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Related Concept Videos

Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

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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...
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Pathophysiology of Heart Failure01:17

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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...
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Heart Failure I: Introduction01:27

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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...
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Heart Failure VI: Adjunct Therapies01:22

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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.
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Heart Failure Drugs: Diuretics01:22

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Heart failure and kidney perfusion are interconnected in a complex way. Reduced renal perfusion and venous congestion are two significant factors that contribute to renal dysfunction in heart failure. The kidneys, primarily responsible for fluid balance in the body, are adversely affected due to compromised cardiac output and increased venous pressure. In response to reduced renal perfusion, the kidneys activate neurohumoral mechanisms to restore balance. However, these mechanisms can be...
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Heart Failure V: Medical Management01:30

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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...
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Machine learning-enabled systematic review on coded healthcare data in heart failure research.

Asgher Champsi1,2, Karin T Slater3,4, Simrat Gill1

  • 1Department of Cardiovascular Sciences, University of Birmingham, Medical School, Vincent Drive, Birmingham B15 2TT, UK.

European Heart Journal. Digital Health
|January 23, 2026
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Summary
This summary is machine-generated.

Approximately one-fifth of heart failure studies use coded healthcare data. Machine learning aids evaluation, but transparency in data usage needs improvement, necessitating quality standards like CODE-EHR.

Keywords:
CodingHeart failureMethodologyResearchTransparency

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Area of Science:

  • Cardiology
  • Health Informatics
  • Clinical Research

Background:

  • Coded healthcare data are increasingly utilized in clinical research.
  • Transparency in reporting data usage is crucial for research integrity.
  • Heart failure studies are a significant area for data analysis.

Purpose of the Study:

  • To assess the transparency of reporting coded healthcare data in heart failure studies.
  • To utilize machine learning for large-scale evaluation of data reporting practices.
  • To identify the prevalence of coded healthcare data use in recent heart failure research.

Main Methods:

  • Systematic search of EMBASE and MEDLINE (2015-2020) for heart failure studies.
  • Manual data extraction from a sample of studies to characterize coded healthcare data usage.
  • Development and application of a Natural Language Processing (NLP) model to automate review of a larger study set.

Main Results:

  • Of 4279 identified studies, 21.2% reported using coded healthcare data.
  • Manual review showed only 47.5% of studies clearly described dataset construction and linkage.
  • The NLP model achieved high accuracy (AUC 0.97, F1 0.96) in identifying data usage.

Conclusions:

  • One-fifth of contemporary heart failure research reports using coded healthcare data.
  • Machine learning facilitates scalable evaluation of data reporting.
  • Limited transparency necessitates quality standards (e.g., CODE-EHR) for healthcare data in research.