Mechanical ventilation with high tidal volume and associated mortality in the cardiac intensive care unit
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Use of protective ventilation has been shown to decrease mortality in medical-surgical ICUs. There is limited data on tidal volume use in ventilated patients in the cardiac intensive care unit (CICU). We hypothesized that large tidal volumes are used in the CICU and that they could contribute to an increase in morbidity and mortality. METHODS: We conducted a retrospective chart review of all mechanically ventilated patients with congestive heart failure or cardiac arrest in a single tertiary care CICU between April 2010 and February 2012. Ventilator settings were analyzed and tidal volume for predicted body weight (VT/PBW) was calculated for 51 patients. RESULTS: The median initial tidal volume was 525 ml (IQR: 500-600) and median VT/PBW was 9.3 ml/kg (IQR: 8.3-10.1). Overall mortality was 29.4%. On univariate analysis, patients that received a VT/PBW below the median, mortality was 23.1% (95% CI: 7.9-39.3) compared to 36.0% (95% CI: 17.2-55.0) in patients that received a VT/PBW above themedian (P = 0.31). On multivariate analysis, the OR for death was 9.0 (95% CI: 1.3-62.0, P = 0.03) with VT/PBW above the median. CONCLUSION: Mechanical ventilation with high tidal volumes was associated with increased mortality in patients with congestive heart failure and post cardiac arrest in our CICU.
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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 | 0.001 |
| 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".