Methods for determining optimal positive end-expiratory pressure in patients undergoing invasive mechanical ventilation: a scoping review
Bibliographic record
Abstract
There is significant variability in the application of positive end-expiratory pressure (PEEP) in patients undergoing invasive mechanical ventilation. There are numerous studies assessing methods of determining optimal PEEP, but many methods, patient populations, and study settings lack high-quality evidence. Guidelines make no recommendations about the use of a specific method because of equipoise and lack of high-quality evidence. We conducted a scoping review to determine which methods of determining optimal PEEP have been studied and what gaps exist in the literature. We searched five databases for primary research reports studying methods of determining optimal PEEP among adults undergoing invasive mechanical ventilation. Data abstracted consisted of the titration method, setting, study design, population, and outcomes. Two hundred and seventy-one studies with 17,205 patients met the inclusion criteria, including 73 randomized controlled trials (RCTs) with 10,733 patients. We identified 22 methods. Eleven were studied with an RCT. Studies enrolled participants within an intensive care unit (ICU) (216/271, 80%) or operating room (55/271, 20%). Most ICU studies enrolled patients with acute respiratory distress syndrome (162/216, 75%). The three most studied methods were compliance (73 studies, 29 RCTs), imaging-based methods (65 studies, 11 RCTs), and use of PEEP-FIO2 tables (52 studies, 20 RCTs). Among ICU RCTs, the most common primary outcomes were mortality or oxygenation. Few RCTs assessed feasibility of different methods (n = 3). The strengths and limitations of each method are discussed. Numerous methods of determining optimal PEEP have been evaluated; however, notable gaps remain in the evidence supporting their use. These include specific populations (normal lungs, patients weaning from mechanical ventilation) and using alternate outcomes (ventilator-free days and feasibility) and they present significant opportunities for future study. Open Science Framework ( https://osf.io/atzqc ); first posted, 19 July 2022.
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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 | 0.039 | 0.134 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.033 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".