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Related Concept Videos

Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

554
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...
554
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

189
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...
189
Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

155
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.
155
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

130
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...
130
Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

2.5K
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...
2.5K
Heart Failure VII: Nursing Interventions01:30

Heart Failure VII: Nursing Interventions

311
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,...
311

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Related Experiment Video

Updated: Dec 12, 2025

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

448

Big Data Approaches in Heart Failure Research.

Jan D Lanzer1,2,3, Florian Leuschner4,5, Rafael Kramann6,7

  • 1Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany.

Current Heart Failure Reports
|August 13, 2020
PubMed
Summary
This summary is machine-generated.

Big data analyses, including omics and clinical data, offer new insights into heart failure (HF). While promising, challenges remain in translating these big data findings into improved patient care.

Keywords:
Big dataHeart failureMachine learningOmicsSingle cell

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Related Experiment Videos

Last Updated: Dec 12, 2025

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

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In Silico Clinical Trials for Cardiovascular Disease
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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

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Area of Science:

  • Cardiovascular Research
  • Bioinformatics
  • Genomics

Background:

  • Heart failure (HF) research is increasingly leveraging large datasets.
  • Understanding the molecular and clinical profiles of HF patients is crucial.

Purpose of the Study:

  • To review the current applications of big data in heart failure research.
  • To discuss the role of 'omics' and clinical data in HF studies.
  • To explore the limitations and future potential of big data in HF.

Main Methods:

  • Analysis of 'omics' data (genomics, proteomics, etc.) for molecular insights.
  • Examination of clinical datasets for HF phenotyping and prognostic modeling.
  • Application of machine learning and other big data methodologies.

Main Results:

  • 'Omics' data reveal molecular profiles in HF patients.
  • Advanced technologies like single-cell and spatial profiling enhance understanding of cellular heterogeneity and tissue architecture.
  • Big data approaches are improving HF phenotyping and prognostic accuracy.

Conclusions:

  • Big data, particularly 'omics' and clinical data, are transforming heart failure research.
  • Machine learning holds potential for elucidating HF biology.
  • Translating big data-driven insights into clinical practice remains a significant challenge.