BACKGROUND: Artificial pancreas systems may offer a potential major impact on the normalization of metabolic control and preventing hypoglycemic events. This study aims to establish near-normal overnight glucose control and reduce the risk of nocturnal hypoglycemia using the MD-Logic Artificial Pancreas (MDLAP), an algorithm that was developed by our research group. This inpatient feasibility study is the first step towards implementing an overnight closed-loop MDLAP system at the patient's home. SUBJECTS AND METHODS: Seven patients with type 1 diabetes (three adolescents and four adults; mean±SD age, 20.6±4.7 years; duration of diabetes, 9.6±2.6 years; body mass index, 24.3±3.9 kg/m(2); and glycated hemoglobin, 7.8±0.8%) participated in a total of 14 closed-loop overnight sessions. Each participant underwent two closed-loop inpatient sessions starting at dinner alone and at dinner following exercise. The closed-loop inpatient sessions were compared with data derived from nights spent at home with an open-loop system in a similar scenario to the study protocol. RESULTS: The mean percentage of time spent in the near normal glucose range of 63-140 mg/dL was 83±16%, and the median (interquartile range) was 85% (78-92%) for the overnight closed-loop sessions compared with 34±31% and 27% (6-57%) in the homecare open-loop setting, respectively. During the overnight closed-loop sessions at dinner alone 92±9% of the sensor values ranged within target range, compared with 73±19% for the sessions following exercise (P=0.03). No hypoglycemic (<63 mg/dL) events occurred during the closed-loop sessions. CONCLUSION: Closed-loop insulin delivery under MDLAP is a feasible and safe solution to control overnight glycemia.
BACKGROUND: Artificial pancreas systems may offer a potential major impact on the normalization of metabolic control and preventing hypoglycemic events. This study aims to establish near-normal overnight glucose control and reduce the risk of nocturnal hypoglycemia using the MD-Logic Artificial Pancreas (MDLAP), an algorithm that was developed by our research group. This inpatient feasibility study is the first step towards implementing an overnight closed-loop MDLAP system at the patient's home. SUBJECTS AND METHODS: Seven patients with type 1 diabetes (three adolescents and four adults; mean±SD age, 20.6±4.7 years; duration of diabetes, 9.6±2.6 years; body mass index, 24.3±3.9 kg/m(2); and glycated hemoglobin, 7.8±0.8%) participated in a total of 14 closed-loop overnight sessions. Each participant underwent two closed-loop inpatient sessions starting at dinner alone and at dinner following exercise. The closed-loop inpatient sessions were compared with data derived from nights spent at home with an open-loop system in a similar scenario to the study protocol. RESULTS: The mean percentage of time spent in the near normal glucose range of 63-140 mg/dL was 83±16%, and the median (interquartile range) was 85% (78-92%) for the overnight closed-loop sessions compared with 34±31% and 27% (6-57%) in the homecare open-loop setting, respectively. During the overnight closed-loop sessions at dinner alone 92±9% of the sensor values ranged within target range, compared with 73±19% for the sessions following exercise (P=0.03). No hypoglycemic (<63 mg/dL) events occurred during the closed-loop sessions. CONCLUSION: Closed-loop insulin delivery under MDLAP is a feasible and safe solution to control overnight glycemia.
Authors: Daniel A Finan; Thomas W McCann; Linda Mackowiak; Eyal Dassau; Stephen D Patek; Boris P Kovatchev; Francis J Doyle; Howard Zisser; Henry Anhalt; Ramakrishna Venugopalan Journal: J Diabetes Sci Technol Date: 2014-01-01
Authors: Jérôme Place; Antoine Robert; Najib Ben Brahim; Patrick Keith-Hynes; Anne Farret; Marie-Josée Pelletier; Bruce Buckingham; Marc Breton; Boris Kovatchev; Eric Renard Journal: J Diabetes Sci Technol Date: 2013-11-01
Authors: Victor W Zhong; Jamie L Crandell; Christina M Shay; Penny Gordon-Larsen; Stephen R Cole; Juhaeri Juhaeri; Anna R Kahkoska; David M Maahs; Michael Seid; Gregory P Forlenza; Elizabeth J Mayer-Davis Journal: J Diabetes Complications Date: 2017-04-20 Impact factor: 2.852
Authors: Jennifer L Sherr; Neha S Patel; Camille I Michaud; Miladys M Palau-Collazo; Michelle A Van Name; William V Tamborlane; Eda Cengiz; Lori R Carria; Eileen M Tichy; Stuart A Weinzimer Journal: Diabetes Care Date: 2016-05-05 Impact factor: 19.112