Genome Sequencing is Critical for Forecasting Outcomes Following Congenital Cardiac Surgery

Area of Science:

• Genomics
• Cardiology
• Artificial Intelligence

Background:

• Exome and whole genome sequencing have advanced understanding of genetic disorders.
• Predictive utility of genetic sequencing for clinical outcomes needs further study.

Purpose of the Study:

• To investigate the predictive value of genome sequencing for clinical outcomes after congenital heart defect (CHD) surgery using AI.
• To analyze genotype-phenotype correlations in CHD patients.

Main Methods:

• Utilized AI technologies on exome sequencing data from 2,253 CHD patients.
• Correlated damaging genotypes in specific gene categories with post-operative outcomes.
• Examined the influence of CHD phenotype, surgical complexity, and anomalies.

Main Results:

• Damaging genotypes in chromatin-modifying and cilia-related genes correlated with increased risk of adverse outcomes (mortality, cardiac arrest, prolonged ventilation).
• Genetic risk was amplified by CHD phenotype, surgical complexity, and extra-cardiac anomalies.
• Absence of damaging genotypes reduced the risk of adverse post-operative outcomes.

Conclusions:

• Genome sequencing, enhanced by AI, improves the prediction of outcomes following congenital cardiac surgery.
• Genetic information provides valuable insights for risk stratification and personalized patient management.