Abstract 10343: Prospective Multi-Modality Imaging Comparisons of Left Ventricular Quantification in Aortic Valve Regurgitation and Stenosis

Circulation(2022)

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摘要
Background: Cardiac magnetic resonance (CMR) has become reference standard for chamber quantification, with complimentary utility of valvular heart disease CMR may provide additional insights to transthoracic echocardiography (TTE) into chamber remodeling in aortic valve disease. We prospectively compared chamber quantification measured by TTE and CMR between AR and AS patients. Methods: In this study, we prospectively recruited moderate-to-severe AR (n=101) or AS (n=44) patients undergoing clinically-indicated TTE and CMR within 3-months of each other. TTE and CMR measurements were compared using Pearson correlation coefficient and Bland-Altman plots. Results: AR patients were significantly younger (57±15 versus 69±15 years, P<0.001), with lower proportion of females (26% versus 43%, P=0.050), being symptomatic (42% versus 68%, P=0.004), and higher proportion having bicuspid aortic valve (45% versus 27%, P=0.064) and dilated thoracic aorta (52% versus 23%, P=0.002) than AS patients. Moderate-to-high correlations were observed by TTE- and CMR-left ventricular parameters in both valve groups (r=0.50-0.80). The Figure shows that eft ventricular volumes were significantly higher by CMR than TTE, while ejection fraction (LVEF) were similar, and left ventricular global longitudinal strain (LVGLS) was more negative by CMR-SENC technique than TTE-velocity vector imaging technique. By CMR, AR patients had significantly higher left ventricular volumes (P<0.001), lower LVEF (P=0.033) and similar LVGLS (P=0.593) than AS patients. Conclusion: CMR left ventricular parameters had moderate-to-high correlations but systematic differences in measurements compared in aortic valve disease patients. Expected differences were observed between CMR-derived left ventricular remodeling of AR and AS.
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关键词
aortic valve regurgitation,left ventricular quantification,left ventricular,imaging,multi-modality
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