Alleviating carbohydrate counting with a FiASP-plus-pramlintide closed-loop delivery system (artificial pancreas): Feasibility and pilot studies.

AIM: To assess whether a FiASP-and-pramlintide closed-loop system has the potential to replace carbohydrate counting with a simple meal announcement (SMA) strategy (meal priming bolus without carbohydrate counting) without degrading glycaemic control compared with a FiASP closed-loop system. MATERIALS AND METHODS: We conducted a 24-hour feasibility study comparing a FiASP system with full carbohydrate counting (FCC) with a FiASP-and-pramlintide system with SMA. We conducted a subsequent 12-day outpatient pilot study comparing a FiASP-and-placebo system with FCC, a FiASP-and-pramlintide system with SMA, and a FiASP-and-placebo system with SMA. Basal-bolus FiASP-and-pramlintide were delivered at a fixed ratio (1 U:10 µg). Glycaemic outcomes were measured, surveys evaluated gastrointestinal symptoms and diabetes distress, and participant interviews helped establish a preliminary coding framework to assess user experience. RESULTS: Seven participants were included in the feasibility analysis. Time spent in 3.9-10 mmol/L was similar between both interventions (81%-84%). Four participants were included in the pilot analysis. Time spent in 3.9-10 mmol/L was similar between the FiASP-and-placebo with FCC and FiASP-and-pramlintide with SMA interventions (70%), but was lower in the FiASP-and-placebo with SMA intervention (60%). Time less than 3.9 mmol/L and gastrointestinal symptoms were similar across all interventions. Emotional distress was moderate at baseline, after the FiASP-and-placebo with FCC and SMA interventions, and fell after the FiASP-and-pramlintide with SMA intervention. SMA reportedly afforded participants flexibility and reduced mealtime concerns. CONCLUSIONS: The FiASP-and-pramlintide system has the potential to substitute carbohydrate counting with SMA without degrading glucose control.

Fully Automated Artificial Pancreas for Adults With Type 1 Diabetes Using Multiple Hormones: Exploratory Experiments.

OBJECTIVES: A fully automated insulin-pramlintide-glucagon artificial pancreas that alleviates the burden of carbohydrate counting without degrading glycemic control was iteratively enhanced until convergence through pilot experiments on adults with type 1 diabetes. METHODS: Nine participants (age, 37±13 years; glycated hemoglobin, 7.7±0.7%) completed two 27-hour interventions: a fully automated multihormone artificial pancreas and a comparator insulin-alone artificial pancreas with carbohydrate counting. The baseline algorithm was a model-predictive controller that administered insulin and pramlintide in a fixed ratio, with boluses triggered by a glucose threshold, and administered glucagon in response to low glucose levels. RESULTS: The baseline multihormone dosing algorithm resulted in noninferior time in target range (3.9 to 10.0 mmol/L) (71%) compared with the insulin-alone arm (70%) in 2 participants, with minimal glucagon delivery. The algorithm was modified to deliver insulin and pramlintide more aggressively to increase time in range and maximize the benefits of glucagon. The modified algorithm displayed a similar time in range for the multihormone arm (79%) compared with the insulin-alone arm (83%) in 2 participants, but with undesired glycemic fluctuations. Subsequently, we reduced the glucose threshold that triggers glucagon boluses. This resulted in inferior glycemic control for the multihormone arm (81% vs 91%) in 2 participants. Thereafter, a model-based meal-detection algorithm to deliver insulin and pramlintide boluses closer to mealtimes was added and glucagon was removed. The final dual-hormone system had comparable time in range (81% vs 83%) in the last 3 participants. CONCLUSION: The final version of the fully automated system that delivered insulin and pramlintide warrants a randomized controlled trial.

Modelling glucose dynamics during moderate exercise in individuals with type 1 diabetes.

The artificial pancreas is a closed-loop insulin delivery system that automatically regulates glucose levels in individuals with type 1 diabetes. In-silico testing using simulation environments accelerates the development of better artificial pancreas systems. Simulation environments need an accurate model that captures glucose dynamics during exercise to simulate real-life scenarios. We proposed six variations of the Bergman Minimal Model to capture the physiological effects of moderate exercise on glucose dynamics in individuals with type 1 diabetes. We estimated the parameters of each model with clinical data using a Bayesian approach and Markov chain Monte Carlo methods. The data consisted of measurements of plasma glucose, plasma insulin, and oxygen consumption collected from a study of 17 adults with type 1 diabetes undergoing aerobic exercise sessions. We compared the models based on the physiological plausibility of their parameters estimates and the deviance information criterion. The best model features (i) an increase in glucose effectiveness proportional to exercise intensity, and (ii) an increase in insulin action proportional to exercise intensity and duration. We validated the selected model by reproducing results from two previous clinical studies. The selected model accurately simulates the physiological effects of moderate exercise on glucose dynamics in individuals with type 1 diabetes. This work offers an important tool to develop strategies for exercise management with the artificial pancreas.

Reducing the need for carbohydrate counting in type 1 diabetes using closed-loop automated insulin delivery (artificial pancreas) and empagliflozin: A randomized, controlled, non-inferiority, crossover pilot trial.

AIM: To assess whether adding empagliflozin to closed-loop automated insulin delivery could reduce the need for carbohydrate counting in type 1 diabetes (T1D) without worsening glucose control. MATERIALS AND METHODS: In an open-label, crossover, non-inferiority trial, 30 adult participants with T1D underwent outpatient automated insulin delivery interventions with three random sequences of prandial insulin strategy days: carbohydrate counting, simple meal announcement (no carbohydrate counting) and no meal announcement. During each sequence of prandial insulin strategies, participants were randomly assigned empagliflozin (25 mg/day) or not, and crossed over to the comparator. Mean glucose for carbohydrate counting without empagliflozin (control) was compared with no meal announcement with empagliflozin (in the primary non-inferiority comparison) and simple meal announcement with empagliflozin (in the conditional primary non-inferiority comparison). RESULTS: Participants were aged 40 ± 15 years, had 27 ± 15 years diabetes duration and HbA1c of 7.6% ± 0.7% (59 ± 8 mmol/mol). The system with no meal announcement and empagliflozin was not non-inferior (and thus reasonably considered inferior) to the control arm (mean glucose 10.0 ± 1.6 vs. 8.5 ± 1.5 mmol/L; non-inferiority p = .94), while simple meal announcement and empagliflozin was non-inferior (8.5 ± 1.4 mmol/L; non-inferiority p = .003). Use of empagliflozin on the background of automated insulin delivery with carbohydrate counting was associated with lower mean glucose, corresponding to a 14% greater time in the target range. While no ketoacidosis was observed, mean fasting ketones levels were higher on empagliflozin (0.22 ± 0.18 vs. 0.13 ± 0.11 mmol/L; p < .001). CONCLUSIONS: Empagliflozin added to automated insulin delivery has the potential to eliminate the need for carbohydrate counting and improves glycaemic control in conjunction with carbohydrate counting, but does not allow for the elimination of meal announcement.

Titration of Long-Acting Insulin Using Continuous Glucose Monitoring and Smart Insulin Pens in Type 1 Diabetes: A Model-Based Carbohydrate-Free Approach.

Objective: Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D), consisting of long-acting insulin to cover fasting conditions and rapid-acting insulin to cover meals. Titration of long-acting insulin is needed to achieve satisfactory glycemia but is challenging due to inter-and intra-individual metabolic variability. In this work, a novel titration algorithm for long-acting insulin leveraging continuous glucose monitoring (CGM) and smart insulin pens (SIP) data is proposed. Methods: The algorithm is based on a glucoregulatory model that describes insulin and meal effects on blood glucose fluctuations. The model is individualized on patient's data and used to extract the theoretical glucose curve in fasting conditions; the individualization step does not require any carbohydrate records. A cost function is employed to search for the optimal long-acting insulin dose to achieve the desired glycemic target in the fasting state. The algorithm was tested in two virtual studies performed within a validated T1D simulation platform, deploying different levels of metabolic variability (nominal and variance). The performance of the method was compared to that achieved with two published titration algorithms based on self-measured blood glucose (SMBG) records. The sensitivity of the algorithm to carbohydrate records was also analyzed. Results: The proposed method outperformed SMBG-based methods in terms of reduction of exposure to hypoglycemia, especially during the night period (0 am-6 am). In the variance scenario, during the night, an improvement in the time in the target glycemic range (70-180 mg/dL) from 69.0% to 86.4% and a decrease in the time in hypoglycemia (<70 mg/dL) from 10.7% to 2.6% was observed. Robustness analysis showed that the method performance is non-sensitive to carbohydrate records. Conclusion: The use of CGM and SIP in people with T1D using MDI therapy has the potential to inform smart insulin titration algorithms that improve glycemic control. Clinical studies in real-world settings are warranted to further test the proposed titration algorithm. Significance: This algorithm is a step towards a decision support system that improves glycemic control and potentially the quality of life, in a population of individuals with T1D who cannot benefit from the artificial pancreas system.

A Meal Detection Algorithm for the Artificial Pancreas: A Randomized Controlled Clinical Trial in Adolescents With Type 1 Diabetes.

OBJECTIVE: We developed a meal detection algorithm for the artificial pancreas (AP+MDA) that detects unannounced meals and delivers automatic insulin boluses. RESEARCH DESIGN AND METHODS: We conducted a randomized crossover trial in 11 adolescents aged 12-18 years with HbA1c ≥7.5% who missed one or more boluses in the past 6 months. We compared 1) continuous subcutaneous insulin infusion (CSII), 2) artificial pancreas (AP), and 3) AP+MDA. Participants underwent three 9-h interventions involving breakfast with a bolus and lunch without a bolus. RESULTS: In AP+MDA, the meal detection time was 40.0 (interquartile range 40.0-57.5) min. Compared with CSII, AP+MDA decreased the 4-h postlunch incremental area under the curve (iAUC) from 24.1 ± 9.5 to 15.4 ± 8.0 h ⋅ mmol/L (P = 0.03). iAUC did not differ between AP+MDA and AP (19.6 ± 10.4 h ⋅ mmol/L, P = 0.21) or between AP and CSII (P = 0.33). The AP+MDA reduced time >10 mmol/L (58.0 ± 26.6%) compared with CSII (79.6 ± 27.5%, P = 0.02) and AP (74.2 ± 20.6%, P = 0.047). CONCLUSIONS: The AP+MDA improved glucose control after an unannounced meal.

Comparison Between Closed-Loop Insulin Delivery System (the Artificial Pancreas) and Sensor-Augmented Pump Therapy: A Randomized-Controlled Crossover Trial.

Objective: Several studies have shown that closed-loop automated insulin delivery (the artificial pancreas) improves glucose control compared with sensor-augmented pump therapy. We aimed to confirm these findings using our automated insulin delivery system based on the iPancreas platform. Research Design and Methods: We conducted a two-center, randomized crossover trial comparing automated insulin delivery with sensor-augmented pump therapy in 36 adults with type 1 diabetes. Each intervention lasted 12 days in outpatient free-living conditions with no remote monitoring. The automated insulin delivery system used a model predictive control algorithm that was a less aggressive version of our earlier dosing algorithm to emphasize safety. The primary outcome was time in the range 3.9-10.0 mmol/L. Results: The automated insulin delivery system was operational 90.2% of the time. Compared with the sensor-augmented pump therapy, automated insulin delivery increased time in range (3.9-10.0 mmol/L) from 61% (interquartile range 53-74) to 69% (60-73; P = 0.006) and increased time in tight target range (3.9-7.8 mmol/L) from 37% (30-49) to 45% (35-51; P = 0.011). Automated insulin delivery also reduced time spent below 3.9 and 3.3 mmol/L from 3.5% (0.8-5.4) to 1.6% (1.1-2.7; P = 0.0021) and from 0.9% (0.2-2.1) to 0.5% (0.2-1.1; P = 0.0122), respectively. Time spent below 2.8 mmol/L was 0.2% (0.0-0.6) with sensor-augmented pump therapy and 0.1% (0.0-0.4; P = 0.155) with automated insulin delivery. Conclusions: Our study confirms findings that automated insulin delivery improves glucose control compared with sensor-augmented pump therapy. ClinicalTrials.gov no. NCT02846831.

Postprandial hyperglycaemia following insulin suspensions by the artificial pancreas: Implications for bolus calculators.

Conventional bolus calculators apply negative prandial corrections when premeal glucose levels are low. However, no study has evaluated the need for this negative correction with closed-loop systems. We analysed data retrospectively from a cohort study evaluating a closed-loop artificial pancreas system conducted in a diabetes camp over a period of 11 days. Meal boluses with negative correction (n = 98) of 47 participants aged 8 to 22 years were examined. If there was no insulin-on-board from previous boluses at mealtime, the postprandial hyperglycaemia rate increased with increased duration of insulin suspension (P = .03), with odds ratios being exaggerated by 17% per 10 minutes of suspension. However, if there was insulin-on-board from previous boluses, the hyperglycaemia rate did not change with increased duration of insulin suspension (P = .24). When there was no insulin-on-board, the rate of hyperglycaemia after meals preceded by no suspension was 21% (3/14), compared with 52% (12/23) and 64% (9/14) after meals preceded by suspensions of ≥50 and ≥70 minutes, respectively. Meal size did not influence these results. We conclude that, in the absence of insulin-on-board, negative prandial corrections may not be necessary following long insulin suspensions.

A pilot non-inferiority randomized controlled trial to assess automatic adjustments of insulin doses in adolescents with type 1 diabetes on multiple daily injections therapy.

BACKGROUND: Multiple daily injections (MDI) therapy for type 1 diabetes involves basal and bolus insulin doses. Non-optimal insulin doses contribute to the lack of satisfactory glycemic control. We aimed to evaluate the feasibility of an algorithm that optimizes daily basal and bolus doses using glucose monitoring systems for MDI therapy users. METHODS: We performed a pilot, non-inferiority, randomized, parallel study at a diabetes camp comparing basal-bolus insulin dose adjustments made by camp physicians (PA) and a learning algorithm (LA), in children and adolescents on MDI therapy. Participants wore a glucose sensor and underwent 11 days of daily dose adjustments in either arm. Algorithm adjustments were reviewed and approved by a physician. The last 7 days were examined for outcomes. RESULTS: Twenty-one youths (age 13.3 [SD, 3.7] years; 13 females; HbA1c 8.6% [SD, 1.8]) were randomized to either group (LA [n = 10] or PA [n = 11]). The algorithm made 293 adjustments with a 92% acceptance rate from the camp physicians. In the last 7 days, the time in target glucose (3.9-10 mmol/L) in LA (39.5%, SD, 20.7) was similar to PA (38.4%, SD, 15.6) (P = .89). The number of hypoglycemic events per day in LA (0.3, IQR, [0.1-0.6]) was similar to PA (0.2, IQR, [0.0-0.4]) (P = .42). There was no incidence of severe hypoglycemia nor ketoacidosis. CONCLUSIONS: In this pilot study, glycemic outcomes in the LA group were similar to the PA group. This algorithm has the potential to facilitate MDI therapy, and longer and larger studies are warranted.

A Novel Dual-Hormone Insulin-and-Pramlintide Artificial Pancreas for Type 1 Diabetes: A Randomized Controlled Crossover Trial.

OBJECTIVE: The rapid insulin-alone artificial pancreas improves glycemia in type 1 diabetes but daytime control remains suboptimal. We propose two novel dual-hormone artificial pancreas systems. RESEARCH DESIGN AND METHODS: We conducted a randomized crossover trial comparing a rapid insulin-alone artificial pancreas with rapid insulin-and-pramlintide and with regular insulin-and-pramlintide artificial pancreas systems in adults with type 1 diabetes. Participants were assigned to the interventions in random order during three 24-h inpatient visits. Each visit was preceded by an outpatient hormonal open-loop run-in period of 10-14 days. The dual-hormone artificial pancreas delivered pramlintide in a basal-bolus manner, using a novel dosing algorithm, with a fixed ratio relative to insulin. The primary outcome was time in the range 3.9-10.0 mmol/L. RESULTS: Compared with the rapid insulin-alone artificial pancreas system, the rapid insulin-and-pramlintide system increased the time in range from 74% (SD 18%) to 84% (13%) (P = 0.0014), whereas the regular insulin-and-pramlintide system did not change the time in range (69% [19%]; P = 0.22). The increased time in range with the rapid insulin-and-pramlintide system was due to improved daytime control (daytime time in range increased from 63% [23%] to 78% [16%], P = 0.0004). There were 11 (1 per 2.5 days) hypoglycemic events (<3.3 mmol/L with symptoms or <3.0 mmol/L irrespective of symptoms) with the rapid insulin-alone system, compared with 12 (1 per 2.3 days) and 18 (1 per 1.4 days) with the rapid and regular insulin-and-pramlintide systems, respectively. Gastrointestinal symptoms were reported after 0% (0 of 112) of meals with the rapid insulin-alone system, compared with 6% (6 of 108) and 11% (11 of 104) with the rapid and regular insulin-and-pramlintide systems, respectively; none of the symptoms were severe. CONCLUSIONS: A novel rapid insulin-and-pramlintide artificial pancreas improves glucose control compared with a rapid insulin-alone artificial pancreas (ClinicalTrials.gov number NCT02814123).

The Efficacy of Basal Rate and Carbohydrate Ratio Learning Algorithm for Closed-Loop Insulin Delivery (Artificial Pancreas) in Youth with Type 1 Diabetes in a Diabetes Camp.

Background: Optimizing programmed basal rates and carbohydrate ratios may improve the performance of the artificial pancreas. We tested, in a diabetes camp, the efficacy of a learning algorithm that updates daily basal rates and carbohydrate ratios in the artificial pancreas. Materials and Methods: We conducted a randomized crossover trial in campers and counselors aged 8-21 years with type 1 diabetes on pump therapy. Participants underwent 2 days of artificial pancreas alone and 6 days of artificial pancreas with learning. During the artificial pancreas with learning, programmed basal rates and carbohydrate ratios were updated daily based on the learning algorithm's recommendations. All algorithm recommendations were reviewed for safety by camp physicians. The primary outcome was the time in target range (3.9-10 mmol/L) of the last 2 days of each intervention. Results: Thirty-four campers (age 13.9 ± 3.9, hemoglobin A1c 8.3% ± 0.2%) were included. Ninety-six percent of algorithm recommendations were approved by the camp physicians. Participants were in closed-loop mode 74% of the time. There was no difference between interventions in time in target (55%-55%; P = 0.71) nor in hypoglycemia events (0.8-0.9 events per day; P = 0.63). This was despite changes in programmed basal rate ranging from -21% to +117%, and changes in breakfast, lunch, and supper carbohydrate ratios from -17% to +40%, -36% to +37%, and -35% to +63%, respectively. Morever, postprandial hyperglycemia and hypoglycemia did not decrease in participants whose carbohydrate ratios were decreased (more insulin boluses) and increased (less insulin boluses), respectively. Conclusions: In camp settings, despite adjustments to programmed basal rates and carbohydrate ratios, the learning algorithm did not change glycemia, which may point toward limited effect of these adjustments in environments with large day-to-day variability in insulin needs. Longer randomized studies in real-world settings are required to further assess the efficacy of automatic adjustments of programmed basal rates and carbohydrate ratios.

Accuracy of FreeStyle Libre in Adults with Type 1 Diabetes: The Effect of Sensor Age.

Background: FreeStyle Libre is a factory-calibrated continuous 14-day glucose sensor. Little is known about the accuracy of FreeStyle Libre as a function of sensor age. Methods: We assessed the accuracy of FreeStyle Libre in 14 adults with type 1 diabetes. Each study participant attended our research facility for two or three 24-h visits, during which they wore a FreeStyle Libre aged 0-1 day, 5-7 days, or 13-14 days. Plasma glucose levels were measured every 10-30 min using YSI2300 STAT Plus Analyser. Participants also wore Dexcom G5® glucose sensor aged 1-2 days. We assessed sensors' accuracy using mean absolute relative difference (MARD) between FreeStyle Libre, the Dexcom G5 sensor, and plasma glucose. Results: We had 1930 pairs of FreeStyle Libre sensor-plasma glucose measurements, collected from 36 FreeStyle Libre sensors, 18 of which were sensors aged 0-1 day, 9 were sensors aged 5-7 days, and 9 were sensors aged 13-14 days. The mean and median MARD for FreeStyle Libre sensors aged 0-1 days were 14.5% and 11.2%, respectively, and for sensors aged 13-14 days were 14.7% and 11.2%, respectively, but for sensors aged 5-7 days were 7.8% and 6.6%, respectively (P = 0.03 vs. sensors aged 0-1 days, and P = 0.06 vs. sensors aged 13-14 days). The percentage of points falling in the potentially dangerous zones C, D, or E in Clarke's error grid analysis were 1.9% for FreeStyle Libre sensors aged 0-1 day, 0.2% for sensors aged 5-7 days, and 0.4% for sensors aged 13-14 days. The overall accuracy of FreeStyle Libre and Dexcom G5 sensor was the same (mean MARD 12.8% and 12.5%, respectively; P = 0.57). Conclusions: FreeStyle Libre's accuracy is adequate during its entire lifetime but is least accurate during its first and last days. ClinicalTrials.gov Identifier: NCT02814123.

The potential impact of intelligent power wheelchair use on social participation: perspectives of users, caregivers and clinicians.

PURPOSE: To explore power wheelchair users', caregivers' and clinicians' perspectives regarding the potential impact of intelligent power wheelchair use on social participation. METHODS: Semi-structured interviews were conducted with power wheelchair users (n = 12), caregivers (n = 4) and clinicians (n = 12). An illustrative video was used to facilitate discussion. The transcribed interviews were analyzed using thematic analysis. RESULTS: Three main themes were identified based on the experiences of the power wheelchair users, caregivers and clinicians: (1) increased social participation opportunities, (2) changing how social participation is experienced and (3) decreased risk of accidents during social participation. CONCLUSION: Findings from this study suggest that an intelligent power wheelchair would enhance social participation in a variety of important ways, thereby providing support for continued design and development of this assistive technology. IMPLICATIONS FOR REHABILITATION: An intelligent power wheelchair has the potential to: Increase social participation opportunities by overcoming challenges associated with navigating through crowds and small spaces. Change how social participation is experienced through "normalizing" social interactions and decreasing the effort required to drive a power wheelchair. Decrease the risk of accidents during social participation by reducing the need for dangerous compensatory strategies and minimizing the impact of the physical environment.

Exploring powered wheelchair users and their caregivers' perspectives on potential intelligent power wheelchair use: a qualitative study.

Power wheelchairs (PWCs) can have a positive impact on user well-being, self-esteem, pain, activity and participation. Newly developed intelligent power wheelchairs (IPWs), allowing autonomous or collaboratively-controlled navigation, could enhance mobility of individuals not able to use, or having difficulty using, standard PWCs. The objective of this study was to explore the perspectives of PWC users (PWUs) and their caregivers regarding if and how IPWs could impact on current challenges faced by PWUs, as well as inform current development of IPWs. A qualitative exploratory study using individual interviews was conducted with PWUs (n = 12) and caregivers (n = 4). A semi-structured interview guide and video were used to facilitate informed discussion regarding IPWs. Thematic analysis revealed three main themes: (1) "challenging situations that may be overcome by an IPW" described how the IPW features of obstacle avoidance, path following, and target following could alleviate PWUs' identified mobility difficulties; (2) "cautious optimism concerning IPW use revealed participants" addresses concerns regarding using an IPW as well as technological suggestions; (3) "defining the potential IPW user" revealed characteristics of PWUs that would benefit from IPW use. Findings indicate how IPW use may help overcome PWC difficulties and confirm the importance of user input in the ongoing development of IPWs.