Evidence map›Paper›PMID 20215457›Full record

Trial reportDiabetes care2010

Evaluation of an algorithm to guide patients with type 1 diabetes treated with continuous subcutaneous insulin infusion on how to respond to real-time continuous glucose levels: a randomized controlled trial.

Alicia J Jenkins, Balasubramanium Krishnamurthy, James D Best, Fergus J Cameron, Peter G Colman, Steven Farish, Peter S Hamblin, Michele A O'Connell, Christine Rodda, Kevin Rowley and 2 more

Erratum issuedOpen access · hybridAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Diabetes care, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Article
  7. Article
  8. Influences on Technology Use and Efficacy in Type 1 Diabetes.Journal of diabetes science and technology · 2016
    Review
  9. Article
  10. Review
  11. Review
4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

12 authors at 7 institutions in 1 country.

Alicia J JenkinsDepartment of Medicine, The University of Melbourne, St Vincent's Hospital, Fitzroy, Victoria, Australia.
Balasubramanium Krishnamurthy
James D Best
Fergus J Cameron
Peter G Colman
Steven Farish
Peter S Hamblin
Michele A O'Connell
Christine Rodda
Kevin Rowley
Helena Teede
David N O'Neal
St Vincent's Hospital · AURoyal Children's Hospital · AUUniversity of Melbourne · AUFootscray Hospital · AUMonash University · AUNorthern Health · AUThe Royal Melbourne Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate an algorithm guiding responses of continuous subcutaneous insulin infusion (CSII)-treated type 1 diabetic patients using real-time continuous glucose monitoring (RT-CGM). RESEARCH DESIGN AND

methodsSixty CSII-treated type 1 diabetic participants (aged 13-70 years, including adult and adolescent subgroups, with A1C <or=9.5%) were randomized in age-, sex-, and A1C-matched pairs. Phase 1 was an open 16-week multicenter randomized controlled trial. Group A was treated with CSII/RT-CGM with the algorithm, and group B was treated with CSII/RT-CGM without the algorithm. The primary outcome was the difference in time in target (4-10 mmol/l) glucose range on 6-day masked CGM. Secondary outcomes were differences in A1C, low (<or=3.9 mmol/l) glucose CGM time, and glycemic variability. Phase 2 was the week 16-32 follow-up. Group A was returned to usual care, and group B was provided with the algorithm. Glycemia parameters were as above. Comparisons were made between baseline and 16 weeks and 32 weeks.

resultsIn phase 1, after withdrawals 29 of 30 subjects were left in group A and 28 of 30 subjects were left in group B. The change in target glucose time did not differ between groups. A1C fell (mean 7.9% [95% CI 7.7-8.2to 7.6% [7.2-8.0]; P < 0.03) in group A but not in group B (7.8% [7.5-8.1] to 7.7 [7.3-8.0]; NS) with no difference between groups. More subjects in group A achieved A1C <or=7% than those in group B (2 of 29 to 14 of 29 vs. 4 of 28 to 7 of 28; P = 0.015). In phase 2, one participant was lost from each group. In group A, A1C returned to baseline with RT-CGM discontinuation but did not change in group B, who continued RT-CGM with addition of the algorithm.

conclusionsEarly but not late algorithm provision to type 1 diabetic patients using CSII/RT-CGM did not increase the target glucose time but increased achievement of A1C <or=7%. Upon RT-CGM cessation, A1C returned to baseline.

Indexed as

AlgorithmsInfusions, SubcutaneousAdolescentAdultBlood GlucoseDiabetes Mellitus, Type 1FemaleGlycated HemoglobinHumansHypoglycemic AgentsInsulinMaleModels, TheoreticalYoung AdultBlood Glucoseglucosylated hemoglobin AGlycated HemoglobinHypoglycemic AgentsInsulin

Identifiers

PMID20215457
PMCPMC2875432
OpenAlexW2039291946

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.