A fully artificial pancreas versus a hybrid artificial pancreas for type 1 diabetes: a single-centre, open-label, randomised controlled, crossover, non-inferiority trial.

BACKGROUND: For people with type 1 diabetes, there is currently no automated insulin delivery system that does not require meal input. We aimed to assess the efficacy of a novel faster-acting insulin aspart (Fiasp) plus pramlintide fully closed-loop system that does not require meal input. METHODS: In this open-label, randomised controlled, crossover, non-inferiority trial we compared the Fiasp (Novo Nordisk, Bagsværd, Denmark) plus pramlintide closed-loop system with no meal input (fully artificial pancreas) and the Fiasp-alone closed-loop system with precise carbohydrate counting (hybrid artificial pancreas). Adults (≥18 years) who had a clinical diagnosis of type 1 diabetes for at least 12 months, had glycated haemoglobin 12% or lower, and had been on insulin pump therapy for at least 6 months were enrolled at McGill University Health Centre, Montreal, QC, Canada. The Fiasp plus pramlintide fully closed-loop system delivered pramlintide in a basal-bolus manner with a fixed ratio of 10 µg:U relative to insulin. A research staff member counted the carbohydrate content of meals to input in the hybrid closed-loop system. Participants completed the two full-day crossover interventions in a random order allocated by a computer-generated code implementing a blocked randomisation (block size of four). The primary outcome was the percentage of time spent within the glucose target range (3·9-10·0 mmol/L), with a 6% non-inferiority margin, assessed in all participants who completed both interventions. This trial is registered with ClinicalTrials.gov, NCT03800875. FINDINGS: Between Feb 8, 2019, and Sept 19, 2020, we enrolled 28 adults, of whom 24 completed both interventions and were included in analyses. The percentage of time spent in the target range was 74·3% (IQR 61·5-82·8) with the fully closed-loop system versus 78·1% (66·3-87·5) with the hybrid Fiasp-alone closed-loop system (paired difference 2·6%, 95% CI -2·4 to 12·2; non-inferiority p=0·28). Eight (33%) participants had at least one hypoglycaemia event (<3·3 mmol/L) with the fully closed-loop system compared with 14 (58%) participants with the hybrid closed-loop system (2200-2200 h). Non-mild nausea was reported by three (13%) participants and non-mild bloating by one (4%) participant with the fully closed-loop system compared with zero participants with the hybrid closed-loop system. INTERPRETATION: The Fiasp plus pramlintide fully closed-loop system was not non-inferior to the Fiasp-alone hybrid closed-loop system for the overall percentage of time in the glucose target range. However, participants still spent a high percentage of time within the target range with the fully-closed loop system. Outpatient studies comparing the fully closed-loop hybrid systems with patient-estimated, rather than precise, carbohydrate counting are warranted. FUNDING: Diabetes Canada.

Long-term use of the hybrid artificial pancreas by adjusting carbohydrate ratios and programmed basal rate: A reinforcement learning approach.

BACKGROUND AND OBJECTIVES: The hybrid artificial pancreas regulates glucose levels in people with type 1 diabetes. It delivers (i) insulin boluses at meal times based on the meals' carbohydrate content and the carbohydrate ratios (CRs) and (ii) insulin basal, between meals and at night, continuously modulated around individual-specific programmed basal rate. The CRs and programmed basal rate significantly vary between individuals and within the same individual with type 1 diabetes, and using suboptimal values in the hybrid artificial pancreas may degrade glucose control. We propose a reinforcement learning algorithm to adaptively optimize CRs and programmed basal rate to improve the performance of the hybrid artificial pancreas. METHODS: The proposed reinforcement learning algorithm was designed using the Q-learning approach. The algorithm learns the optimal actions (CRs and programmed basal rate) by applying them to the individual's state (previous day's glucose levels and insulin delivery) based on an exploration and exploitation trade-off. First, outcomes from our simulator were compared to those of a clinical study in 23 individuals with type 1 diabetes and have yielded similar results. Second, the learning algorithm was tested using the simulator with two scenarios. Scenario 1 has fixed meal sizes and ingestion times and scenario 2 has a more realistic eating behavior with random meal sizes, ingestion times, and carbohydrate counting errors. RESULTS: After about five weeks, the reinforcement learning algorithm improved the percentage of time spent in target range from 67% to 86.7% in scenario 1 and 65.5% to 86% in scenario 2. The percentage of time spent below 4.0 mmol/L decreased from 9% to 0.9% in scenario 1 and 9.5% to 1.1% in scenario 2. CONCLUSIONS: Results indicate that the proposed algorithm has the potential to improve glucose control in people with type 1 diabetes using the hybrid artificial pancreas. The proposed algorithm is a key in making the hybrid artificial pancreas adaptive for the long-term real life outpatient studies.

A Model-Based Insulin Dose Optimization Algorithm for People With Type 1 Diabetes on Multiple Daily Injections Therapy.

OBJECTIVE: Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D) including basal insulin doses to keep glucose levels constant during fasting conditions and bolus insulin doses with meals. Optimal insulin dosing is critical to achieving satisfactory glycemia but is challenging due to inter- and intra-individual variability. Here, we present a novel model-based iterative algorithm that optimizes insulin doses using previous-day glucose, insulin, and meal data. METHODS: Our algorithm employs a maximum-a-posteriori method to estimate parameters of a model describing the effects of changes in basal-bolus insulin doses. Then, parameter estimates, their confidence intervals, and the goodness of fit, are combined to generate new recommendations. We assessed our algorithm in three ways. First, a clinical data set of 150 days (15 participants) were used to evaluate the proposed model and the estimation method. Second, 60-day simulations were performed to demonstrate the efficacy of the algorithm. Third, a sample 6-day clinical experiment is presented and discussed. RESULTS: The model fitted the clinical data well with a root-mean-square-error of 1.75 mmol/L. Simulation results showed an improvement in the time in target (3.9-10 mmol/L) from 64% to 77% and a decrease in the time in hypoglycemia (< 3.9 mmol/L) from 8.1% to 3.8%. The clinical experiment demonstrated the feasibility of the algorithm. CONCLUSION: Our algorithm has the potential to improve glycemic control in people with T1D using MDI. SIGNIFICANCE: This work is a step forward towards a decision support system that improves their quality of life.