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Updated: Oct 11, 2025

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
Myocardial Strain and Association With Clinical Outcomes in Danon Disease: A Model for Monitoring Progression of
Quan M Bui1, Kimberly N Hong1, Megan Kraushaar1
1Division of Cardiovascular Medicine Department of Medicine University of California, San Diego San Diego CA.
Abstract:
Background Myocardial strain can identify subclinical left ventricular dysfunction in various cardiac diseases, but its association with clinical outcomes in genetic cardiomyopathies remains unknown. Herein, we assessed myocardial strain in patients with Danon disease (DD), a rare X-linked autophagic disorder that causes severe cardiac manifestations. Methods and Results Echocardiographic images were reviewed and used to calculate myocardial strain from a retrospective, international registry of patients with DD. Regression analyses were performed to evaluate for an association of global longitudinal strain (GLS) and ejection fraction with the composite outcome (death, ventricular assist device, heart transplantation, and implantable cardioverter defibrillator for secondary prevention). A total of 22 patients with DD (male 14 [63.6%], median age 16.5 years) had sufficient echocardiograms for analysis. Absolute GLS was reduced with a mean of 12.2% with an apical-sparing pattern observed. Univariable regression for GLS and composite outcome showed an odds ratio of 1.32 (95% CI, 1.02-1.71) with P=0.03. For receiver operating characteristic analysis, the areas under the curve for GLS and ejection fraction were 0.810 (P=0.02) and 0.605 (P=0.44), respectively. An absolute GLS cutoff of 10.0% yielded a true positive rate of 85.7% and false positive rate of 13.3%. Conclusions In this cohort of patients with DD, GLS may be a useful assessment of myocardial function and may predict clinical outcomes. This study highlights the potential use of myocardial strain phenotyping to monitor disease progression and potentially to predict clinical outcomes in DD and other genetic cardiomyopathies.
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