Literature DB >> 32669412

Time in Range for Multiple Technologies in Type 1 Diabetes: A Systematic Review and Network Meta-analysis.

Anthony Pease1,2, Clement Lo1,2, Arul Earnest1, Velislava Kiriakova2, Danny Liew1,3, Sophia Zoungas4,2,3.   

Abstract

BACKGROUND: Time in range is a key glycemic metric, and comparisons of management technologies for this outcome are critical to guide device selection.
PURPOSE: We conducted a systematic review and network meta-analysis to compare and rank technologies for time in glycemic ranges. DATA SOURCES: We searched Evidenced-Based Medicine Reviews, CINAHL, Embase, MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PROSPERO, PsycInfo, PubMed, and Web of Science until 24 April 2019. STUDY SELECTION: We included randomized controlled trials ≥2 weeks' duration comparing technologies for management of type 1 diabetes in adults (≥18 years of age), excluding pregnant women. DATA EXTRACTION: Data were extracted using a predefined template. Outcomes were percent time with sensor glucose levels 3.9-10.0 mmol/L (70-180 mg/dL), >10.0 mmol/L (180 mg/dL), and <3.9 mmol/L (70 mg/dL). DATA SYNTHESIS: We identified 16,772 publications, of which 14 eligible studies compared eight technologies comprising 1,043 participants. Closed-loop systems led to greater percent time in range than any other management strategy, and mean percent time in range was 17.85 (95% predictive interval 7.56-28.14) longer than with usual care of multiple daily injections with capillary glucose testing. Closed-loop systems ranked best for percent time in range or above range with use of Surface Under the Cumulative RAnking curve (SUCRA) (98.5% and 93.5%, respectively). Closed-loop systems also ranked highly for time below range (SUCRA 62.2%). LIMITATIONS: Overall risk of bias ratings were moderate for all outcomes. Certainty of evidence was very low.
CONCLUSIONS: In the first integrated comparison of multiple management strategies considering time in range, we found that the efficacy of closed-loop systems appeared better than all other approaches.
© 2020 by the American Diabetes Association.

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Year:  2020        PMID: 32669412     DOI: 10.2337/dc19-1785

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  6 in total

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Authors:  Hood Thabit; Rayhan Lal; Lalantha Leelarathna
Journal:  Diabet Med       Date:  2021-09-28       Impact factor: 4.359

Review 2.  Current Status and Emerging Options for Automated Insulin Delivery Systems.

Authors:  Gregory P Forlenza; Rayhan A Lal
Journal:  Diabetes Technol Ther       Date:  2022-03-14       Impact factor: 7.337

3.  Glycemic Outcomes in Baseline Hemoglobin A1C Subgroups in the International Diabetes Closed-Loop Trial.

Authors:  Laya Ekhlaspour; Marissa Town; Dan Raghinaru; John W Lum; Sue A Brown; Bruce A Buckingham
Journal:  Diabetes Technol Ther       Date:  2022-02-08       Impact factor: 7.337

Review 4.  Continuous Glucose Monitor, Insulin Pump, and Automated Insulin Delivery Therapies for Type 1 Diabetes: An Update on Potential for Cardiovascular Benefits.

Authors:  Meghan E Pauley; Kalie L Tommerdahl; Janet K Snell-Bergeon; Gregory P Forlenza
Journal:  Curr Cardiol Rep       Date:  2022-10-24       Impact factor: 3.955

5.  The Evolution of Hemoglobin A1c Targets for Youth With Type 1 Diabetes: Rationale and Supporting Evidence.

Authors:  Maria J Redondo; Ingrid Libman; David M Maahs; Sarah K Lyons; Mindy Saraco; Jane Reusch; Henry Rodriguez; Linda A DiMeglio
Journal:  Diabetes Care       Date:  2021-01-11       Impact factor: 19.112

Review 6.  Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review.

Authors:  Chirath Hettiarachchi; Elena Daskalaki; Jane Desborough; Christopher J Nolan; David O'Neal; Hanna Suominen
Journal:  JMIR Diabetes       Date:  2022-02-24
  6 in total

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