Precision phenotyping from routine laboratory parameters for out of hospital survival prediction in an all comers prospective PCI registry.

ABSTRACT: Out-of-hospital mortality in coronary artery disease (CAD) is particularly high and established adverse event prediction tools are yet to be available. Our study aimed to investigate whether precision phenotyping can be performed using routine laboratory parameters for the prediction of out-of-hospital survival in a CAD population treated by percutaneous coronary intervention (PCI). All patients treated by PCI and discharged alive in a tertiary center between January 2016 - December 2022 that have been included prospectively in the local registry were analyzed. 115 parameters from the PCI registry and 266 parameters derived from routine laboratory testing were used. An extreme gradient-boosted decision tree machine learning (ML) algorithm was trained and used to predict all-cause and cardi

SUBMITTER: Calburean PA 

PROVIDER: S-EPMC11496522 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature