Low-Tidal-Volume Ventilation and Mortality in Patients With Acute Brain Injury
Bibliographic record
Abstract
BACKGROUND: Low-tidal-volume ventilation (LTVV) improves outcomes in critically ill patients, but its impact in patients with acute brain injuries (ABIs) is less certain. RESEARCH QUESTION: What is the association between LTVV and mortality in mechanically ventilated patients with ABI? STUDY DESIGN AND METHODS: We did a secondary analysis of a prospective observational study (NCT03400904; https://clinicaltrials.gov/study/NCT03400904). We compared LTVV (≤ 8 mL/kg predicted body weight [PBW]) with tidal volumes > 8 mL/kg PBW over the first 7 days of mechanical ventilation. Alternate analyses used lower thresholds for LTVV. Marginal structural Cox models were used to evaluate the association between LTVV and ICU mortality up to 60 days. Stabilized inverse probability treatment and censoring weights were generated using multivariable logistic regression adjusted for baseline and time-dependent confounders. RESULTS: A total of 1,510 patients from 73 ICUs across 18 countries were included. The mean age was 52 years, 513 patients (34.0%) were female, and the most common ABI etiology was traumatic brain injury (n = 726; 48.1%). ARDS developed in 137 patients (9.2%). In patients receiving LTVV, adjusted incidence of ICU mortality was 40.2% (95% CI, 19.2%-61.1%), vs 59.7% (95% CI, 44.0%-75.4%) in patients receiving tidal volumes > 8 mL/kg PBW (marginal hazard ratio, 0.54; 95% CI, 0.33-0.88). There was no heterogeneity of treatment effect in subgroup analyses, and sensitivity analyses for unmeasured confounding yielded similar results. However, associations were less clear at lower thresholds of LTVV. INTERPRETATION: In this predominantly non-ARDS cohort of patients with ABI, LTVV over the first 7 days of mechanical ventilation was associated with lower ICU mortality up to 60 days, vs tidal volumes > 8 mL/kg PBW. Future research should investigate effects in patients with ABI and ARDS, use of lower LTVV thresholds, and impact on additional end points including functional outcomes and adverse events.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".