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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Pathophysiology of Heart Failure01:17

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Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
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Heart Failure I: Introduction01:27

Heart Failure I: Introduction

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Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...
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Cardiomyopathy II: Dilated Cardiomyopathy01:30

Cardiomyopathy II: Dilated Cardiomyopathy

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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 III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

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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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Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

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Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...
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Related Experiment Video

Updated: Jan 11, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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From patterns to prognosis: machine learning-derived clusters in advanced heart failure.

Murat Karaçam1, Barkın Kültürsay2, Deniz Mutlu3

  • 1Department of Cardiology, Bitlis State Hospital, Bitlis, Türkiye.

Frontiers in Cardiovascular Medicine
|November 10, 2025
PubMed
Summary

Machine learning identified two advanced heart failure (HF) phenotypes. One group had better hemodynamics and prognosis, while the other faced higher mortality risk due to biventricular dysfunction and poor exercise capacity.

Keywords:
advanced heart failuremachine learningphenotypingrisk stratificationunsupervised clustering

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Area of Science:

  • Cardiology
  • Computational Biology
  • Medical Informatics

Background:

  • Advanced heart failure (HF) presents complex heterogeneity, challenging traditional prognostication and personalized treatment.
  • Existing classification systems may not fully capture the nuances required for tailored patient care in advanced HF.

Purpose of the Study:

  • To employ unsupervised machine learning to identify distinct clinical subgroups within advanced HF patients.
  • To assess the prognostic implications of these identified phenotypes on long-term clinical outcomes.

Main Methods:

  • Retrospective analysis of 524 advanced HF patients with comprehensive clinical, echocardiographic, hemodynamic, and exercise data.
  • K-means clustering applied to standardized multidimensional data to define patient phenotypes.
  • Kaplan-Meier analysis and Cox regression used to evaluate outcomes (mortality, LVAD, transplant) by cluster.

Main Results:

  • Two distinct phenotypes were identified through clustering.
  • Cluster 1: Patients with preserved hemodynamics and functional status, associated with a favorable prognosis.
  • Cluster 2: Older patients with biventricular dysfunction, elevated pulmonary pressures, and reduced exercise capacity, exhibiting significantly higher adverse event rates (HR: 3.84; p < 0.001).

Conclusions:

  • Machine learning successfully delineated two advanced HF phenotypes with divergent clinical characteristics and prognoses.
  • This data-driven phenotyping approach offers potential for improved risk stratification in advanced HF.
  • Findings may guide the development of individualized therapeutic strategies for this vulnerable patient population.