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

• Published in: Journal of diabetes science and technology Vol. 16; no. 1; pp. 29 - 39
• Main Authors: Armiger, Ryan, Reddy, Monika, Oliver, Nick S., Georgiou, Pantelis, Herrero, Pau
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2022
• Subjects: Adolescent; Adult; Algorithms; Blood Glucose; Blood Glucose Self-Monitoring; Diabetes Mellitus, Type 1 - drug therapy; Humans; Hypoglycemic Agents - therapeutic use; Insulin - therapeutic use; Insulin Infusion Systems; Pancreas, Artificial; Special Section: The Artificial Pancreas: Predictive Algorithm Strategies; in silico trials; type 1 diabetes; do-it-yourself; automatic insulin delivery; artificial pancreas; bio-inspired technology
• ISSN: 1932-2968; 1932-3107; 1932-3107
• DOI: 10.1177/19322968211060074

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.