Factors Associated with Failure of Non-invasive Positive Pressure Ventilation in a Critical Care Helicopter Emergency Medical Service
Bibliographic record
Abstract
INTRODUCTION: Non-invasive positive pressure ventilation (NIPPV) is used to treat severe acute respiratory distress. Prehospital NIPPV has been associated with a reduction in both in-hospital mortality and the need for invasive ventilation. HYPOTHESIS/PROBLEM: The authors of this study examined factors associated with NIPPV failure and evaluated the impact of NIPPV on scene times in a critical care helicopter Emergency Medical Service (HEMS). Non-invasive positive pressure ventilation failure was defined as the need for airway intervention or alternative means of ventilatory support. METHODS: A retrospective chart review of consecutive patients where NIPPV was completed in a critical care HEMS was conducted. Factors associated with NIPPV failure in univariate analyses and from published literature were included in a multivariable, logistic regression model. RESULTS: From a total of 44 patients, NIPPV failed in 14 (32%); a Glasgow Coma Scale (GCS)<15 at HEMS arrival was associated independently with NIPPV failure (adjusted odds ratio 13.9; 95% CI, 2.4-80.3; P=.003). Mean scene times were significantly longer in patients who failed NIPPV when compared with patients in whom NIPPV was successful (95 minutes vs 51 minutes; 39.4 minutes longer; 95% CI, 16.2-62.5; P=.001). CONCLUSION: Patients with a decreased level of consciousness were more likely to fail NIPPV. Furthermore, patients who failed NIPPV had significantly longer scene times. The benefits of NIPPV should be balanced against risks of long scene times by HEMS providers. Knowing risk factors of NIPPV failure could assist HEMS providers to make the safest decision for patients on whether to initiate NIPPV or proceed directly to endotracheal intubation prior to transport.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".