Evidence map›Paper›PMID 29542346›Full record

ArticleJournal of diabetes science and technology2018

Accuracy Beyond ISO: Introducing a New Method for Distinguishing Differences Between Blood Glucose Monitoring Systems Meeting ISO 15197:2013 Accuracy Requirements.

Scott Pardo, Rimma M Shaginian, David A Simmons

Abstract read
In one paragraph

Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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.

Scott Pardo1 Ascensia Diabetes Care, Parsippany, NJ, USA.
Rimma M Shaginian2 Ascensia Diabetes Care, Basel, Switzerland.
David A Simmons1 Ascensia Diabetes Care, Parsippany, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes treatment is intended to maintain near-normal glycemic levels. Self-monitoring of blood glucose (SMBG) allows patients to track their BG levels compared with glycemic targets and is associated with improved health outcomes. Because of the importance of SMBG, it is essential that results are accurate to prevent errors in nutritional intake and drug dosing. This study presents a new methodology to evaluate the accuracy of BG monitoring systems (BGMSs).

methodsSensitivity analyses were performed using real and simulated BGMS data to compute probabilities that, for any BG value, the BGMS result would be within prescribed error bounds and confidence limits compared with laboratory reference values. Multiple BG value ranges were used.

resultsProbability curves were created using data from 3 simulated BGMSs and anonymized data from 3 real-world BGMSs. Accuracy probability curves from capillary fingertip blood samples (actual clinical data) showed that all 3 real-world BGMSs met EN ISO 15197:2015 accuracy criteria, since 99.63%, 99.63%, and 99.81% of results from the 3 BGMSs were within ±15 mg/dL or ±15% of reference for BG <100 mg/dL and ≥100 mg/dL, respectively. However, there was identifiable variability between BGMSs if BG was <70 mg/dL; one BGMS showed further reductions in accuracy if BG was <50 mg/dL.

conclusionsProbability curves highlight the importance of BGMS accuracy to help achieve optimal glycemic control while avoiding hypoglycemia or hyperglycemia. This may be especially significant in very low BG ranges where small errors in BGMS measurements can have substantial impacts on patient-related outcomes, including hypoglycemia risk.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2HumansReference ValuesBlood Glucoseblood glucoseblood glucose self-monitoringdiabetes mellitushypoglycemiaISO 15197:2013

Identifiers

PMID29542346
PMCPMC6154250

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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.