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

32
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...
32
Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

1.8K
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...
1.8K
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

37
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...
37
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

23
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...
23
Heart Failure III: Clinical Manifestations01:26

Heart Failure III: Clinical Manifestations

42
Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...
42
Heart Failure VII: Nursing Interventions01:30

Heart Failure VII: Nursing Interventions

139
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,...
139

You might also read

Related Articles

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

Sort by
Same author

Heart Failure Through the Lens of Complexity.

American journal of physiology. Heart and circulatory physiology·2026
Same author

GLP-1-Based Therapies in Type 1 Diabetes: Emerging Evidence on Cardiovascular, Renal, and Safety Outcomes.

Diabetes, obesity & metabolism·2026
Same author

Robotic-Assisted Thoracic Surgery in the Immunotherapy Era: Navigating Altered Anatomy, Oncologic Precision, and the Future of Integrated Platforms.

Journal of clinical medicine·2026
Same author

Transcatheter Aortic Valve Implantation in Cancer Patients: A Contemporary Review of the Specific Challenges, the Outcomes, Risk Stratification, and Decision-Making.

Medicina (Kaunas, Lithuania)·2026
Same author

Acute High Intensity Interval Exercise Promotes Circulating Progenitor Cell Mobilization and Improves Microcirculation in Patients with Chronic Heart Failure.

Journal of cardiovascular development and disease·2026
Same author

Optimal Antithrombotic Therapy for Peripheral Artery Disease: A Systematic Review and Network Meta-Analysis.

Journal of the American Heart Association·2026

Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
04:05

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis

Published on: June 30, 2023

2.2K

Prognostic Models in Heart Failure: Hope or Hype?

Spyridon Skoularigkis1, Christos Kourek2, Andrew Xanthopoulos1

  • 1Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece.

Journal of Personalized Medicine
|August 27, 2025
PubMed
Summary

Accurate heart failure (HF) prognostication is vital for patient care. While current tools exist, novel approaches like AI and multi-omics offer promise for personalized risk assessment, but require validation and integration.

Keywords:
heart failureoutcomesprognostic modelsriskscoresstratification

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
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

213

Related Experiment Videos

Last Updated: Sep 10, 2025

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
04:05

Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis

Published on: June 30, 2023

2.2K
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
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

213

Area of Science:

  • Cardiology
  • Medical Informatics
  • Biostatistics

Background:

  • Heart failure (HF) presents a significant global health challenge, marked by high morbidity, mortality, and healthcare expenditures.
  • Effective prognostication is essential for tailoring treatments, allocating resources, and guiding patient management strategies.

Purpose of the Study:

  • To review the current landscape of prognostic models for heart failure.
  • To identify limitations of existing tools and explore emerging technologies for improved risk stratification.

Main Methods:

  • Comprehensive literature review of prognostic models in heart failure, encompassing clinical scores, biomarkers, imaging, and artificial intelligence.
  • Analysis of the generalizability, data integration, and clinical utility of various prognostic tools.
  • Exploration of novel approaches including machine learning, multi-omics, and remote monitoring.

Main Results:

  • Existing prognostic models, while useful, often suffer from poor generalizability, static data reliance, and limited clinical integration.
  • Emerging technologies like machine learning and multi-omics show potential for dynamic and individualized HF risk assessment.
  • Challenges remain in interpretability, validation, and ethical implementation of advanced prognostic methods.

Conclusions:

  • Prognostic models hold significant potential to revolutionize heart failure management.
  • Successful clinical translation requires continuous updating, external validation, and seamless integration into healthcare workflows.
  • Addressing limitations through interdisciplinary collaboration and patient-centered innovation is crucial for realizing the full impact of prognostic tools.