Effect of individualized PEEP titration by ultrasonography on perioperative pulmonary protection and postoperative cognitive function in patients with chronic obstructive pulmonary disease
Bibliographic record
Abstract
Abstract Objective To evaluate the effect of the individualized positive end-expiratory pressure (PEEP) lung protection ventilation strategy by combining driving pressure (ΔP) and pulmonary ultrasound (LUS)-based titration on lung function and postoperative cognitive function in patients with chronic obstructive pulmonary disease (COPD) during laparoscopic surgery. Methods A total of 108 patients with COPD undergoing laparoscopic gastrointestinal surgery under general anesthesia were included in this study. They were randomly divided into three groups (n = 36): traditional volume ventilation group (Group C), fixed PEEP 5 cmH2O group (Group P), and ΔP combined with LUS-based PEEP titration in the resuscitation room group (Group T). All three groups were given volume ventilation mode, I:E = 1:2; In group C, VT was 10 mL/kg and PEEP was 0 cmH2O; In groups P and T, VT was 6 mL/kg and PEEP was 5 cmH2O; After mechanical ventilation for 15 min in Group T, ΔP in combination with LUS was used to titrate PEEP. The oxygenation index (PaO2/FiO2), airway platform pressure (Pplat), dynamic lung compliance (Cdyn), Montreal Cognitive Assessment (MoCA), and venous interleukin-6(IL-6) were recorded at the corresponding time points, and the final PEEP value in Group T was recorded. Results The final PEEP value of Group T was (6.4 ± 1.2) cmH2O; Compared with groups C and P: PaO2/FiO2 and Cdyn in Group T were significantly increased (P < 0.05) and value of IL-6 was significantly decreased (P < 0.05) at the corresponding time points. Compared with group C, the MoCA score on day 7 after surgery in Group T was significantly higher (P < 0.05). Conclusion Compared with the traditional ventilation strategy, the individualized ΔP combined with LUS-based PEEP titration in patients with COPD during the perioperative period of laparoscopic surgery can play a better role in lung protection and can improve postoperative cognitive function.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".