Abstract 4370407: Impact of Donor and Recipient Risk Matching on Survival After Pediatric Heart Transplantation
Bibliographic record
Abstract
Background: Pediatric heart transplantation (HT) is limited by donor numbers and graft quality, although recipient clinical acuity may have the greatest impact on post-HT survival. Increasingly, more marginal donors are being used with reasonable outcomes, but often these are implanted into the sickest recipients. If acceptance of higher risk donors could provide excellent post-HT outcomes for low-risk recipients, then this could improve utilization of marginal donors while also increasing availability of lower risk donors for high-risk recipients. Methods: A retrospective cohort analysis of the Pediatric Heart Transplant Society (PHTS) database was performed for all pediatric (age <18 years) patients (n=5920) undergoing primary HT from 1/1/2010−6/30/2024. Separate donor and recipient risk scores were developed using multivariable multiphase parametric hazard modeling. Patients were stratified into low-, medium-, or high-risk categories for each donor-recipient pair, according to tertiles of the predicted 1-year survival estimates from the risk models. Results: Overall, 1-year post-HT survival was 92%. Donor risk factors included age <3 vs 3-17 years (HR 1.74), oversized height vs well-matched (HR 1.97) and head trauma as cause of death (HR 0.69) (p<0.05 for all). Recipient risk factors included congenital heart disease (HR 3.91), age (HR 0.98), panel reactive antibodies >10% (HR 1.46), waitlist interval (HR 1.16), renal dysfunction (HR 1.87), induction therapy (HR 0.69), ventilator (HR 1.46), extracorporeal membrane oxygenation (HR 3.65), and any ventricular assist device (HR 1.66) at time of HT (p<0.05 for all). Low-risk recipients tended to be matched with low- or medium-risk donors, while high-risk recipients tended to be matched with medium- or high-risk donors (Table 1). Low-risk recipients had similar 1-year survival regardless of donor risk, while high-risk recipients had worst 1-year survival with medium- or high-risk donors (Figure 1). Conclusion: Recipient risk profile has a greater impact on 1-year graft survival compared to donor risk. For low-risk recipients, donor risk profile has less impact on survival. By increasing the use of marginal donor organs for low-risk recipients and thereby increasing availability of low-risk organs for the sickest recipients, it may be possible to decrease waitlist mortality and improve post-HT outcomes for the entire cohort.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".