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Record W2057917074 · doi:10.1097/aln.0000000000000499

Lung Ultrasonography for the Detection of Anesthesia-induced Lung Atelectasis

2014· letter· en· W2057917074 on OpenAlexafffundabout
Martin Girard, Vincent Généreux, Audrey Monastesse

Bibliographic record

VenueAnesthesiology · 2014
Typeletter
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersUniversité de Montréal
KeywordsAtelectasisMedicineLungUltrasoundMechanical ventilationRadiologyPathologyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

We have read with great interest the article by Acosta et al.1 exploring the use of lung ultrasound as a mean to detect intraoperative atelectasis. However, despite their statement to the contrary, the occurrence of B lines in the setting of atelectasis has already been described by others. Although B lines were initially thought to originate from the interaction of the ultrasound beam with thickened subpleural interlobular septa found in alveolar-interstitial pathologies,2 recent work has challenged this hypothesis. In an elegant series of experiments, Soldati et al.3–5 have shown that B lines are observed when the ultrasound beam interacts at the pleural surface with lung tissue of a specific density. Although this can occur with the replacement of subpleural air by an ultrasound-conductive substance (e.g., water, pus, blood, and fibrous tissue), the withdrawal of air (e.g., resorption atelectasis) will also lead to the genesis of B lines. Demonstrating this last point, in an ex vivo animal model of graded atelectasis, B lines were observed in increasing numbers with increasing atelectasis. Pathologic examination of the excised lungs showed diffusely compressed alveoli mixed with sporadic areas of normally expanded distal air spaces.5The study by Acosta et al. comes at an interesting moment. The recent publication of two randomized controlled trials6,7 exploring the impact of intraoperative mechanical ventilation parameters on postoperative pulmonary complications has generated much interest.8 Although some have linked the negative results of the PROVHILO (PROtective Ventilation using HIgh versus LOw positive end-expiratory pressure) trial to a lack of regular recruitment maneuvers and a positive end-expiratory pressure set too high,9 others blame the use of inappropriately high tidal volumes in the control group for the positive results in the IMPROVE (Intraoperative PROtective VEntilation) trial.10 In the absence of imagery supporting claims of atelectasis or overdistention, the culprits usually blamed for the development of postoperative pulmonary complications, it is unlikely this question will be resolved before more data becomes available. Interestingly, recent anesthesiology literature has demonstrated the advantage of hemodynamic optimization11 championing the concept that individualization is preferable to a “one size fits all” approach. Likewise, individualization of mechanical ventilation parameters might be an interesting avenue to explore if we wish to decrease the occurrence of postoperative respiratory complications. This hypothesis is supported by spiral computed tomography studies reporting significant interpatient variability in the amount of atelectasis induced by general anesthesia.12,13 Therefore, we believe that bedside monitoring to detect lung atelectasis or overdistention is needed. Although magnetic resonance imaging and computed tomography fulfill this requirement, they cannot be used in an intraoperative setting except in specially designed operating rooms and could not realistically be repeated throughout a procedure. Because lung ultrasonography can be performed at the bedside and is devoid of any ionizing radiation, the present study by Acosta et al., although interesting in and of itself, is an important milestone toward establishing lung ultrasonography as a tool to optimize intraoperative mechanical ventilation parameters. Other investigators have described loss of aeration scales that have allowed the study of the therapeutic effects of antibiotics in ventilator-associated pneumonia,14 the effect of different levels of positive end-expiratory pressure in patients with acute respiratory distress syndrome on lung reexpansion15 and the detection of patients likely to fail extubation after a successful spontaneous breathing trial.16 Although not developed specifically for the diagnosis and monitoring of anesthesia-induced atelectasis, the use of these scales would have been an interesting addition to the study by Acosta et al. Whether to optimize intraoperative mechanical ventilation parameters or to assist anesthesiologists in the care of hypoxemic patients, lung ultrasonography is likely to have a bright future in our operating rooms.Supported by Grant of the Fondation 2013, Fondation d’anesthésiologie et réanimation du Québec; Grant of the Fonds de développement 2014, Département d’anesthésiologie, Université de Montréal.The authors declare no competing interests.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.317
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations9
Published2014
Admission routes3
Has abstractyes

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