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Related Concept Videos

Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

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...
Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
Cardiomyopathy I: Introduction and Classification01:25

Cardiomyopathy I: Introduction and Classification

Cardiomyopathy, or CMP, is a group of diseases affecting the myocardial structure, impairing its ability to pump blood effectively. This condition can lead to arrhythmias, heart failure, or sudden cardiac death.Cardiomyopathies are classified into primary and secondary categories:Primary Cardiomyopathy refers to conditions involving only the heart muscle that are often idiopathic (of unknown cause) or genetic. They primarily affect the myocardium without the involvement of other systemic...
Cardiomyopathy II: Dilated Cardiomyopathy01:30

Cardiomyopathy II: Dilated Cardiomyopathy

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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Related Experiment Video

Updated: Jun 5, 2026

Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model
03:45

Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model

Published on: August 8, 2022

Machine Learning-based Model for Major Adverse Cardiac Event Prediction in Patients with Hypertrophic Cardiomyopathy.

Thomas Geyer1,2,3, Christopher McIntosh1,2,4, Vishesh Sood1,2

  • 1Joint Department of Medical Imaging, University Medical Imaging Toronto, Toronto General Hospital, University Health Network (UHN), Toronto, Canada.

Radiology. Cardiothoracic Imaging
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning model effectively predicts major adverse cardiac events (MACEs) in hypertrophic cardiomyopathy (HCM) patients. Key cardiac MRI findings significantly improve risk stratification for heart conditions.

Keywords:
CardiacHeartHyperplasiaHypertrophyMRIMachine LearningModel Training

Related Experiment Videos

Last Updated: Jun 5, 2026

Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model
03:45

Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model

Published on: August 8, 2022

Area of Science:

  • Cardiology
  • Medical Imaging
  • Machine Learning

Background:

  • Hypertrophic cardiomyopathy (HCM) is a significant cause of cardiac morbidity and mortality.
  • Accurate risk stratification for major adverse cardiac events (MACEs) is crucial for managing HCM patients.
  • Existing risk models may not fully incorporate advanced imaging and machine learning capabilities.

Purpose of the Study:

  • To develop and validate a machine learning-based model for predicting MACEs in patients with HCM.
  • To identify key predictors of MACEs, particularly those derived from cardiac magnetic resonance imaging (CMR).
  • To compare the performance of the machine learning model against established risk stratification tools.

Main Methods:

  • Retrospective cohort study of 604 patients undergoing CMR for HCM evaluation (2015-2022).
  • Cardiac MRI sequences included cine, T1/T2 mapping, and late gadolinium enhancement (LGE).
  • A penalized Cox proportional hazards model (CoxNet) was trained using 33 variables, including clinical, genetic, echocardiographic, and CMR data, with cross-validation.

Main Results:

  • The CoxNet model achieved a C-index of 0.75 for MACE prediction, demonstrating favorable performance.
  • Model performance was comparable to the 2014 European Society of Cardiology sudden cardiac death risk model (C-index 0.67, P=.07).
  • Significant predictors included apical aneurysm, LV end-systolic volume index, extensive LGE, native T1 z-score, and male sex.

Conclusions:

  • A machine learning model utilizing routinely available clinical and CMR variables accurately predicts MACEs in HCM.
  • Cardiac MRI features are critical determinants for improving risk stratification in hypertrophic cardiomyopathy.
  • This model offers a promising tool for enhanced MACE prediction and personalized patient management.