Abstract 13723: Waitlist Outcomes for Pediatric Heart Transplantation in the Current Era: An Analysis of the PHTS Database
Bibliographic record
Abstract
Introduction: Waitlist mortality (WM) remains high in pediatric heart transplantation (HT). Allocation policy is a potential tool to help improve WM. This study aims to identify patients listed status 1A at highest risk for WM to potentially inform future allocation policy changes. Hypothesis: Significant variation in WM will exist even within highest urgency waiting status (1A). Methods: The pediatric heart transplant society database was queried for all patients <18 years of age listed for HT between January 1, 2010 to December 31, 2021. WM was defined by death while awaiting transplant or being removed from the waitlist due to clinical deterioration. Kaplan-Meier analysis, log-rank testing, and Cox-proportional hazard models were created to determine association with WM. Subgroup analysis was performed in 1A patients based upon body surface area (BSA) at time of listing, cardiac diagnosis, and presence of mechanical circulatory support. Results: Among 5,974 children listed, 3928 (65.8%) were status 1A. Patients listed 1A had a higher burden of WM (p<0.01) compared to patients listed status 1B or status 2. Among 1A patients, BSA<0.3m 2 , congenital heart disease (CHD), and lower eGFR was associated with higher WM. VAD support at listing was associated with lower WM (figure, all p-values<0.01) except in the single ventricle cohort (CHD-SV) (HR 2.13, p<0.01). For BSA<0.3m 2 listings, diagnosis other than dilated cardiomyopathy was associated with increased risk of WM . Prior cardiac surgery was associated with WM in the BSA 0.3-0.7m 2 and >0.7m 2 groups. ECMO was associated with increased risk of WM in all cohorts (HR 2.26, p<0.01). Conclusions: Significant variability exists in WM in patients listed 1A. Future changes to the pediatric allocation system should factor in the increased risk of WM in patients supported by ECMO, CHD-SV on VAD support, and small children with CHD, RCM or HCM.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".