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.

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 HbA_1c ≥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.

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.

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.

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.

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.

Mortality, Length of Stay, and Cost Implications of Procedural Bleeding After Percutaneous Interventions Using Large-Bore Catheters

Importance

Bleeding complications after percutaneous transcatheter interventions that used large-bore catheters are frequent and associated with high mortality and morbidity.

Objective

To describe the incidence of bleeding complications among patients undergoing contemporary endovascular interventions involving large-bore catheters and its association with in-hospital mortality, length of stay, and health care cost.

Design, Setting, and Participants

This retrospective cohort study analyzed all 17 672 patients from the Healthcare Cost and Utilization Project's National Inpatient Sample database who were recorded as having undergone a transcatheter aortic valve replacement (n = 3223), an endovascular aneurysm repair (n = 12 633), or a percutaneous left ventricular assist device implant (n = 1816) between January 1, 2012, and December 31, 2013. Bleeding complication was defined as any transfusion, any hemorrhage or hematoma, or the need for percutaneous or surgical intervention to control the bleeding event. Health care costs were determined by multiplying the total charge for each visit by the cost to charge ratios reported for each hospital code in the database. Data were collected from the database on April 29, 2016.

Main Outcomes and Measures

Adjusted association between bleeding complications and mortality was determined by multivariable logistic regression. Length of stay and total health care costs were compared using multivariable linear regression between patients who did and patients who did not have bleeding complications.

Results

Bleeding complications occurred in 3128 patients (17.7%) (1984 men and 1144 women, with a mean [SD] age of 75.6 [11.9] years). Bleeding was associated with higher mortality (adjusted odds ratio, 2.70; 95% CI, 2.27-3.22; P < .001) and longer hospital stay (adjusted multiplicative difference, 2.14; 95% CI, 2.06-2.16; P < .001). Median (interquartile range) total health care costs were $48 663 ($32 620-$71 547) for patients with bleeding complications compared with $29 968 ($21 924-$43 287) for patients without a bleeding complication (adjusted multiplicative difference, 1.55; 95% CI, 1.52-1.59; P < .001).

Conclusions and Relevance

Periprocedural bleeding was common among patients who underwent transcatheter intervention using large-bore catheters and was associated with a statistically significant increase in mortality, length of stay, and cost.