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Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

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...
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

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...
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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...
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

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...
Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

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.

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Related Experiment Video

Updated: Jul 10, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction.

Trevor Winger1,2, Cagri Ozdemir3, Shanti L Narasimhan4

  • 1Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA.

Diagnostics (Basel, Switzerland)
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

A time-adaptive machine learning model effectively predicts heart failure with reduced ejection fraction severity. Personalizing predictions with patient data significantly improves accuracy, aiding tailored interventions for chronic disease management.

Keywords:
heart failure with reduced ejection fractionmachine learningpassive-aggressive classifierpersonalized machine learningtime-adaptive machine learning

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A Surgical Model of Heart Failure with Preserved Ejection Fraction in Tibetan Minipigs
07:09

A Surgical Model of Heart Failure with Preserved Ejection Fraction in Tibetan Minipigs

Published on: February 18, 2022

Related Experiment Videos

Last Updated: Jul 10, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

A Surgical Model of Heart Failure with Preserved Ejection Fraction in Tibetan Minipigs
07:09

A Surgical Model of Heart Failure with Preserved Ejection Fraction in Tibetan Minipigs

Published on: February 18, 2022

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Heart failure with reduced ejection fraction (HFrEF) requires individualized management.
  • Predicting HFrEF severity and disease progression presents a clinical challenge.

Purpose of the Study:

  • To evaluate a time-adaptive Passive-Aggressive classifier for HFrEF severity prediction.
  • To assess the model's ability to capture patient-specific disease trajectories.

Main Methods:

  • Utilized a time-adaptive Passive-Aggressive classifier with clinical data and Brain Natriuretic Peptide levels.
  • Personalized the model by sequentially incorporating 0-9 clinical visits per patient.
  • Assessed model adaptability and effectiveness using accuracy and reliability metrics.

Main Results:

  • Model accuracy and reliability significantly improved with progressive patient data incorporation.
  • One-Versus-Rest AUC increased from 0.4884 (zero visits) to 0.8253 (nine visits).
  • Demonstrated ability to handle diverse patient presentations and dynamic disease progression.

Conclusions:

  • Time-adaptive machine learning, specifically the Passive-Aggressive classifier, shows promise for HFrEF management.
  • Patient-specific predictions facilitate early detection and tailored interventions.
  • Adaptive models can enhance clinical workflows for chronic disease management.