Additional prognostic value of fully-automatic Global Longitudinal Strain using machine learning

Abstract

Abstract Funding Acknowledgements Type of funding sources: None. Background Several studies described the independent prognostic value of left ventricular global longitudinal strain (GLS) using cardiovascular magnetic resonance (CMR) to predict cardiovascular events. However, the potential interest of GLS using a fully automatic method assessed at rest during a stress CMR exam was not well established. Aim To assess the prognostic value of GLS to predict all-cause death using a fully automatic machine learning algorithm without human correction in consecutive patients referred for stress CMR. Methods Between 2016 and 2018, all consecutive patients referred for stress CMR were included and followed for the occurrence of all-cause death. A fully automatic machine learning algorithm was trained and validated unseen CMR studies (MAGNETOM Aera and Skyra, Siemens Healthcare, Erlangen, Germany) to assess the GLS from long-axis cine images acquired at rest. The algorithm combines multiple deep learning networks for detection and segmentation with an active contours approach. Cox regressions were performed to determine the prognostic value of GLS. Results Among 9,883 consecutive patients who underwent stress CMR between 2016 and 2018 retrospectively included, the automatic GLS was successfully computed in 9,638 (97.5%) patients (67% male, mean age 66±12 years). A total of 510 (5.3%) deaths were observed during a median (IQR) follow-up period of 4.5 (3.7–5.2) years. GLS, the presence of inducible ischemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of death (hazard ratio, HR: 1.22 [95% CI, 1.17–1.26]; HR: 2.23 [95% CI, 1.61–3.10]; and HR: 2.04 [95% CI, 1.41–2.95], respectively, all p<0.001). After adjustment for traditional risk factors, inducible ischemia and LGE, GLS was an independent predictor of a higher incidence of death (adjusted HR: 1.14 [95% CI, 1.08–1.20]). Automatic GLS showed an incremental prognostic value to predict death compared to traditional risk factors, inducible ischemia and LGE (C-statistic improvement: 0.05; NRI=0.146; IDI=0.244; all p<0.001). Conclusions Automatic GLS measured at rest has an incremental prognostic value to predict all-cause death above traditional risk factors, and other stress CMR parameters.