Management of critically ill patients receiving noninvasive and invasive mechanical ventilation in the emergency department
Bibliographic record
Abstract
Patients requiring noninvasive and invasive ventilation frequently present to emergency departments, and may remain for prolonged periods due to constrained critical care services. Emergency clinicians often do not receive the same education on management of mechanical ventilation or have similar exposure to these patients as do their critical care colleagues. The aim of this review was to synthesize the evidence on management of patients requiring noninvasive and invasive ventilation in the emergency department including indications, clinical applications, monitoring priorities, and potential complications. Noninvasive ventilation is recommended for patients with acute exacerbation of chronic obstructive pulmonary disease or cardiogenic pulmonary edema. Less evidence supports its use in asthma and other causes of acute respiratory failure. Use of noninvasive ventilation in the prehospital setting is relatively new, and some evidence suggests benefit. Monitoring priorities for noninvasive ventilation include response to treatment, respiratory and hemodynamic stability, noninvasive ventilation tolerance, detection of noninvasive ventilation failure, and identification of air leaks around the interface. Application of injurious ventilation increases patient morbidity and mortality. Lung-protective ventilation with low tidal volumes based on determination of predicted body weight and control of plateau pressure has been shown to reduce mortality in patients with acute respiratory distress syndrome, and some evidence exists to suggest this strategy should be used in patients without lung injury. Monitoring of the invasively ventilated patient should focus on assessing response to mechanical ventilation and other interventions, and avoiding complications, such as ventilator-associated pneumonia. Several key aspects of management of noninvasive and invasively ventilated patients are discussed, with a particular emphasis on initiation and ongoing monitoring priorities focused on maintaining patient safety and improving patient outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".