Abstract 4344136: The Genetic Basis of Early Mortality in Neonates with Single Ventricle Disease: An NC-DEFINE Prospective Observational Cohort Study
Bibliographic record
Abstract
Background: Single ventricle disease (SVD) is the most severe form of congenital heart disease. Despite surgical advances improving survival, heart failure (HF) remains a key contributor to early morbidity and mortality, especially in the first months of life. Defining genetic drivers of HF in SVD could enable early risk stratification and guide precision therapies to improve outcomes. Hypothesis: Ultra-rare variants in genes associated with dilated cardiomyopathy (DCM) increase HF risk in neonates with SVD. Approach: Neonates ≤21 days old with SVD were prospectively enrolled and followed at Duke (n=40). An additional 69 individuals (0.2–65y) from Duke and UNC formed an ambispective cohort. Chromosomal abnormalities were excluded. Ultra-rare (MAF AllofUs population. Results: In the prospective cohort, 8 (20%) neonates developed severe HF, 11 (28%) developed MM HF, and 21 (52%) remained HF free, with mean follow-up of 2 years. Hosting a DCM-associated LP/P variant was linked to an 11-fold increased risk of severe HF ( P =0.0009), while VUSs increased MM HF risk 6-fold ( P =0.01). Most HF occurred within the first month. Findings were independently validated in the NCH cohort, where LP/P variants reduced freedom from severe HF ( P =0.0007). Associations were attenuated in the ambispective cohort, suggesting survivor bias and underscoring the importance of early detection and risk stratification. Compared with the AllofUs cohort, the prospective cohort had a higher prevalence of LP/P variants ( P <0.0001) but similar VUS burden, supporting a model in which LP/P variants drive primary disease risk, while low-penetrant variants may modify susceptibility in the context of SVD. Conclusion: This study provides the first prospective evidence linking DCM-associated variants to significant risk for early-onset HF in SVD. These findings, independently validated in external cohorts, underscore the potential for genetic screening to inform early risk stratification and family counseling in this high-risk population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".