High Positive End-expiratory Pressure (PEEP) with Recruitment Maneuvers versus Low PEEP during General Anesthesia for Surgery: A Bayesian Individual Patient Data Meta-analysis of Three Randomized Clinical Trials
Bibliographic record
Abstract
BACKGROUND: The influence of high positive end-expiratory pressure (PEEP) with recruitment maneuvers on the occurrence of postoperative pulmonary complications after surgery is still not definitively established. Bayesian analysis can help to gain further insights from the available data and provide a probabilistic framework that is easier to interpret. The objective was to estimate the posterior probability that the use of high PEEP with recruitment maneuvers is associated with reduced postoperative pulmonary complications in patients with intermediate-to-high risk under neutral, pessimistic, and optimistic expectations regarding the treatment effect. METHODS: Multilevel Bayesian logistic regression analysis was performed on individual patient data from three randomized clinical trials carried out on surgical patients at intermediate to high risk for postoperative pulmonary complications. The main outcome was the occurrence of postoperative pulmonary complications in the early postoperative period. This study examined the effect of high PEEP with recruitment maneuvers versus low PEEP ventilation. Priors were chosen to reflect neutral, pessimistic, and optimistic expectations of the treatment effect. RESULTS: Using a neutral, pessimistic, or optimistic prior, the posterior mean odds ratio for high PEEP with recruitment maneuvers compared to low PEEP was 0.85 (95% credible interval, 0.71 to 1.02), 0.87 (0.72 to 1.04), and 0.86 (0.71 to 1.02), respectively. Regardless of prior beliefs, the posterior probability of experiencing a beneficial effect exceeded 90%. Subgroup analysis indicated a more pronounced effect in patients who underwent laparoscopy (odds ratio, 0.67 [0.50 to 0.87]) and those at high risk for postoperative pulmonary complications (odds ratio, 0.80 [0.53 to 1.13]). Sensitivity analysis, considering severe postoperative pulmonary complications only or applying a different heterogeneity prior, yielded consistent results. CONCLUSIONS: High PEEP with recruitment maneuvers demonstrated a moderate reduction in the probability of postoperative pulmonary complication occurrence, with a high posterior probability of benefit observed consistently across various prior beliefs, particularly among patients who underwent laparoscopy.
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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 | 0.086 | 0.136 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.063 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".