No survival benefit associated with waiting for non‐lung donor heart transplants for adult recipients with congenital heart disease
Bibliographic record
Abstract
BACKGROUND: Adults with congenital heart disease (CHD) awaiting heart transplant (HT) have higher mortality and waitlist removal due to clinical deterioration than those without CHD. The selective use of non-lung donors (NLD) to recover donor pulmonary vasculature to assist in graft implantation may be a contributing factor and is supported by consensus statements despite the recent use of pericardium or graft material as an alternative in pulmonary vascular reconstruction. The impact of selecting NLD for CHD recipients on wait time and mortality has not been evaluated. METHODS/RESULTS: In the United Network for Organ Sharing (UNOS) Registry, 1271 HT recipients age ≥ 18 with CHD were identified between 1987 and 2016, 68% of which had NLDs. Prior to HT, NLD recipients were significantly less likely to be listed UNOS Status 1A, require mechanical ventilation, or intra-aortic balloon pump support. There was no difference in mean waitlist time (254 vs. 278 days, p = .31), 1-year mortality (82% vs. 80%, p = .81; adjusted odds ratio 1.32, 95% confidence interval [CI] 0.96-1.83, p = .08), or overall mortality (adjusted hazard ratio 1.08, 95% CI 0.86-1.36, p = .48) between recipients from NLD and concomitant lung donors. CONCLUSIONS: Adult CHD patients who are less critically ill or listed at a lower status are more likely to receive HT from NLD. There is no overall mortality benefit associated with this practice. While specific cases may necessitate waiting for NLD, programs need to re-evaluate whether this should remain a more widespread practice among CHD patients.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".