Arrhythmia in hypertrophic cardiomyopathy: Risk prediction using contrast enhanced MRI, T1 mapping, and personalized...

Area of Science:

• Cardiology
• Medical Imaging
• Computational Biology

Background:

• Hypertrophic cardiomyopathy (HCM) is a genetic heart disease characterized by myocardial fibrosis.
• HCM is a leading cause of sudden cardiac death (SCD) in younger individuals, primarily due to ventricular arrhythmias (VA).
• Current risk stratification methods inadequately identify patients who would benefit from an implantable cardioverter defibrillator (ICD) for primary prevention.

Purpose of the Study:

• To develop and evaluate a novel risk prediction approach for VA in HCM patients.
• To combine late gadolinium contrast-enhanced (LGE) MRI and T1 mapping for enhanced fibrosis assessment.
• To compare imaging biomarker predictions with a virtual heart computational risk assessment approach.

Main Methods:

• Integrated short-axis LGE-MRI with post-contrast T1 maps to define fibrosis thresholds.
• Utilized combined LGE-T1 maps to derive imaging biomarkers for VA risk prediction.
• Compared the predictive performance of biomarkers and LGE-T1 virtual heart models against clinical VA outcomes.

Main Results:

• Standard image-based biomarkers (hypertrophy, signal heterogeneity, fibrotic border complexity) failed to differentiate high-risk from low-risk patients for VA.
• LGE-T1 virtual heart technology demonstrated statistically significant and superior performance in predicting VA risk within the HCM cohort.
• The virtual heart models accurately predicted VA risk, outperforming all individual image-based metrics.

Conclusions:

• Combined LGE-T1 MR imaging analysis of imaging biomarkers alone is insufficient for discriminating VA risk in HCM.
• Hybrid LGE-T1 virtual heart models show significant promise for accurate VA risk prediction in HCM.
• This advanced computational approach may enhance SCD risk stratification, optimizing ICD implantation decisions for primary prevention in HCM patients.