Abstract 11567: Congenital Heart Disease Classification Improves SRTR Risk Estimation of Waitlist and Early Post-Transplant Mortality for Pediatric Heart Transplantation
Bibliographic record
Abstract
Introduction: Risk estimation for waitlist and 1-year post-transplant mortality utilized by SRTR for pediatric heart transplantation dichotomizes patients based upon presence or absence of congenital heart disease (CHD). Hypothesis: Current SRTR risk estimations can be improved by adding further detail in categorization of CHD. Methods: A retrospective analysis of the Pediatric Heart Transplant Society (PHTS) database of pediatric (<18yo) heart transplant listings from 2010 to 2020 was performed, excluding patients listed for retransplantation. Cox hazard models were developed for waitlist mortality during the first year following listing (WaitDeath) and one year post-transplant mortality (TxDeath). The initial model was constructed using covariates included in the current SRTR model and a second model was developed by adding CHD classifications (SRTR+) including: single ventricle vs. biventricular heart disease, ventricular morphology, and surgical interventions. Results: The WaitDeath model included 5,790 patients in which 722 of which died. In the SRTR WaitDeath model, CHD had a HR of 1.8 (95%CI of 1.4-2.3) compared to cardiomyopathy patients. Where as in SRTR+ model, patients with different types of CHD had HR’s that ranged from 1.5 to 2.1 (Table 1). The TxDeath model included 4,185 patients in which 304 which died. (Table 1) In the SRTR model of TxDeath, patients with CHD had a HR of 3.9 (2.9-5.1) compared to cardiomyopathy patients, whereas in the SRTR+ model patients with different types of CHD had HRs that ranged from 2.4-5.3 (Table 1). Conclusions: Inclusion of granular CHD data may improve waitlist and post-transplant mortality risk estimation in pediatric heart transplant patients, allowing for more refinement in allocation policies and understanding of difference in post-transplant outcomes across sites. This data supports further refinement of current SRTR risk adjustment models.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".