Evidence map›Paper›PMID 33511578›Full record

ArticleExperimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association2022

Choice of Continuous Glucose Monitoring Systems May Affect Metrics: Clinically Relevant Differences in Times in Ranges.

Guido Freckmann, Stefan Pleus, Sebastian Schauer, Manuela Link, Nina Jendrike, Delia Waldenmaier, Cornelia Haug, Andreas Stuhr

Abstract read
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In one paragraph

Article in Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Observational
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Hemoglobin ADiabetes care · 2021
    Review
  14. Article
  15. Continuous Glucose Monitoring in the Hospital.Endocrinology and metabolism (Seoul, Korea) · 2021
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Guido FreckmannInstitut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany.ORCID 0000-0002-0406-9529
Stefan PleusInstitut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany.ORCID 0000-0003-4629-7754
Sebastian SchauerInstitut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany.ORCID 0000-0002-9873-0989
Manuela LinkInstitut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany.
Nina JendrikeInstitut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany.
Delia WaldenmaierInstitut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany.ORCID 0000-0003-3280-2369
Cornelia HaugInstitut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany.
Andreas StuhrAscensia Diabetes Care, Parsippany, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundContinuous glucose monitoring-derived parameters are becoming increasingly important in the treatment of people with diabetes. The aim of this study was to assess whether these parameters, as calculated from different continuous glucose monitoring systems worn in parallel, are comparable. In addition, clinical relevance of differences was investigated.

methodsA total of 24 subjects wore a FreeStyle Libre (A) and a Dexcom G5 (B) sensor in parallel for 7 days. Mean glucose, coefficient of variation, glucose management indicator and time spent in different glucose ranges were calculated for each system. Pairwise differences between the two different continuous glucose monitoring systems were computed for these metrics.

resultsOn average, the two CGM systems indicated an identical time in range (67.9±10.2 vs. 67.9±11.5%) and a similar coefficient of variation; both categorized as unstable (38.1±5.9 vs. 36.0±4.8%). In contrast, the mean time spent below and above range, as well as the individual times spent below, in and above range differed substantially. System A indicated about twice the time spent below range than system B (7.7±7.2 vs. 3.8±2.7%, p=0.003). This could have led to different therapy recommendations in approximately half of the subjects. DISCUSSION: The differences in metrics found between the two continuous glucose monitoring systems may result in different therapy recommendations. In order to make adequate clinical decisions, measurement performance of CGM systems should be standardized and all available information, including the HbA1c, should be utilized.

Indexed as

Blood Glucose Self-MonitoringDiabetes Mellitus, Type 1Blood GlucoseGlucoseHumansBlood GlucoseGlucose

Identifiers

PMID33511578

What Socratic holds

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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 Socratic graph.