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Aims Risk stratification and individual risk prediction plays a key role in making treatment decisions in patients with complex coronary artery disease (CAD). The aim of this study was to assess whether machine learning (ML) algorithms can improve discriminative ability and identify unsuspected, but potentially important, factors in the prediction of long-term mortality following percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) in patients with complex CAD. Methods and results To predict long-term mortality, the ML algorisms were applied to the SYNTAXES database with 75 pre-procedural variables including demographic and clinical factors, blood sampling, imaging, and patient-reported outcomes. The discriminative ability and feature importance of the ML model was assessed in the derivation cohort of the SYNTAXES trial using a 10-fold cross validation approach. The ML model showed an acceptable discrimination (AUC = 0.76) in cross-validation. C-reactive protein, patient-reported pre-procedural mental status, Gamma-glutamyl transferase, and HbA1c were identified as important variables predicting 10-year mortality. Conclusions ML algorithms disclosed unsuspected, but potentially important prognostic factors of very long-term mortality among patients with CAD. A “mega-analysis” based on large randomized or non-randomized data, so called “BIG Data”, may be warranted to confirm these findings.

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