Impact of Deceased Donor Management on Donor Heart Use and Recipient Graft Survival
Bibliographic record
Abstract
BACKGROUND: Current risk-adjusted models used to predict donor heart use and cardiac graft survival from organ donors after brain death (DBDs) do not include bedside critical care data. We sought to identify novel independent predictors of heart use and graft survival to better understand the relationship between donor management and transplantation outcomes. STUDY DESIGN: We conducted a prospective observational study of DBDs managed from 2008 to 2013 by 10 organ procurement organizations. Demographic data, critical care parameters, and treatments were recorded at 3 standardized time points during donor management. The primary outcomes measures were donor heart use and cardiac graft survival. RESULTS: From 3,433 DBDs, 1,134 hearts (33%) were transplanted and 969 cardiac grafts (85%) survived after 684 ± 392 days of follow-up. After multivariable analysis, independent positive predictors of heart use included standard criteria donor status (odds ratio [OR] 3.93), male sex (OR 1.68), ejection fraction > 50% (OR 1.64), and partial pressure of oxygen to fraction of inspired oxygen ratio > 300 (OR 1.31). Independent negative predictors of heart use included donor age (OR 0.94), BMI > 30 kg/m2 (OR 0.78), serum creatinine (OR 0.83), and use of thyroid hormone (OR 0.78). As for graft survival, after controlling for known recipient risk factors, thyroid hormone dose was the only independent predictor (OR 1.04 per μg/h). CONCLUSIONS: Modifiable critical care parameters and treatments predict donor heart use and cardiac graft survival. The discordant relationship between thyroid hormone and donor heart use (negative predictor) vs cardiac graft survival (positive predictor) warrants additional investigation.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| 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".