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Record W3111719012 · doi:10.1213/ane.0000000000005554

Difficult Airway Management in Adult Coronavirus Disease 2019 Patients: Statement by the Society of Airway Management

2021· review· en· W3111719012 on OpenAlexaff
Lorraine J. Foley, Felipe Urdaneta, Lauren Berkow, Michael F. Aziz, Paul Baker, Narasimhan Jagannathan, William H. Rosenblatt, Tracey Straker, David T. Wong, Carin A. Hagberg

Bibliographic record

VenueAnesthesia & Analgesia · 2021
Typereview
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineAirway managementCoronavirus disease 2019 (COVID-19)Airway2019-20 coronavirus outbreakStatement (logic)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusPandemicIntensive care medicineAnesthesiaVirologyInternal medicine

Abstract

fetched live from OpenAlex

The coronavirus disease 2019 (COVID-19) disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), often results in severe hypoxemia requiring airway management. Because SARS-CoV-2 virus is spread via respiratory droplets, bag-mask ventilation, intubation, and extubation may place health care workers (HCW) at risk. While existing recommendations address airway management in patients with COVID-19, no guidance exists specifically for difficult airway management. Some strategies normally recommended for difficult airway management may not be ideal in the setting of COVID-19 infection. To address this issue, the Society for Airway Management (SAM) created a task force to review existing literature and current practice guidelines for difficult airway management by the American Society of Anesthesiologists Task Force on Management of the Difficult Airway. The SAM task force created recommendations for the management of known or suspected difficult airway in the setting of known or suspected COVID-19 infection. The goal of the task force was to optimize successful airway management while minimizing exposure risk. Each member conducted a literature review on specific clinical practice section utilizing standard search engines (PubMed, Ovid, Google Scholar). Existing recommendations and evidence for difficult airway management in the COVID-19 context were developed. Each specific recommendation was discussed among task force members and modified until unanimously approved by all task force members. Elements of Appraisal of Guidelines Research and Evaluation (AGREE) Reporting Checklist for dissemination of clinical practice guidelines were utilized to develop this statement. Airway management in the COVID-19 patient increases HCW exposure risk. Difficult airway management often takes longer and may involve multiple procedures with aerosolization potential, and strict adherence to personal protective equipment (PPE) protocols is mandatory to reduce risk to providers. When a patient's airway risk assessment suggests that awake tracheal intubation is an appropriate choice of technique, and procedures that may cause increased aerosolization of secretions should be avoided. Optimal preoxygenation before induction with a tight seal facemask may be performed to reduce the risk of hypoxemia. Unless the patient is experiencing oxygen desaturation, positive pressure bag-mask ventilation after induction may be avoided to reduce aerosolization. For optimal intubating conditions, patients should be anesthetized with full muscle relaxation. Videolaryngoscopy is recommended as a first-line strategy for airway management. If emergent invasive airway access is indicated, then we recommend a surgical technique such as scalpel-bougie-tube, rather than an aerosolizing generating procedure, such as transtracheal jet ventilation. This statement represents recommendations by the SAM task force for the difficult airway management of adults with COVID-19 with the goal to optimize successful airway management while minimizing the risk of clinician exposure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.672
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.318
Teacher spread0.294 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations37
Published2021
Admission routes1
Has abstractyes

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