OBJECTIVE The Pediatric Artificial Pancreas (PedArPan) project tested a children-specific version of the modular model predictive control (MMPC) algorithm in 5- to 9-year old children during a camp. RESEARCH DESIGN AND METHODS A 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 sensor-augmented pump (SAP). RESULTS Overnight 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 = 0.002), without a significant change of time-in-target, mean: 56.0% (SD 22.5%) vs. 59.7% (21.2%) (P = 0.430), but with increased mean glucose 173 mg/dL (SD 36) vs. 150 mg/dL (SD 39) (P = 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% (SD 13.5) vs. 63.1% (SD 11.0) (P = 0.022) and increased mean glucose 169 mg/dL (SD 23) vs. 147 mg/dL (SD 23) (P < 0.001). CONCLUSIONS This 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.

Objective: This study assessed the accuracy of real-time continuous glucose monitoring system (RTCGMS) devices in an intensive care unit (ICU) to determine whether the septic status of the patient has any influence on the accuracy of the RTCGMS. Subjects and Methods: In total, 41 patients on insulin therapy were included. Patients were monitored for 72 h using RTCGMS. Arterial blood glucose (ABG) samples were obtained following the protocol established in the ICU. The results were evaluated using paired values (excluding those used for calibration) with the performance assessed using numerical accuracy. Nonparametric tests were used to determine statistically significant differences in accuracy. Results: In total, 956 ABG/RTCGMS pairs were analyzed. The overall median relative absolute difference (RAD) was 13.5%, and the International Organization for Standardization (ISO) criteria were 68.1%. The median RADs reported for patients with septic shock, with sepsis, and without sepsis were 11.2%, 14.3%, and 16.3%, respectively ( P <0.05). Measurements meeting the ISO criteria were 74.5%, 65.6%, and 63.7% for patients with septic shock, with sepsis, and without sepsis, respectively ( P <0.05). Conclusions: 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.

Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized 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, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator. Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week 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 washout period. The primary end-point was percentage time in range (TIR, 3.9–10.0 mmol/L [70–180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender. Results: Fifty-four 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 hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia. Conclusions: The PEPPER system was safe, but did not change glycemic 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. Clinical trial registration: ClinicalTrials.gov NCT03849755

Background: Continuous glucose monitors (CGMs) present a problem of lack of accuracy, especially in the lower range, sometimes leading to missed or false hypoglycemia. A new algorithm is presented here aimed at improving the measurement accuracy and hypoglycemia detection. Its core is the estimation of blood glucose (BG) in real time (RT) from CGM intensity readings using autoregressive (AR) models. Methods: Eighteen patients with type 1 diabetes were monitored for three days (one at the hospital and two at home) using the CGMS® Gold. For these patients, BG samples were taken every 15 min for 2 h after meals and every half hour otherwise during the first day. The relationship between the current measured by the CGMS Gold and BG was learned by an AR model, allowing its RT estimation. New capillary glucose measurements were used to correct the model BG estimations. Results: A total of 563 paired points were obtained from BG and monitor readings to validate the new algorithm. 98.5% of paired points fell in zones A+B of the Clarke error grid analysis with the proposed algorithm. The overall mean and median relative absolute differences (RADs) were 9.6% and 6.7%. Measurements meeting International Organization for Standardization (ISO) criteria were 88.7%. In the hypoglycemic range, the mean and median RADs were 8.1% and 6.0%, and measurements meeting ISO criteria were 86.7%. The sensitivity and specificity with respect to hypoglycemia detection were 91.5% and 95.0%. Conclusions: The performance measured with both clinical and numerical accuracy metrics illustrates the improved accuracy of the proposed algorithm compared with values presented in the literature. A significant improvement in hypoglycemia detection was also observed.

OBJECTIVE: To explore the experiences of children with type 1 diabetes and their parents taking part in an artificial pancreas (AP) clinical trial during a 7-day summer camp. RESEARCH DESIGN AND METHODS: A semistructured interview, composed of 14 questions based on the Technology Acceptance Model, was conducted at the end of the clinical trial. Participants also completed the Diabetes Treatment Satisfaction Questionnaire (DTSQ, parent version) and the AP Acceptance Questionnaire. RESULTS: Thirty children, aged 5–9 years, and their parents completed the study. A content analysis of the interviews showed that parents were focused on understanding the mechanisms, risks, and benefits of the new device, whereas the children were focused on the novelty of the new system. The parents’ main concerns about adopting the new system seemed related to the quality of glucose control. The mean scores of DTSQ subscales indicated general parents’ satisfaction (44.24 ± 5.99, range 32–53) and trustful views of diabetes control provided by the new system (7.8 ± 2.2, range 3–12). The AP Acceptance Questionnaire revealed that most parents considered the AP easy to use (70.5%), intended to use it long term (94.0%), and felt that it was apt to improve glucose control (67.0%). CONCLUSIONS: 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.

Context Closed-loop control (CLC) for the management of type 1 diabetes (T1D) is a novel method for optimizing glucose control, and strategies for individualized implementation are being developed. Objective To analyze glycemic control in an overnight CLC system designed to “reset” the patient to near-normal glycemic targets every morning. Design Randomized, crossover, multicenter clinical trial. Participants Forty-four subjects with T1D requiring insulin pump therapy. Intervention Sensor-augmented pump therapy (SAP) at home vs 5 nights of CLC (active from 23:00 to 07:00) in a supervised outpatient setting (research house or hotel), with a substudy of 5 nights of CLC subsequently at home. Main Outcome Measure The percentage of time spent in the target range (70 to 180 mg/dL measured using a continuous glucose monitor). Results Forty subjects (age, 45.5 ± 9.5 years; hemoglobin A1c, 7.4% ± 0.8%) completed the study. The time in the target range (70 to 180 mg/dL) significantly improved in CLC vs SAP over 24 hours (78.3% vs 71.4%; P = 0.003) and overnight (85.7% vs 67.6%; P < 0.001). The time spent in a hypoglycemic range (<70 mg/dL) decreased significantly in the CLC vs SAP group over 24 hours (2.5% vs 4.3%; P = 0.002) and overnight (0.9% vs 3.2%; P < 0.001). The mean glucose level at 07:00 was lower with CLC than with SAP (123.7 vs 145.3 mg/dL; P < 0.001). The substudy at home, involving 10 T1D subjects, showed similar trends with an increased time in target (70 to 180 mg/dL) overnight (75.2% vs 62.2%; P = 0.07) and decreased time spent in the hypoglycemic range (<70 mg/dL) overnight in CLC vs SAP (0.6% vs 3.7%; P = 0.03). Conclusion Overnight-only CLC increased the time in the target range over 24 hours and decreased the time in hypoglycemic range over 24 hours in a supervised outpatient setting. A pilot extension study at home showed a similar nonsignificant trend.