Successful MACE risk stratification in hypertrophic cardiomyopathy patients using different 2D speckle-tracking TTE

Koya Ozawa1, Nobusada Funabashi1, Hiroyuki Takaoka1

  • 1Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba City, Chiba 260-8670, Japan.

Insights

Global longitudinal strain (GLS) measured by 2D transthoracic echocardiography (TTE) effectively predicts major adverse cardiac events (MACE) in hypertrophic cardiomyopathy (HCM) patients. This strain measurement offers valuable prognostic information for these individuals.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Echocardiography

Background:

  • Hypertrophic cardiomyopathy (HCM) is a significant cause of sudden cardiac death.
  • Predicting adverse cardiac events in HCM patients is crucial for management.
  • Left ventricular (LV) myocardial strain analysis offers a novel approach to risk stratification.

Purpose of the Study:

  • To compare the predictive ability of 2D global longitudinal strain (GLS) and global circumferential strain (GCS) for major adverse cardiac events (MACE).
  • To evaluate these strain parameters in hypertrophic cardiomyopathy (HCM) patients without obstructive coronary arteries.

Main Methods:

  • Retrospective analysis of 41 symptomatic HCM patients using 2D speckle tracking transthoracic-echocardiography (TTE).
  • Measurement of LV GLS and GCS.
  • Follow-up for a median of 30 months to record MACE occurrence.

Main Results:

  • 2D LV GLS was a significant predictor of MACE (univariate HR 1.89, P=0.019; multivariate HR 2.144, P=0.013).
  • 2D LV GCS was not a significant predictor of MACE (univariate HR 1.07, P=0.118).
  • ROC analysis showed superior sensitivity and specificity for MACE prediction with 2D LV GLS compared to GCS.

Conclusions:

  • 2D LV GLS is a superior predictor of poor prognosis in HCM patients without obstructive coronary arteries.
  • Both 2D LV GLS and GCS can predict adverse outcomes, but GLS demonstrates greater predictive power.
Abstract

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