The Impact of Ultrasound-Guided Positive End-Expiratory Pressure Titration on Weaning Outcomes in Acute Type I Respiratory Failure
Bibliographic record
Abstract
Optimal positive end-expiratory pressure (PEEP) is crucial in managing mechanically ventilated patients with acute type I respiratory failure. Lung ultrasound (LUS) offers a bedside method for personalized PEEP titration. This systematic review critically appraises the evidence regarding the impact of LUS-guided PEEP strategies on weaning outcomes. Methods: A systematic literature search of PubMed, Scopus, and Cochrane Library was conducted for studies published between January 2010 and August 2025. Randomized controlled trials (RCTs) and prospective observational studies comparing LUS-guided PEEP to standard care in adults with hypoxemic respiratory failure were included. Primary outcomes were weaning success and duration; secondary outcomes included oxygenation, lung aeration, and ventilator-free days. Study quality was assessed using the Cochrane Risk of Bias tool and Newcastle-Ottawa Scale. Results: Eight studies (4 RCTs, 4 observational) involving 612 patients were included. The evidence suggests that LUS-guided PEEP may improve weaning success (pooled risk ratio 1.28, 95% CI 1.10–1.49) and reduce weaning duration compared to standard care. It consistently improved secondary outcomes like PaO₂/FiO₂ ratio and LUS aeration scores. However, the overall quality of evidence was moderate, limited by heterogeneity in LUS protocols and PEEP titration algorithms. Conclusion: LUS-guided PEEP represents a promising, physiology-oriented approach to mechanical ventilation that may enhance weaning efficiency. Current evidence, while encouraging, is derived from small, heterogeneous studies. Future large-scale, multicenter RCTs employing standardized LUS protocols are needed to confirm its benefit on patient-centered outcomes and facilitate clinical implementation.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".