A randomized clinical trial for meal bolus decision using learning-based control in adults with type 2 diabetes.

BACKGROUND:We proposed an artificial-pancreas-like algorithm (AP-A) which could automatically determine the pre-prandial insulin dose based on intermittently scanned continuous glucose monitoring (isCGM) data trajectories in multiple dose injection (MDI) therapy. We aim to determine whether pre-prandial insulin dose adjustments guided by the AP-A is as effective and safe as physician decisions. METHODS:We performed a randomized, single-blind, clinical trial at a tertiary, referral hospital in Beijing, China. Type 2 diabetes participants were eligible if they were aged  18 years, with a glycated hemoglobin of 8.0% or higher. Eligible participants were randomly assigned (1:1) to the AP-A arm supervised by physician and the conventional physician treatment arm. The primary objective was to compare percentage time spent with sensor glucose level in 3.9-10.0 mmol/L (TIR) between the two study arms. Safety was assessed by the percentage time spent with sensor glucose level below 3.0 mmol/L (TBR). RESULTS:140 participants were screened, of whom 119 were randomly assigned to AP-A arm (n = 59) or physician arm (n = 60). The TIR achieved by the AP-A arm was statistically non-inferior compared with the control arm (72.4% (63.3-82.1) vs. 71.2% (54.9-81.4)), with a median difference of 1.33% (95% CI, -6.00 to 10.94, non-inferiority margin -7.5%). TBR was also statistically non-inferior between the AP-A and control arms (0.0% (0.0-0.0) vs. 0.0% (0.0-0.0), respectively; median difference (95% CI, 0.00% (0.00 to 0.00), non-inferiority margin 2.0%). CONCLUSIONS:The AP-A supported physician titration of pre-prandial insulin dosage offers non-inferior glycemic control compared with optimal physician care in type 2 diabetes.