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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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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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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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Identifying congestion phenotypes using unsupervised machine learning in acute heart failure.

Tripti Rastogi1, Olivier Hutin1, Jozine M Ter Maaten2

  • 1Université de Lorraine, Inserm, DCAC, Centre D'Investigation Clinique-Plurithématique 14-33, CHRU-Nancy, F-CRIN iNI-CRCT (Cardiovasculaire and Renal Clinical Trialists), 4, rue du Morvan, 54500 Vandœuvre-Lès-Nancy, France.

European Heart Journal. Digital Health
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PubMed
Summary
This summary is machine-generated.

Data-driven clustering identified five heart failure (HF) congestion phenotypes. Certain phenotypes, like global congestion, significantly increase the risk of HF hospitalization and death, suggesting distinct pathophysiologies.

Keywords:
CongestionHeart failure phenotypesProtein biomarkersRandom forestUnsupervised clustering

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Area of Science:

  • Cardiology
  • Biostatistics
  • Systems Biology

Background:

  • Heart failure (HF) categorization can be improved using data-driven clustering.
  • Understanding HF pathophysiology through congestion phenotypes is crucial for prognosis.

Purpose of the Study:

  • To identify distinct acute heart failure phenotypes based on pulmonary and systemic congestion levels (tissue and intravascular).
  • To assess the association of these phenotypes with the composite outcome of HF hospitalization and death.
  • To explore underlying biological pathways through network analysis of protein data.

Main Methods:

  • Clustering techniques were applied to 19 clinical, laboratory, and echocardiographic congestion markers in the Nancy-HF cohort (n=741).
  • The clustering model was validated in the BIOSTAT-CHF cohort (n=4254).
  • Network analysis of 363 proteins was used to identify biological pathways associated with each phenotype.

Main Results:

  • Five distinct congestion phenotypes were identified: PTC-dilated LV, PTC-HFpEF, PTC/STC-AF, PIVC-dilated LA/LV, and Global congestion.
  • The PTC, STC-AF and Global congestion phenotypes were associated with a significantly higher risk of the composite outcome compared to PTC-dilated LV.
  • The Global congestion phenotype showed significantly higher risk in the BIOSTAT-CHF cohort.
  • Network analysis revealed an association between immune response pathways and all phenotypes, with specific pathways linked to PTC-HFpEF, PTC/STC-AF, and PIVC-dilated LA/LV.

Conclusions:

  • Clustering techniques reveal distinct clinical congestion profiles in worsening HF, linked to prognosis and underlying pathophysiologies.
  • These identified phenotypes offer insights into HF heterogeneity and can be utilized via an online model to assess patient risk.
  • The findings highlight the potential for tailored therapeutic strategies based on specific HF congestion phenotypes.