in either study group. CONCLUSIONS: The advanced AID system improves glycaemic control without increasing the risk of hypoglycaemia in inpatients receiving nutrition support, particularly in patients with acute severe pancreatitis.
Source: Diabetes & Metabolism
Authors: Yunying Cai, Le Ma, Linting Xun, Yanmin Chen, Heng Su
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,490
Award: G5136601435
OpenAlex ID: W7148827791
