Abstract
Abstract Background Ejection fraction is the standard metric to analyze cardiac function in the left (LV) or right (RV) ventricles. However, these global metrics are not able to characterize patients in which the heart compensates for regional dysfunction. More sensitive metrics are needed to detect subclinical regional dysfunction before cardiac remodeling results in changes in ejection fraction (EF) and global longitudinal strain (GLS). Fast-SENC intramyocardial strain (fSENC) is a unique cardiac magnetic resonance imaging (CMR) modality that measures intramyocardial contraction in 1 heartbeat per image plane. This prospective registry compares segmental fSENC to standard CMR calculations (e.g. LVEF, volumes, mass, etc.) in patients with mitral valve disease. Methods A single center, prospective registry of CMR scans acquired with a 1.5T scanner were evaluated for standard CMR calculations as well as fSENC scans. Intramyocardial LV & RV strain was quantified with MyoStrain software. Three short axis scans (basal, midventricular, & apical) were used to calculate peak strain in 16 LV & 6 RV longitudinal segments while three long axis scans (2-, 3-, & 4-chamber) were used to calculate 21 LV & 5 RV circumferential segments. Results A total of 493 scans in 424 patients with moderate or severe mitral regurgitation were included in the study. Patients had an average (± stdev) age of 60 (15) yrs and BMI of 27 (4) kg/m2; 63% had arterial hypertension, 19% diabetes mellitus, 10% atrial fibrillation, 15% pulmonary disease, and 32% coronary artery disease. Figure 1 shows the non-linear relationship between segmental fSENC strain (% of normal LV segments ≤−17%) versus LVEF (R=0.81). Conclusion Segmental fSENC detects subclinical LV dysfunction before changes in LVEF. Evaluating segmental longitudinal and circumferential fSENC peak strain provides an alternative metric that shows consistent changes in cardiac function in patients with mitral valve disease irrespective of global calculations that are dependent on loading conditions. Funding Acknowledgement Type of funding source: None
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