Machine-learning score using stress CMR for death prediction in patients with suspected or known CAD

Abstract

Abstract Funding Acknowledgements Type of funding sources: None. BACKGROUND In patients with suspected or known coronary artery disease (CAD), traditional prognostic risk assessment is based upon a limited selection of clinical and imaging findings. Machine learning (ML) methods can take into account a greater number and complexity of variables. PURPOSE To investigate the feasibility and accuracy of ML using stress CMR and clinical data to predict 10-year all-cause mortality in patients with suspected or known CAD, and compared its performance to existing clinical or CMR scores. METHODS Between 2008 and 2018, a retrospective cohort study with a median follow-up of 6.0 years (interquartile range: 5.0-8.0) included all consecutive patients referred for stress CMR. Twenty-three clinical and 11 stress CMR parameters were evaluated. Machine learning involved automated feature selection by random survival forest, model building with a multiple fractional polynomial algorithm, and 5 repetitions of 10-fold stratified cross-validation. The primary outcome was all-cause death based on the electronic National Death Registry. RESULTS Of 31,752 consecutive patients (mean age 63.7 ± 12.1 years and 65.7% males), 2,679 (8.4%) died with 206,453 patient-years of follow-up. ML score (ranging 0 to 10 points) exhibited a higher area-under-the-curve compared with C-CMR-10-score, ESC-score, QRISK3-score, FRS and stress CMR data alone for prediction of 10-year all-cause mortality (ML: 0.76 vs. C-CMR-10-score: 0.68, ESC-score: 0.66, QRISK3-score: 0.64, FRS: 0.63, extent of inducible ischemia: 0.66, extent of LGE: 0.65, all p < 0.001). CONCLUSIONS The ML score including clinical and stress CMR data exhibited a higher prognostic value to predict 10-year death compared with all traditional clinical or CMR scores. Abstract Figure. Random survival Forest: ML score Abstract Figure. Prognostic Value of ML score

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