scholarly journals Controlling the AP Controller: Controller Performance Assessment and Modification

2019 ◽  
Vol 13 (6) ◽  
pp. 1091-1104 ◽  
Author(s):  
Iman Hajizadeh ◽  
Nicole Hobbs ◽  
Sediqeh Samadi ◽  
Mert Sevil ◽  
Mudassir Rashid ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Adaptive Learning ◽  
Glucose Concentration ◽  
Blood Glucose Concentration ◽  
Artificial Pancreas ◽  
Target Range ◽  
Modification System ◽  
Loop Control ◽  
Metabolic Characteristics ◽  
Controller Performance

Background: Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time. Methods: A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time. Controller performance assessment and modification system also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects. Results: Using a multivariable simulation platform for T1D, the CPAMS is used to enhance the BGC regulation in people with T1D by means of automated insulin delivery with an adaptive learning predictive controller. Controller performance assessment and modification system increases the percentage of time in the target range (70-180) mg/dL by 52.3% without causing any hypoglycemia and hyperglycemia events. Conclusions: The results demonstrate a significant improvement in the multivariable AP controller performance by using CPAMS.

scholarly journals SystematicallyIn SilicoComparison of Unihormonal and Bihormonal Artificial Pancreas Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoteng Gao ◽  
Huangjiang Ning ◽  
Youqing Wang
Keyword(s):  
Diabetes Mellitus ◽  
Closed Loop ◽  
Blood Glucose Concentration ◽  
Artificial Pancreas ◽  
Glucose Regulation ◽  
Closed Loop Control ◽  
Loop Control ◽  
Promising Direction ◽  
P Type

Automated closed-loop control of blood glucose concentration is a daily challenge for type 1 diabetes mellitus, where insulin and glucagon are two critical hormones for glucose regulation. According to whether glucagon is included, all artificial pancreas (AP) systems can be divided into two types: unihormonal AP (infuse only insulin) and bihormonal AP (infuse both insulin and glucagon). Even though the bihormonal AP is widely considered a promising direction, related studies are very scarce due to this system’s short research history. More importantly, there are few studies to compare these two kinds of AP systems fairly and systematically. In this paper, two switching rules, P-type and PD-type, were proposed to design the logic of orchestrates switching between insulin and glucagon subsystems, where the delivery rates of both insulin and glucagon were designed by using IMC-PID method. These proposed algorithms have been compared with an optimal unihormonal system on virtual type 1 diabetic subjects. Thein silicoresults demonstrate that the proposed bihormonal AP systems have outstanding superiorities in reducing the risk of hypoglycemia, smoothing the glucose level, and robustness with respect to insulin/glucagon sensitivity variations, compared with the optimal unihormonal AP system.

scholarly journals Dual-hormone closed loop system using a liquid stable glucagon formulation versus insulin-only closed loop system compared to a predictive low glucose suspend system: an open label, outpatient, single center, crossover, randomized control trial

2020 ◽  
Author(s):  
Leah M. Wilson ◽  
Peter G. Jacobs ◽  
Katrina L. Ramsey ◽  
Navid Resalat ◽  
Ravi Reddy ◽  
...  
Keyword(s):  
Closed Loop ◽  
Artificial Pancreas ◽  
Loop System ◽  
Target Range ◽  
Closed Loop Control ◽  
Closed Loop System ◽  
Entire Study ◽  
Loop Control ◽  
Study Duration ◽  
Low Glucose

<b>Objective: </b>To assess the efficacy and feasibility of a dual-hormone closed loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed loop system and a predictive low glucose suspend system. <p><b>Research Design and Methods:</b> In a 76-hour, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: (1) dual-hormone (DH) closed loop control, (2) insulin-only single-hormone (SH) closed loop control and (3) predictive low glucose suspend (PLGS). The primary endpoint was percent time in hypoglycemia (<70 mg/dL) from start of in-clinic aerobic exercise (45mins at 60% VO<sub>2max</sub>) to 4 hours after.</p> <p><b>Results:</b> DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [0.0-4.2], SH 8.3% [0.0-12.5], p=0.025). There was an increased time in hyperglycemia (>180mg/dL) during and after exercise for DH vs SH (20.8% DH vs. 6.3% SH, p=0.038). Mean glucose during the entire study duration was: DH 159.2, SH 151.6, PLGS 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70-180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, p=0.044). For the entire study duration, DH had 28.2% time in hyperglycemia versus 25.1% for SH (p=0.044) and 34.7% for PLGS (p=0.140). Four participants experienced nausea related to glucagon leading 3 to withdraw from the study. </p> <p><b>Conclusions:</b> The glucagon formulation demonstrated feasibility in a closed loop system. The dual-hormone system reduced hypoglycemia during and after exercise with some increase in hyperglycemia.</p>

scholarly journals Dual-hormone closed loop system using a liquid stable glucagon formulation versus insulin-only closed loop system compared to a predictive low glucose suspend system: an open label, outpatient, single center, crossover, randomized control trial

2020 ◽  
Author(s):  
Leah M. Wilson ◽  
Peter G. Jacobs ◽  
Katrina L. Ramsey ◽  
Navid Resalat ◽  
Ravi Reddy ◽  
...  
Keyword(s):  
Closed Loop ◽  
Artificial Pancreas ◽  
Loop System ◽  
Target Range ◽  
Closed Loop Control ◽  
Closed Loop System ◽  
Entire Study ◽  
Loop Control ◽  
Study Duration ◽  
Low Glucose

<b>Objective: </b>To assess the efficacy and feasibility of a dual-hormone closed loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed loop system and a predictive low glucose suspend system. <p><b>Research Design and Methods:</b> In a 76-hour, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: (1) dual-hormone (DH) closed loop control, (2) insulin-only single-hormone (SH) closed loop control and (3) predictive low glucose suspend (PLGS). The primary endpoint was percent time in hypoglycemia (<70 mg/dL) from start of in-clinic aerobic exercise (45mins at 60% VO<sub>2max</sub>) to 4 hours after.</p> <p><b>Results:</b> DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [0.0-4.2], SH 8.3% [0.0-12.5], p=0.025). There was an increased time in hyperglycemia (>180mg/dL) during and after exercise for DH vs SH (20.8% DH vs. 6.3% SH, p=0.038). Mean glucose during the entire study duration was: DH 159.2, SH 151.6, PLGS 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70-180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, p=0.044). For the entire study duration, DH had 28.2% time in hyperglycemia versus 25.1% for SH (p=0.044) and 34.7% for PLGS (p=0.140). Four participants experienced nausea related to glucagon leading 3 to withdraw from the study. </p> <p><b>Conclusions:</b> The glucagon formulation demonstrated feasibility in a closed loop system. The dual-hormone system reduced hypoglycemia during and after exercise with some increase in hyperglycemia.</p>

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms

2021 ◽  
pp. 193229682110600
Author(s):  
Ryan Armiger ◽  
Monika Reddy ◽  
Nick S. Oliver ◽  
Pantelis Georgiou ◽  
Pau Herrero
Keyword(s):  
Glycemic Control ◽  
In Silico ◽  
Control Algorithm ◽  
Artificial Pancreas ◽  
Target Range ◽  
Loop Control ◽  
Percentage Time ◽  
Do It Yourself ◽  
Lower Variability ◽  
Baseline Algorithm

Background: User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available. Methods: The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator. Results: Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%). Conclusions: Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

Modeling and qualitative analysis of diabetes therapies with state feedback control

2014 ◽  
Vol 07 (04) ◽  
pp. 1450035 ◽  
Author(s):  
Mingzhan Huang ◽  
Xinyu Song
Keyword(s):  
Plasma Glucose ◽  
State Feedback ◽  
Closed Loop ◽  
Blood Glucose Concentration ◽  
Artificial Pancreas ◽  
Insulin Injection ◽  
State Feedback Control ◽  
Insulin Degradation ◽  
Closed Loop Control ◽  
Loop Control

For the therapies of diabetes mellitus, a novel mathematical model with two state impulses: impulsive injection of insulin and impulsive injection of glucagon, is proposed. To avoid hypoglycemia and hyperglycemia, the injections of insulin and glucagon are determined by closely monitoring the plasma glucose level of the patients. By using differential equation geometry theory, the existence of periodic solution and the attraction region of the system have been obtained, which ensures that injections in such an automated way can keep the blood glucose concentration under control. The simulation results verify that the better insulin injection strategy in closed-loop control is a larger dose but longer interval rather than a smaller dose but shorter interval. Besides, our numerical analysis reveals that medicine studies and practice that slow down the insulin degradation are helpful for the plasma glucose control. Our findings can provide significant guidance in both design of artificial pancreas and clinical treatment.

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