Prognostic Power of Quantitative Assessment of Functional Mitral Regurgitation and Myocardial Scar Quantification by Cardiac Magnetic Resonance.

Abstract

The severity classification of functional mitral regurgitation (FMR) remains controversial despite adverse prognosis and rapidly evolving interventions. Furthermore, it is unclear if quantitative assessment with cardiac magnetic resonance can provide incremental risk stratification for patients with ischemic cardiomyopathy (ICM) or non-ICM (NICM) in terms of FMR and late gadolinium enhancement (LGE). We evaluated the impact of quantitative cardiac magnetic resonance parameters on event-free survival separately for ICM and NICM, to assess prognostic FMR thresholds and interactions with LGE quantification. Patients (n=1414) undergoing cardiac magnetic resonance for cardiomyopathy (ejection fraction<50%) assessment from April 1, 2001 to December 31, 2017 were evaluated. The primary end point was all-cause death, heart transplant, or left ventricular assist device implantation during follow-up. Multivariable Cox analyses were conducted to determine the impact of FMR, LGE, and their interactions with event-free survival. There were 510 primary end points, 395/782 (50.5%) in ICM and 114/632 (18.0%) in NICM. Mitral regurgitation-fraction per 5% increase was independently associated with the primary end point, hazards ratios (95% CIs) of 1.04 (1.01-1.07; P=0.034) in ICM and 1.09 (1.02-1.16; P=0.011) in NICM. Optimal mitral regurgitation-fraction threshold for moderate and severe FMR were ≥20% and ≥35%, respectively, in both ICM and NICM, based on the prediction of the primary outcome. Similarly, optimal LGE thresholds were ≥5% in ICM and ≥2% in NICM. Mitral regurgitation-fraction×LGE emerged as a significant interaction for the primary end point in ICM (P=0.006), but not in NICM (P=0.971). Mitral regurgitation-fraction and LGE are key quantitative cardiac magnetic resonance biomarkers with differential associations with adverse outcomes in ICM and NICM. Optimal prognostic thresholds may provide important clinical risk prognostication and may further facilitate the ability to derive selection criteria to guide therapeutic decision-making.