Evidence map›Paper›PMID 23294784›Full record

ArticleJournal of diabetes science and technology2012

Minimizing the impact of time lag variability on accuracy evaluation of continuous glucose monitoring systems.

Cosimo Scuffi, Fausto Lucarelli, Francesco Valgimigli

Abstract read
In one paragraph

Article in Journal of diabetes science and technology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Inpatient Continuous Glucose Monitoring and Glycemic Outcomes.Journal of diabetes science and technology · 2017
    Review
  8. Article
  9. Time Delay of CGM Sensors: Relevance, Causes, and Countermeasures.Journal of diabetes science and technology · 2015
    Article
  10. Review
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Cosimo ScuffiA. Menarini Diagnostics, Scientific and Technology Affairs, Florence, Italy. cscuffi@menarini.it
Fausto Lucarelli
Francesco Valgimigli

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite all commercially available continuous glucose monitoring (CGM) systems being designed to operate in the extracellular interstitial fluid, and even though there is a well-recognized time lag between the interstitial and the venous compartments, the accuracy of the CGM device readings is still evaluated against the glucose concentration in venous blood (VB) samples, thus resulting in a perceived decrease in accuracy. This article explains how different time lag compensation methods (no compensation, compensation with a fixed delay, compensation with a variable delay based on an intercompartmental diffusional model) have an impact on how CGM accuracy is evaluated.

methodsThe data set used consisted of 210 CGM/blood glucose data pairs from 18 diabetes subjects (15 type 1 and 3 type 2) selected from a data base collected during two independent clinical trials. All CGM measurements were performed using the GlucoMen ®Day CGM system (A. Menarini Diagnostics, Italy), and the reference VB glucose measurements by means of a standard laboratory instrument. For each applied time lag compensation method, the CGM accuracy evaluation was performed as recommended by the POCT05-A consensus guideline.

resultsThe perceived accuracy of the CGM device significantly improved when applying both the fixed or the variable delay compensation method. However, it is worth noting how the variable delay method, which relies on a closer description of the intercompartmental diffusion processes, provided the best perception of the clinical accuracy of the device.

conclusionsWhen assessing the accuracy of a CGM system, a crucial step in data analysis is to account for time lag, which enables minimization of the apparent decline in system accuracy.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringDiabetes MellitusHumansTime FactorsBlood Glucose

Identifiers

PMID23294784
PMCPMC3570879

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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.