Machine-learning score using stress CMR for death prediction in patients with suspected or known CAD. (3rd October 2022)

• Record Type: Journal Article
• Authors: Pezel, T; Sanguineti, F; Garot, P; Unterseeh, T; Champagne, S; Toupin, S; Morisset, S; Hovasse, T; Ah-Sing, T; Nicol, M; Hamzi, L; Dillinger, J G; Henry, P; Bousson, V; Garot, J
• Is Part Of: European heart journal. Volume 43(2022)Supplement 2
• Publication Date: 2022-10-03
• Subjects: Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehac544.283
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: 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. The external validation cohort of the ML score was performed in another center. 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). The ML score exhibited also a good area-under-the-curve in the external cohort (AUC: 0.75). 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. Funding Acknowledgement: Type of funding sources: None.