Quantitative Assessment of Viral Dispersion Associated with Respiratory Support Devices in a Simulated Critical Care Environment
Bibliographic record
Abstract
Abstract Rationale Patients with severe coronavirus disease (COVID-19) require supplemental oxygen and ventilatory support. It is unclear whether some respiratory support devices may increase the dispersion of infectious bioaerosols and thereby place healthcare workers at increased risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Objectives To quantitatively compare viral dispersion from invasive and noninvasive respiratory support modalities. Methods This study used a simulated ICU room with a breathing-patient simulator exhaling nebulized bacteriophages from the lower respiratory tract with various respiratory support modalities: invasive ventilation (through an endotracheal tube with an inflated cuff connected to a mechanical ventilator), helmet ventilation with a positive end-expiratory pressure (PEEP) valve, noninvasive bilevel positive-pressure ventilation, nonrebreather face masks, high-flow nasal oxygen (HFNO), and nasal prongs. Measurements and Main Results Invasive ventilation and helmet ventilation with a PEEP valve were associated with the lowest bacteriophage concentrations in the air, and HFNO and nasal prongs were associated with the highest concentrations. At the intubating position, bacteriophage concentrations associated with HFNO (2.66 × 104 plaque-forming units [PFU]/L of air sampled), nasal prongs (1.60 × 104 PFU/L of air sampled), nonrebreather face masks (7.87 × 102 PFU/L of air sampled), and bilevel positive airway pressure (1.91 × 102 PFU/L of air sampled) were significantly higher than those associated with invasive ventilation (P < 0.05 for each). The difference between bacteriophage concentrations associated with helmet ventilation with a PEEP valve (4.29 × 10–1 PFU/L of air sampled) and bacteriophage concentrations associated with invasive ventilation was not statistically significant. Conclusions These findings highlight the potential differential risk of dispersing virus among respiratory support devices and the importance of appropriate infection prevention and control practices and personal protective equipment for healthcare workers when caring for patients with transmissible respiratory viral infections such as SARS-CoV-2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".