Impact of Airway Humidification Strategy in Mechanically Ventilated COVID-19 Patients
Bibliographic record
Abstract
BACKGROUND: Humidification of inspiratory gases is mandatory in all mechanically ventilated patients in ICUs, either with heated humidifiers (HHs) or with heat and moisture exchangers (HMEs). In patients with COVID-19, the choice of the humidification device may have relevant impact on patients' management as demonstrated in recent studies. We reported data from 2 ICUs using either HME or HH. METHODS: Data from patients with COVID-19 requiring invasive mechanical ventilation during the first wave in 2 ICUs in Québec City were reviewed. In one ICU, HMEs were used, whereas heated-wire HHs were used in the other ICU. We compared ventilator settings and arterial blood gases at day one after adjustment of ventilator settings. Episodes of endotracheal tube occlusions (ETOs) or subocclusions and a strategy to limit the risk of under-humidification were reported. On a bench test, we measured humidity with psychrometry with HH at different ambient temperature and evaluated the relation with heater plate temperature. RESULTS: was higher (48 vs 42 mm Hg) despite higher minute ventilation (171 vs 145 mL/kg/min predicted body weight [PBW]). We also reported 3 ETOs occurring in the ICU using HH. The hygrometric bench study reported a strong correlation between heater plate temperatures of the HH and humidity delivered. After implementation of measures to avoid under-humidification, including heater plate temperature monitoring, no more ETOs occurred. CONCLUSIONS: removal with lower dead space) and on complications related to low humidity, including ETOs that may be present with heated-wire HHs when used with high ambient temperatures.
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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 | 0.002 |
| 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".