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Incremental Predictive Value of Longitudinal Axis Strain and Late Gadolinium Enhancement Using Standard CMR Imaging in Patients with Aortic Stenosis.

Lucia Agoston-Coldea | Kunal Bheecarry | Carmen Cionca | Cristian Petra | Lelia Strimbu | Camelia Ober | Silvia Lupu | Daniela Fodor | Teodora Mocan
Journal of clinical medicine | 2019

To analyse the predictive ability and incremental value of left ventricular longitudinal axis strain (LAS) and late gadolinium enhancement (LGE) using standard cardiovascular magnetic resonance (CMR) imaging for the diagnosis and prognosis of severe aortic stenosis (AS) in patients with an indication for aortic valve replacement. We conducted a prospective study on 52 patients with severe AS and 52 volunteers. The evaluation protocol included standard biochemistry tests, novel biomarkers of myocardial fibrosis, 12-lead electrocardiograms and 24-hour Holter, the 6-minute walk test and extensive echocardiographic and CMR imaging studies. Outcomes were defined as the composite of major cardiovascular events (MACEs). Among AS patients, most (n = 17, 77.2%) of those who exhibited LGE at CMR imaging had MACEs during follow-up. Kaplan⁻Meier curves for event-free survival showed a significantly higher rate of MACEs in patients with LGE (p < 0.01) and decreased LAS (p < 0.001). In Cox regression analysis, only reduced LAS (hazard ratio 1.33, 95% CI (1.01 to 1.74), p < 0.01) and the presence of LGE (hazard ratio 11.3, 95% CI (1.82 to 70.0), p < 0.01) were independent predictors for MACEs. The predictive value increased if both LGE and reduced LAS were added to left ventricular ejection fraction (LVEF). None of the biomarkers of increased collagen turnover exhibited any predictive value for MACEs. LAS by CMR is an independent predictor of outcomes in patients with AS and provides incremental value beyond the assessment of LVEF and the presence of LGE.

Pubmed ID: 30717180

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Statistical software for ROC curve analysis. MedCalc performs several statistical tests such as method comparison, method evaluation, reference intervals, and meta-analysis.

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