Precision phenotyping from routine laboratory parameters for machine learning out-of-hospital survival prediction using 4D time-dependent SHAP plots in an all-comers prospective PCI registry

Abstract

Introduction 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).