The Association Between Extravascular Lung Water and Critical Care Outcomes Following Bilateral Lung Transplantation
Bibliographic record
Abstract
Primary graft dysfunction (PGD) is a form of acute respiratory failure that complicates 30% of bilateral lung transplants. Higher grades of PGD correlate with higher severity of respiratory failure and unfavorable outcomes. Immediate PGD determination posttransplant' however, is not always predictive of PGD over subsequent days or intensive care unit outcomes. We aimed to evaluate whether extravascular lung water index (ELWI) measured immediately post bilateral lung transplant was associated with higher severity of PGD at 72 h and duration of mechanical ventilation. Methods: We conducted a prospective, observational study of bilateral lung transplant patients admitted to the intensive care unit. ELWI measurements were performed at admission, 6, 12, 24, 36, 48, 60, and 72 h following transplant or until extubation. We evaluated the association between admission ELWI and 72-h PGD grade and duration of mechanical ventilation. Results: < 0.001]). Using multivariable Poisson regression analysis adjusting for confounders, admission ELWI elevation was associated with higher severity of PGD at 72 h (incidence rate ratio [IRR], 1.06; 95% confidence interval, 1.01-1.12) and duration of mechanical ventilation (IRR, 1.62; 95% confidence interval, 1.23-2.14). The combination of an ELWI of ≥13 mL/kg and partial pressure of oxygen/fraction of inspired oxygen ≤ 100 within 6 h of admission had high sensitivity (75%) and specificity (100%) for grade 3 PGD at 72 h (area under the curve, 0.95) and performed better than ELWI or partial pressure of oxygen/fraction of inspired oxygen alone. Conclusions: Our exploratory study demonstrates an association between admission ELWI and high grades of PGD at 72 h and longer duration of ventilation. These results provide the impetus to study whether goal-directed ELWI algorithms can improve transplant outcomes.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".