Detection of latent obstruction in patients with hypertrophic cardiomyopathy using parameters derived from resting echocardiography

利用静息超声心动图衍生的参数检测肥厚型心肌病患者的潜在梗阻

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Abstract

BACKGROUND: Diagnosis of left ventricular outflow tract (LVOT) obstruction in hypertrophic cardiomyopathy (HCM) by echocardiography requires specific provocation tests. We aimed to explore the echocardiographic parameters (under resting conditions) associated with latent obstruction in patients with HCM. METHODS: Echocardiographic data of 138 patients (65 with resting obstruction, 38 with latent obstruction, and 35 without obstruction) with HCM were analyzed. Under the resting condition, parameters of basal septal morphology [IVSa (area of basal septum protruding into the LVOT), LA (depth of IVSa relative to the LVOT), LB (length of IVSa in the direction of the left ventricular long axis), and S-IVSa (IVSa divided by LB)], the angle between the mitral valvular orifice and ascending aorta (MV-AO angle), and routine echocardiographic parameters were measured and compared among the three groups. Further analyses were conducted on patients with and without latent LVOT obstruction. Associations between parameters and latent obstruction were assessed using multivariate logistic regression analysis and receiver operating characteristic (ROC) curves. RESULTS: The MV-AO angle, LA, S-IVSa, length of the posterior mitral leaflet, and presence of abnormal muscle bundles (MBs) were significantly different between patients with and without latent obstruction (P<0.001, P=0.006, P=0.016, and P=0.001, respectively). In multivariate analyses, the MV-AO angle, presence of abnormal MBs, and S-IVSa were independently associated with latent obstruction (P<0.001, P=0.013, and P=0.022, respectively). The ROC curves of the model consisting of these parameters showed a stronger association with latent obstruction; the area under the ROC curve was 0.954 [95% confidence interval (CI): 0.878-0.989]. CONCLUSIONS: Combining S-IVSa with the MV-AO angle and abnormal MBs evaluated at rest can effectively identify patients with provocable obstruction, making the new model a valuable tool for improving the detection rate of latent obstruction and benefiting more patients.

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