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

AbstractIntroductionOut-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).Materials and methodsAll 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 cardiovascular-cause survival.ResultsA total of 7186 PCI hospitalizations for 5797 patients were included with more than 610.000 laboratory values. All-cause and cardiovascular cause mortality was 17.5% and 12.2%, respectively, during a median follow-up time of 1454 (687 – 2072) days. The integrated area under the receiver operator characteristic curve for prediction of all-cause and cardiovascular cause mortality by the ML on the validation dataset was 0.844 and 0.837, respectively (all p<0.001). The integrated area under the precision-recall curve for prediction of all-cause and cardiovascular cause mortality by the ML on the validation dataset was 0.647 and 0.589, respectively (all p<0.001).ConclusionPrecise survival prediction in CAD can be achieved using routine laboratory parameters. ML outperformed clinical risk scores in predicting out-of-hospital mortality in a prospective all-comers PCI population.