AI-based Fully-Automated Stress Left Ventricular Ejection Fraction as a Prognostic Marker in Patients Undergoing Stress-CMR.

ABSTRACT

AIM: To determine in patients undergoing stress CMR whether fully automated stress artificial intelligence (AI)-based left ventricular ejection fraction (LVEFAI) can provide incremental prognostic value to predict death above traditional prognosticators. MATERIEL AND RESULTS: Between 2016 and 2018, we conducted a longitudinal study that included all consecutive patients referred for vasodilator stress CMR. LVEFAI was assessed using AI-algorithm combines multiple deep learning networks for LV segmentation. The primary outcome was all-cause death assessed using the French National Registry of Death. Cox regression was used to evaluate the association of stress LVEFAI with death after adjustment for traditional risk factors and CMR findings.In 9,712 patients (66±15 years, 67% men), there was an excellent correlation between stress LVEFAI and LVEF measured by expert (LVEFexpert) (r=0.94, p<0.001). Stress LVEFAI was associated with death (median [IQR] follow-up 4.5 [3.7-5.2] years) before and after adjustment for risk factors (adjusted hazard ratio [HR], 0.84 [95% CI, 0.82-0.87] per 5% increment, p<0.001). Stress LVEFAI had similar significant association with death occurrence compared with LVEFexpert. After adjustment, stress LVEFAI value showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-statistic improvement 0.11; NRI=0.250; IDI=0.049, all p<0.001; LR-test p<0.001), with an incremental prognostic value over LVEFAI determined at rest. CONCLUSION: AI-based fully automated LVEF measured at stress is independently associated with the occurrence of death in patients undergoing stress CMR, with an additional prognostic value above traditional risk factors, inducible ischemia and LGE.