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Related Concept Videos

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

Heart Failure Drugs: Inhibitors of Renin-Angiotensin System

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...
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

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

Heart Failure VII: Nursing Interventions

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,...

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Related Experiment Video

Updated: Jun 7, 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

Diversity and Inclusion Within Datasets in Heart Failure: A Systematic Review.

Elinor Laws1, Maria Charalambides2, Sonam Vadera3

  • 1University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom; Institute of Applied Health Science, University of Birmingham, United Kingdom.

JACC. Advances
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

AI for heart failure (HF) needs diverse data. Most datasets lack demographic details, with 89% of reported race/ethnicity being White, limiting AI equity. More accessible and transparent data is crucial for equitable AI in HF.

Keywords:
artificial intelligencedigital healthdiversityequityhealth datahealth technologyinclusion

More Related Videos

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
03:47

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients

Published on: July 12, 2024

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

Related Experiment Videos

Last Updated: Jun 7, 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

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
03:47

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients

Published on: July 12, 2024

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

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Health Equity

Background:

  • Heart failure (HF) affects over 64 million globally, necessitating advanced diagnostic and treatment tools.
  • Artificial intelligence (AI) shows promise for early HF diagnosis and treatment stratification.
  • The efficacy of AI in HF is contingent upon the representativeness of the data used throughout its lifecycle.

Purpose of the Study:

  • To identify and characterize datasets used in AI development for HF.
  • To assess data diversity and inclusivity within these datasets.
  • To evaluate dataset accessibility, geographical origin, metadata reporting, and composition.

Main Methods:

  • Systematic literature search of MEDLINE and Embase (2012-2022).
  • Independent screening of articles by two reviewers to identify relevant datasets.
  • Analysis of dataset documentation focusing on accessibility, origin, metadata, and composition.

Main Results:

  • 72 datasets from 23 countries, encompassing over 2 million individuals, were identified.
  • Reporting of age (86%) and sex/gender (85%) was common; race/ethnicity (29%) and socioeconomic status (11%) were less frequently reported.
  • Of datasets reporting race/ethnicity, 89% were predominantly "White" or "Caucasian"; only 28% of all datasets were fully accessible.

Conclusions:

  • Inconsistent reporting of key demographics like sex, gender, and socioeconomic status in HF datasets.
  • There is a critical need for transparently reported and accessible datasets for AI development in HF.
  • Generating and reporting demographic data, with appropriate safeguards, is essential for creating equitable AI-driven health technologies.