80: Genome sequencing predicts outcomes after congenital cardiac surgery

Watkins WS et al et al., Nature Communications - A prospective observational study of 2,253 Pediatric Cardiac Genomics Consortium patients shows that whole-exome sequencing combined with AI genome interpretation and Bayesian networks improves prediction of adverse outcomes after congenital cardiac surgery. Damaging de novo variants in chromatin-modifying genes and recessive/biallelic variants in cilia-related genes increase risk of mortality, cardiac arrest, and prolonged ventilation, especially when combined with specific CHD phenotypes, surgical complexity, and extracardiac anomalies. Key terms: congenital heart disease, genome sequencing, chromatin-modifying genes, cilia genes, Bayesian networks.

Study Highlights:
In 2,253 CHD patients the AI tool GEM identified putative damaging genotypes in 10.6% of individuals. Damaging de novo chromatin variants increased probabilities of mortality, cardiac arrest, and prolonged ventilation (≈1.6–1.8-fold), while recessive cilia genotypes showed similar relative risk increases. Risks were amplified in specific contexts (LVO/HLHS, HTX, STAT4/5 surgeries and presence of extracardiac anomalies) and absence of damaging genotypes was associated with reduced risk. Bayesian network models quantified these conditional dependencies to enable personalized risk estimates.

Conclusion:
Genome sequencing, interpreted with AI and integrated into probabilistic clinical models, enriches outcome forecasting after congenital cardiac surgery and can inform preoperative risk stratification and targeted perioperative strategies.

Music:
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Article title:
Genome sequencing is critical for forecasting outcomes following congenital cardiacsurgery

First author:
Watkins WS et al

Journal:
Nature Communications

DOI:
10.1038/s41467-025-61625-0

Reference:
Watkins WS et al., Genome sequencing is critical for forecasting outcomes following congenital cardiacsurgery. Nature Communications (2025) 16:6365. https://doi.org/10.1038/s41467-025-61625-0

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

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QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2025-07-19.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited transcript sections covering CHD outcome prediction, AI-based phenotyping and GEM genomic interpretation, Bayesian network linkage of genotype to phenotype to outcomes, chromatin- and cilia-associated variant effects, ECAs, surgical complexity, rapid sequencing implications, and study limitations.
- transcript topics: CHD post-surgical outcome prediction; AI-based phenotyping using Fyler codes (LVO, HTX, AVC, CTD, OTH); GEM damaging variants in chromatin-modifying and cilia genes; Bayesian networks linking genotype to phenotype and outcomes; Impact of extracardiac anomalies and surgical complexity; Clinical implications of rapid genome sequencing for perioperative care

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 7
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
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