Literature DB >> 27981804

MD-Logic overnight type 1 diabetes control in home settings: A multicentre, multinational, single blind randomized trial.

Revital Nimri1, Natasa Bratina2, Olga Kordonouri3, Magdalena Avbelj Stefanija2, Maryam Fath3, Torben Biester3, Ido Muller1, Eran Atlas1, Shahar Miller1, Aviel Fogel1, Moshe Phillip1,4, Thomas Danne3, Tadej Battelino2,5.   

Abstract

AIMS: To evaluate the safety, efficacy and need for remote monitoring of the MD-Logic closed-loop system during short-term overnight use at home.
METHODS: Seventy-five patients (38 male; aged 10-54 years; average A1c, 7.8% ± 0.7%, 61.8 ± 7.2 mmol/mol) were enrolled from 3 clinical sites. Patients were randomly assigned to participate in 2 overnight crossover periods, each including 4 consecutive nights, 1 under closed-loop control and 1 under sensor-augmented pump (SAP) therapy in the patient's home. Both study arms were supervised using a remote-monitoring system in a blinded manner. Primary endpoints were time spent with glucose levels below 70 mg/dL and percentage of nights in which mean overnight glucose levels were within 90 to 140 mg/dL.
RESULTS: The median [interquartile range] percentage of time spent in hypoglycaemia was significantly lower on nights when MD-Logic was used, compared to SAP therapy (2.07 [0, 4.78] and 2.6 [0, 10.34], respectively; P = .004) and the percentage of individual nights with a mean overnight glucose level in target was significantly greater (75 [42, 75] and 50 [25,75], respectively; P = .008). The time spent in target range was increased by a median of 28% (P = .001), with the same amount of insulin (10.69 [7.28, 13.94] and 10.41[6.9, 14.07], respectively; P = .087). The remote monitoring triggered calls for hypoglycaemia at twice the rate during SAP therapy compared to closed-loop control (62 and 29, respectively; P = .002).
CONCLUSIONS: The MD-Logic system demonstrated a safe and efficient profile during overnight use by children, adolescents and adults with type 1 diabetes and, therefore, provides an effective means of mitigating the risk of nocturnal hypoglycaemia.
© 2016 John Wiley & Sons Ltd.

Entities:  

Keywords:  zzm321990MD-Logic; closed-loop; nocturnal hypoglycaemia; remote-monitoring; type 1

Mesh:

Substances:

Year:  2017        PMID: 27981804     DOI: 10.1111/dom.12852

Source DB:  PubMed          Journal:  Diabetes Obes Metab        ISSN: 1462-8902            Impact factor:   6.577


  15 in total

1.  Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting.

Authors:  Gregory P Forlenza; Faye M Cameron; Trang T Ly; David Lam; Daniel P Howsmon; Nihat Baysal; Georgia Kulina; Laurel Messer; Paula Clinton; Camilla Levister; Stephen D Patek; Carol J Levy; R Paul Wadwa; David M Maahs; B Wayne Bequette; Bruce A Buckingham
Journal:  Diabetes Technol Ther       Date:  2018-04-16       Impact factor: 6.118

2.  Artificial Intelligence Methodologies and Their Application to Diabetes.

Authors:  Mercedes Rigla; Gema García-Sáez; Belén Pons; Maria Elena Hernando
Journal:  J Diabetes Sci Technol       Date:  2017-05-25

Review 3.  Artificial Pancreas: Current Progress and Future Outlook in the Treatment of Type 1 Diabetes.

Authors:  Rozana Ramli; Monika Reddy; Nick Oliver
Journal:  Drugs       Date:  2019-07       Impact factor: 9.546

Review 4.  Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes.

Authors:  David Rodbard
Journal:  Diabetes Technol Ther       Date:  2017-06       Impact factor: 6.118

5.  Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes.

Authors:  Rayhan A Lal; Laya Ekhlaspour; Korey Hood; Bruce Buckingham
Journal:  Endocr Rev       Date:  2019-12-01       Impact factor: 19.871

Review 6.  Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic.

Authors:  Nakul Aggarwal; Mahnoor Ahmed; Sanjay Basu; John J Curtin; Barbara J Evans; Michael E Matheny; Shantanu Nundy; Mark P Sendak; Carmel Shachar; Rashmee U Shah; Sonoo Thadaney-Israni
Journal:  NAM Perspect       Date:  2020-11-30

7.  Outpatient Randomized Crossover Automated Insulin Delivery Versus Conventional Therapy with Induced Stress Challenges.

Authors:  Ravinder Jeet Kaur; Sunil Deshpande; Jordan E Pinsker; Wesley P Gilliam; Shelly McCrady-Spitzer; Isabella Zaniletti; Donna Desjardins; Mei Mei Church; Francis J Doyle Iii; Walter K Kremers; Eyal Dassau; Yogish C Kudva
Journal:  Diabetes Technol Ther       Date:  2022-04-25       Impact factor: 7.337

Review 8.  Insulin delivery and nocturnal glucose control in children and adolescents with type 1 diabetes.

Authors:  Martin Tauschmann; Roman Hovorka
Journal:  Expert Opin Drug Deliv       Date:  2017-08-18       Impact factor: 6.648

Review 9.  [Individualization of diabetes treatment by automated insulin delivery].

Authors:  T Biester; K Dovc; A Chobot; M Tauschmann; T Kapellen
Journal:  Monatsschr Kinderheilkd       Date:  2021-07-13       Impact factor: 0.416

10.  Reduced Worries of Hypoglycaemia, High Satisfaction, and Increased Perceived Ease of Use after Experiencing Four Nights of MD-Logic Artificial Pancreas at Home (DREAM4).

Authors:  Claudia Ziegler; Alon Liberman; Revital Nimri; Ido Muller; Simona Klemenčič; Nataša Bratina; Sarah Bläsig; Kerstin Remus; Moshe Phillip; Tadej Battelino; Olga Kordonouri; Thomas Danne; Karin Lange
Journal:  J Diabetes Res       Date:  2015-10-25       Impact factor: 4.011

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