Since risk stratification data represents a key domain of biomarker validation, we compared associations between outcomes and various cardiovascular magnetic resonance (CMR) metrics quantifying myocardial fibrosis (MF) in noninfarcted myocardium: extracellular volume fraction (ECV), native T1, post contrast T1, and partition coefficient. Background: MF associates with vulnerability to adverse events e.g., mortality and hospitalization for heart failure (HHF), but investigators still debate its optimal measurements; most histologic validation data show strongest ECV correlations with MF. Methods: We enrolled 1714 consecutive patients without amyloidosis or hypertrophic cardiomyopathy from a single CMR referral center serving an integrated healthcare network. We measured T1 (MOLLI) in noninfarcted myocardium, averaged from 2 short axis slices (basal and mid) before and 15-20 minutes after a gadolinium contrast bolus. We compared chi square (χ 2) values from CMR MF measures in univariable and multivariable Cox regression models. We assessed "dose-response" relationships in Kaplan Meier curves using log-rank statistics for quartile strata. We also computed net reclassification improvement (NRI) and integrated discrimination improvement (IDI for Cox models with ECV vs. native T1. Results: Over a median of 5.6 years, 374 events occurred after CMR (162 HHF events and 279 deaths, 67 with both). ECV yielded best separation of Kaplan-Meier curves and highest log-ranks statistics. In univariable and multivariable models, ECV associated most strongly with outcomes, demonstrating the highest χ 2 values. Native T1 or post contrast T1 did not associate with outcomes in the multivariable model. ECV provided added prognostic value to models with native T1, e.g., in multivariable models IDI=0.

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Background Global longitudinal shortening (GL‐Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated artificial intelligence (AI) solution can be of strong clinical interest. Methods and Results The model was implemented on cardiac magnetic resonance scanners with automated in‐line processing. Reproducibility was evaluated in a scan–rescan data set (n=160 patients). The prognostic association with adverse events (death or hospitalization for heart failure) was evaluated in a large patient cohort (n=1572) and compared with feature tracking global longitudinal strain measured manually by experts. Automated processing took ≈1.1 seconds for a typical case. On the scan–rescan data set, the model exceeded the precision of human expert (coefficient of variation 7.2% versus 11.1% for GL‐Shortening, P =0.0024; 6.5% versus 9.1% for MAPSE, P =0.0124). The minimal detectable change at 90% power was 2.53 percentage points for GL‐Shortening and 1.84 mm for MAPSE. AI GL‐Shortening correlated well with manual global longitudinal strain ( R 2 =0.85). AI MAPSE had the strongest association with outcomes (χ 2 , 255; hazard ratio [HR], 2.5 [95% CI, 2.2–2.8]), compared with AI GL‐Shortening (χ 2 , 197; HR, 2.1 [95% CI,1.9–2.4]), manual global longitudinal strain (χ 2 , 192; HR, 2.1 [95% CI, 1.9–2.3]), and left ventricular ejection fraction (χ 2 , 147; HR, 1.8 [95% CI, 1.6–1.9]), with P <0.001 for all. Conclusions Automated in‐line AI‐measured MAPSE and GL‐Shortening can deliver immediate and highly reproducible results during cardiac magnetic resonance scanning. These results have strong associations with adverse outcomes that exceed those of global longitudinal strain and left ventricular ejection fraction.