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

Haidar, Ahmad; Legault, Laurent; Raffray, Marie; Gouchie‐Provencher, Nikita; Jacobs, Peter G.; Fathi, Anas El; Rutkowski, Joanna; Messier, Virginie; Rabasa‐Lhoret, Rémi

doi:10.1089/dia.2020.0365

Cited by 13 publications

(10 citation statements)

References 31 publications

(52 reference statements)

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“…Commercially available AID systems are hybrid closed-loop systems, and they require these manual entries by the user. AID systems, also called artificial pancreas (AP), consist of a CGM, an insulin pump, and a closed-loop control algorithm that manipulates the insulin infusion rate delivered by the pump based on the recent CGM values reported [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. More advanced AID systems that use a multivariable approach [10,[24][25][26] use additional inputs from wearable devices (such as wristbands) to automatically detect the occurrence of physical activity and incorporate this information to the automated control algorithms for a fully automated AID system [27].…”

Section: Introductionmentioning

confidence: 99%

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

et al. 2022

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Objective: The interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID). Methods: Two challenges in time-series data collected in daily living are addressed: data quality improvement and the detection of unannounced disturbances of BGC. CGM data have missing values for varying periods of time and outliers. People may neglect reporting their meal and physical activity information. In this work, novel methods for preprocessing real-world data collected from people with T1D and the detection of meal and exercise events are presented. Four recurrent neural network (RNN) models are investigated to detect the occurrences of meals and physical activities disjointly or concurrently. Results: RNNs with long short-term memory (LSTM) with 1D convolution layers and bidirectional LSTM with 1D convolution layers have average accuracy scores of 92.32% and 92.29%, and outperform other RNN models. The F1 scores for each individual range from 96.06% to 91.41% for these two RNNs. Conclusions: RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation.

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Section: Introductionmentioning

confidence: 99%

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

et al. 2022

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“…The algorithms did not recommend automatic boluses outside of mealtimes. Instead, when needed, basal rates were modulated to deliver an amount of insulin equivalent to a corrective bolus over several 10‐minute increments, as with our previous closed‐loop study 15 …”

Section: Methodsmentioning

confidence: 99%

“…The pilot study used the iPancreas closed-loop system 15,16 (Oregon Health & Science University, OR, USA) with the same dosing algorithms and glucose targets as the feasibility study. This system consists of a glucose sensor (Dexcom G5), non-commercial t:slim insulin pumps (Tandem Diabetes Care, CA, USA) and a cellphone (Nexus 5, LG Electronics).…”

Section: Pilot Study Proceduresmentioning

confidence: 99%

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

Tsoukas

Cohen

Legault

et al. 2021

Diabetes Obesity Metabolism

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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.

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“…Table 1 shows the characteristics of 41 studies [20, included in the systematic review (1,398 total patients). Nine trials compared insulin pumps, and 32 trials compared sensor-augmented pumps.…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis

Kang

Hwang

Kwon

et al. 2022

Diabetol Metab Syndr

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Background The purpose of this study was to assess the effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes. Methods We searched PubMed, EMBASE, Cochrane Central, and the Web of Science to December 2021. The eligibility criteria for study selection were randomized controlled trials comparing artificial pancreas systems (MPC, PID, and fuzzy algorithms) with conventional insulin therapy in type 1 diabetes patients. The heterogeneity of the overall results was identified by subgroup analysis of two factors including the intervention duration (overnight and 24 h) and the follow-up periods (< 1 week, 1 week to 1 month, and > 1 month). Results The meta-analysis included a total of 41 studies. Considering the effect on the percentage of time maintained in the target range between the MPC-based artificial pancreas and conventional insulin therapy, the results showed a statistically significantly higher percentage of time maintained in the target range in overnight use (10.03%, 95% CI [7.50, 12.56] p < 0.00001). When the follow-up period was considered, in overnight use, the MPC-based algorithm showed a statistically significantly lower percentage of time maintained in the hypoglycemic range (−1.34%, 95% CI [−1.87, −0.81] p < 0.00001) over a long period of use (> 1 month). Conclusions Overnight use of the MPC-based artificial pancreas system statistically significantly improved glucose control while increasing time maintained in the target range for outpatients with type 1 diabetes. Results of subgroup analysis revealed that MPC algorithm-based artificial pancreas system was safe while reducing the time maintained in the hypoglycemic range after an overnight intervention with a long follow-up period (more than 1 month).

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

Cited by 13 publications

References 31 publications

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

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

Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis

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