High Tidal Volumes in Mechanically Ventilated Patients Increase Organ Dysfunction after Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: High tidal volumes in patients with acute respiratory distress syndrome and acute lung injury lead to ventilator-induced lung injury and increased mortality. We evaluated the impact of tidal volumes on cardiac surgery outcomes. METHODS: We examined prospectively recorded data from 3,434 consecutive adult patients who underwent cardiac surgery. Three groups of patients were defined based on the tidal volume delivered on arrival at the intensive care unit: (1) low: below 10, (2) traditional: 10-12, and (3) high: more than 12 ml/kg of predicted body weight. We assessed risk factors for three types of organ failure (prolonged mechanical ventilation, hemodynamic instability, and renal failure) and a prolonged stay in the intensive care unit. RESULTS: The mean tidal volume/actual weight was 9.2 ml/kg, and the tidal volume/predicted body weight was 11.5 ml/kg. Low, traditional, and high tidal volumes were used in 724 (21.1%), 1567 (45.6%), and 1,143 patients (33.3%), respectively. Independent risks factors for high tidal volumes were body mass index of 30 or more (odds ratio [OR] 6.25; CI: 5.26-7.42; P < 0.001) and female sex (OR 4.33; CI: 3.64-5.15; P < 0.001). In the multivariate analysis, high and traditional tidal volumes were independent risk factors for organ failure, multiple organ failure, and prolonged stay in the intensive care unit. Organ failures were associated with increased intensive care unit stay, hospital mortality, and long-term mortality. CONCLUSION: Tidal volumes of more than 10 ml/kg are risk factors for organ failure and prolonged intensive care unit stay after cardiac surgery. Women and obese patients are particularly at risk of being ventilated with injurious tidal volumes.
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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 | 0.004 |
| 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 | 0.001 |
| 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".