Clinical value of electrical impedance tomography (EIT) in the management of patients with acute respiratory failure: a single centre experience
Bibliographic record
Abstract
Abstract Objective. We will describe our clinical experience using electrical impedance tomography (EIT) in the management of mechanical ventilation in patients with acute respiratory failure and to determine to which extent EIT-guided positive end-expiratory pressure (PEEP) setting differed from clinically set values. Approach. We conducted a retrospective, observational cohort study performed in a hub centre for the treatment of acute respiratory failure and veno-venous extracorporeal membrane oxygenation (ECMO). Main results. Between January 2017 and December 2019, EIT was performed 54 times in 41 patients, not feasible only in one case because of signal instability. More than 50% was on veno-venous ECMO support. In 16 cases (30%), EIT was used for monitoring mechanical ventilation, i.e. to evaluate recruitability or sigh setting. In 37 cases (70%), EIT was used to set PEEP both with incremental (11 cases in nine patients) and decremental (26 cases, 18 patients) PEEP trial. Clinical PEEP before the decremental PEEP trial (PEEP PRE ) was 14.1 ± 3.4 cmH2O and clinical PEEP set by clinicians after the PEEP trial (PEEP POST ) was 13.6 ± 3.1 (p = ns). EIT analyses demonstrated that more hypoxic patients were higher derecruited when compared to less hypoxic patients that were, on the contrary, more overdistended (p < 0.05). No acute effects of PEEP adjustment based on EIT on respiratory mechanics or regional EIT parameters modification were observed. Significance. The variability of EIT findings in our population confirmed the need to provide ventilation settings individually tailored and EIT was confirmed to be an optimal useful clinical bedside noninvasive tool to provide real-time monitoring of the PEEP effect and ventilation distribution.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".