<b>Background: </b>Time-in-range
is a key glycaemic metric, and comparisons of management technologies for this
outcome are critical to guide device selection.
<p><b> </b></p>
<p><b>Purpose: </b>We
conducted a systematic review and network meta-analysis to compare and rank
technologies for time in glycaemic ranges.</p>
<p> </p>
<p><b>Data sources: </b>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.</p>
<p> </p>
<p><b>Study selection: </b>We included randomised controlled trials <u>></u>2
weeks duration comparing technologies for management of type 1 diabetes in
adults (<u>></u>18 years of age), excluding pregnant women. </p>
<p> </p>
<p><b>Data extraction: </b>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). </p>
<p><b> </b></p>
<p><b>Data synthesis: </b>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). </p>
<p><b> </b></p>
<p><b>Limitations: </b>Overall risk of bias ratings were moderate for all
outcomes. Certainty of evidence was very low.</p>
<p><b> </b></p>
<p><b>Conclusions: </b>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. </p>
Identifying clinical criteria to predict Type 1 diabetes, as defined by absolute insulin deficiency: a systematic review protocol
