Clinical implementation of advanced respiratory monitoring with esophageal pressure and electrical impedance tomography: results from an international survey and focus group discussion
Bibliographic record
Abstract
BACKGROUND: Popularity of electrical impedance tomography (EIT) and esophageal pressure (Pes) monitoring in the ICU is increasing, but there is uncertainty regarding their bedside use within a personalized ventilation strategy. We aimed to gather insights about the current experiences and perceived role of these physiological monitoring techniques, and to identify barriers and facilitators/solutions for EIT and Pes implementation. METHODS: Qualitative study involving (1) a survey targeted at ICU clinicians with interest in advanced respiratory monitoring and (2) an expert focus group discussion. The survey was shared via international networks and personal communication. An in-person discussion session on barriers, facilitators/solutions for EIT implementation was organized with an international panel of EIT experts as part of a multi-day EIT meeting. Pes was not discussed in-person, but we found the focus group results relevant to Pes as well. This was confirmed by the survey results and four additional Pes experts that were consulted. RESULTS: We received 138 survey responses, and 26 experts participated in the in-person discussion. Survey participants had diverse background [physicians (54%), respiratory therapists (19%), clinical researchers (15%), and nurses (6%)] with mostly > 10 year ICU experience. 84% of Pes users and 74% of EIT users rated themselves as competent to expert users. Techniques are currently primarily used during controlled ventilation for individualization of PEEP (EIT and Pes), and for monitoring lung mechanics and lung stress (Pes). EIT and Pes are considered relevant techniques to guide ventilation management and is helpful for educating clinicians; however, 57% of EIT users and 37% of Pes users agreed that further validation is needed. Lack of equipment/materials, evidence-based guidelines, clinical protocols, and/or the time-consuming nature of the measurements are main reasons hampering Pes and EIT application. Identified facilitators/solutions to improve implementation include international guidelines and collaborations between clinicians/researcher and manufacturers, structured courses for training and use, easy and user-friendly devices and standardized analysis pipelines. CONCLUSIONS: This study revealed insights on the role and implementation of advanced respiratory monitoring with EIT and Pes. The identified barriers, facilitators and strategies can serve as input for further discussions to promote the development of EIT-guided or Pes-guided personalized ventilation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 teacher head, 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".