Time_in_range_network_meta-analysis_appendix.docx (695.68 kB)

Time in range for multiple technologies in type 1 diabetes: a systematic review and network meta-analysis

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posted on 17.07.2020 by Anthony Pease, Clement Lo, Arul Earnest, Velislava Kiriakova, Danny Liew, Sophia Zoungas
Background: Time-in-range is a key glycaemic 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 glycaemic ranges.

Data sources: We searched All Evidenced Based Medicine Reviews, CINAHL, EMBASE, MEDLINE, MEDLINE In-Process and other non-indexed citations, PROSPERO, PsycINFO, PubMed, and Web of Science until 24 April, 2019.

Study selection: We included randomised 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 3.9–10.0mmol/l (70–180mg/dL), >10.0mmol/L (180mg/dL), and <3.9mmol/L (70mg/dL).

Data synthesis: We identified 16,772 publications, of which 14 eligible studies compared eight technologies comprising 1,043 participants. Closed loop systems lead to greater percent time-in-range than any other management strategy and was 17.85 (95% predictive interval [PrI] 7.56–28.14) higher than usual care of multiple daily injections with capillary glucose testing. Closed loop systems ranked best for percent time-in-range or above range utilising 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.

Funding

No funding was received for this work.

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