Ventilation Practices and Critical Events during Transport of Ventilated Patients outside of Hospital: A Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Little is known about mechanical ventilation practices during patient transport outside of hospital in the civilian setting, although these practices may have clinical impact. Objective. We set out to describe ventilation practice, the use of lung-protective ventilation strategies, administration of sedation and neuromuscular blockade, and related critical events during out-of-hospital transport of ventilated patients. METHODS: We conducted a population-based retrospective cohort study. Ventilator, pharmacy, and clinical data were extracted from the database of the provincial transport medicine agency in Ontario, Canada. Patients at risk for acute lung injury were identified by explicit screening criteria and lung-protective ventilation was assessed according to evidence-based thresholds. Critical events occurring during transport consisting of clinical deterioration or resuscitative procedures were recorded. RESULTS. We identified 1,735 mechanically ventilated adults who received out-of-hospital transport. Volume control and pressure control were the most commonly used ventilation modes. The median tidal volume delivered during transport was 500 mL (interquartile range 450-600) with positive end-expiratory pressure (PEEP) of 5 cmH(2)O (5-7) and peak inspiratory pressure of 24 cmH(2)O (20-29). Most patients (92%) were ventilated with peak pressures < or = 35 cmH(2)O; 22% of patients were ventilated with PEEP < 5 cmH(2)O. Ventilation in patients at risk of acute lung injury was not significantly different, and 68% of this subgroup was ventilated within lung-protective thresholds. Sedation was administered in 1,235 transports (71.2%) with frequent repeat administration. Neuromuscular blockade was administered in 385 transports (22.2%). Critical events occurred during 297 (17.1%) transports, due primarily to new-onset hypotension (n = 208). New in-transit hypotension was independently associated with sedative administration. CONCLUSIONS: In-transit mechanical ventilation practices are variable, although patient exposure to potentially injurious pressures and volumes is uncommon. The application of PEEP is modest. In-transit hypotension is common and associated with sedative administration. The extent to which these practices impact patient outcome is unclear.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".