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Related Concept Videos

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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Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

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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...
729
Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

278
Additional therapies for treating patients with heart failure (HF) may include procedural interventions, supplemental oxygen, the management of sleep disorders, and nutritional therapy.Procedural InterventionsImplantable Cardioverter-Defibrillator: For patients at risk of life-threatening arrhythmias due to severe left ventricular dysfunction, an Implantable Cardioverter-Defibrillator (ICD) can detect and terminate these arrhythmias, preventing sudden cardiac death and improving survival rates.
278
Heart Failure Drugs: Diuretics01:22

Heart Failure Drugs: Diuretics

825
Heart failure and kidney perfusion are interconnected in a complex way. Reduced renal perfusion and venous congestion are two significant factors that contribute to renal dysfunction in heart failure. The kidneys, primarily responsible for fluid balance in the body, are adversely affected due to compromised cardiac output and increased venous pressure. In response to reduced renal perfusion, the kidneys activate neurohumoral mechanisms to restore balance. However, these mechanisms can be...
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Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

227
Medical Management of Acute Decompensated Heart Failure (ADHF)The primary goals of therapy for patients hospitalized with acute decompensated heart failure (ADHF) include:Relieving symptomsOptimizing volume statusSupporting oxygenation and ventilationMaintaining cardiac output (CO) and end-organ perfusionIdentifying and addressing the cause of ADHFPreventing complicationsProviding patient education on factors precipitating HF exacerbationPlanning for dischargeOngoing monitoring and assessment...
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Related Experiment Video

Updated: Jan 23, 2026

Author Spotlight: Investigating HR-Dependent Cardiac Function in Mouse Models Through a Novel Atrial-Pacing Approach
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Using machine learning to characterize heart failure across the scales.

M Peirlinck1, F Sahli Costabal2, K L Sack3,4

  • 1Biofluid, Tissue and Solid Mechanics for Medical Applications (IBiTech, bioMMeda), Ghent University, Ghent, Belgium.

Biomechanics and Modeling in Mechanobiology
|June 27, 2019
PubMed
Summary
This summary is machine-generated.

A stretch-driven cardiac growth model accurately predicts myocyte changes in heart failure. This multiscale modeling approach, combining simulation and machine learning, offers insights into heart failure progression and personalized treatments.

Keywords:
Bayesian inferenceGaussian process regressionGrowth and remodelingHeart failureMachine learningMultiscaleUncertainty quantification

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Area of Science:

  • Cardiovascular Research
  • Computational Biology
  • Biomedical Engineering

Background:

  • Heart failure is a progressive condition with complex structural and functional changes.
  • Multiscale cardiac growth models offer personalized insights but their predictive power is unclear.

Purpose of the Study:

  • To quantify the predictive power of a stretch-driven growth model in a porcine heart failure model.
  • To assess the model's ability to predict cellular-scale changes using subject-specific simulations.

Main Methods:

  • Utilized a chronic porcine heart failure model over 8 weeks.
  • Employed hierarchical modeling, Bayesian inference, and Gaussian process regression for uncertainty quantification.
  • Propagated experimental uncertainties through a multiscale computational growth model.

Main Results:

  • Stretch was identified as the primary stimulus for myocyte lengthening.
  • The stretch-driven growth model explained a significant portion of observed myocyte morphological changes.
  • Quantified agreement between experimental measurements and computational predictions at the cellular scale.

Conclusions:

  • A stretch-driven multiscale model can effectively predict key aspects of cardiac cellular adaptation in heart failure.
  • This approach facilitates the development of next-generation models for exploring heart failure contributors.
  • Machine learning integration enhances holistic understanding and potential treatment strategies for heart failure.