Background: Myocardial perfusion reflects the macro- and microvascular coronary circulation. Recent quantitation developments using cardiovascular magnetic resonance (CMR) perfusion permit automated measurement clinically. We explored the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR, the ratio of stress to rest MBF). Methods: A two center study of patients with both suspected and known coronary artery disease referred clinically for perfusion assessment. Image analysis was performed automatically using a novel artificial intelligence approach deriving global and regional stress and rest MBF and MPR. Cox proportional hazard models adjusting for co-morbidities and CMR parameters sought associations of stress MBF and MPR with death and major adverse cardiovascular events (MACE), including myocardial infarction, stroke, heart failure hospitalization, late (&gt;90 day) revascularization and death. Results: 1049 patients were included with median follow-up 605 (interquartile range 464-814) days. There were 42 (4.0%) deaths and 188 MACE in 174 (16.6%) patients. Stress MBF and MPR were independently associated with both death and MACE. For each 1ml/g/min decrease in stress MBF the adjusted hazard ratio (HR) for death and MACE were 1.93 (95% CI 1.08-3.48, P=0.028) and 2.14 (95% CI 1.58-2.90, P&lt;0.0001) respectively, even after adjusting for age and co-morbidity. For each 1 unit decrease in MPR the adjusted HR for death and MACE were 2.45 (95% CI 1.42-4.24, P=0.001) and 1.74 (95% CI 1.36-2.22, P&lt;0.0001) respectively. In patients without regional perfusion defects on clinical read and no known macrovascular coronary artery disease (n=783), MPR remained independently associated with death and MACE, with stress MBF remaining associated with MACE only. Conclusions: In patients with known or suspected coronary artery disease, reduced MBF and MPR measured automatically inline using artificial intelligence quantification of CMR perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcomes.

The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence Based Approach Using Perfusion Mapping