LAUSR

BACKGROUND The prognostic value of myocardial fibrosis in patients with hypertrophic cardiomyopathy (HCM) has been well-established. Although cardiac magnetic resonance (CMR) is the method of choice in its revealing as the presence of late gadolinium enhancement (LGE), this technique still has limited availability in daily clinical practice. Two-dimensional speckle tracking echocardiography (2D STE) seems to be helpful in verification which HCM patient has the highest probability of LGE presence and hence needs to be qualified to CMR. While the majority of HCM patients have a patchy pattern of myocardial fibrosis, the aim of this study was to evaluate whether segmental rather than global longitudinal strain is more accurate in the identification of the presence of LGE. METHODS Forty-six HCM patients had transthoracic echocardiography and CMR imaging performed. Each patient had global longitudinal strain and rotation parameters calculated, as well as segmental analyses for wall thickness, longitudinal strain, and LGE presence based on 736 segments of the left ventricle (LV). The presence of LGE in CMR was confirmed on a per-segment basis, which was similar to LV segments in the echocardiographic examination. All patients were divided into two groups according to the CMR result: LGE (+) and LGE (-). RESULTS Receiver-operating characteristic analyses identified peak global longitudinal strain and peak twisting velocity with the cut-off values -14.4% and 116°/s respectively as the accurate predictors of LGE presence in CMR, whereas segmental longitudinal strain of -12.5% cut-off value had the highest area under the curve value (87.4%, confidence interval 84.5-90.3%), with 93.7% sensitivity, 86.5% negative predictive value, and 55% specificity. CONCLUSIONS Segmental longitudinal strain with the cut-off value of -12.5% has the highest discriminatory power for LGE presence and seems to be more adequate than global speckle tracking parameters in identification of HCM patients with strong indications for CMR for more accurate risk stratification.