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.

scholarly journals Use of a do-it-yourself artificial pancreas system is associated with better glucose management and higher quality of life among adults with type 1 diabetes

2020 ◽  
Vol 11 ◽  
pp. 204201882095014
Author(s):  
Zekai Wu ◽  
Sihui Luo ◽  
Xueying Zheng ◽  
Yan Bi ◽  
Wen Xu ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Artificial Pancreas ◽  
Pump Therapy ◽  
Quality Of Life Scale ◽  
Do It Yourself ◽  
Glucose Management

Background: Previous studies show that the use of do-it-yourself artificial pancreas system (DIYAPS) may be associated with better glycemic control characterized by improved estimated hemoglobin A1c (eHbA1c) and time in range among adults with type 1 diabetes (T1D). However, few studies have demonstrated the changes in laboratory-measured HbA1c, which is a more accepted index for glycemic control, after using a DIYAPS. Methods: This is a retrospective before-after study approaching patients who reported self-use of AndroidAPS. The main inclusion criteria included: T1D; aged ⩾18 years; having complete record of ⩾3 months of continuous AndroidAPS use; with laboratory-measured HbA1c and quality of life scale data before and after 3 months of AndroidAPS use; and not pregnant. The primary outcome was the change in HbA1c between baseline and 3 months after initiation of AndroidAPS use. Results: Overall, 15 patients (10 females) were included; the median age was 32.2 years (range: 19.2–69.4), median diabetes duration was 9.7 years (range: 1.8–23.7) and median baseline HbA1c was 7.3% (range: 6.4–10.1). The 3 months of AndroidAPS use was associated with substantial reductions in HbA1c [6.79% (SD: 1.29) versus 7.63% (SD: 1.06), p = 0.002] and glycemic variability when compared with sensor-augmented pump therapy. A lower level of fear of hypoglycemia [22.13 points (SD: 6.87) versus 26.27 points (SD: 5.82), p = 0.010] was also observed after using AndroidAPS. Conclusions: The 3 months of AndroidAPS use was associated with significant improvements in glucose management and quality of life among adults with T1D.

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 A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study

2019 ◽  
Vol 14 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Chengyuan Liu ◽  
Parizad Avari ◽  
Yenny Leal ◽  
Marzena Wos ◽  
Kumuthine Sivasithamparam ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Insulin Dose ◽  
Safety System ◽  
Target Range ◽  
Percentage Time ◽  
Viable Solution ◽  
System Functioning ◽  
Safety Constraint

Background: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. Methods: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycemia; and a personalized safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycemic control and safety system functioning were compared between the two-weeks run-in period and the end point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. Results: Overall, glycemic control improved over the evaluated period. In particular, percentage time in the hypoglycemia range (<3.0 mmol/l) significantly decreased from 0.82% (0.05-4.79) at run-in to 0.33% (0.00-0.93) at endpoint ( P = .02). This was associated with a significant increase in percentage time in target range (3.9-10.0 mmol/l) from 52.8% (38.3-61.5) to 61.3% (47.5-71.7) ( P = .03). There was also a reduction in number of carbohydrate recommendations. Conclusion: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes.

Control and Decision-Making Support in a Personalized Insulin Therapy System

2021 ◽  
Vol 26 (2) ◽  
pp. 162-171
Author(s):  
E.L. Litinskaia ◽  
Keyword(s):  
Blood Glucose ◽  
Glycemic Control ◽  
Insulin Therapy ◽  
Closed Loop ◽  
Operation Time ◽  
Diabetes Mellitus Type 1 ◽  
Target Range ◽  
Loop Control ◽  
Closed Loop Systems ◽  
Glycemic Regulation

Insulin therapy automation is an actual research line in the glycemic control of diabetes mellitus type 1 patients. Development of closed-loop systems and methods will allow blood glucose maintaining in the physiological range. The work proposes the personalized insulin therapy system considered as a closed-loop control system based on feedback and external disturbances compensation principles. Automatic feedback-based glycemic control includes proportional reg-ulation of basal insulin infusion rate in relation to optimized thresholds inside the target range. To achieve bidirectional glycemic regulation the author proposes model predictive control for calculation of not only optimal profile of bolus infusion but also recommended corrective dose of carbohydrates. Besides, the comparative analysis of trends in measured and predicted profiles of blood glucose allows detecting and compensation of its unpredicted deviations. In silico testing of developed algorithms on nine virtual adults for 72 hours shows an ability for glucose maintaining in the target range for whole system operation time.

scholarly journals Using a Do-It-Yourself Artificial Pancreas: Perspectives from Patients and Diabetes Providers

2020 ◽  
Vol 14 (5) ◽  
pp. 860-867 ◽  
Author(s):  
Walter Palmer ◽  
Siri Atma W. Greeley ◽  
Lisa R. Letourneau-Freiberg ◽  
Rochelle N. Naylor
Keyword(s):  
Glycemic Control ◽  
Artificial Pancreas ◽  
Patient Survey ◽  
Endocrine Society ◽  
The Us ◽  
Time In Range ◽  
Do It Yourself ◽  
Provider Survey ◽  
Diabetes Educators ◽  
E Mail

Background: A growing number of people with diabetes are choosing to adopt do-it-yourself artificial pancreas system (DIYAPS) despite a lack of approval from the US Food and Drug Administration. We describe patients’ experiences using DIYAPS, and patient and diabetes providers’ perspectives on the use of such technology. Methods: We distributed surveys to patients and diabetes providers to assess each group’s perspectives on the use of DIYAPS. The patient survey also assessed glycemic control and impact on sleep. The patient survey was distributed in February 2019 via Facebook and Twitter ( n = 101). The provider survey was distributed via the American Association of Diabetes Educators’ e-mail newsletter in April 2019 and the Pediatric Endocrine Society membership e-mail list in May 2019 ( n = 152). Results: Patients overwhelmingly described improvements in glycemic control and sleep quality: 94% reported improvement in time in range, and 64% reported improvement in all five areas assessed. Eighty-nine percent of patients described DIYAPS as “Safe” or “Very Safe,” compared to only 27% of providers. Most felt encouraged by their diabetes provider to continue using DIYAPS, but few described providers as knowledgeable regarding its use. Providers cited a lack of experience with such systems and an inability to troubleshoot them as their most significant challenges. Conclusions: Despite evidence that DIYAPS usage is increasing, our surveys suggest that patients’ adoption of this technology and trust in it is outpacing that of diabetes providers. Providers must be aware of this growing population of patients and familiarize themselves with DIYAPS to support patients using this technology.

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 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>

In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems

2020 ◽  
Vol 22 (2) ◽  
pp. 112-120 ◽  
Author(s):  
Chiara Toffanin ◽  
Milos Kozak ◽  
Zdenek Sumnik ◽  
Claudio Cobelli ◽  
Lenka Petruzelkova
Keyword(s):  
Open Source ◽  
In Silico ◽  
Artificial Pancreas ◽  
New Paradigm ◽  
Safety And Efficacy ◽  
Do It Yourself

scholarly journals Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes—An In Silico Proof-of-Concept

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3168 ◽  
Author(s):  
Cappon ◽  
Facchinetti. ◽  
Sparacino. ◽  
Georgiou ◽  
Herrero
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Real Time ◽  
In Silico ◽  
Blood Glucose Concentration ◽  
Insulin Dose ◽  
Target Range ◽  
Glycemic Status ◽  
Meal Time

In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (± 13.89) to 67.00 (± 11.54; p < 0.01) without increasing hypoglycemia.

scholarly journals Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement

2019 ◽  
Vol 13 (6) ◽  
pp. 1035-1043 ◽  
Author(s):  
Emilia Fushimi ◽  
Patricio Colmegna ◽  
Hernán De Battista ◽  
Fabricio Garelli ◽  
Ricardo Sánchez-Peña
Keyword(s):  
In Silico ◽  
Artificial Pancreas ◽  
Glucose Monitoring ◽  
Automatic Regulation ◽  
In Vivo Evaluation ◽  
Loop Control ◽  
Pilot Clinical Study ◽  
Intrapatient Variability ◽  
Basal Bolus

Background: Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses—the so-called automatic regulation of glucose (ARG)—was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. Method: An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. Results: The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). Conclusion: In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.

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