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 V: Medical Management01:30

Heart Failure V: Medical Management

16
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...
16
Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

19
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...
19
Heart Failure VII: Nursing Interventions01:30

Heart Failure VII: Nursing Interventions

121
The first step in nursing management of a patient with heart failure involves thoroughly assessing the patient's medical history.Subjective Data: Obtain the patient's medical history of coronary artery disease, hypertension, myocardial infarction, and symptoms like dyspnea, orthopnea, and paroxysmal nocturnal dyspnea.Objective Data: Conduct a physical examination to identify findings such as jugular vein distention, pulmonary crackles, tachycardia, murmurs, peripheral edema, and vital signs,...
121
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

21
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...
21
Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

17
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.
17
Mitral Regurgitation IV: Nursing Management01:28

Mitral Regurgitation IV: Nursing Management

51
Mitral regurgitation (MR) is a condition where the mitral valve does not close properly, leading to the backward flow of blood from the left ventricle into the left atrium during systole. This condition can arise from various causes, including rheumatic fever, infective endocarditis, or degenerative valve disease. Effective nursing management is crucial to optimizing patient outcomes and involves comprehensive assessment and targeted interventions.Comprehensive Patient AssessmentA detailed...
51

You might also read

Related Articles

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

Sort by
Same author

Machine learning-based prediction of mortality and hospitalization in diabetic patients with heart failure with preserved ejection fraction: the GUARDIAN-P risk score.

European heart journal. Digital health·2026
Same author

Feasibility and long-term outcomes of recanalization for De novo small vessel chronic total occlusions.

International journal of cardiology. Heart & vasculature·2026
Same author

Novel percutaneous retrieval of a self-entangled decapolar catheter using a deflectable sheath and guidewire counter-traction.

Journal of cardiothoracic surgery·2026
Same author

Triglyceride-glucose index and subclinical left ventricular dysfunction across cardiovascular-kidney-metabolic syndrome stages: a 7-year retrospective cohort study.

Frontiers in endocrinology·2026
Same author

Heterogeneity between insulin and proinsulin in the potency for insulin autoantibodies in newly diagnosed type 1 diabetes children.

Clinical and experimental immunology·2026
Same author

2026 Taiwan society of lipids and atherosclerosis consensus statement for the identification and management of patients receiving suboptimally tolerable statins.

Journal of the Formosan Medical Association = Taiwan yi zhi·2026

Related Experiment Video

Updated: Jul 29, 2025

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

6.5K

Personalized Management for Heart Failure with Preserved Ejection Fraction.

Chang-Yi Lin1, Heng-You Sung1, Ying-Ju Chen2

  • 1Division of Cardiology, Department of Internal Medicine, MacKay Memorial Hospital, No. 92, Sec. 2, Zhongshan N. Road, Taipei 10449, Taiwan.

Journal of Personalized Medicine
|May 27, 2023
PubMed
Summary

Artificial intelligence (AI) can identify distinct patient groups in heart failure with preserved ejection fraction (HFpEF). Further research is needed to integrate AI-driven phenotyping into clinical practice for better HFpEF management.

Keywords:
artificial intelligenceclusterheart failure with preserved ejection fractionlatent class analysismachine learningphenotype

More Related Videos

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

1.9K
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

1.6K

Related Experiment Videos

Last Updated: Jul 29, 2025

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

6.5K
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

1.9K
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

1.6K

Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Heart failure with preserved ejection fraction (HFpEF) presents diverse clinical phenotypes due to multiple underlying mechanisms and comorbidities.
  • Understanding these phenotypes is crucial for advancing HFpEF pathophysiology, treatment strategies, and patient outcomes.

Purpose of the Study:

  • To explore the potential of artificial intelligence (AI) in phenotyping HFpEF using multi-dimensional data.
  • To highlight the gap between AI capabilities and current clinical practice guidelines for HFpEF.

Main Methods:

  • AI-based phenotyping utilizing clinical, biomarker, and imaging data.
  • Analysis of existing data to identify distinct HFpEF phenotypes.

Main Results:

  • Accumulating data suggests AI can effectively phenotype HFpEF patients.
  • Current guidelines and consensus lack integration of AI-driven phenotyping in daily practice.

Conclusions:

  • AI-based phenotyping holds promise for advancing HFpEF management.
  • Further studies are required to validate AI findings and standardize clinical implementation for HFpEF.