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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 II: Pathophysiology01:29

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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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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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Heart Failure III: Clinical Manifestations01:26

Heart Failure III: Clinical Manifestations

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Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...
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Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

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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...
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Machine Learning Reveals Novel Pediatric Heart Failure Phenotypes with Distinct Mortality and Hospitalization

Muhammad Junaid Akram1,2, Asad Nawaz1,2, Lingjuan Liu1,2

  • 1Ministry of Education Key Laboratory of Child Development and Disorders, Department of Pediatric Cardiology, National Clinical Key Cardiovascular Specialty, National Clinical Research Center for Child Health and Disorders, Children's Hospital of Chongqing Medical University, Chongqing 400014, China.

Diagnostics (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

Machine learning identified three distinct pediatric heart failure phenotypes with unique risks and treatments. These findings challenge current classifications and suggest phenotype-specific management for better outcomes.

Keywords:
cardiomyopathymachine learningpediatric heart failurephenotypingprecision medicineunsupervised clustering

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Area of Science:

  • Cardiology
  • Pediatrics
  • Machine Learning

Background:

  • Pediatric heart failure (PHF) is complex and inadequately classified by adult-centric parameters.
  • Existing systems fail to capture the developmental and pathophysiological nuances of PHF.
  • Novel approaches are needed to define PHF phenotypes for improved management.

Purpose of the Study:

  • To utilize machine learning to identify distinct pediatric heart failure (PHF) phenotypes.
  • To characterize these phenotypes based on clinical, biomarker, and echocardiographic data.
  • To determine unique outcomes and therapeutic implications for each identified PHF phenotype.

Main Methods:

  • A multicenter retrospective study of 2903 pediatric heart failure patients (≤18 years).
  • Unsupervised machine learning (k-means clustering with PCA) applied to 99 variables.
  • Phenotypes were compared for mortality, hospitalization rates, and treatment responses.

Main Results:

  • Three distinct PHF phenotypes were identified: Chronic Hypertensive/Cardiorenal, Preterm/CHD-Associated HF, and Fulminant Myocarditis.
  • Each phenotype exhibited unique characteristics, mortality rates (2.5% to 5.1%), hospitalization patterns, and treatment responses.
  • Significant differences (p < 0.001) were observed between clusters regarding clinical profiles and outcomes.

Conclusions:

  • Machine learning successfully delineated three PHF phenotypes with distinct risk profiles and therapeutic needs.
  • Findings challenge current PHF classification systems and support phenotype-specific management strategies.
  • Future research should focus on targeted interventions for arrhythmia prevention and immunomodulation, alongside specialized care pathways.