Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: a randomised crossover trial.


Journal

The Lancet. Digital health
ISSN: 2589-7500
Titre abrégé: Lancet Digit Health
Pays: England
ID NLM: 101751302

Informations de publication

Date de publication:
02 2020
Historique:
entrez: 1 9 2020
pubmed: 31 8 2020
medline: 31 8 2020
Statut: ppublish

Résumé

Automated closed-loop control (CLC), known as the "artificial pancreas" is emerging as a treatment option for Type 1 Diabetes (T1D), generally superior to sensor-augmented insulin pump (SAP) treatment. It is postulated that evening-night (E-N) CLC may account for most of the benefits of 24-7 CLC; however, a direct comparison has not been done. In this trial (NCT02679287), adults with T1D were randomised 1:1 to two groups, which followed different sequences of four 8-week sessions, resulting in two crossover designs comparing SAP vs E-N CLC and E-N CLC vs 24-7 CLC, respectively. Eligibility: T1D for at least 1 year, using an insulin pump for at least six months, ages 18 years or older. Primary hypothesis: E-N CLC compared to SAP will decrease percent time <70mg/dL (3.9mmol/L) measured by continuous glucose monitoring (CGM) without deterioration in HbA Ninety-three participants were randomised and 80 were included in the analysis, ages 18-69 years; HbA1c levels 5.4-10.6%; 66% female. Compared to SAP, E-N CLC reduced overall time <70mg/dL from 4.0% to 2.2% () resulting in an absolute difference of 1.8% (95%CI: 1.2-2.4%), p<0.0001. This was accompanied by overall reduction in HbA In type 1 diabetes, evening-night closed-loop control was superior to sensor-augmented pump therapy, achieving most of the glycaemic benefits of 24-7 closed-loop.

Sections du résumé

Background
Automated closed-loop control (CLC), known as the "artificial pancreas" is emerging as a treatment option for Type 1 Diabetes (T1D), generally superior to sensor-augmented insulin pump (SAP) treatment. It is postulated that evening-night (E-N) CLC may account for most of the benefits of 24-7 CLC; however, a direct comparison has not been done.
Methods
In this trial (NCT02679287), adults with T1D were randomised 1:1 to two groups, which followed different sequences of four 8-week sessions, resulting in two crossover designs comparing SAP vs E-N CLC and E-N CLC vs 24-7 CLC, respectively. Eligibility: T1D for at least 1 year, using an insulin pump for at least six months, ages 18 years or older. Primary hypothesis: E-N CLC compared to SAP will decrease percent time <70mg/dL (3.9mmol/L) measured by continuous glucose monitoring (CGM) without deterioration in HbA
Findings
Ninety-three participants were randomised and 80 were included in the analysis, ages 18-69 years; HbA1c levels 5.4-10.6%; 66% female. Compared to SAP, E-N CLC reduced overall time <70mg/dL from 4.0% to 2.2% () resulting in an absolute difference of 1.8% (95%CI: 1.2-2.4%), p<0.0001. This was accompanied by overall reduction in HbA
Interpretation
In type 1 diabetes, evening-night closed-loop control was superior to sensor-augmented pump therapy, achieving most of the glycaemic benefits of 24-7 closed-loop.

Identifiants

pubmed: 32864597
doi: 10.1016/S2589-7500(19)30218-3
pmc: PMC7453908
mid: NIHMS1549717
doi:

Substances chimiques

Insulin 0

Banques de données

ClinicalTrials.gov
['NCT02679287']

Types de publication

Journal Article Randomized Controlled Trial Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Pagination

e64-e73

Subventions

Organisme : NIDDK NIH HHS
ID : R01 DK085623
Pays : United States

Commentaires et corrections

Type : CommentIn

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Auteurs

Boris P Kovatchev (BP)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Laura Kollar (L)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Stacey M Anderson (SM)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Charlotte Barnett (C)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Marc D Breton (MD)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Kelly Carr (K)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Rachel Gildersleeve (R)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Mary C Oliveri (MC)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Christian A Wakeman (CA)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

Sue A Brown (SA)

University of Virginia Center for Diabetes Technology, Charlottesville, VA USA.

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Classifications MeSH