Using machine learning to predict five-year transplant-free survival among infants with hypoplastic left heart syndrome.

ABSTRACT: Hypoplastic left heart syndrome (HLHS) is a congenital malformation commonly treated with palliative surgery and is associated with significant morbidity and mortality. Risk stratification models have often relied upon traditional survival analyses or outcomes data failing to extend beyond infancy. Individualized prediction of transplant-free survival (TFS) employing machine learning (ML) based analyses of outcomes beyond infancy may provide further valuable insight for families and healthcare providers along the course of a staged palliation. Data from both the Pediatric Heart Network (PHN) Single Ventricle Reconstruction (SVR) trial and Extension study (SVR II), which extended cohort follow up for five years was used to develop ML-driven models predicting TFS. Models incrementally incorp

SUBMITTER: Smith AH 

PROVIDER: S-EPMC10894293 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature