Hemodynamic failure and graft dysfunction after lung transplant: A possible clinical continuum with immediate and long‐term consequences
Bibliographic record
Abstract
INTRODUCTION: The postoperative hemodynamic management after lung transplant (LUTX) is guided by limited evidence. We aimed to describe and evaluate risk factors and outcomes of postoperative vasoactive support of LUTX recipients. METHODS: In a single-center retrospective analysis of consecutive adult LUTX, two cohorts were identified: (1) patients needing prolonged vasoactive support (>12 h from ICU admission) (VASO+); (2) or not (VASO-). Postoperative hemodynamic characteristics were thoroughly analyzed. Risk factors and outcomes of VASO+ versus VASO- cohorts were assessed by multivariate logistic regression and propensity score matching. RESULTS: One hundred and thirty-eight patients were included (86 (62%) VASO+ versus 52 (38%) VASO-). Vasopressors (epinephrine, norepinephrine, dopamine) were used in the first postoperative days (vasoactive inotropic score at 12 h: 6 [4-12]), while inodilators (dobutamine, levosimendan) later. Length of vasoactive support was 3 [2-4] days. Independent predictors of vasoactive use were: LUTX indication different from cystic fibrosis (p = .003), higher Oto score (p = .020), longer cold ischemia time (p = .031), but not preoperative cardiac catheterization. VASO+ patients showed concomitant hemodynamic and graft impairment, with longer mechanical ventilation (p = .010), higher primary graft dysfunction (PGD) grade at 72 h (PGD grade > 0 65% vs. 31%, p = .004, OR 4.2 [1.54-11.2]), longer ICU (p < .001) and hospital stay (p = .013). Levosimendan as a second-line inodilator appeared safe. CONCLUSIONS: Vasoactive support is frequently necessary after LUTX, especially in recipients of grafts of lesser quality. Postoperative hemodynamic dysfunction requiring vasopressor support and graft dysfunction may represent a clinical continuum with immediate and long-term consequences. Further studies may elucidate if this represents a possible treatable condition.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".