Performance of the Omnipod Personalized Model Predictive Control Algorithm with Meal Bolus Challenges in Adults with Type 1 Diabetes

Autor:	Mark P. Christiansen, Lauren M. Huyett, Thomas A. Peyser, Jason O'Connor, Gregory P. Forlenza, Trang T. Ly, Jennifer E. Layne, Joon Bok Lee, Eyal Dassau, Bruce A. Buckingham, R. Paul Wadwa
Rok vydání:	2018
Předmět:	Adult Blood Glucose Male Pancreas Artificial Endocrinology Diabetes and Metabolism Artificial pancreas 030209 endocrinology & metabolism Young Adult 03 medical and health sciences 0302 clinical medicine Endocrinology Bolus (medicine) immune system diseases Diabetes mellitus medicine Humans Hypoglycemic Agents Insulin Closed-loop 030212 general & internal medicine Omnipod Type 1 diabetes Meal business.industry Postprandial Investigational Device Feeding Behavior Original Articles Middle Aged Postprandial Period medicine.disease tubeless Insulin pump Medical Laboratory Technology Model predictive control Diabetes Mellitus Type 1 Automated insulin delivery Female business Algorithm Algorithms
Zdroj:	Diabetes Technology & Therapeutics
ISSN:	1557-8593 1520-9156
DOI:	10.1089/dia.2018.0138
Popis:	Background: This study assessed the safety and performance of the Omnipod® personalized model predictive control (MPC) algorithm using an investigational device in adults with type 1 diabetes in response to overestimated and missed meal boluses and extended boluses for high-fat meals. Materials and Methods: A supervised 54-h hybrid closed-loop (HCL) study was conducted in a hotel setting after a 7-day outpatient open-loop run-in phase. Adults aged 18–65 years with type 1 diabetes and HbA1c 6.0%–10.0% were eligible. Primary endpoints were percentage time in hypoglycemia
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1dd54c3d473d2321468b4e597b9caac0 https://doi.org/10.1089/dia.2018.0138 Zobrazit plný text záznamu