Background:The Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system provides personalised bolus advice for people with Type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalised carbohydrate recommendations and dynamic bolus insulin constraint. We evaluated the safety and feasibility of the PEPPER system compared to a standard bolus calculator. Methods:This was an open-labelled multicentre randomized controlled cross-over study. Following 4week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12-weeks. Participants then crossed over after a wash-out period. The primary end-point was percentage time in range (TIR, 3.9mmol/L-10.0mmol/L (70-180mg/dL)). Secondary outcomes included glycaemic variability, quality of life, and outcomes on the safety system and insulin recommender.Results: 54 participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 (52.1-67.8) % vs 58.4 (49.6-64.3) % respectively, p=0.27). For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Conclusions:The PEPPER system was safe but did not change glycaemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness.

OBJECTIVEThe Pediatric Artificial Pancreas (PedArPan) project tested a children-specific version of the modular model predictive control (MMPC) algorithm in 5-to 9-yearold children during a camp. RESEARCH DESIGN AND METHODSA total of 30 children, 5-to 9-years old, with type 1 diabetes completed an outpatient, open-label, randomized, crossover trial. Three days with an artificial pancreas (AP) were compared with three days of parent-managed sensoraugmented pump (SAP). RESULTSOvernight time-in-hypoglycemia was reduced with the AP versus SAP, median (25 th -75 th percentiles): 0.0% (0.0-2.2) vs. 2.2% (0.0-12.3) (P 5 0.002), without a significant change of time-in-target, mean: 56.0% (SD 22.5) vs. 59.7% (21.2) (P 5 0.430), but with increased mean glucose 173 mg/dL (36) vs. 150 mg/dL (39) (P 5 0.002). Overall, the AP granted a threefold reduction of time-in-hypoglycemia (P < 0.001) at the cost of decreased time-in-target, 56.8% (13.5) vs. 63.1% (11.0) (P 5 0.022) and increased mean glucose 169 mg/dL (23) vs. 147 mg/dL (23) (P < 0.001). CONCLUSIONSThis trial, the first outpatient single-hormone AP trial in a population of this age, shows feasibility and safety of MMPC in young children. Algorithm retuning will be performed to improve efficacy.Only three artificial pancreas (AP) trials have focused on the prepubertal population so far: two single-hormone AP studies, performed inpatient for less than 1 day (1,2) and a recent dual-hormone AP study, performed in a camp for 5 days (3). Here we report the first outpatient single-hormone AP trial focusing on 5-to 9-year-old children.Data were collected in the Pediatric Artificial Pancreas (PedArPan) camp, where sensor-augmented pump (SAP) therapy was compared with the modular model predictive control algorithm (MMPC) (4,5), running on the wearable platform Diabetes Assistant (DiAs) (6).

The results showed that the septic status of patients influenced the accuracy of the RTCGMS in the ICU. Accuracy was significantly better in patients with septic shock in comparison with the other patient cohorts.

Participants manifested a positive attitude toward the AP. Further studies are required to explore participants' perceptions early in the AP development to individualize the new treatment as much as possible, and to tailor it to respond to their needs and values.

Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.