Electrical Impedance Tomography–based Ventilation Patterns in Patients after Major Surgery
Bibliographic record
Abstract
Abstract Rationale General anesthesia and mechanical ventilation have negative impacts on the respiratory system, causing heterogeneous distribution of lung aeration, but little is known about the ventilation patterns of postoperative patients and their association with clinical outcomes. Objectives To clarify the phenotypes of ventilation patterns along a gravitational direction after surgery by using electrical impedance tomography (EIT) and to evaluate their association with postoperative pulmonary complications (PPCs) and other relevant clinical outcomes. Methods Adult postoperative patients at high risk for PPCs, receiving mechanical ventilation on ICU admission (N = 128), were prospectively enrolled between November 18, 2021 and July 18, 2022. PPCs were prospectively scored until hospital discharge, and their association with phenotypes of ventilation patterns was studied. The secondary outcomes were the times to wean from mechanical ventilation and oxygen use and the length of ICU stay. Measurements and Main Results Three phenotypes of ventilation patterns were revealed by EIT: phenotype 1 (32% [n = 41], a predominance of ventral ventilation), phenotype 2 (41% [n = 52], homogeneous ventilation), and phenotype 3 (27% [n = 35], a predominance of dorsal ventilation). The median PPC score was higher in phenotype 1 and phenotype 3 than in phenotype 2. The median time to wean from mechanical ventilation was longer in phenotype 1 versus phenotype 2. The median duration of ICU stay was longer in phenotype 1 versus phenotype 2. The median time to wean from oxygen use was longer in phenotype 1 and phenotype 3 than in phenotype 2. Conclusions Inhomogeneous ventilation patterns revealed by EIT on ICU admission were associated with PPCs, delayed weaning from mechanical ventilation and oxygen use, and a longer ICU stay.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".