Critically Ill Patients with COVID-19: A Narrative Review on Prone Position
Bibliographic record
Abstract
Abstract Introduction: Prone position improves mortality in patients with acute respiratory distress syndrome (ARDS). The impact of prone position in critically ill COVID-19 patients remains to be determined. In this review, we describe mechanisms of action of prone position, systematically appraise current experience of prone position in COVID-19 patients and highlight unique considerations for prone position practices during this pandemic.Methods: For our systematic review, we searched PubMed, Scopus and EMBASE from January 1, 2020 to April 16th 2020. After completion of our search, we became aware of four relevant publications during article preparation that were published in May and June 2020 and these studies were reviewed for eligibility and inclusion. We included all studies reporting clinical characteristics of patients admitted to hospital with COVID-19 disease who received respiratory support with high flow nasal cannula, non-invasive or MV and reported the use of prone position. Full text of eligible articles was reviewed and data regarding study design patient characteristics, interventions and outcomes were extracted.Results: We found 7 studies (total 1899 patients) describing prone position in COVID-19. Prone position has been increasingly used in non-intubated patients with COVID-19; studies show high tolerance, improvement in oxygenation and lung recruitment. Published studies lacked description of important clinical outcomes (e.g., mortality, duration of mechanical ventilation).Conclusions: Based on the findings of our review, we recommend prone position in patients with moderate to severe COVID-19 ARDS as per existing guidelines. A trial of prone position should be considered for non-intubated COVID-19 patients with hypoxemic respiratory failure, as long as this does not result in a delay in intubation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.000 |
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.
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".