Evidence map›Paper›PMID 29969995›Full record

ArticleBMC anesthesiology2018

Automated closed-loop management of body temperature using forced-air blankets: preliminary feasibility study in a porcine model.

Jörg Peter, Kathrin Klingert, Wilfried Klingert, Karolin Thiel, Alfred Königsrainer, Christian Grasshoff, Wolfgang Rosenstiel, Martin Schenk

Open access · goldAbstract read
In one paragraph

Article in BMC anesthesiology, 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
0.2field-weighted citation impact, top 47% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 1 institution in 1 country.

Jörg PeterDepartment of Computer Engineering, University of Tübingen, Sand 13, Tübingen, 72076, Germany.
Kathrin KlingertDepartment of Anaesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, Tübingen, 72076, Germany.
Wilfried KlingertDepartment of General, Visceral and Transplant Surgery, University Hospital Tübingen, Paul-Ehrlich-Straße 36, Tübingen, 72076, Germany.
Karolin ThielDepartment of General, Visceral and Transplant Surgery, University Hospital Tübingen, Paul-Ehrlich-Straße 36, Tübingen, 72076, Germany.
Alfred KönigsrainerDepartment of General, Visceral and Transplant Surgery, University Hospital Tübingen, Paul-Ehrlich-Straße 36, Tübingen, 72076, Germany.
Christian GrasshoffDepartment of Anaesthesiology and Intensive Care Medicine, University Hospital Tübingen, Hoppe-Seyler-Straße 3, Tübingen, 72076, Germany.
Wolfgang RosenstielDepartment of Information Technology and Applied Medical Informatics, University Hospital Tübingen, Sand 13, Tübingen, 72076, Germany.
Martin SchenkDepartment of General, Visceral and Transplant Surgery, University Hospital Tübingen, Paul-Ehrlich-Straße 36, Tübingen, 72076, Germany. martin.schenk@med.uni-tuebingen.de.ORCID 0000-0002-0402-7708
University of Tübingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundManagement of a patient's body temperature is an important aspect of care that should be addressed by targeted temperature management (TTM). Often, non-invasive methods like forced-air blankets are used. Especially in the operating room this management may be a subsidiary and repetitive task requiring constant observation of the patient's body temperature and adaption using the limited set of available settings. Thus, automation of TTM is a feasible target to improve patient outcome and reduce caregiver workload.

methodsA Philips IntelliVue MP 50 patient monitor with an arterial PiCCO catheter system was used to measure patient blood temperature. Thermal management was performed with a 3M Bair Hugger 755 warming unit with forced air blankets. The warming unit was extended by a computer interface to allow for remote and automated control. A proposed closed-loop algorithm reads the measured temperature and performs automated control of the 3M Bair Hugger. Evaluation was performed in an experimental intensive care setting for animal studies. Two fully automated trials are compared with two manual and two uncontrolled trials in the same study setting using six female pigs for prolonged observation times of up to 90 hours in each trial.

resultsThe developed system and proposed algorithm allow more precise temperature management by keeping a set target temperature within a range of ± 0.5 °C in 88% of the observation time and within a range of ± 1.0 °C at all times. The proposed algorithm yielded better performance than did manual control or uncontrolled trials. It was able to adapt to individual patient needs as it is more dynamic than look-up table approaches with fixed settings for various temperatures.

conclusionsClosed-loop TTM using non-invasive forced-air warming blankets was successfully tested in a porcine study with the proposed hardware interface and control algorithm. This automation can be beneficial for patient outcome and can reduce caregiver workload and patient risk in clinical settings. As temperature readings are most often available, existing devices like the 3M Bair Hugger can easily be expanded. However, even if clinical application is feasible, open questions regarding approval and certification of such automated systems within the current legal situation still need to be answered.

Indexed as

Bedding and LinensBody TemperatureAlgorithmsAnimalsAutomationFeasibility StudiesFemaleSwineAutomationClosed-loopForced-air blanketsIntensive careMedical frameworkMicrocontrollerPatient monitoringTargeted temperature managementWarming unit

Identifiers

PMID29969995
PMCPMC6029032
OpenAlexW2809819421

What OpenQuestion holds

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LicenceCC BY
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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.