Evidence map›Paper›PMID 20642855›Full record

ArticleBiomedical engineering online2010

In silico evaluation of a control system and algorithm for automated insulin infusion in the ICU setting.

José L Ortiz, Marcelo W Guarini, Gisella R Borzone, Pablo R Olmos

Open access · goldAbstract read
In one paragraph

Article in Biomedical engineering online, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 40% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

4 authors at 1 institution in 1 country.

José L OrtizDepartment of Electrical Engineering, College of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
Marcelo W Guarini
Gisella R Borzone
Pablo R Olmos
Pontificia Universidad Católica de Chile · CL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is known that tight control of glucose in the Intensive Care Unit reduces morbidity and mortality not only in diabetic patients but also in those non-diabetics who become transiently hyperglycemic. Taking advantage of a recently marketed subcutaneous glucose sensor we designed an Automatic Insulin Infusion System (AIIS) for inpatient treatment, and tested its stability under simulated clinical conditions.

methodsThe system included: reference glucose, glucose sensor, insulin and glucose infusion controllers and emergency infusion logic. We carried out computer simulations using Matlab/Simulink, in both common and worst-case conditions.

resultsThe system was capable of controlling glucose levels without entering in a phase of catastrophic instability, even under severe simulated challenges. Care was taken to include in all simulations the 5-10 minute delay of the subcutaneous glucose signal when compared to the real-time serum glucose signal, a well-known characteristic of all subcutaneous glucose sensors.

conclusionsWhen tested in-Silico, a commercially available subcutaneous glucose sensor allowed the stable functioning of a proportional-derivative Automatic Insulin Infusion System, which was able to maintain glucose within acceptable limits when using a well-established glucose response model simulating a patient. Testing of the system in vivo using animal models is now warranted.

Indexed as

AlgorithmsComputational BiologyInsulin Infusion SystemsIntensive Care UnitsSoftwareAutomationBlood GlucoseDiabetes MellitusGlucoseHumansReproducibility of ResultsBlood GlucoseGlucose

Identifiers

PMID20642855
PMCPMC2918623
OpenAlexW2118746325

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.