Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

57
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...
57
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

82
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...
82
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

36
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...
36
Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

1.9K
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...
1.9K
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

95
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...
95
Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

52
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...
52

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Artificial Intelligence Identification of Heart Failure With Preserved Ejection Fraction Substrate in Cardiac Surgery Patients.

Annals of thoracic surgery short reports·2026
Same author

Screening Strategies for Identification of Transthyretin Amyloid Cardiomyopathy.

JACC. Heart failure·2026
Same author

Predicting Heart Failure From 12-Lead ECGs Using AI: A HeartShare/AMP-HF Pooled Cohort Analysis.

Journal of the American College of Cardiology·2026
Same author

Behavioral Intelligence in Action: Implementation Science and Cardiovascular Learning Health Systems.

JACC. Advances·2025
Same author

Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.

JACC. Advances·2025
Same author

Improvement in breathlessness following bariatric surgery as measured by a new heart failure-specific health-related quality of life instrument: a prospective longitudinal study.

Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery·2025

Related Experiment Video

Updated: Sep 28, 2025

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

2.0K

Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.

Faraz S Ahmad1, Yuan Luo2, Ramsey M Wehbe3

  • 1Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA. Electronic address: https://twitter.com/FarazA.

Heart Failure Clinics
|March 28, 2022
PubMed
Summary

Machine learning offers promise for understanding and treating heart failure with preserved ejection fraction (HFpEF). Careful consideration of potential pitfalls is crucial for appropriate application and interpretation of these advanced computational methods.

Keywords:
Artificial intelligenceDeep learningHeart failureMachine learningNatural language processing

More Related Videos

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

6.6K
Author Spotlight: Exploring the Relationship Between Lipotoxicity and HFpEF
03:42

Author Spotlight: Exploring the Relationship Between Lipotoxicity and HFpEF

Published on: March 29, 2024

1.7K

Related Experiment Videos

Last Updated: Sep 28, 2025

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

2.0K
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

6.6K
Author Spotlight: Exploring the Relationship Between Lipotoxicity and HFpEF
03:42

Author Spotlight: Exploring the Relationship Between Lipotoxicity and HFpEF

Published on: March 29, 2024

1.7K

Area of Science:

  • Cardiology
  • Biomedical Informatics
  • Computational Medicine

Background:

  • Heart failure with preserved ejection fraction (HFpEF) is a complex cardiovascular condition.
  • Current understanding of HFpEF pathogenesis and targeted therapies remains limited.
  • Machine learning (ML) presents novel opportunities for advancing HFpEF research.

Purpose of the Study:

  • To explore the potential utility of machine learning in understanding HFpEF.
  • To identify common pitfalls associated with machine learning application in HFpEF research.
  • To guide appropriate interpretation and application of ML in precision medicine for HFpEF.

Main Methods:

  • Review of machine learning principles and their application in complex clinical syndromes.
  • Discussion of potential benefits of ML for targeted therapies and mechanistic insights in HFpEF.
  • Analysis of common challenges and limitations in ML studies relevant to cardiovascular research.

Main Results:

  • Machine learning algorithms can learn from data to potentially improve HFpEF therapies.
  • ML holds promise for guiding precision medicine approaches in complex conditions like HFpEF.
  • Several potential pitfalls exist in ML studies that require careful consideration.

Conclusions:

  • Machine learning has considerable promise for advancing the mechanistic understanding and treatment of HFpEF.
  • Awareness of ML pitfalls is essential for accurate interpretation and effective clinical application.
  • Appropriate use of ML can enhance targeted therapies and precision medicine for HFpEF patients.