Electrical impedance tomography-guided positive end-expiratory pressure titration in ARDS: a systematic review and meta-analysis
Bibliographic record
Abstract
Assessing efficacy of electrical impedance tomography (EIT) in optimizing positive end-expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) patients to enhance respiratory system mechanics and prevent ventilator-induced lung injury (VILI), compared to traditional methods. We carried out a systematic review and meta-analysis, spanning literature from January 2012 to May 2023, sourced from Scopus, PubMed, MEDLINE (Ovid), Cochrane, and LILACS, evaluated EIT-guided PEEP strategies in ARDS versus conventional methods. Thirteen studies (3 randomized, 10 non-randomized) involving 623 ARDS patients were analyzed using random-effects models for primary outcomes (respiratory mechanics and mechanical power) and secondary outcomes (PaO 2 /FiO 2 ratio, mortality, stays in intensive care unit (ICU), ventilator-free days). EIT-guided PEEP significantly improved lung compliance ( n = 941 cases, mean difference (MD) = 4.33, 95% confidence interval (CI) [2.94, 5.71]), reduced mechanical power ( n = 148, MD = − 1.99, 95% CI [− 3.51, − 0.47]), and lowered driving pressure ( n = 903, MD = − 1.20, 95% CI [− 2.33, − 0.07]) compared to traditional methods. Sensitivity analysis showed consistent positive effect of EIT-guided PEEP on lung compliance in randomized clinical trials vs. non-randomized studies pooled (MD) = 2.43 (95% CI − 0.39 to 5.26), indicating a trend towards improvement. A reduction in mortality rate (259 patients, relative risk (RR) = 0.64, 95% CI [0.45, 0.91]) was associated with modest improvements in compliance and driving pressure in three studies. EIT facilitates real-time, individualized PEEP adjustments, improving respiratory system mechanics. Integration of EIT as a guiding tool in mechanical ventilation holds potential benefits in preventing ventilator-induced lung injury. Larger-scale studies are essential to validate and optimize EIT’s clinical utility in ARDS management.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".