Evidence map›Paper›PMID 33914229›Full record

ArticleJournal of clinical monitoring and computing2022

Volatile anesthetic gas concentration sensing using flow sensor fusion for use in Austere settings.

Patrick R Kolbay, Joseph A Orr, Kai Kück

Abstract read
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Article in Journal of clinical monitoring and computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Patrick R KolbayDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA. patrick.kolbay@utah.edu.
Joseph A OrrDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA.ORCID 0000-0002-1892-0660
Kai KückDepartment of Anesthesiology, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA.

Funding

National Science Foundation i-CorpsUtah Space Grant Consortium 80NSSC20M0103
6 · The paper itself

Abstract

Flow sensors are often sensitive to the presence of volatile anesthetics. However, this sensitivity provides a unique opportunity to combine flow sensors of differing technological principles as an alternative to measuring volatile anesthetic gas concentration, particularly for austere settings. To determine the feasibility of flow sensor fusion for volatile anesthetic concentrations monitoring, eight flow sensors were tested with isoflurane, sevoflurane, and desflurane, ranging in concentrations from 0-4.5%, 0-3.5%, and 0-18%, respectively. Pairs of flow sensors were fit to the volatile anesthetic gas concentration with a leave-one-out cross-validation method to reduce the likelihood of overfitting. Bland-Altman was used for the final evaluation of sensor pair performance. Several sensor pairs yielded limits of agreement comparable to the rated accuracy of a commercial infrared spectrometer. The ultrasonic and orifice-plate flowmeters yielded the most combinations of viable sensor pairs for all three volatile anesthetic gases. Conclusion: Measuring volatile anesthetic gases using flow sensor fusion is a feasible low-cost, low-maintenance alternative to infrared spectroscopy. In this study, testing was done under steady-state conditions in 100% oxygen. Further testing is necessary to ensure sensor fusion performance under conditions that are more reflective of the clinical use case.

Indexed as

Anesthetics, InhalationIsofluraneMethyl EthersHumansSevofluraneAnesthetics, InhalationIsofluraneMethyl EthersSevofluraneFlow sensorInhalational anestheticsSensor fusionVolatile anesthetic monitoring

Identifiers

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Registered trials

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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.