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Enregistrement W4220685846 · doi:10.1097/aln.0000000000004193

Pressure Support Ventilation and Atelectasis: Comment

2022· letter· en· W4220685846 sur OpenAlexaff
Cédrick Zaouter, Alex Moore, François Martin Carrier, Julie Girard, Martin Girard

Notice bibliographique

RevueAnesthesiology · 2022
Typeletter
Langueen
DomaineMedicine
ThématiqueUltrasound in Clinical Applications
Établissements canadiensUniversité de MontréalMcGill University Health Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineAtelectasisPressure support ventilationVentilation (architecture)AnesthesiaIntensive care medicineMechanical ventilationInternal medicineLungMechanical engineering

Résumé

récupéré en direct d'OpenAlex

We read with great interest the article by Jeong et al.1 titled “Pressure Support versus Spontaneous Ventilation during Anesthetic Emergence—Effect on Postoperative Atelectasis: A Randomized Controlled Trial.” Although many studies have looked at the potential effects of various intraoperative open lung ventilation strategies on postoperative pulmonary outcomes, recent evidence suggests that their potential benefits may be limited if no action is taken to minimize lung derecruitment during the emergence period.2 Considering that postoperative atelectasis plays a central role in the development of postoperative pulmonary complications, and that maintaining positive pressure during emergence may help preserve lung aeration,3 the research question of Jeong et al. is of paramount importance. However, we have some concerns regarding key aspects of the study’s methodology.First, we were especially worried about elements used to define and measure the incidence of atelectasis, the study’s primary outcome. The authors’ definition (more than three lung sections with a non-zero atelectasis score) is not standard4 and has not been previously validated. Can the authors specify whether their definition was selected before conducting the study to reassure readers on the absence of data-driven threshold selection? Performing sensitivity analyses looking at different thresholds for the number of atelectatic lung sections necessary to classify the outcome would better assess the robustness of their findings.Second, we were puzzled to read that Jeong et al. not only used a modified and unvalidated echographic pulmonary aeration loss score5 but also introduced their own modifications, potentially further weakening the validity of their primary outcome classification. In particular, loss of lung sliding with lung pulse is not a sign of atelectasis but rather a sign of a well-aerated lung without ventilation. This finding could have indicated the presence of a mucous plug which may have been resolved after a simple coughing fit without causing any atelectasis. Including this sign in their atelectasis score seems problematic. We encourage the authors to use the lung ultrasound score, a validated echographic loss of aeration score, to report their results.6Third, their study was underpowered for their anticipated effect size. Using the same assumptions (an incidence of 53% in the control group and 37% in the intervention group for an absolute estimated effect of 16%), we calculated that a sample size of 302 patients would have been necessary even before considering a 15% dropout rate. Their greater-than-anticipated observed effect explains why their results achieved statistical significance. However, underpowered studies are prone to inflated results with positive results that are more likely to be false positives.7Fourth, the authors’ definition of hypoxemia, a secondary outcome, may lead to missing important clinical effects resulting from their intervention. A punctual event of oxygen saturation measured by pulse oximetry greater than 92% may be not be clinically significant in comparison with a prolonged postoperative need for high fractional inspired oxygen tension. Can the authors provide data on this secondary outcome using a time-weighted need for organ support, such as oxygen-free days or cumulative postoperative oxygen administration?The imaging study by Jeong et al. is an essential first step in clarifying the role of assisted ventilatory modes during anesthesia emergence. However, there is still a lot of work to be done to answer the salient question: Are assisted ventilatory modes an important part of an open lung strategy at emergence that may lead to a decreased incidence of postoperative pulmonary complications?Dr. Girard is a paid consultant for the point-of-care ultrasonography group of GE Healthcare (Milwaukee, Wisconsin). The other authors declare no competing interests.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,035
score de la tête « metaresearch » (Gemma)0,232
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,042
Score d'incertitude au seuil0,185

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0350,232
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,005
Bibliométrie0,0020,003
Études des sciences et des technologies0,0030,005
Communication savante0,0050,010
Science ouverte0,0110,003
Intégrité de la recherche0,0420,058
Charge utile insuffisante (le modèle a refusé de juger)0,0100,009

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,317
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2022
Routes d'admission1
Résumé présentoui

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