Airway Management in the Operating Room and Interventional Suites in Known or Suspected COVID-19 Adult Patients: A Practical Review
Bibliographic record
Abstract
Current evidence suggests that coronavirus disease 2019 (COVID-19) spread occurs via respiratory droplets (particles >5 µm) and possibly through aerosol. The rate of transmission remains high during airway management. This was evident during the 2003 severe acute respiratory syndrome epidemic where those who were involved in tracheal intubation had a higher risk of infection than those who were not involved (odds ratio 6.6). We describe specific airway management principles for patients with known or suspected COVID-19 disease for an array of critical care and procedural settings. We conducted a thorough search of the available literature of airway management of COVID-19 across a variety of international settings. In addition, we have analyzed various medical professional body recommendations for common procedural practices such as interventional cardiology, gastroenterology, and pulmonology. A systematic process that aims to protect the operators involved via appropriate personal protective equipment, avoidance of unnecessary patient contact and minimalization of periprocedural aerosol generation are key components to successful airway management. For operating room cases requiring general anesthesia or complex interventional procedures, tracheal intubation should be the preferred option. For interventional procedures, when tracheal intubation is not indicated, cautious conscious sedation appears to be a reasonable approach. Awake intubation should be avoided unless it is absolutely necessary. Extubation is a high-risk procedure for aerosol and droplet spread and needs thorough planning and preparation. As updates and modifications in the management of COVID-19 are still evolving, local guidelines, appraised at regular intervals, are vital in optimizing clinical management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".