The Importance of Circulation in Airway Management
Bibliographic record
Abstract
OBJECTIVE: To identify the modifiable and nonmodifiable risk factors associated with postintubation hypotension (PIH) among trauma patients who required endotracheal intubation (ETI) in the trauma bay. BACKGROUND: ETI has been associated with hemodynamic instability, termed PIH, yet its risk factors in trauma patients remain underinvestigated. METHODS: This is a prospective observational study at a level I trauma center over 4 years (2019-2022). All adult (≥18) trauma patients requiring ETI in the trauma bay were included. Blood pressure was monitored both preintubation and postintubation. Multivariable logistic regression analysis was performed to identify the modifiable and nonmodifiable factors associated with PIH. RESULTS: Seven hundred eight patients required ETI in the trauma bay, of which, 435 (61.4%) developed PIH. The mean (SD) age was 43 (21) years and 71% were male. Median [interquartile range] arrival Glasgow Coma Scale was 7 [3-13]. Patients who developed PIH had a lower mean (SD) preintubation systolic blood pressure [118 (46) vs 138 (28), P <0.001] and higher median [interquartile range] Injury Severity Score: 27 [21-38] versus 21 [9-26], P <0.001. Multivariable regression analysis identified body mass index >25, increasing Injury Severity Score, penetrating injury, spinal cord injury, preintubation packed red blood cell requirements, and diabetes mellitus as nonmodifiable risk factors associated with increased odds of PIH. In contrast, preintubation administration of 3% hypertonic saline and vasopressors were identified as the modifiable factors significantly associated with reduced PIH. CONCLUSIONS: More than half of the patients requiring ETI in the trauma bay developed PIH. This study identified modifiable and nonmodifiable risk factors that influence the development of PIH, which will help physicians when considering ETI upon patient arrival. LEVEL OF EVIDENCE: Level III-Prognostic study.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".