Evidence map›Paper›PMID 23555075›Full record

ArticleBioMed research international2013

The use of continuous glucose monitoring combined with computer-based eMPC algorithm for tight glucose control in cardiosurgical ICU.

Petr Kopecký, Miloš Mráz, Jan Bláha, Jaroslav Lindner, Stĕpán Svačina, Roman Hovorka, Martin Haluzík

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 2 pooled it
2.7field-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

33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 53 citations in OpenAlex.

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  16. Continuous Glucose Monitoring in the Hospital.Endocrinology and metabolism (Seoul, Korea) · 2021
    Review
  17. Facilitators and Barriers to Nursing Implementation of Continuous Glucose Monitoring (CGM) in Critically Ill Patients With COVID-19.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2021
    Article
  18. Article
  19. Diabetes Technology in the Inpatient Setting for Management of Hyperglycemia.Endocrinology and metabolism clinics of North America · 2020
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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

7 authors at 2 institutions in 2 countries.

Petr KopeckýDepartment of Anaesthesia, Resuscitation and Intensive Medicine, 1st Faculty of Medicine and General University Hospital, Charles University in Prague, U Nemocnice 2, 128 08 Prague 2, Czech Republic.
Miloš Mráz
Jan Bláha
Jaroslav Lindner
Stĕpán Svačina
Roman Hovorka
Martin Haluzík
Charles University · CZUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimIn postcardiac surgery patients, we assessed the performance of a system for intensive intravenous insulin therapy using continuous glucose monitoring (CGM) and enhanced model predictive control (eMPC) algorithm.

methodsGlucose control in eMPC-CGM group (n = 12) was compared with a control (C) group (n = 12) treated by intravenous insulin infusion adjusted according to eMPC protocol with a variable sampling interval alone. In the eMPC-CGM group glucose measured with a REAL-Time CGM system (Guardian RT) served as input for the eMPC adjusting insulin infusion every 15 minutes. The accuracy of CGM was evaluated hourly using reference arterial glucose and Clarke error-grid analysis (C-EGA). Target glucose range was 4.4-6.1 mmol/L.

resultsOf the 277 paired CGM-reference glycemic values, 270 (97.5%) were in clinically acceptable zones of C-EGA and only 7 (2.5%) were in unacceptable D zone. Glucose control in eMPC-CGM group was comparable to C group in all measured values (average glycemia, percentage of time above, within, and below target range,). No episode of hypoglycemia (<2.9 mmol) occurred in eMPC-CGM group compared to 2 in C group.

conclusionOur data show that the combination of eMPC algorithm with CGM is reliable and accurate enough to test this approach in a larger study population.

Indexed as

AgedAlgorithmsBlood GlucoseDiabetes Mellitus, Type 1FemaleHumansInfusions, IntravenousInsulinIntensive Care UnitsMaleMiddle AgedPostoperative PeriodBlood GlucoseInsulin

Identifiers

PMID23555075
PMCPMC3595106
OpenAlexW2029998845

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.