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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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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

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 VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

428
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.
428
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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Heart Failure Drugs: Inhibitors of Renin-Angiotensin System01:26

Heart Failure Drugs: Inhibitors of Renin-Angiotensin System

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The activation of the sympathetic nervous system and the renin-angiotensin-aldosterone system (RAAS) contributes to cardiac remodeling, and inhibiting the RAAS is a pharmacological target in heart failure management. As a result, neurohumoral modulation is a crucial treatment principle for managing heart failure. This approach involves using medications like ACE inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, mineralocorticoid receptor antagonists (MRAs), and neutral...
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Related Experiment Video

Updated: Feb 21, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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Forecasting left ventricular systolic dysfunction in heart failure with artificial intelligence.

Teya Bergamaschi1,2,3,4, Tiffany Yau2,3,4,5, Payal Chandak6,7,8

  • 1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

Eclinicalmedicine
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model, PULSE-HF, accurately predicts worsening left ventricular ejection fraction (LVEF) in heart failure patients using ECGs. This tool aids in identifying at-risk individuals for timely intervention.

Keywords:
Deep learningHeart failureLVEF

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Objective assessment of left ventricular function is crucial for guiding heart failure (HF) therapy.
  • Left ventricular ejection fraction (LVEF) is dynamic, and declines are linked to increased morbidity and mortality.
  • Identifying patients at risk of LVEF decline can improve prognostication and enable timely intervention.

Purpose of the Study:

  • To develop and validate a deep learning model (PULSE-HF) that predicts changes in left ventricular systolic function from electrocardiograms (ECGs) in heart failure patients.
  • To assess the model's ability to identify patients likely to have an LVEF below 40% within one year.

Main Methods:

  • Developed a deep learning model, PULSE-HF, integrating 12-lead ECG waveforms with prior LVEF measurements.
  • Retrospectively developed and tested the model on data from one hospital (Jan 2000-June 2021).
  • Externally validated the model on retrospective cohorts from two additional hospitals (data collected between Jan 2000-June 2021 and 2008-2019).

Main Results:

  • PULSE-HF demonstrated strong discriminatory ability, with areas under the receiver operating characteristic curve (AUROC) of 87.5-91.4% across three HF cohorts for predicting LVEF < 40% within a year.
  • For patients with baseline LVEF > 40%, PULSE-HF identified those at risk of worsening LVEF with AUROCs of 81.6-86.3%.
  • Model performance remained consistent across various subgroups and a simplified lead I version showed similar performance to the 12-lead model.

Conclusions:

  • PULSE-HF robustly predicts worsening LVEF in patients with a prior diagnosis of heart failure.
  • This deep learning approach offers a valuable platform for identifying patients at increased risk of worsening systolic dysfunction.
  • The model's ability to predict LVEF decline from ECGs can facilitate proactive management strategies in heart failure care.