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A Meal Detection Algorithm for the Artificial Pancreas: A Randomized Controlled Clinical Trial in Adolescents With Type 1 Diabetes

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posted on 04.12.2020, 23:27 by Emilie Palisaitis, Anas El Fathi, Julia E. von Oettingen, Ahmad Haidar, Laurent Legault
Background: We developed a meal detection algorithm for the artificial pancreas that detects unannounced meals and delivers an automatic insulin bolus.

Methods: We conducted a randomized crossover trial in 11 adolescents aged 12-18 years with HbA1c≥7.5% who missed≥1 bolus in the past six months. We compared (i) CSII, (ii) artificial pancreas (AP), and (iii) artificial pancreas with a meal detection algorithm (AP+MDA). Participants underwent three 9-hour interventions involving breakfast with a bolus and lunch without a bolus.

Results: In AP+MDA, the meal detection time was 40.0 [40.0–57.5] minutes. Compared to CSII, AP+MDA decreased the 4-hour post-lunch iAUC from 24.1±9.5 h.mmol/L 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) nor between AP and CSII (p=0.33). The AP+MDA reduced time>10mmol/L (58.0±26.6%) compared to 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.

Funding

AH received research support/consulting fees from Eli Lilly, Medtronic, AgaMatrix, and Dexcom, and has pending patents in the artificial pancreas area. LL has pending patents in the field of artificial pancreas, received consulting fees from Dexcom, and has received support for clinical trials from Merck, Astra-Zeneca and Sanofi. No other competing financial interests at the time of writing the manuscript were reported.

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