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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

55
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...
55
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

78
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...
78
Heart Failure III: Clinical Manifestations01:26

Heart Failure III: Clinical Manifestations

67
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...
67
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
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

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Related Experiment Video

Updated: Sep 26, 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

243

Multivariable prognostic model for heart failure in Chinese Han population-based setting.

Man Huang1,2, Lei Xiao1,2, Yang Sun1,2

  • 1Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China.

ESC Heart Failure
|April 22, 2022
PubMed
Summary

A new prognostic model combines genetic risk scores related to autophagy, traditional risk factors, and NT-proBNP to predict heart failure outcomes. This approach improves prediction accuracy for heart failure prognosis, aiding treatment and prevention strategies.

Keywords:
AutophagyGenetic risk factorGenetic risk scoreHeart failureModelPrognosis

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Last Updated: Sep 26, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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A Surgical Model of Heart Failure with Preserved Ejection Fraction in Tibetan Minipigs
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Area of Science:

  • Genetics and Precision Medicine
  • Cardiovascular Research
  • Molecular Biology and Autophagy

Background:

  • Heart failure (HF) prognosis is influenced by genetic predisposition and impaired autophagy.
  • Autophagy-related genes (ARGs) play a role in HF pathogenesis.
  • Integrating genetic factors with traditional risk factors (TRF) may improve HF outcome prediction.

Purpose of the Study:

  • To construct a prognostic model for heart failure (HF) by combining polygenetic background related to autophagy.
  • To integrate autophagy-related genetic risk scores (GRS) and genetic risk factors (GRF) with TRF and NT-proBNP.
  • To evaluate the predictive power of the combined model for HF prognosis.

Main Methods:

  • Transcriptomic data analysis identified differentially expressed ARGs.
  • Whole exome sequencing and clinical data from HF patients were used to develop GRS and GRF.
  • Five models were compared using receiver operating characteristic curves, including GRS, TRF, NT-proBNP, and GRF.

Main Results:

  • A genetic risk score (GRS) based on 11 autophagy-related gene variants was strongly associated with cardiac mortality in HF patients.
  • Patients in the highest GRS tertile showed significantly higher risks of adverse clinical outcomes.
  • The model combining GRS, TRF, GRF, and NT-proBNP demonstrated the highest discrimination power (AUC = 0.777), particularly in specific patient subgroups.

Conclusions:

  • The integrated model incorporating autophagy-related GRS, TRF, GRF, and NT-proBNP effectively distinguishes between better and worse HF prognosis.
  • This multi-component model offers a promising strategy for personalized HF treatment and prevention.
  • Genetic profiling related to autophagy pathways can enhance cardiovascular risk assessment in heart failure.