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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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Related Experiment Video

Updated: Jul 22, 2025

Evaluation of Right Ventricular Function in Experimental Models of Pulmonary Arterial Hypertension
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Development and validation of machine learning algorithms to predict posthypertensive origin in left ventricular

Maxime Beneyto1, Ghada Ghyaza2, Eve Cariou1

  • 1Cardiac Imaging Centre, Toulouse University Hospital, 31059 Toulouse, France.

Archives of Cardiovascular Diseases
|July 20, 2023
PubMed
Summary

Machine learning effectively predicts the hypertensive origin of left ventricular hypertrophy, reducing the need for extensive patient workups. This tool aids in differentiating causes of left ventricular hypertrophy.

Keywords:
Artificial intelligenceHypertensionHypertensive cardiomyopathyHypertrophic cardiomyopathySupervised machine learning

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Left ventricular hypertrophy (LVH) is frequently associated with hypertension, but non-hypertensive causes significantly impact patient management.
  • A thorough workup is crucial for identifying the etiology of LVH, especially with increasing prevalence of mild cases.
  • There is a need for tools to accurately assess the pretest probability of hypertensive LVH.

Purpose of the Study:

  • To develop and validate machine learning models for predicting the hypertensive origin of LVH.
  • Utilize first-line clinical, laboratory, and echocardiographic variables for prediction.
  • Improve diagnostic accuracy and streamline patient management.

Main Methods:

  • Retrospective analysis of 591 patients with LVH (maximal wall thickness ≥12mm).
  • Data split into training and testing sets.
  • Trained and validated decision tree, random forest, and support vector machine algorithms.

Main Results:

  • All models demonstrated strong predictive performance (AUCs ranging from 0.82 to 0.90).
  • Support vector machine achieved high specificity (0.96) and sensitivity (0.31) after threshold selection.
  • Key predictor variables were consistent across algorithms; online calculators were developed.

Conclusions:

  • Machine learning models accurately predict the hypertensive origin of LVH.
  • Clinical implementation can decrease the number of etiological workups required for LVH patients.
  • These tools support efficient and targeted patient evaluation.