Evaluation of Positive End-Expiratory Pressure Strategies in Patients With Coronavirus Disease 2019–Induced Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
Background: Different positive end-expiratory pressure (PEEP) strategies are available for subjects with coronavirus disease 2019 (COVID-19)–induced acute respiratory distress syndrome (ARDS) requiring invasive mechanical ventilation. We aimed to evaluate three conventional PEEP strategies on their effects on respiratory mechanics, gas exchanges, and hemodynamics. Methods: This is a prospective, physiologic, multicenter study conducted in China. We recruited 20 intubated subjects with ARDS and confirmed COVID-19. We first set PEEP by the ARDSnet low PEEP–fraction of inspired oxygen (F IO 2 ) table. After a recruitment maneuver, PEEP was set at 15, 10, and 5 cm H 2 O for 10 min, respectively. Among these three PEEP levels, best-compliance PEEP was the one providing the highest respiratory system compliance; best-oxygenation PEEP was the one providing the highest Pa O 2 (partial pressure of arterial oxygen)/FIO 2 . Results: At each PEEP level, we assessed respiratory mechanics, arterial blood gas, and hemodynamics. Among three PEEP levels, plateau pressure, driving pressure, mechanical power, and blood pressure improved with lower PEEP. The ARDSnet low PEEP–F IO 2 table and the best-oxygenation strategies provided higher PEEP than the best-compliance strategy (11 ± 6 cm H 2 O vs. 11 ± 3 cm H 2 O vs. 6 ± 2 cm H 2 O, p = 0.001), leading to higher plateau pressure, driving pressure, and mechanical power. The three PEEP strategies were not significantly different in gas exchange. The subgroup analysis showed that three PEEP strategies generated different effects in subjects with moderate or severe ARDS ( n = 12) but not in subjects with mild ARDS ( n = 8). Conclusions: In our cohort with COVID-19–induced ARDS, the ARDSnet low PEEP/F IO 2 table and the best-oxygenation strategies led to higher PEEP and potentially higher risk of ventilator-induced lung injury than the best-compliance strategy. Clinical Trial Registration: www.ClinicalTrials.gov , identifier: NCT04359251.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".