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Related Concept Videos

Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

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Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
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Dilated cardiomyopathy, or DCM, is a progressive myocardial disorder characterized by ventricular chamber dilation and contractile dysfunction.EtiologyVarious factors can cause DCM, including hypertension and heavy alcohol intake, which contribute to the weakening and enlargement of the heart muscle. Viral infections, such as Coxsackievirus B, adenoviruses, and influenza, can lead to DCM by causing inflammation and damage to heart tissue. Certain chemotherapeutic agents, including daunorubicin,...
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Cardiomyopathy IV: Restrictive Cardiomyopathy01:29

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Restrictive cardiomyopathy (RCM) is a rare heart muscle disease characterized by impaired ventricular filling due to stiffened ventricular walls, leading to significant diastolic dysfunction.EtiologyRestrictive cardiomyopathy can arise from both inherited and acquired diseases, many of which are systemic. It is categorized into four main types: infiltrative, storage, non-infiltrative, and endomyocardial diseases.Infiltrative diseases, such as amyloidosis, lead to RCM by depositing amyloid...
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Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model
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Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic

Aurore Lyon1, Rina Ariga2, Ana Mincholé1

  • 1Department of Computer Science, University of Oxford, Oxford, United Kingdom.

Frontiers in Physiology
|March 30, 2018
PubMed
Summary
This summary is machine-generated.

Computational analysis of electrocardiograms (ECG) identified four hypertrophic cardiomyopathy (HCM) phenotypes. Primary T wave inversion in HCM patients indicates higher sudden cardiac death (SCD) risk and distinct hypertrophy patterns.

Keywords:
computational clusteringe-cardiologyelectrocardiographyhypertrophic cardiomyopathyphenotyping

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Computational Biology

Background:

  • Ventricular arrhythmias are a primary cause of sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM).
  • Current risk stratification methods for HCM lack robust electrophysiological biomarkers.
  • ECG analysis offers potential for identifying distinct HCM phenotypes and assessing SCD risk.

Purpose of the Study:

  • To identify distinct HCM phenotypes using computational ECG analysis.
  • To correlate these phenotypes with clinical risk factors and cardiac magnetic resonance (CMR) imaging findings.
  • To evaluate the potential of ECG-based phenotyping for SCD risk stratification in HCM.

Main Methods:

  • High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 controls were analyzed.
  • Mathematical modeling and computational clustering were employed to identify ECG phenotypes.
  • Clinical data and CMR imaging were used to assess hypertrophy extent and distribution within identified subgroups.

Main Results:

  • Three HCM phenotypes were initially identified based on QRS morphology, with no significant differences in arrhythmic risk or hypertrophy distribution.
  • Incorporating T wave analysis revealed four phenotypes, including a group (1A) with normal QRS and primary T wave inversion.
  • Phenotype 1A exhibited significantly higher HCM Risk-SCD scores and a predominance of coexisting septal and apical hypertrophy compared to other groups.

Conclusions:

  • Computational ECG phenotyping can classify HCM patients into distinct subgroups based on ECG features.
  • Primary T wave inversion, independent of QRS abnormalities, is associated with increased SCD risk and specific hypertrophy patterns in HCM.
  • ECG-based phenotyping holds promise as a novel, independent tool for SCD risk stratification in HCM patients.