Pretransplant Diabetes, Not Donor Age, Predicts Long-Term Outcomes in Cardiac Transplantation
Bibliographic record
Abstract
BACKGROUND AND AIM: Accepting donors of advanced age may increase the number of hearts available for transplantation. Objectives were to review the outcomes of using cardiac donors 50 years of age and older and to identify predictors of outcome at a single institution. METHODS: A retrospective analysis of all adult cardiac transplants (n = 338) performed at our institution between 1988 and 2002 was conducted. RESULTS: Of these, 284 patients received hearts from donors <50 years old and 54 received hearts from donors > or =50 years old. Recipients of hearts from older donors had a greater frequency of pretransplant diabetes (19% vs 33%), renal failure (16% vs 30%), and dialysis (3% vs 9%). There were no differences in ICU or postoperative length of stay, days ventilated, or early rejection episodes. Recipients of older donor hearts, however, had increased perioperative mortality (7% vs 17%; p = 0.03). Multivariate analysis identified older donors (OR 2.599; p = 0.03) and donor ischemia time (OR 1.006; p = 0.002) as significant predictors of perioperative mortality. Actuarial survival at 1 (87% vs 74%), 5 (76% vs 69%), and 10 (59% vs 58%) years was similar (p = 0.08) for the two groups. Separate multivariate analyses identified pretransplant diabetes as the sole predictor of long-term survival (HR 1.659; p = 0.02) and transplant coronary disease (HR 2.486; p = 0.003). CONCLUSIONS: Despite increased perioperative mortality, donors > or =50 years old may be used with long-term outcomes similar to those of younger donor hearts. This has potential to expand the donor pool. Pretransplant diabetes has a significant impact on long-term outcomes in cardiac transplantation and requires further 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 | 0.001 | 0.004 |
| 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".